Tech Talks Daily

What happens when an AI agent pursues an approved task in a way nobody expected, using access that reaches further than it needs?

On Tech Talks Daily, I'm joined by Jonathan Trayers, a director at Ekco, to discuss the security decisions behind agentic AI. The conversation focuses on what changes when a system can act with credentials, connect to business applications, and trigger workflows. A useful answer in a chat window and an automated action inside a company create different responsibilities.

Jonathan describes working with both deterministic workflows, where rules specify what should happen, and agentic steps, where AI interprets a situation before deciding what to do. Bringing those approaches together raises practical questions about boundaries. Which actions can the system take, what information can it access, and where should a person remain involved?

What interested me was his emphasis on limiting the impact of unexpected behavior. Predicting every failure is unrealistic, so he argues for reducing the area an agent can affect. The permissions attached to its identity help determine that boundary. An agent given broad access may be able to create problems across several systems, even when its original assignment sounds narrow and reasonable.

We discuss separate identities for agents and their connections, restricted permissions, continuing monitoring, and the ability to revoke access. Jonathan also describes governance as a series of practical decisions about the use case, the risks the business accepts, and the people responsible for approving the deployment. Prompts can express instructions, while controls around the systems an agent reaches need their own attention.

Testing is another part of that process. Jonathan recommends beginning in a controlled environment, observing the outcome, and then using supervised operation before handing over greater responsibility. His invoice example makes the point understandable. Processing an invoice and authorizing its payment involve different consequences, so the workflow should reflect that difference rather than giving one automated process every permission at once.

We also consider what this means for smaller businesses. Jonathan's concern is that companies with limited security resources may already have broad access arrangements, which can be carried into AI deployments. Company size alone does not determine exposure, but understanding accounts, permissions, software, and patching remains part of managing it. His argument is to apply those disciplines as AI adoption develops, rather than waiting for a new platform to solve every governance problem.

Accountability runs through the discussion. Jonathan argues that a business remains responsible for the way it deploys technology and approves access, alongside responsibilities held by vendors and model providers. The precise legal allocation will depend on the circumstances. The operational question for listeners is clearer: who owns the use case, who accepted its risks, and who can act when something changes?

For teams beginning this work, Jonathan recommends one valuable use case and a reusable implementation pattern. Define the boundaries, test the controls, retain human judgment where needed, and use the experience to inform the next deployment. The conversation offers a practical starting point for organizations trying to gain value from AI agents while keeping their access and actions understandable.

Please share your thoughts. How is your organization deciding what AI agents may do, and what would help you keep ownership, permissions, and oversight clear after deployment?

What if many of the problems businesses blame on AI are actually problems with their data? It's a question I wanted to examine when I sat down with Steffen Hoellinger, Field CTO EMEA at Confluent, during Current 2026 in San Francisco. After a year dominated by conversations about AI agents, I'm increasingly interested in the less glamorous work companies need to get right before these technologies can deliver anything useful.

Steffen works with enterprises across Europe, helping them manage real-time data and develop AI applications. Our conversation begins with the difficulties businesses face when information is scattered across departments, applications, and different locations. He explains why simply moving data between systems is no longer enough, and how processing, filtering, and preparing information as it moves can help applications make better decisions.

We also discuss Confluent's Streamhouse announcement and the growing role of Apache Kafka and Flink. Steffen explains how these technologies help businesses process information closer to where it originates, rather than waiting for scheduled transfers and reports. For companies trying to put AI agents into production, he argues that having the right information available when it is needed can make an enormous difference.

One of my favorite moments comes when our conversation turns to where this technology is actually being used. Steffen shares an unexpected example from the America's Cup, where Kafka helped exchange information between a racing yacht and a separate support boat. We also talk about factories and retail stores that can continue operating independently when their connection to the cloud is interrupted.

From there, we examine the particular challenges facing European companies. Data sovereignty, privacy, regulation, and concerns about relying too heavily on public cloud infrastructure all influence technology decisions. Steffen explains why open standards and the ability to operate across different environments matter to businesses that cannot afford extended downtime.

Another subject I wanted to challenge him on was the amount of money being spent on AI models. Steffen argues that companies shouldn't assume an expensive large language model is the answer to every problem. Traditional machine learning, event processing, and simpler analytical techniques can often perform particular jobs faster and at a lower cost. He explains how businesses can combine those approaches, using AI models only where they offer additional value.

We also discuss his experience founding Airy, which Confluent acquired in 2025, and what he's learned about moving from a startup to working with some of the world's largest enterprises. That leads to a wider conversation about governance, accountability, and what happens when organizations begin using large numbers of AI agents across their operations.

Steffen makes an interesting point about business processes that have historically depended on people and software working together. Introducing AI agents will require companies to reconsider how those processes operate, while retaining clear human accountability for the decisions being made.

Towards the end of our conversation, I ask what European CIOs should prioritize over the next 12 months. His advice is to concentrate on the data companies already control, understand where it comes from, establish consistent schemas and governance, and avoid committing too early to a single AI platform.

It's a useful conversation for anyone trying to separate the excitement surrounding AI from the practical work required to make it useful inside a business.

As more organizations prepare to introduce AI agents into everyday operations, are we spending too much time choosing models and not enough time understanding the information those models depend on? Listen to our conversation and share your thoughts.

What happens when an AI agent makes a business decision using information that's already out of date? It's a question I wanted to put to Stephen Deasy, CTO of Confluent, when we met at Current 2026 in San Francisco. After attending more than 20 technology conferences this year, I've heard countless promises about what AI agents will eventually achieve. But there's a growing problem behind those ambitions. Businesses are still struggling to get accurate, current information out of the systems they already own.

Confluent has introduced Streamhouse, a data architecture designed to address that problem. Stephen explains why the company believes existing approaches can leave applications working with delayed or incomplete information, and how the new architecture brings streaming, processing, governance, and querying together. We also discuss Lightning Tables, one of Confluent's major announcements at the event, and what direct access to current Kafka data could mean for developers and their businesses.

I was particularly interested in what this means for companies that have already invested heavily in data warehouses and lakehouses. Stephen makes the distinction between analyzing past performance and running business operations using current information. He explains why Confluent sees these approaches working together, and why existing technology investments can provide the foundation for introducing Streamhouse.

We also discuss the experiences of Instacart and Citizens Bank. Stephen recalls how Instacart faced a sudden increase in demand during the pandemic, when accurate information about orders, inventory, and deliveries became especially important. He also describes how Citizens Bank improved loan fulfillment processes through real-time data streaming. Both examples help explain why the speed at which information moves between business systems can affect customers directly.

The conversation also covers IBM's relationship with Confluent, open technologies including Kafka and Flink, and the announcement that Tableflow is now generally available on Google Cloud. Stephen shares his thoughts on model evaluation, human oversight, and how companies should measure returns from their technology spending.

One of my final questions concerns the misconceptions surrounding Streamhouse. Stephen explains that the architecture brings together approaches many organizations have already been working toward, and that its value lies partly in making those capabilities easier to use together.

With so much money being invested in AI, are businesses paying enough attention to the information those systems depend on? Listen to our conversation and share your thoughts.

What should a business consider before moving AI inference onto infrastructure it owns?

In this episode of Tech Talks Daily, I speak with Paul Speciale, Chief Marketing Officer at Scality, about the decisions behind enterprise AI infrastructure. Following the launch of Scality AI Inference Factory, we discuss why organizations are assessing on-premises inference alongside cloud services, with attention to costs, model control and the location of sensitive data. Paul expects a hybrid approach, with different workloads running in the environments that suit their requirements.

The conversation begins with the work a GPU repeats when an AI session needs to process familiar context. Paul explains how saving and retrieving that information can reduce recomputation, and why storage and networking deserve attention alongside compute. We discuss where the economics change as usage grows and why owning the infrastructure also means taking responsibility for utilization, performance and the operating bill.

We then consider data placement across storage tiers. Paul argues that there is no single best location for every workload, with performance, cost and sovereignty requirements shaping the decision. I ask where automation should stop and human approval should begin. He describes automating health checks, forecasts and recommendations while retaining approval for consequential changes such as deleting data or changing policies.

Cyber resilience is another part of the discussion. Paul explains why an immutable copy needs to be supported by tested recovery, and why sovereignty rules should be checked through attempts to perform actions the system is meant to refuse. We close with the responsibilities infrastructure teams take on as they manage inference directly, from tracking time to first token and context reuse to deciding when a model should change. For leaders assessing their next AI deployment, the conversation offers practical questions about control and the evidence behind the business case. Which workloads would you consider running locally, and what would you want to prove before making that decision? Please share your thoughts with me.

How do you introduce electric vehicles and AI-assisted safety technology into a collection fleet that customers depend on every day?

On Tech Talks Daily, I'm joined by Brett Rogers, Vice President of Operations Technology at Republic Services, to discuss the engineering, infrastructure, and human decisions behind modern waste and recycling operations. Brett's background includes work at Toyota, General Motors, and Cruise. He describes arriving from the automotive sector and discovering how much complexity sits behind a service most people expect to happen without thinking about it.

We give particular attention to electric refuse trucks. Range is part of the calculation, but the vehicle also powers equipment that lifts containers and handles material. Brett explains how Republic Services models energy use across the chassis and collection body, taking account of terrain, climate, speed, and the work required on a route. The goal is to identify where an electric vehicle can replace a conventional truck while maintaining service.

Route selection can produce some surprising answers. A residential truck making frequent stops may benefit from regenerative braking, while a vehicle with fewer container lifts can spend much of its day covering distance. Brett describes why his team began with residential applications, then used operational data to inform expansion into other vehicle types. The number of lifts alone does not tell the whole story.

Charging has its own planning cycle. Brett says utility discussions, design, and construction can require decisions roughly two years before trucks are needed. We discuss why depot readiness has to lead vehicle delivery, alongside the arrangements that keep service running if power is interrupted. He describes spare vehicles and equipment that can accept emergency power, while on-site storage and renewable energy form part of the options being considered. These approaches have different levels of maturity, and the conversation keeps that distinction clear.

Technology-enabled safety is the other major focus. Brett describes adding a bird's-eye camera view to help drivers see around large vehicles, then working with vendors on systems that distinguish people from objects. Spoken alerts such as "pedestrian rear" are intended to tell a driver what is happening and where, without requiring them to keep checking another screen.

The discussion goes beyond installing cameras. Brett says in-cab alerts were associated with an initial reduction of around 20% to 30% in what Republic Services calls unfocused behaviors. He reports a reduction of 95% after coaching was added to the approach. These are company-reported behavioral measures, not a claim that collisions fell by 95%. No evaluation period or underlying methodology is provided in the interview. The useful operational lesson is the connection between technology, explanation, and the opportunity for drivers to correct a recurring behavior.

We also discuss employee adoption. Brett recalls drivers who were initially reluctant to move from diesel trucks and describes how time in the electric vehicles changed their view. He points to lower noise, vibration, and heat, alongside cab design. His account is a reminder that the experience of the person doing the job can determine whether a promising deployment becomes a useful everyday tool.

We finish with routing visibility for municipal customers and plans to use AI and other tools to improve operations. For fleet leaders, the conversation offers practical considerations around duty cycles, charging resilience, driver support, and measuring results. For everyone else, it reveals the work behind a truck arriving on the right street at the right time.

I'd welcome your thoughts. What would give you the most confidence in an electric fleet rollout, and how should organizations demonstrate that their safety technology is helping drivers and communities?

What would make you comfortable giving an AI agent the authority to act inside a live business process?

In this episode of Tech Talks Daily, I speak with Valerie Henderson, CEO of Caylent, about what has to happen between a successful demonstration and a system a business can depend on. The conversation centers on production readiness, including access controls, spending limits, traceability, workflow design, and the people responsible for the outcome.

Valerie describes why introducing agents requires changes to how work gets done. If an organization adds new technology without reconsidering the handoffs and decisions around it, the hoped-for productivity gains can remain difficult to achieve. Her advice is to begin with a bounded problem where automating a few steps could provide a clear benefit, then build on the evidence rather than starting with an ambitious network of agents.

We discuss the difference between read-only workloads and agents that can alter live systems. Valerie says many customers are already comfortable with agents retrieving information, while autonomous operations such as self-healing infrastructure are concentrated among the most advanced customers she sees. Those observations reflect her experience with Caylent engagements. They do not establish a universal level of enterprise adoption.

For me, one of the most useful questions is what meaningful human oversight looks like. Valerie is clear that people remain accountable for the instructions and access they give agents. She emphasizes deliberate testing, appropriate governance, and the ability to understand what a system has done. We also discuss how agents can repeat work without producing anything useful, creating a bill that needs to be considered alongside accuracy and reliability.

The conversation moves into Caylent's own operating model. Valerie describes moving away from assumptions built around human time and materials, making existing technical knowledge easier to reuse, and changing delivery incentives to reward speed and customer satisfaction. It is a practical reminder that organizations adopting AI may need to reconsider what they reward as well as which tools they buy.

She also shares a customer example involving Smarsh. According to Valerie, redesigning its software delivery process with agentic AI freed engineering capacity that could be reinvested in the business. She describes that capacity in financial terms, but the interview does not establish a measurement period or separate capacity value from realized cash savings. The useful lesson is the connection between a defined objective and a decision about how to use the resulting capacity.

We touch on Caylent's agentic AI readiness research, while keeping the broader discussion focused on practical experience. The useful question for leaders is what controls and evidence should come before greater autonomy, and how they will reassess that decision as the system changes.

Finally, Valerie explains how she thinks about success for employees as well as customers. She wants technologists to have time for difficult problems and opportunities to develop their skills. She also argues that customers should be able to explain the value of the work they have undertaken. Model performance is part of that assessment, alongside the environment, permissions, costs, and business process in which the agent operates.

I would love to hear how your team is making these decisions, so please share your thoughts. Which responsibilities are you ready to delegate to an AI agent, and what evidence would you need before going further?

What makes an AI fall alert useful to the person who needs help and the team responsible for responding?

In this episode of Tech Talks Daily, I speak with Mike Link, chief operating officer at KamiVision, about deploying computer vision in residential care. We discuss the connection between the sensor, the people receiving its notifications, and the decisions that follow. The conversation also looks at privacy, connectivity, false alarms, and why a system that works in a care facility needs further work before it can serve a wide range of homes.

Mike describes KamiVision's path from home-security cameras to its KamiCare platform. His account of fall detection involves three stages. A device identifies a possible event, a cloud model filters notifications, and a person reviews a blurred, encrypted clip before an alert reaches the user. The human review is part of the service he describes, so it belongs in any discussion of how the system performs.

I ask about the company's 99.5% accuracy claim and what buyers should understand before relying on a number like that. Mike explains the filtering process, but the interview does not establish a test population, a missed-event rate, or a complete statistical definition. His answer is a starting point for further questions about performance across the whole service, rather than proof that a device detects 99.5% of all falls by itself.

One of the most useful examples concerns a resident found on the floor. Mike describes how reviewing an event can help staff understand whether someone fell or deliberately lowered themselves because they needed help with a task. That information may lead to a change in a routine or room layout. It is an example of how visual context can inform care, rather than an independently measured claim that the technology prevents injuries.

We also discuss the difference between a managed care setting and a home. A residential facility can have relatively consistent room layouts and lighting. Homes introduce furniture, pets, several rooms, and areas outside a sensor's view. Mike talks about investigating other sensing technologies to address those gaps. He presents those ideas as work under consideration, not a set of generally available capabilities.

Connectivity is another practical issue. Mike says the version discussed uses Wi-Fi and that a cellular option has been considered. He also explains that the consumer version was being tested with families and was not yet available to purchase at the time of recording. That distinction between an established care-facility offering and a product still being developed matters when assessing where the technology can be used today.

Privacy and consent are part of deployment. Mike says facilities obtain consent from residents or an appropriate representative, and he describes the service as focused on event clips rather than live viewing into a room. Those explanations help frame the conversation a buyer should have about access, retention, review, and the information people are agreeing to share.

Finally, we discuss the business case. Mike connects the value of event information with staff response, communication with families, and adjustments to care. He also describes possible reporting and risk-identification capabilities that had not been deployed. The interview offers a view of the operational questions involved, without establishing a controlled clinical result or a fully quantified return on investment.

I would love to hear your thoughts, so please share your experiences. How should care providers assess whether an AI alert improves the work people do, while respecting the privacy and choices of those receiving care?

What can a crypto exchange's proof of reserves establish, and what might still be missing from the picture?

On Tech Talks Daily, I'm joined by Harvey Liu, co-founder and CEO of LeveX, to discuss transparency, platform engineering, and the responsibilities that come with operating a centralized exchange. Harvey brings a computer science background, an MBA, and experience in venture capital. Our conversation looks at how becoming a founder changed the questions he asks about growth, spending, product delivery, and customer assets.

We begin with the community idea behind LeveX. Harvey recalls meeting other crypto investors and wanting an online environment where people could discuss ideas alongside trading. He describes that as part of the motivation for building the company. The interview also makes clear that some social features remain in development or are planned for a later release. A community vision and a finished product roadmap are different things, and the discussion gives listeners a view of that work in progress.

The central technology question is proof of reserves. Harvey explains why a snapshot should be considered in context, including its date, the assets it contains, and the liabilities it may not reveal. He discusses the limitations of relying on a platform's own token and the difficulty of understanding obligations outside the information shown. Publishing reserve information can contribute evidence, but the conversation does not establish that a snapshot alone proves a business is solvent or that customer assets are protected under every condition.

We also discuss the engineering approach Harvey says LeveX uses. He describes Merkle-tree verification, user account inclusion, and separating parts of the reporting process from the live trading environment. The challenge is to provide useful information while managing privacy and system load. His explanation is a founder's account of the architecture, rather than an independent technical audit or a live demonstration.

From there, the conversation turns to the realities of building the business. Harvey says he underestimated how much product development investment would be needed to compete with larger teams. He also describes the difference between taking a minority investment position and being responsible for the operation itself. Cash flow, customer growth, infrastructure decisions, and the consequences of a failure become immediate concerns rather than topics for a periodic review.

Security and AI bring further tradeoffs. Harvey describes security reviews, operational controls, and the ability to suspend certain functions if something appears wrong. He also discusses AI-assisted development and analysis, alongside agents being built to help with order placement. These ideas raise questions about access, authorization, accuracy, and responsibility. The interview does not demonstrate those systems or establish that AI-generated analysis improves trading results.

Availability is another part of the discussion. Harvey says LeveX restricts access from jurisdictions including the United States, the United Kingdom, Singapore, and mainland China. He describes licensing work as being in progress, rather than announcing completed authorizations. This episode examines a technology business and its founder's decisions. It does not recommend opening an account or imply the service is available in every listener's location.

We close with Harvey's views on decentralized exchanges and plans for additional social features, competitions, and copy trading. These remain company plans as described at recording, without a verified delivery schedule or a promise of financial returns. For listeners interested in platform design and founder accountability, the useful thread is how to question evidence, distinguish current operations from future ambitions, and assess what a company is actually claiming.

Please share your thoughts. What should exchanges make easier to understand about their reserves, liabilities, and operating controls so people can assess their claims with greater clarity?

What needs to happen before an IT team can trust an AI agent to resolve a problem rather than simply report it?

In this episode of Tech Talks Daily, I welcome Jed Ayres, CEO of ControlUp, and Gadi Feldman, Chief Product Officer, to discuss autonomous endpoint management and the practical work behind it. We begin with a familiar problem for enterprise IT teams, plenty of monitoring data and recurring issues that still require investigation, ticket transfers and manual intervention. Jed explains why the aim is to follow an operational problem through to an authorized action and then verify that the issue has been resolved.

Gadi describes how specialist agents could bring together expertise across areas such as endpoint management, networking and storage. We discuss the connections those agents need to existing tools and why an understanding of the technology is only part of the job. Agents also need access to company procedures, priorities and knowledge, much as a new member of an IT team needs to learn how the organization works. Gadi explains how configurable human approval and other controls form part of that approach, with teams able to begin with analysis and recommendations before allowing additional actions.

Jed shares a CHRISTUS Health example involving an audio driver issue across approximately 900 devices, illustrating the value of seeing a common pattern rather than treating every device as a separate support problem. We also discuss what a useful business case should measure. Alongside the time spent by IT, leaders need to consider the employee's experience of waiting for help or finding a workaround. Device performance and software usage can also inform decisions about equipment replacement and unused licenses.

The conversation considers what becomes possible when repetitive work takes less of the team's time, while acknowledging the need to establish trust and define the limits of autonomy. For leaders looking for a starting point, Jed describes ControlUp's seven-day challenge, and Gadi recommends understanding the fleet already in place. Which recurring IT problem would you address first, and what evidence would convince your team that automation was helping? Please share your thoughts with me.

When would you trust your ERP system to move from answering a question to carrying out the work?

Recording at SuiteWorld in Las Vegas, I spoke with Brian Chess, who leads AI product and technology for Oracle NetSuite, about what that progression looks like in practice. Brian was an early developer when the business was called NetLedger, later left to build a company, and returned with a different perspective on the needs of small and growing businesses. Our conversation focuses on how AI can help people understand their systems, complete unfamiliar tasks, and gradually delegate work without losing control of the process.

One of Brian's most useful ideas is AI acting as a teacher. He describes users asking Ask Oracle who has logged into their account, or asking it to explain a script they do not fully understand. The benefit is the answer and the explanation of how to find it. Someone who previously needed help from a colleague can begin to understand a part of the system for themselves.

He also shares an example involving NetSuite customer MCNC. When the payroll administrator was unavailable, Brian says a controller who had not been trained on the process used Ask Oracle for guidance. The outcome he describes is straightforward and meaningful: payroll ran on time. It is a customer account from the interview, rather than an independently assessed result, but it illustrates how useful AI assistance can be without requiring a dramatic productivity claim.

Financial close provides another practical example. Brian describes starting with the spreadsheet a finance team already uses, modelling that process inside NetSuite, and introducing automation in stages. I recognise the appeal of that approach from my own time supporting month-end and year-end work in IT. Teams need to understand how the process will run, especially when a mistake can affect reporting or the system of record.

We discuss how permissions apply as software gains the ability to act. Brian says Ask Oracle operates with the permissions of the user, while an agent receives an assigned role governing its access. He describes a progression from read-only analysis to limited changes as users gain confidence. Human approval remains available for transactions, particularly while teams refine instructions and account for cases they may have missed.

Business context and shared knowledge are part of the same discussion. Brian explains how written skills can capture procedures and the intended business outcome, making knowledge available beyond the colleague who usually handles the task. We also talk about accountability. He describes transaction records showing which agent acted and when, together with an activity log. Those records help establish what happened, although they should not be confused with a complete explanation of every internal model decision.

The conversation also covers whether to build AI capabilities or use those embedded in a business platform. Brian's answer depends on the role the capability plays in a company's competitive position, the expertise available, and the time it wants to invest. His view of ROI is similarly specific to the business. Comparing vendor contracts with billing records may reveal savings, but the same result does not necessarily transfer to a company with different suppliers and arrangements.

We finish with the opportunity Brian sees for smaller businesses that have fewer specialists available. AI assistance may help people undertake work they previously needed a consultant or experienced colleague to explain. The question is how to extend that access while retaining appropriate permissions, review, and responsibility.

I would love to hear your thoughts, so please share your experiences. Where would conversational ERP make the biggest difference in your business, and which actions would you still want a person to approve?

What if the biggest gain from AI comes from reducing the distance between the person who understands a problem and the person building its solution?

On Tech Talks Daily, I'm joined by Ben Grinnell, Chief AI and Capabilities Officer at Newton, a strategic delivery consultancy working with private and public sector organizations. Ben's career stretches from an early web consultancy to enterprise technology delivery and today's AI tools. He describes returning from retirement after reading a draft book about vibe coding and realizing he wanted to be part of what came next.

What interested me was his practical explanation of how AI can change delivery. When a consultant or business specialist can test an idea while hearing the problem directly, fewer handoffs may mean less lost context and quicker feedback. Ben sees an opportunity to build useful solutions closer to the work. He also warns against confusing a demonstration that looks finished with software that is ready to run reliably. Security, architecture, testing, and maintainability still need people who know which questions to ask.

We discuss the relationship between business teams and IT. Employees have been building spreadsheets, macros, and local workarounds for years, so the challenge of technology appearing outside central oversight is familiar. AI changes the speed and scope of that activity. Ben argues for keeping technical expertise close to the business, connecting small projects to existing enterprise investments, and producing value early enough that people want to participate.

Trust runs through the conversation. Ben describes a progression from private use of AI assistants to shared agents that appear within a team's existing tools. Visibility can help colleagues understand what is happening, but easier access to company information can also expose old permissions problems. A document that has sat unnoticed on a shared site can become easier to find, making sensible access controls part of adoption rather than an afterthought.

One of the more surprising examples concerns legacy modernization. Drawing on his experience with caseworkers using different workarounds, Ben discusses separating a technical platform replacement from changes to how people work. He describes AI-assisted rewriting that preserves existing behavior before a later, more gradual process redesign. The conversation presents this as an approach to consider, rather than a demonstrated deployment or a reason to preserve security defects.

We also examine AI measurement. Ben challenges the emphasis on how many roles a company can remove and suggests looking at revenue, client service, profitability, and the repetitive work people no longer have to do. His argument is that the way a program is described affects whether employees support it. Leaders still need evidence that a project works, alongside an understanding of how their success measures influence behavior.

We finish with Newton's hands-on AI work, including internal hackathons, model access, and Ben's account of investment in sovereign compute for inference costs and UK data requirements. He makes a useful distinction between giving new users a strong first experience and asking experienced users to improve efficiency. Cost control and adoption need to be considered together.

I'd welcome your thoughts. How is your organization measuring AI value, and which small improvement has persuaded people to change the way they work?

What if your ERP system came to you through a conversation, instead of asking you to learn its menus before you could get anything done?

Recording at SuiteWorld in Las Vegas, I welcomed Evan Goldberg, founder and executive vice president of NetSuite at Oracle, back to the podcast. We last spoke two years earlier about the company's origins. This time, the discussion centres on his description of AI as the UI and what that could mean for the people using business software every day.

Evan describes an ERP experience in which users explain what they want in natural language. One example is adding a dashboard metric by describing the information it should show. If the result is not quite right, the user can explain what needs to change. For me, the useful question is whether that makes software easier to fit around a person's work and how reliably it understands the request.

We discuss the knowledge behind those answers. Evan describes SuiteSkills as a way to bring together information about NetSuite and the procedures businesses use within it. He connects that work with consistent answers through NetSuite Next and through external tools using the AI connector service. His ambition is to support the models and ways of working customers choose, while making the information returned from NetSuite dependable. It is a product direction he describes, rather than an independently tested guarantee that every model will behave identically.

The conversation then moves from answers to actions. Evan points to order approvals, accruals, and agents looking for exceptions that deserve attention. He argues for beginning with a process people already want to improve and deciding which parts can run with human approval or greater autonomy. The appropriate balance will depend on the company and the work involved. A conversational interface does not remove the need to decide where authority belongs.

Personalisation is another part of adoption. Evan's view is that people are more likely to keep using an AI feature when they can explain how they want it to work. The dashboard example makes that concrete, but it also raises a wider question about how companies maintain consistent procedures while allowing people to adapt their tools.

We also discuss smaller businesses and the opportunity to access capabilities without building a large specialist team. Evan describes a startup offering and his belief that the same AI models can help companies of different sizes. The practical considerations still include eligibility, the work a team needs to accomplish, and the controls around its business information.

For partners, he sees an opportunity to make industry expertise available through reusable skills. A specialist's knowledge of a particular niche could inform how Ask Oracle helps customers, while the partner spends time developing that expertise further. It is an interesting view of how advice and software configuration may change as AI becomes part of delivery.

I also ask about software pricing. If agents complete tasks that once required someone to log in, what happens to the traditional per-user model? Evan acknowledges the question but offers a counterpoint: easier access to information and personal agents could encourage people to remain users. He does not announce a pricing change, leaving us with a business-model question that is still developing.

We finish with an unexpected customer conversation at Disneyland, which Evan uses to illustrate interest from everyday business users. It is a memorable anecdote rather than a measure of adoption, but it brings the discussion back to the people who ultimately decide whether these features earn a place in their working day.

I would love to hear your thoughts, so please share your experiences. If AI becomes the interface to your business systems, what would you want it to understand before you let it act on your behalf?

What needs to happen before AI agents can reliably complete work across the systems that run a business?

In this episode of Tech Talks Daily, I welcome back Irfan Khan, Chief Product Officer for SAP Data & Analytics, to discuss the distance between a useful AI pilot and a dependable operational workflow. Our conversation begins with the autonomous enterprise and what that means for teams working across finance, procurement, supply chain and HR. Irfan explains why agents need a shared understanding of business activity, including the state of purchase orders and inventory, before coordination can produce a useful result.

We discuss the data quality, permissions and governance questions that become harder to manage as deployments grow. Irfan also considers how leaders should measure AI performance. Generating additional content or consuming additional tokens tells us something about activity, but leaders still need to understand the effect on productivity, accuracy and the cost of completing work. He shares how SAP has approached developer experimentation and why teams need to become deliberate about which tasks benefit from AI consumption.

The conversation also covers company memory, an initiative Irfan describes as a way to bring institutional process knowledge into agent workflows. We discuss how employee trust depends on the task, why order entry requires a different level of precision from a forecast, and how open architecture can preserve choices as models and infrastructure develop. Irfan then explains SAP's approach to connecting SAP and non-SAP data through SAP Business Data Cloud and the wider SAP Business AI Platform.

For leaders assessing the next stage of their AI programs, the discussion offers practical questions about context, access, cost and the business outcomes they want to improve. You can find the resources discussed in the accompanying show notes at TechTalksNetwork.com. Where is your organization finding the biggest obstacle to operational AI, and what would help you move forward?

When AI makes writing code faster, how do you decide whether your team is building the right thing?

In this episode of Tech Talks Daily, I speak with Johnny Halife, CTO at Southworks, about what happens to software engineering when code generation becomes easier but decisions still require experience. The pressure to deliver faster is familiar. The harder question is whether teams have the review capacity, testing processes, and deployment infrastructure to turn that extra output into useful software.

Johnny describes a different way of directing AI coding agents. Engineers need to specify the outcome, define what finished work should look like, and provide checks that can show whether it meets the required standard. Constantly telling an assistant what to do next can keep a person busy without giving the system enough responsibility to complete a coherent piece of work. We discuss how to create tasks that are small enough to review and substantial enough to delegate.

We also talk about context. Giving an AI system access to institutional knowledge sounds sensible, but adding every document to every conversation creates its own problems. Johnny describes borrowing from open source contribution practices, including README files, contribution guidelines, and records of architectural decisions. His approach combines an explanation of why the system works as it does with directions for finding additional information when needed. It is a useful way to think about onboarding an agent without expecting it to absorb the entire history of a business at once.

The phrase human in the loop comes up frequently in AI discussions, so I ask what it should mean in practice. Johnny describes checks before work begins and a review of test results and other evidence at the end, supported by progress updates along the way. The person responsible needs to set expectations and assess the outcome. Being available for an alert is only one part of that responsibility.

An unnamed streaming platform project provides a practical example. Johnny explains how Southworks developed the supporting process in stages, improving documentation, tests, and the infrastructure around the developer workflow. He reports a tenfold increase across requests, changes, and delivered value, although we do not have an independently verified measure or a detailed breakdown. His broader observation is that the same improvements made the environment easier for human engineers to understand and work in.

Finally, we discuss hiring and professional development. Johnny values software and cloud knowledge, curiosity, clear writing, time management, and the judgment to choose between competing possibilities. AI can make experimentation quicker, but someone still needs to decide which experiment deserves attention and when to stop.

I would love to hear how this compares with your own experience, so please share your thoughts. Where does work now wait in your engineering team, and how are you helping people make better decisions about what to build?

What happens behind the scenes when a cross-border payment uses stablecoins, and how much of that should the customer need to understand?

In this episode of Tech Talks Daily, I speak with Shah Ramezani, co-founder and CEO of Noah, about the infrastructure that connects stablecoins to business payments. The discussion goes beyond the transfer itself to the practical work required to move between currencies, choose a route, verify identities, and reconcile the result.

Shah starts with the friction he sees in correspondent banking and the opportunity for a different settlement layer. He explains how stablecoins can move quickly between participants and then connect to local payment methods at either end. His example of a payment from the United States to Argentina helps explain the choices involved, including whether the recipient wants local currency or a dollar-denominated stablecoin. It is an illustrative scenario rather than a named customer case or a universal comparison of costs and delivery times.

I ask what makes that experience work technically. Shah breaks the process into liquidity, routing, compliance, settlement, and reconciliation. The available partner, customer type, destination, and pricing can all affect how a payment is handled. Noah's role, as he describes it, is to bring those components together so partners can connect without building every part themselves.

Identity is another part of the conversation. Shah discusses know-your-customer and know-your-business checks, including arrangements in which partners rely on checks performed by Noah and receive the information they need. His account makes clear that reducing repeated document requests requires coordination among the organizations involved. It also raises questions about who is responsible for each step and how that responsibility changes across markets.

One idea I found most interesting is that users may encounter stablecoin infrastructure without choosing to hold or handle stablecoins themselves. Shah describes payments in which the technology operates behind a familiar interface. For a business considering that approach, the useful comparison is the full payment journey, including conversion, local payout, support, and each participant's obligations.

We also discuss banks' continuing role. Shah rejects the idea that they disappear and instead describes a different distribution of responsibilities across payment networks and financial institutions. The conversation gives listeners a way to consider how new settlement technology can coexist with established banking relationships.

Trust and regulation remain part of that assessment. Shah argues that stronger regulation provides reassurance, while acknowledging the difficulty of serving customers across jurisdictions with different reporting obligations, identity requirements, and fraud patterns. His confidence in the market's direction is his perspective. It should not be read as a guarantee that every stablecoin, provider, or transaction carries the same protections.

Finally, Shah discusses Noah's plans to broaden its capabilities and invest in product development and hiring. He describes an opportunity to make the payment experience simpler and less complex for the user. For me, the accompanying question is how providers make their responsibilities clear when the underlying process spans several organizations.

I would love to hear your thoughts, so please share your experiences. When you assess a new payment route, how do you weigh speed and cost against clarity about responsibility, access to funds, and help when something goes wrong?

What happens when the pressure to complete an ERP transformation leaves access governance waiting until after go-live?

In this episode of Tech Talks Daily, I speak with Chris Radkowski, GRC Expert and Advisor at Pathlock, about the business consequences of access decisions.

Chris previously spent 17 years at SAP and brings experience across access control, governance and security. We discuss why ERP risk belongs in transformation planning, with examples that connect system availability to manufacturing, incoming deliveries and the processes that keep a business operating.

The conversation considers privileged access inside business applications. An identity does not need to administer a server to hold permissions that affect sensitive information, supplier records or financial transactions.

Chris explains why the combinations of entitlements assigned to a person deserve attention, and how that assessment becomes more complicated as processes extend across several systems. AI agents and automated workflows add to the challenge when they can carry out transactions quickly or operate through credentials associated with a user.

We also discuss the limits of scheduled access reviews. Chris describes the work involved in checking whether an inappropriate permission was used, including the manual effort that can arise when teams need to investigate historical transactions. He argues for combining access information with activity and transaction data, so teams can examine an unexpected pattern and initiate a review rather than waiting for the next certification cycle.

For leaders planning an ERP or AI transformation, Chris recommends considering role design, access assignment, certification and emergency access early in the program. We discuss how changes between SAP ECC and S/4HANA can affect the permissions and transactions on which existing controls depend.

Our conversation offers practical questions about who can act, what they actually do and how the organization responds when operations are under pressure.

Which access decisions would you want resolved before your next go-live, and where are the gaps today? Please share your thoughts with me.

What happens when an organization restores its systems after an incident but employees still cannot connect to them?

In this episode of Tech Talks Daily, I speak with Sterling Wilson, Global Field CTO for Business Continuity and Disaster Recovery at IGEL, about the endpoint recovery gap. Sterling explains why a laptop that is encrypted, unstable or being preserved for investigation can prevent work from resuming even when applications and infrastructure are available. We discuss why continuity planning needs to account for the device people use to reach those services.

The conversation examines IGEL Emergency Mode, an administrator-led capability for supported IGEL Dual Boot-enabled endpoints. Sterling describes moving those devices into IGEL OS so employees can reach resources IT has approved while security teams continue investigating the Windows environment. He emphasizes that this is an alternative access path, rather than Windows repair, threat detection or a replacement for the wider incident response process.

I ask how organizations should prioritize employees and services during a disruption. Sterling recommends making those decisions around business functions and dependencies rather than seniority, with the priorities established before a crisis. We also discuss who has authority to initiate a transition, whether people can still sign in and reach their applications, and how a team returns devices to its chosen operating state afterward.

Sterling draws a distinction between a recovery time objective and the time it actually takes people to resume work. That brings the discussion back to testing, including scenarios where management systems, connectivity or other dependencies may be unavailable. For leaders assessing business resilience, the conversation offers a way to look beyond infrastructure status and examine whether the organization can deliver its services again. When did your team last test recovery from an employee's perspective, and what did it reveal? Please share your thoughts with me.

What does an investor actually own when a share becomes a token, and who keeps the official record accurate?

In this episode of Tech Talks Daily, I speak with Dan Kramer, Chief Executive Officer of Equiniti, about the infrastructure behind tokenized securities.

While much of the discussion focuses on trading venues and settlement, Dan brings the conversation back to the record of ownership and the responsibilities of a transfer agent, or registrar in the UK. He explains why issuers want a consistent register rather than separate versions of ownership across different systems.

We discuss the difference between shares issued by a company and tokens created by a third party to represent an interest in an underlying asset. Dan explains why voting rights, information rights and exposure to another organization deserve attention when assessing a tokenized product.

We also consider how existing market infrastructure connects to this work and why the technology itself cannot answer every question about rights and responsibilities.

The conversation then turns to AI in shareholder services. I ask Dan about accountability when software assists with proxy voting, and we discuss the importance of being able to understand how decisions were reached.

He also shares Equiniti's approach to employee training and its ambition to make interactions with its systems available through natural language by the end of 2027. This is a future target, rather than a description of capabilities already available across the business.

Dan also discusses the planned Bullish acquisition and how the companies intend to bring together issuer relationships and blockchain infrastructure.

We close with a practical example of a customer portal moving from a Monday discussion to a Thursday delivery, illustrating his view of how development is changing.

For leaders assessing tokenization, the conversation offers useful questions about ownership, controls and the experience shareholders should receive. Which questions would your organization need answered before considering tokenized shares?

Please share your thoughts with me.

What good is a new treatment if patients still struggle to get the approvals, appointments, and support needed to receive it?

In this episode of Tech Talks Daily, I speak with Amir Sharif, president and co-founder of Leap, about the operational infrastructure behind specialty care. While AI in drug discovery attracts attention, the journey from a prescription to treatment often depends on disconnected organizations, incomplete information, and manual coordination. Amir describes how Leap combines drug sourcing, distribution, care coordination, and employer benefit arrangements to address some of those gaps.

His interest in patient experience grew through work helping build healthcare businesses including Care/of and Tend. Those experiences led him to question why everyday expectations around scheduling, pricing, and customer support can be so difficult to meet in healthcare. Specialty therapies bring another level of complexity because they can require clinical administration, recurring appointments, insurance authorization, and expensive medication procurement.

We look at what happens after a physician writes a prescription. Approval may depend on the patient's health plan, supporting medical information, an administering provider, and a permitted site of care. Coordinating those pieces can fall to the patient, even while they are dealing with an illness. Amir explains why access depends on the administrative process as well as the treatment itself.

Drug pricing provides another part of the picture. Under a buy and bill arrangement, a provider purchases a drug, administers it, and bills for it. Amir says the margin added to the acquisition cost can range from 40 percent to 400 percent. These are his reported figures rather than a finding independently established in this episode. He describes Leap's model as passing its drug acquisition cost to the employer or plan sponsor and coordinating administration through home infusion or partner clinical sites, depending on patient preference and clinical requirements.

The technology discussion becomes particularly practical when Amir describes the data behind identifying eligible patients. Leap receives feeds containing diagnosis codes, claims, and prior authorization information. Its data engine normalizes those feeds so the team can act on signals that a patient is receiving, or may soon be prescribed, a therapy within the program's scope. This is a care coordination process. Amir repeatedly says Leap does not replace the prescriber or take control of clinical treatment decisions.

He also describes an average patient encounter involving over 20 coordination touchpoints. Many external parties still communicate by email or fax rather than through software interfaces. Leap uses agentic workflows to support appointment scheduling, shipment tracking, and medical order processing. According to Amir, those workflows remain supervised by care teams, and patients speak with a person when they contact the service.

For business leaders assessing AI returns, his account of model costs is especially useful. Some workflows the team considered two quarters earlier would have cost tens or hundreds of dollars per run in token consumption, he says. As models become cheaper and more capable, different operational uses become viable. The interview does not provide a measured before and after cost comparison, so the lesson is about evaluating changing economics rather than declaring a proven return for every workflow.

Leap reports a patient Net Promoter Score of 92, with surveys sent after every encounter. Amir describes this as evidence of patient satisfaction, alongside feedback gathered by care teams. Satisfaction is useful, but it should not be confused with proof of improved clinical outcomes or faster treatment across every patient. Employers can request an impact analysis comparing historical claims with the proposed cost of Leap's service.

Where would you begin when assessing specialty care, the drug price, the time patients lose, or the coordination needed to keep treatment on track? Listen to the conversation and share your thoughts with me.

What happens to a good idea when the person who understands the problem best cannot express it confidently in the language used by the rest of the team?

In this episode of Tech Talks Daily, I speak with Gonçalo Gaiolas, Chief Product Officer at DeepL, about real time voice translation and the work required to make a multilingual conversation feel natural. Our discussion moves from the business cost of language barriers to the technical choices behind speaker identity, tone, speed, and the handling of sensitive meeting data.

Gonçalo begins with a cost that is difficult to place on a spreadsheet. An employee may understand a local market, a product, or a customer need but struggle to contribute in the company's shared language. The organization can miss that knowledge even when the person is present in the meeting. Customers also tell DeepL that language barriers affect the time needed to make decisions and understand nuance. These are experiences he describes, rather than independently measured results presented in the interview.

Live translation introduces a demanding set of constraints. People pause, interrupt, change direction, and build meaning at different speeds across languages. A system needs enough context to produce an accurate translation while responding quickly enough to remain useful. Waiting can interrupt the flow, but responding too early can lose the meaning of the sentence. Tone creates another challenge because urgency, hesitation, and a question can carry information that the translated words alone do not capture.

We discuss DeepL's approach to retaining the characteristics of individual speakers. Gonçalo describes separating speakers and carrying elements of voice, rhythm, and emotion into the translated audio. He also explains that some original audio remains audible at a lower level. His aim is to help listeners follow who is speaking while understanding the translated conversation. He says users have options around retaining vocal identity, acknowledging that people may feel differently about using that capability.

The enterprise adoption discussion is equally practical. Gonçalo describes a desktop app intended to provide a consistent experience across Zoom, Microsoft Teams, and Google Meet. A global workforce may use one platform internally and encounter several others when speaking with customers and partners. For him, working across those environments is part of the product's usefulness, alongside the quality of the translation itself.

Accuracy remains a recurring theme. Gonçalo describes testing with real users and evaluating whether the result works in the customer's context. He gives the example of an engineering department whose acronyms and specialist terms can confuse a model. DeepL's customization capabilities, he says, let customers supply terminology and guidance about how language should be handled. The example offers a useful reminder that a fluent sentence can still misinterpret the way a particular team speaks.

We also discuss procurement and governance. Meeting audio can contain sensitive business information, so buyers need to understand technical architecture, policies, and how their data is processed. Gonçalo says DeepL does not use customer data to train its own models. The interview does not supply the contractual scope, retention periods, or detailed controls behind that statement. Those remain questions for buyers to check against the service and agreement they intend to use.

Examples range from conversations between parents and teachers to contact centers, large events, and use alongside human interpreters. Gonçalo describes customer demand that extends beyond the scenarios the team originally expected. I also ask about translating podcasts, which leads to a discussion of possible uses outside live meetings. We keep that possibility separate from a confirmed podcast production service or a promise about this show's availability in other languages.

I'd welcome your thoughts. Where could voice translation help someone contribute knowledge your organization is currently missing, and what would you need to test before relying on it?

What if the most useful industrial robots are the ones helping experienced people continue working in safer conditions?

Recording from the show floor at IFS Unleashed 2026 in Orlando, I spoke with David Askey, CEO of Ascend Robotics, about construction and maintenance machines working in spaces that are difficult or dangerous for people to reach.

We discussed the gap between a robotics demonstration and equipment that can perform reliably at a customer site, where conditions rarely match the original plans.

David shares a ship painting example in which he reports work completed three to four times faster than with typical human crews, alongside approximately twenty five percent material savings. He also describes more consistent coating thickness and explains why people continue to monitor and direct the work.

A separate example involves cleaning and coating a factory ceiling during a data center conversion, where live electrical infrastructure added to the difficulty of the job.

One of the most interesting parts of our conversation concerns the people operating these machines. David describes hiring experienced workers from painting crews as robot operators and working with unions as the equipment enters the market. For workers affected by the physical demands of construction, operating a robot can provide a way to keep contributing their knowledge from a different working environment. We also discuss how robots adapt to buildings as they actually exist and how operators help them decide what to paint and what to avoid.

David explains how AI tools have accelerated his team's software development and why connecting the data collected by robots to enterprise systems is a separate challenge. He describes IFS as a bridge between work happening in the field and the information management teams need.

For anyone considering robotics, the conversation offers practical questions about reliability, safety, integration and the outcomes worth measuring. Where could robots support your workforce, and what would you need to see before putting them to work? Please share your thoughts with me.

What happens when individual AI agents begin working together as digital teams, and how much more can businesses achieve when automation moves beyond a single task?

Recording live at IFS Unleashed 2026 in Orlando, Neil C. Hughes reconnects with Somya Kapoor, CEO and Co-founder of IFS Loops, to discuss how digital workers have developed since their previous conversation in New York.

Last year, the focus was on individual digital workers performing specific tasks. Now, IFS is bringing several workers together to support wider business processes, with early customer deployments providing evidence of what these systems can achieve.

Somya explains why the initial approach centered on low-risk, high-impact workflows rather than attempting to automate complex processes all at once. She shares how some customers who began with a single digital worker have expanded to two or three, creating opportunities to automate more work across procurement, asset management, and field service.

The conversation also examines customer examples from IFS Unleashed. Somya discusses KLM's reported efficiency improvements, the potential annual savings associated with invoice processing at Eight Group, and how Kodiak Gas is using a digital worker to make spare-parts identification and purchasing significantly faster.

These examples provide an interesting perspective on enterprise AI at a time when many organizations are still struggling to move beyond pilot projects. Rather than beginning with large transformation programs, Somya describes an approach built around focused deployments, measurable results, and the opportunity to expand when the initial benefits become clear.

But the technology is only part of the challenge. Somya also explains why change management can become a greater obstacle than building the agents themselves, particularly when employees and business leaders are unfamiliar with allowing digital workers to perform operational tasks.

As AI agents become increasingly capable of working together, will organizations find greater success by starting small and building confidence through results? And could digital teams provide a more practical route from AI experimentation to everyday business value?

Listen to the conversation and share your thoughts on where digital workers could make the greatest difference in your organization.

What if the biggest obstacle to industrial AI isn't the technology itself, but the data businesses are trying to use with it? And how much could manufacturers and industrial operators achieve if they could connect information from across the entire life of their assets?

Recording live at IFS Unleashed 2026 in Orlando, Neil C. Hughes speaks with Shawn Gregerson, VP APM & MES Business Development at AVEVA, about how industrial companies are using AI, operational data, and predictive maintenance to improve reliability and make more informed business decisions.

Shawn explains why industrial AI is increasingly becoming a data challenge rather than an algorithmic one. Although AI models and analytical techniques have developed significantly, many organizations still struggle with disconnected information spread across engineering, operations, maintenance, and asset management systems.

The conversation examines how AVEVA is bringing these different sources together through its industrial knowledge graph technology, creating a more reliable foundation for AI applications that need to understand the relationships between assets, processes, and operational conditions.

They also discuss the growing financial pressures facing industrial organizations. Shawn highlights the increasing cost of downtime and the challenge of maintaining assets that are operating beyond their original design life.

With AVEVA and IFS working together on Continuous Asset Decision Intelligence, or CADi, the discussion turns to connecting asset planning, design, operations, maintenance, and optimization so that decisions can be informed by real operating conditions.

Shawn also shares where industrial AI is already delivering results, including predictive maintenance systems that monitor equipment health, identify early warning signs, and help maintenance teams determine what action to take before failures occur. Other applications include predictive quality, manufacturing throughput, and the use of generative AI to make industrial information easier to access.

Looking beyond individual applications, they consider how connected industrial data could support agentic workflows and enable businesses to automate more of the tasks that employees currently perform manually.

As industrial organizations face pressure to improve efficiency while extending the life of existing equipment, could connecting their operational data deliver greater value than investing in increasingly sophisticated AI models alone?

Listen to the conversation and share your thoughts on how industrial AI is changing maintenance, asset management, and operational decision-making.

Could your enterprise data support a reliable report and still leave an AI agent unsure what the numbers mean?

In this episode of Tech Talks Daily, I speak with Saurabh Gupta, CEO of The Modern Data Company, about the difference between being analytics ready and AI ready.

Saurabh's background includes data strategy and governance work at Thoughtworks, Washington DC government, and international organizations. Our conversation focuses on the information around data that machines need when they begin consuming it directly.

A familiar reporting process can depend on human knowledge that never becomes part of the underlying dataset. Saurabh describes a system-of-record report exported to Excel, adjusted, and then used for compliance reporting. The spreadsheet and the original source now tell different stories. If an AI system encounters multiple copies, it needs a way to understand which version is appropriate and why.

We discuss how context includes quality, lineage, transformations, meaning, usage, and permissions. Those details often live across separate tools and teams. Bringing them together is part of the challenge of making data useful to agents, rather than simply making it available.

The distinction becomes clearer through a furniture company's date fields. Saurabh describes 14 fields that were tagged similarly but carried different business meanings. A warehouse, an ecommerce team, and a delivery team can use a label in different ways. An answer based on the wrong interpretation may look convincing while failing to address the question.

Governance also needs to reach the point of consumption. Saurabh explains why one person may be allowed to see a name, address, and date of birth while another needs the same fields masked. His approach is to connect those permissions with how the data is used, including when a machine becomes the consumer.

We then discuss DataOS, which The Modern Data Company describes as a platform for data products. Saurabh argues for starting with the outcome a user wants, then working backward to the necessary sources and controls. He explains how the company brings data from different locations into that process, while acknowledging that moving or centralizing data can still be appropriate where latency requires it.

The interview also addresses measurement. Saurabh clarifies the company's 90% claim as faster use-case delivery or time saved. It is not a 90% increase in adoption. He describes tracking data interactions to identify stale processing and report schedules that do not reflect actual use. The company also reports potential reductions in data ownership costs, but the interview does not provide enough detail to treat those figures as a universal return.

For CIOs and CDOs, his practical recommendation is to begin with a business problem and implement incrementally. A clearer view of which data people consume can help teams decide what to process, what to retire, and which investments serve a useful outcome.

We also discuss how AI may change the division of work between consumers, analysts, and engineers. Saurabh's forecast is a strong one. The broader issue is how organizations preserve business interpretation and accountability as additional reporting tasks move into automated systems.

The resources supplied after the interview include the company website, the Modern Data 101 practitioner community, and The Modern Data Report 2026. They offer destinations for listeners who want to continue learning about the team's approach.

What would make your data genuinely ready for AI, and where do definitions, permissions, or inconsistent copies still create uncertainty? Please listen and share your thoughts.

What happens when marketing software starts learning from customer behavior instead of following a campaign rule that someone wrote weeks ago?

In this episode of Tech Talks Daily, I speak with Neha Mittal, co-founder and CEO of JustAI, about AI agents, reinforcement learning, and a different approach to personalization. Neha spent over a decade working on growth and retention at companies including Twitter and Pinterest. Her experience repeatedly brought her back to the same problem: teams were running sequential experiments while the interests and behavior of their users kept changing.

Neha describes how a series of small A/B tests could consume weeks without producing a durable answer. Changing a button from red to blue might teach a team something, but the larger opportunity was to keep experimenting and learning across many decisions. Seeing similar problems at different companies helped shape the idea behind JustAI.

We discuss why she considers marketing a promising environment for autonomous agents. It involves a high volume of decisions, frequent feedback, and experiments that can often be assessed quickly.

Opens, purchases, and ignored messages can all contribute to learning. Neha contrasts those conditions with higher-consequence fields such as medicine and law, while explaining that strategy and judgment remain responsibilities for people.

The conversation also separates generating content from choosing an action. A language model can help produce an email. The reinforcement learning approach Neha describes aims to learn which decision supports longer-term customer value. The question becomes what message, timing, or channel makes sense for a particular person in the current moment, rather than which static segment or campaign they belong to.

JustAI divides the work across Strategy, Creative, Decisioning, and Data agents. Neha explains how opportunities feed into messaging, how the decisioning system learns from responses, and how the data agent returns insights to the process. Marketers can review customer-facing creative, provide brand guidance, and adjust strategy when the company's products or priorities change.

Her hypermarketer vision raises another question about how teams grow. Neha imagines one marketer having the capacity traditionally associated with a much larger team, with agents handling repetitive execution and experimentation. She presents this as a direction for the business rather than a demonstrated productivity ratio for every customer. The marketer still needs to translate business goals into instructions and judge whether the results make sense.

We bring the discussion back to current examples. Neha describes Coursera and ClickUp delegating the creation and selection of messaging variants. She also discusses the importance of human review for Better's financial-services communications. These examples show why the level of autonomy should depend on the task and the consequences of getting it wrong.

The supplied booking notes report over $100 million in customer revenue influenced and a $17 million funding round. Those company claims provide background, but the interview does not establish a revenue attribution method, incremental lift, or a complete return-on-investment calculation. The practical value of the conversation lies in understanding what work is being delegated and what people continue to own.

For me, the interesting issue is how to create a feedback loop that improves relevance without letting operational speed run ahead of customer trust or business judgment.

Which marketing decisions would you delegate to AI agents today, and what would you want to review before a message reaches a customer? Please listen and share your thoughts. 5. Suggested URL

Is your company using AI to improve the buying experience, or to carry out the same processes faster?

In this episode of Tech Talks Daily, I speak with Jonathan Kvarfordt, known as Coach K, about business strategy, go-to-market AI, and the relationship between people and automated systems. Jonathan introduces himself as VP of Marketing at 1mind and brings experience training revenue leaders and teams to assess where AI can support their work.

We begin with his framework for optimizing, amplifying, or reinventing a process. The point is to recognize the difference between making an existing task easier and considering whether the task should be done differently. Jonathan also acknowledges that organizations have different levels of readiness. Changing everything at once is not a practical answer for every business.

The conversation becomes especially useful when we discuss measurement. Rather than beginning with licenses or prompt activity, Jonathan recommends identifying the business outcome and working backward. Is the goal improved quota attainment, a higher win rate, reduced churn, or another result? What is preventing it today? Sometimes the answer concerns process or people rather than AI.

We then turn to the customer journey. Jonathan describes how 1mind aims to help buyers get product information, receive demonstrations, and answer technical questions before moving into a conversation with a person. His argument is that a website visitor may already be researching a specific decision, so an experience built only around awareness and meeting bookings can leave their immediate questions unanswered.

The practical question is how to support that buyer at the moment they need help. Jonathan talks about website assistance, sales engineering, product guidance, and customer success. He sees opportunities to provide information when a team cannot staff every interaction, while giving people additional context when they take over.

We also discuss the human side of AI adoption. Jonathan describes the continued value of emotional intelligence, relationships, and strategy. He challenges the assumption that every buyer always wants to speak with a person, but also recognizes that providing facts is different from understanding someone's motivations. The opportunity depends on the individual, the task, and the trust they place in the experience.

A comparison between two companies using the same outbound tool brings the discussion back to operational reality. In Jonathan's account, one company had clearer customer definitions, messaging, and data. The other used a broad target and expected the technology to compensate. His lesson is to examine what the system receives before concluding that another purchase will solve the problem.

For leaders assessing AI value, we talk about conversion rates, selling time, and the parts of a buyer's journey a system can support. Establishing a baseline matters. A team cannot confidently claim improved selling time if it never measured how much time people had to sell in the first place.

The interview also contains an inconsistent percentage claim about hallucinations. I have kept those figures out of the promotional package pending clarification. Reliability needs evidence, and the discussion does not provide a benchmark methodology or enough detail to establish a performance guarantee.

We close with Jonathan's advice to remain willing to learn and reconsider familiar habits. He is candid about feeling overwhelmed by the pace of change, which makes the conversation useful for people who are experimenting without feeling they have every answer.

Where does your buying journey create unnecessary effort, and how would you assess whether AI made the experience better for the customer? Please listen and share your thoughts.

What would change if your building could help explain what needs attention, why it matters, and whether the action you took actually worked?

In this episode of Tech Talks Daily, I welcome Bert Van Hoof, CEO of Willow, back to the show to discuss operational AI for buildings and infrastructure. Bert spent about 20 years at Microsoft, where his work included launching Azure Digital Twins. He now works with environments such as hospitals, airports, universities, corporate campuses, and data centers, where the condition of a physical space can directly affect the work happening inside it.

We begin with a problem familiar to many facilities teams. Buildings already have sensors, management systems, meters, maintenance records, and dashboards. Having access to information does not necessarily help an operator decide what to do next. Bert describes operational AI as a way to move through a sequence of questions: what is happening, why is it happening, what might happen next, what should we do, and did the action produce the expected result?

Context gives those questions meaning. A temperature reading alone cannot tell us whether a room is operating correctly. We need to know the room's purpose, occupancy, equipment, weather conditions, and recent maintenance history. Bert explains how digital twins and knowledge graphs connect those relationships so that a system can assess the situation rather than interpret a sensor value in isolation.

The discussion then turns to predictive maintenance. A probability of failure may be useful to an analyst, but a technician also needs to understand the consequence, urgency, and next step. Bert describes how condition-based maintenance can support equipment decisions and how earlier detection can reduce the disruption caused by refrigeration problems or water leaks.

These are practical examples of the types of outcomes Willow targets, rather than a claim that every customer will achieve the same result.

There is a human side to the conversation too. Bert describes operators opening numerous browser tabs each morning to assemble a picture of their buildings. Daily briefings and natural-language questions could reduce that search effort, helping teams focus their attention on the issues that deserve it.

We also discuss the relationship between occupancy, ventilation, energy consumption, comfort, and maintenance. Bert describes Willow's Active Control and the use of schedules and occupancy signals to help buildings respond to how spaces are being used. His argument is that these decisions should account for the whole environment. Saving energy in isolation can create different problems if comfort, reliability, or safety is overlooked.

Trust runs through the interview. A poor recommendation in a hospital has different consequences from an awkward paragraph in a document. Bert argues for explainable recommendations, operational evidence, defined limits, and verification after an action. He also sees experienced facilities engineers as part of the solution, with responsibility for setting objectives and judging trade-offs.

For me, the larger question is how building operations connect with the organization's purpose. A hospital needs spaces and equipment ready for patient care. An airport needs to account for passengers, flights, gates, and changing demand. Understanding the environment means understanding what it needs to support.

Where would operational AI make the most useful difference in your buildings, and what evidence would you want before allowing it to take action? Please listen and share your thoughts.

How much damage can a proven leadership playbook cause when it is applied to the wrong company? Experience can give executives confidence, but Sameer Kazi argues that confidence becomes dangerous when it replaces diagnosis.

In this episode of Tech Talks Daily, I welcome Sameer Kazi, CEO of the Pantheon Platform, back to the show for a conversation about copy-and-paste management, first-principles thinking, AI adoption, and the value of listening before making changes. Pantheon supports major websites and provides DevOps tools for agencies and enterprise web teams, placing Sameer close to the changing relationship between SaaS, web development, and agentic website creation.

Sameer describes a familiar mistake in technology companies. A new executive arrives with a formula that worked elsewhere, hires people from a previous team, and assumes the same inputs will produce the same growth. In the early SaaS era, for example, a company might have treated its sales efficiency ratio as a direct signal to hire another quota-bearing sales representative.

Product-led growth, changing buying behavior, and different forms of product market fit have weakened that assumption. Adding salespeople does not automatically create demand.

His alternative begins with product market fit and the way buyers discover, evaluate, and purchase a product. From there, leaders can study the mechanics across marketing, sales, customer success, support, and product development.

Sameer argues that rapidly growing AI businesses do not come with a settled operating manual, so executives need curiosity and evidence instead of a formula copied from the past.

Customer feedback provides one source of that evidence. Pantheon runs a public Slack channel where customers and partners speak directly with the company. Sameer says those conversations can reveal customer experience problems and, in some cases, signal a platform slowdown before observability tools detect it.

A small piece of feedback may not represent every customer, but it can give an experienced leader a reason to investigate immediately.

We also discuss AI return on investment and the growing cost of adding token consumption to an organization's existing workforce and infrastructure. Sameer does not claim to have one universal metric. Instead, he recommends looking at customer reactions to AI across sales, support, and internal processes. If AI makes a process faster for the company but worse for the buyer, customer evidence should reveal the problem.

The conversation then turns to data ownership. Sameer compares the hesitation companies once felt about moving data into the cloud with the speed at which some now provide business data to frontier AI labs. He expects small language models and open-weight models to give companies additional choices, including the option to run systems on their own hardware and isolate proprietary information where appropriate.

One of the strongest leadership examples comes from Sameer's arrival at Pantheon. He promised to speak with every employee and spent four months holding small roundtables. The format began with introductions and favorite movies before moving to a direct invitation: tell me what works, what needs attention, and what should be addressed first.

Sameer describes the result as a treasure trove of information from people closest to customers, prospects, products, and daily operations.

That approach also challenged the lazy assumption that long-serving employees represent outdated thinking. Sameer found experienced practitioners who had built agencies, developed large fleets of websites, and worked directly with customers.

Recognizing that knowledge can strengthen commitment, while dismissing it can leave employees disconnected and encourage quiet quitting.

Can leaders keep the principles that made them successful while treating every familiar method as a hypothesis to be tested? Listen to the episode and share how your organization balances executive experience with customer evidence and employee knowledge.

What matters most when you land in another country and your phone has no data? The cheapest plan may have looked like the obvious choice before departure, but priorities change quickly when you need a map, a translator, a ride to the hotel, or a way to tell your family you arrived safely.

In this episode of Tech Talks Daily, I speak with Alfonso Mata Villa, Head of Strategy at Holafly, about the growth of travel eSIMs and why they have moved from a specialist product to a far easier alternative to physical SIM cards and traditional roaming.

Alfonso explains that modern smartphones now make it possible to keep a normal number active while adding a separate data connection for an international trip. The result is a product that feels less like replacing a phone service and more like adding connectivity for a destination.

We talk through the practical experience for someone using an eSIM for the first time, including installation, activation, turning off the home data connection, and avoiding an unexpected roaming bill. Alfonso also describes Holafly's Always On benefit, which the company says gives customers one gigabyte of data each month so they can access basic services after arrival before buying another plan.

The apparent simplicity hides a demanding operating model. Holafly works across over 200 markets, where network quality, device behavior, carrier restrictions, hardware differences, and local regulation can all vary.

Alfonso explains why providers need strong operator relationships, redundancy, and the ability to move traffic when coverage becomes unreliable. His argument is that travelers should never have to see the complexity required to make a connection work.

That raises a useful question for anyone comparing travel eSIM providers. Price and data allowance matter, but what happens after the purchase? We discuss network reliability, data routing, latency, access to local services, privacy, and the speed of customer support when an installation or connection fails. Alfonso says Holafly treats multilingual support, unlimited data, transparent pricing, and reliable connectivity as parts of the product rather than extras.

The conversation also looks at the changing relationship between eSIM companies and mobile operators. Travel eSIMs compete with traditional roaming, but they still depend on operator networks. Alfonso sees an increasingly connected model in which operators supply infrastructure while global providers package connectivity for travelers and distribute it through airlines, travel agencies, hotels, online travel platforms, and financial services.

There is also a risk of leaving some travelers behind. Older phones may lack eSIM support, while carrier-locked devices and regional differences can create additional barriers. Alfonso argues that progress should be measured by whether the technology becomes simple enough for almost any traveler to use, rather than by technical sophistication alone.

If mobile connectivity can become another digital part of planning a trip, will travel eSIMs finally retire the anxiety and surprise costs associated with international roaming? Listen to the episode and share your experiences with eSIMs, roaming charges, and staying connected abroad.

Could your board explain where the organization's most valuable data resides, who can control it, and how quickly it could be recovered

In this episode of Tech Talks Daily, I speak with Adam Gale, CTO for Cybersecurity and Artificial Intelligence at NetApp, about why digital sovereignty is moving from compliance teams into boardroom discussions.

Adam argues that data has measurable business value, so decisions about its location, ownership, protection, and recoverability should be treated with the same seriousness as other financial risks.

We discuss how the US CLOUD Act, Europe's sovereignty agenda, and wider geopolitical tensions are changing cloud decisions. Adam explains that sovereignty extends beyond physical data location.

Hardware supply chains, software development, labor, access, operational control, and recovery all influence the level of sovereignty an organization may require. His advice is to treat sovereignty as a scale rather than impose the same controls on every workload.

The conversation also turns to sovereign AI. Adam describes why businesses and governments want greater control over training data, intellectual property, model activity, and audit records. He explains how poisoned data can produce plausible but unsafe outputs, and why controlled AI environments, traceable logs, and immutable records matter when systems begin making consequential decisions.

We then connect data visibility with business value. Adam shares how uncertainty over data location can affect a company acquisition and explains why insurers increasingly ask organizations to prove how information is protected and recovered.

He also describes the growing role of behavior analytics, automated responses, immutable storage, and storage-level ransomware protection in limiting damage and restoring trusted data.

If digital sovereignty begins with responsibility, control, and security, who owns those decisions in your organization today?

Listen to the conversation and share your thoughts with me.

Why do promising AI pilots struggle when they become services that real customers depend on?

In this episode of Tech Talks Daily, I'm joined by David Hughes, Chief Technology Officer at Stelia, to talk about the engineering decisions behind enterprise AI deployment. Dave's responsibilities cover networking, cloud infrastructure, and customer-facing products, giving him a view of what happens when an application meets the physical systems expected to support it.

We begin with a question that can get lost in the excitement around a successful demonstration. What problem was the system designed to solve, and what happens when the number of users changes? Dave describes reviewing a proof of concept that had worked for one external customer, only to identify a messaging component he believed would become a bottleneck under heavier demand. His lesson is to understand the intended workload before treating a working prototype as a production service.

The discussion also offers a balanced view of cloud computing. Dave praises elastic resources and programmatic control, while arguing that demanding AI workloads require teams to understand how storage, networking, and compute interact. A fast network cannot compensate for storage that cannot feed it. His steamship analogy makes the point familiar: the engine needs a steady supply of fuel if it is going to maintain its speed.

We talk about lessons from high performance computing, Dave's experience at Genesis Cloud, and the practical constraints of finding data center space and power. He explains why location and available capacity can influence architecture before a team even starts comparing GPUs.

For business leaders, the conversation becomes especially useful when we turn to measurement. Dave describes assessing internal model services through their effect on spending and judging customer projects by their output. His hotel occupancy analogy raises a question for anyone approving an infrastructure budget: how much of the asset's capacity will actually be used?

There is also a tension between engineering discipline and experimentation. Dave describes starting an internal inference service with existing servers and GPUs, then expanding it as demand grew. Proving that people wanted the service helped establish a case for a fuller investment. The lesson is to distinguish a useful experiment from evidence that a system is ready for wider demand.

We close with the Stelia newsroom and its engineer-written technical content. The supplied guest notes also introduce Stelia AI OS, described by the company as a full-stack operating system for enterprise AI. The interview focuses on deployment principles rather than a detailed assessment of that platform.

Where does your organization encounter the biggest obstacle when moving AI into production, and what evidence helps you decide where to invest next? Please share your thoughts.

Can a hotel modernize its technology without turning hospitality into another automated transaction? In this episode of Tech Talks Daily, I speak with Mark Hollyhead, Chief Transformation Officer at Aven Hospitality, about the systems behind thousands of hotels and the operational decisions that affect both employees and guests. Mark brings experience from British Airways, Heathrow Airport, Expedia, and travel management before joining the company carved out from Sabre with support from TPG.

His argument begins with a distinction that many technology programs miss. Guests rarely remember the property management system, API, or data pipeline behind a stay. They remember whether somebody knew their name, solved a problem, explained when the room would be ready, or prevented them from repeating information after a long journey. Technology matters because it can remove the administrative work surrounding those moments. Mark wants frontline employees to have time to be hospitable, especially when a situation requires empathy or judgment. That means looking beyond the promise of convenience and asking whether a system gives staff better information, reduces repeated work, and leaves them free to focus on the person in front of them.

Mark shares an example from a hotel he visits regularly in Dallas. Different employees recognized a few preferences from previous stays, including his connection with Seattle, a request for two bottles of water, and a preference for a corner room. None of those details required the hotel to know everything about him. They gave the employee enough context to make the interaction feel personal.

The example also illustrates the boundary between thoughtful personalization and an experience that feels intrusive. Mark's test is whether the customer would be surprised by what the company knows. If the guest begins wondering how the information was obtained, trust has already weakened. Hotels hold identity, payment, preference, and location data, including the place where somebody is sleeping, so that responsibility deserves careful treatment. For technology investment, Mark begins with friction rather than a product category. He looks for employees rekeying information, building spreadsheets because systems do not communicate, switching among screens, and developing improvised techniques to complete ordinary work. Across one property, five unnecessary minutes may appear minor. Across thousands of employees and repeated tasks, those minutes become expensive.

This approach changes how transformation is measured. Mark says another login, dashboard, or screen usually indicates that software has been added without changing the underlying work. He asks what has disappeared after a release. Fewer clicks, calls, handoffs, and repeated explanations provide clearer evidence than the launch itself.

Hotels also depend on a large network of systems covering reservations, property management, payments, distribution, loyalty, revenue management, customer relationships, and housekeeping. When definitions differ across those systems, employees become the integration layer. Data quality problems then appear at the exact moment a guest expects a simple answer. Mark believes the arrival of agentic commerce will make trusted and consistent information even more important. Travelers who have spent decades filling out search forms may increasingly describe what they need through a conversation. The AI agent will expect accurate room attributes, availability, rates, preferences, and policies from connected sources. Conflicting answers will damage confidence quickly.

The episode also challenges the familiar debate between digital and human service. Mark argues that people do not wake up choosing one or the other. They want an easy experience suited to the circumstances. A traveler arriving after a normal flight may prefer to go directly to the room. A traveler stranded in a snowstorm is unlikely to appreciate a chatbot repeating that it understands their frustration.

His operating principle is to automate predictable work and make it simple. When a situation becomes ambiguous, emotional, or unusual, a person should be able to step in with the context already available. The handoff matters as much as the automation because the employee should not force the guest to repeat the entire story. This has implications for frontline adoption. Mark rejects the assumption that resistance means employees dislike change. People often dislike bad change, especially when a new system makes the job harder and the response is a lengthy training session explaining why it should feel better. Aven involves frontline staff earlier so their knowledge can influence workflows before technology is introduced.

AI can also help prioritize work by showing employees the next action that deserves attention. That does not eliminate human judgment. It can give staff the information and authority required to resolve the most important problem without searching through several systems while an increasingly frustrated guest waits.

The opportunity is a hospitality operation where technology works quietly, reliable information travels with the guest, and employees have greater capacity to create memorable moments. The risk is adding automation to disconnected processes, collecting personal information without a clear purpose, or mistaking another launch for an improved experience.

Where would removing five minutes of friction create the greatest difference for your employees and customers? Listen to the episode and share your thoughts.

When should an AI system be allowed to fix an IT problem without waiting for a human to approve the action?

In this episode of Tech Talks Daily, I speak with Matt Tuson, General Manager for Europe, the Middle East, and Africa at LogicMonitor, about the practical path from reactive IT operations toward autonomous IT. The promise is attractive: fewer repetitive tasks, faster incident resolution, less disruption, and systems that can correct familiar problems before users feel the impact. The difficult part is deciding when the available data, controls, and evidence are strong enough to trust an automated action.

Matt argues that observability is taking on a different responsibility. It once helped engineers answer what happened and why. In an autonomous environment, it must also give AI enough connected context to recommend or execute what should happen next. That means understanding how infrastructure, networks, cloud services, applications, and other operational signals relate to one another. A fast decision based on an incomplete view can resolve the wrong symptom, create duplicate incidents, or make the original problem worse.

Trust therefore begins with the information supplied to the system. Many IT environments contain separate monitoring and management tools introduced by different teams to solve specific problems. Those specialist products may continue to do useful work, but their signals often remain disconnected. Matt describes a business combining nine organizations, each bringing its own tools and operational practices. The challenge was not simply the number of products. It was the inability to connect their outputs into a reliable view of what was happening across the combined environment.

This matters because AI can process poor information as quickly as good information. Matt warns that adding autonomous remediation to incomplete, duplicated, outdated, or poorly contextualized data risks creating automated confusion. False positives multiply, root causes are missed, and teams repeatedly address symptoms while the underlying fault remains. His advice is to start with the business outcome, identify the information needed to support that outcome, and improve the quality and consistency of the data before increasing AI authority.

We also discuss how leaders can decide which actions belong with AI and which should remain under human control. Matt rejects the idea that organizations must choose between full autonomy and complete human approval. He recommends graduated levels of authority based on the action, confidence in the recommendation, business impact, and whether the change can be reversed easily. Ticket enrichment, alert correlation, and routine remediation may offer lower-risk starting points. A change affecting a major service should receive closer human oversight until the system has established a reliable record.

That creates a practical model for earning trust. If an AI recommendation repeatedly matches the action an experienced engineer would have taken, the organization gains evidence that it may be safe to automate that decision. The process can move gradually through support levels and operational complexity, with explainability, governance, and audit records maintained throughout. Human judgment remains where accountability and business consequences demand it.

Matt also addresses the difference between companies making progress with AI and those stuck in pilot purgatory. The stronger performers begin with an operational problem, such as reducing incidents, improving uptime, or accelerating resolution. They invest early in data quality, visibility, collaboration, and governance. AI becomes part of an existing workflow with a defined path from insight to action, rather than a detached experiment looking for a reason to exist.

For IT leaders who want greater autonomy but do not yet trust their environment, Matt's starting point is simple: establish broad visibility, improve operational data, connect isolated sources, and define the actions AI can take independently. Teams must also be able to understand why a system reached its conclusion and inspect what it did afterward. Autonomy should grow through repeated evidence, not through hope or one large leap.

There is a commercial reason to get this right. Faster resolution can reduce the cost of service, protect customer experience, and limit the business impact of downtime. There is also a human benefit. Engineers spend less time sorting duplicate alerts or joining war rooms to prove which team was innocent. They can concentrate on decisions that require experience, judgment, and knowledge of the business.

As observability starts supplying evidence to machines as well as people, what controls would make you comfortable allowing AI to take action inside your IT environment? Listen to the episode and share your thoughts.

What has to change before an AI pilot can become part of a regulated financial workflow?

In this episode, I speak with Tom Carey, President of Broadridge's Global Technology and Operations Business, about the work required to move AI beyond personal productivity tools and prototypes. Tom brings an unusual perspective to the conversation. He studied AI in the 1990s, long before current computing power and language models made commercial deployment practical, and now works where financial market infrastructure, technology and operations meet.

Tom describes four levels of AI productivity. The first helps an individual complete a task. The second turns successful personal patterns into tools that other employees can use. The third places agents inside production workflows while a person verifies the result. The fourth allows agents to operate without constant human review, but only within a controlled environment with monitoring, policies and evidence that the system is behaving as expected.

One of the strongest lessons from our conversation is that AI can force an organization to examine a process it may have accepted for years. Tom says Broadridge removed 30 percent of the email traffic entering one operational group after finding duplicates, messages that required no response and work that should not have existed. His own estimate is that around half the benefit of an AI program may come from simplifying the current process before advanced models are applied. That is an important counterweight to the assumption that every inefficiency needs a language model.

We also discuss the economics of enterprise AI. Tom compares current token-cost surprises with the early cloud projects that moved existing workloads without redesigning them for a different computing model. Broadridge baselines costs, tests use cases through its operations business and monitors token consumption. He shares one example in which a team's default configuration automatically moved to a newer and costlier model, showing why model selection and configuration belong in the operating discipline around AI.

For financial institutions choosing their first agentic workflow, Tom recommends high-volume, rules-based work with known inputs and outputs. A person or an automated test should be able to verify the result. He also favors several smaller agents over one large agent responsible for an entire process because individual steps are easier to inspect, replace and stop when something goes wrong.

As models become widely available, Tom believes business advantage will increasingly depend on the proprietary data, platforms, workflows and governance surrounding them. Does your organization have the process discipline, cost visibility and control framework required to put AI into production? Listen to the episode and share your thoughts with me.

Useful Links

Executive: Tom Carey, President of Broadridge's Global Technology & Operations business

Broadridge Deploys Agentic AI at Institutional Scale Across Capital Markets and Wealth Operations (May 11, 2026)

Broadridge Joins Anthropic's Project Glasswing (June 17, 2026)

GenAI Delivering Now, Tokenization Is Next: Financial Services Enters Period of Accelerating Transformation, Landmark Broadridge Study Finds (February 25, 2026)

LTX Launches Agentic AI in BondGPT, Turning AI Insights into Trading Action (June 16, 2026)

Broadridge Invests in DeepSee, Further Harnessing Agentic AI to Transform Post-trade Operations (January 8, 2026)

What if the most useful measure of workplace technology was not the number of tickets closed, but the amount of productive time returned to employees?

In this episode of Tech Talks Daily, I speak with Kelly Candler, Global Offering Lead for Workplace and Business Process Services at DXC Technology, about moving digital workplace services away from activity metrics and toward employee and business outcomes. Kelly has worked on both sides of the managed services relationship. Before joining DXC, she was a customer of the company and several of its competitors, giving her a practical view of what large enterprises expect from workplace technology and where support models continue to disappoint.

Kelly begins with a simple distinction. Traditional IT reporting often focuses on ticket volumes, response times, and calls answered. Employees care about whether they can do their jobs, how much time they lose, and how quickly they can return to productive work. When the outcome becomes the starting point, support is judged by its impact on the employee rather than the amount of activity generated behind the scenes.

That matters because many companies already have crowded workplace technology estates. Years of investment have produced overlapping tools, legacy applications, automation, device platforms, and service channels. Adding another AI product can increase cost and confusion if it replaces nothing and connects to little. Kelly argues that organizations should understand the technology they already own, identify the employee outcomes they want, and use orchestration to connect those investments rather than discarding them automatically.

DXC positions its Workplace Services offering as a people-centered, AI-native service enabled by DXC OASIS, its orchestration platform for Human+ agentic AI workflows. In the interview, Kelly describes Human+ as a partnership in which AI handles repetitive work while people retain creativity, judgment, and accountability. The intention is to make support more proactive, provide employees with help through familiar channels, and allow experienced teams to concentrate on work that requires human knowledge.

Device performance provides a practical example. In a reactive model, employees discover that a laptop has become slow, unstable, or unusable after productivity has already been lost. Kelly explains that operational data and automation can identify patterns such as declining battery health, storage problems, and application conflicts before the employee experiences a failure. The system may resolve the issue automatically or arrange a replacement before work is interrupted.

Kelly says DXC performs over 82 million proactive checks and remediations annually across over one million managed endpoints. These are company-reported operating figures, but they illustrate the scale at which preventative workplace support is being applied. She expects agentic AI to extend that model by learning from operational data and improving the speed and scope of proactive action.

We also examine DXC's reported outcome figures. The company says Workplace Services can produce a 40 percent reduction in operational complexity, 60 percent fewer service desk calls, resolve 50 percent of device issues before employees notice, and return over 15 hours of productivity to each employee every month. Kelly explains that the productivity calculation considers incident resolution, device availability, PC performance, and onboarding. The actual result will depend on the employee persona, environment, baseline, and method of measurement, so leaders should examine how each figure was calculated before applying it to their own workforce.

The discussion then moves from technology to adoption. Kelly says organizations often know where to run an AI proof of concept but struggle to turn the result into a production capability. DXC's response is an Exponential Blueprint that assesses the environment, maturity, culture, and potential value areas, then helps customers move from a smaller test toward wider use. The wider lesson is that a successful pilot needs a defined route into existing operations, ownership, governance, and measurement.

Governance becomes especially important when Human+ workflows begin making decisions at scale. Kelly notes that human service desk employees already make mistakes, so the standard for AI should focus on risk tolerance, oversight, and what happens when an error occurs rather than assuming perfection. DXC has introduced an AI code of conduct alongside its employee code of conduct. Kelly says humans remain accountable for the actions taken by AI agents, much like the accountable role in a RACI model.

Trust also depends on how leaders explain the changes to employees. Automation can raise concerns about surveillance and job loss. Kelly frames the opportunity as removing repetitive work so people can concentrate on decisions, creativity, and business outcomes. That promise will only be credible if employees understand how the technology is used, what remains under human control, how performance is measured, and where they can challenge an automated decision.

For CEOs deciding where to begin, Kelly recommends support and device management because both affect nearly every employee and can produce measurable results. She also suggests moving toward experience level agreements that connect workplace performance with employee outcomes. The longer-term value comes from understanding the broader environment in which employees work and improving how its parts operate together.

If workplace AI is intended to give people time back, should organizations retire ticket volume as their headline measure and report the employee impact instead? Listen to the episode and share your thoughts.

What does cyber resilience look like when leaders must make a costly decision before anyone fully understands the incident?

In this episode of Tech Talks Daily, I speak with Justin Henkel, CISO at SolarWinds, about moving the security conversation beyond prevention and preparing the wider business to respond, recover, and communicate under pressure. Justin brings 22 years of Air Force experience across intelligence, cyber operations, and special operations, alongside leadership roles in the public and private sectors.

His intelligence background shapes a simple but demanding approach to uncertainty. Technical teams may need time to establish exactly what happened, but business decisions cannot always wait for a complete answer. Leaders need to understand what is known, what remains uncertain, which customers could be affected, and what action limits the potential damage while the investigation continues.

Justin illustrates that tension through an incident involving a database that returned larger responses than expected, including information connected with other customers. The team did not initially know the cause or full extent.

His recommendation was to take the affected product offline while the company investigated. Sales and marketing colleagues were understandably concerned about revenue and reputation, but Justin framed the decision in commercial terms. A temporary loss measured in millions was preferable to allowing one problem to become hundreds of problems with consequences measured in billions.

The incident was ultimately connected with the timing of a database update and a system that failed open rather than failing safely. The example shows why the CISO's job has expanded beyond explaining technical controls. Security leaders must help executives compare immediate commercial pain with the possible cost of leaving an uncertain situation untouched.

Can an organization control employee AI use without making the approved tools so restrictive that people simply work around them?

In this episode of Tech Talks Daily, I speak with Steven Walchek, Co-Founder and CEO of Liminal, about shadow AI, enterprise governance, data privacy, and the growing tension between employee productivity and corporate security.

Steven's career includes leadership roles at FIS and AWS, along with involvement in three successful exits. His experience has given him a close view of how major technology adoption cycles begin inside companies, often before leadership has developed the policies, budgets, and controls needed to manage them.

The story behind Liminal began with an intensive period of customer discovery. Steven and his co-founder spoke with 100 prospective customers in 90 days. Across large and small businesses, they repeatedly heard concerns about what would happen to company data after employees submitted it to generative AI providers.

That concern has grown as AI tools have spread through the workplace. Steven describes two common responses from CIOs. Some permit employees to use almost any AI product, despite limited visibility into licensing terms, data retention, model training, or regulatory exposure. Others attempt to prohibit AI use completely and assume a written policy will stop employees from accessing these services.

Neither response accounts for how people behave when they believe a tool can help them work faster. An employee may use a personal account, take a photograph of a screen, or transfer information onto another device. The company has technically established a policy, but its security team may now have even less visibility into what is happening.

Steven compares this with the early adoption of cloud computing. Developers and business teams could purchase services with a credit card, while finance leaders later discovered rapidly growing AWS bills. Cloud adoption created shadow IT because people had access to useful technology before company controls caught up. Generative AI is producing a similar pattern at greater speed.

We discuss why aggressive security warnings can also produce unintended behavior. If an approved platform repeatedly frightens or reprimands employees for submitting information, they may move to an unapproved tool that creates less friction. From the employee's perspective, the objective is usually straightforward: complete the work and produce a good result. Steven argues that companies need an approach that gives employees a familiar AI experience while providing security teams with governance, model administration, data protection, observability, and an audit trail. He explains how Liminal attempts to combine access to several AI models with controls operating behind the user experience.

We also discuss why listening to employees matters after deployment. Steven shares how customer feedback led Liminal's development team to change a spreadsheet feature within 24 hours. For him, that responsiveness helps businesses introduce governance without forcing people to choose between the approved system and the tool they believe can do the job.

Should enterprise AI governance begin with restrictions, or with a better understanding of what employees are trying to accomplish? Listen to the conversation and share your experience.

How can marketers make better decisions for individual customers without spending their working lives designing, running, and maintaining separate tests?

Recorded at Braze Forge 2026 at the Fontainebleau in Las Vegas, this episode of Tech Talks Daily features my conversation with George Khachatryan, Head of AI Decisioning at Braze. George describes leading product management for AI Decisioning Studio and shares the story behind OfferFit, the company he cofounded before it became part of Braze.

We begin with the practical limits of segmentation and A/B testing. George explains that smaller customer segments can make it harder to collect enough evidence for a useful result, while each new creative option introduces further work. His explanation of reinforcement learning offers a different approach. The marketer defines the objective and the options available to the system, which then experiments, observes the results, and adjusts its decisions.

We discuss the distinction between Decisioning Studio Pro and the newly announced Decisioning Studio Go. George describes Pro as offering flexibility around success metrics and custom data, with data science support required during implementation. Go is designed as a self-service option using data generated within Braze. At the time of recording, he says Go supports email and optimizes click activity with machine clicks filtered out. Marketers choose the journey, creative options, subject lines, calls to action, available timings, frequencies, and guardrails.

The limits are as useful as the possibilities. George recommends a baseline of at least a few thousand clicks per month for a journey using Go, so the model has enough information to learn. He also acknowledges that maximizing clicks will not always maximize conversions. We discuss why those objectives need to be assessed separately, rather than treating improved interaction metrics as proof of additional sales.

George explains how the system can learn from similarities between creative variants and describes daily model retraining as a way to adapt to changing behavior. We also talk about reporting against a business-as-usual control group. He distinguishes the performance reporting available at the time of recording from deeper explanations of why a model made a particular choice, which he describes as an area of ongoing development.

One of the most memorable parts of the conversation concerns customer trust. George recounts arriving with his family for an apartment viewing arranged by an AI assistant, only to discover that no appointment had been booked. His point is that speed and responsiveness lose their value when a company refuses responsibility for the actions of its AI. Transparency and ownership of the customer experience still require human judgment.

We finish with George's advice on readiness, including experience with manual testing, measurement, and customer data. His broader comments about data infrastructure should be considered separately from his description of Go's use of native Braze data.

Which marketing decision would you automate first, and how would you check that it was improving the outcome you actually care about? I'd love you to share your thoughts.

How can marketers use AI to improve the customer experience without filling every channel with increasingly similar content?

Recorded at Braze Forge 2026 in Las Vegas, this episode of Tech Talks Daily features my conversation with Christy Poulos, VP of Product Marketing at Braze. We discuss the practical choices behind AI marketing, from deciding what a campaign should achieve to keeping creative work, compliance, and customer relationships under human direction.

Christy describes Forge as an opportunity for marketers to talk about their craft and the problems they face every day. Technology forms part of that conversation, but her starting point is the work itself. Teams are being asked to adopt AI while also demonstrating a useful return. Her advice is to connect the tools they choose with specific goals for the brand, the team, and the wider business. Producing additional content offers little reassurance if nobody has agreed what success looks like.

We discuss Agentic Standards and the less glamorous work of checking campaigns against rules. Drawing on her experience marketing a regulated product, Christy explains why automating repetitive checks could give teams greater confidence in their output. The marketer still sets the boundaries. Her argument is that campaigns should operate within those boundaries whether a human or an agent prepares them. The interview presents the intended value of these capabilities, rather than independent evidence that they remove compliance risk.

Another part of the conversation concerns where marketers actually want to work. Operator Connect introduces ways to connect AI assistants and other working environments with Braze. Christy discusses campaign briefs created in Claude and the growing interest in collaboration through Slack. She also makes a clear case for retaining the traditional product interface, where teams can review a campaign in context, check standards, and launch it. Some businesses are interested in these connections, while others are still developing their AI skills or learning what the tools can do.

The question of creative quality runs through the interview. Christy believes customers will recognize generic AI content and that this could weaken their relationship with a brand. Her view is that marketers who continue to bring their own creativity and work alongside AI will produce stronger results. We also discuss conversational agents and the possibility of customer interactions that allow people to respond and feel heard, rather than simply receive another broadcast message.

For teams wondering where to begin, Christy recommends Content Optimizer and email content testing as a first step. She describes the possibility of testing many variants, then progressing to Decisioning Studio Go to consider send times and channels. These are her recommendations for adoption, not measured results from a customer case study. Later, she discusses how insights from decisioning products could help marketers contribute to product strategy and business planning.

One of the most memorable examples arrives toward the end, when Christy describes a ride hailing platform in Venezuela using Braze to create an earthquake awareness system. The company is not named in the interview, but the story illustrates her broader point about customers finding applications that product teams had not anticipated.

Where could AI make your marketing more useful to customers, and which decisions should remain with the people who understand them? I'd love you to share your thoughts.

Can an enterprise move quickly with AI when the information it needs remains spread across business units, acquired companies, private data centers, multiple clouds, and systems governed by different privacy rules?

In this episode , I speak with Justin Borgman, co-founder and CEO of Starburst, about the data architecture decisions now affecting how quickly businesses can turn AI investment into useful results. The conversation begins with a reality many established companies will recognize. Their technology estate reflects years of applications, acquisitions, regulatory demands, regional choices, and earlier infrastructure programs.

Justin argues that this history becomes a constraint when AI teams need governed access to information quickly. A company created within the last year may design its data environment around AI from the beginning. A multinational enterprise rarely has that freedom. It must work with valuable data held across different locations while respecting security, privacy, and sovereignty requirements.

The traditional response has been to centralize everything. Justin believes the single source of truth is often an impossible target rather than a finished destination. Drawing on his earlier experience at Teradata, he says even customers using a leading database continued to retain information elsewhere. New applications, company acquisitions, regulations, and changing business demands kept creating additional systems.

His preferred approach is to accept that distributed data will remain part of enterprise life and build an architecture that can work across it. A federated data platform can query information where it lives while giving users a common point of access. This can reduce the time spent moving data before an analyst, executive, application, or AI agent can use it.

Justin illustrates the problem through a large American bank with many lines of business and inherited data silos. Senior leaders could ask commercially important questions, but answering them required analytics teams to write queries, assemble dashboards, and combine information from several systems. The process could take weeks.

He describes how connecting those sources and adding a natural-language interface can reduce that delay. Starburst calls its interface ADA. It allows a user to ask questions in conversational language while drawing on governed enterprise information and its business context. The company presents this as a way to shorten the path from question to insight without forcing every data set into one platform first.

Business value remains harder to prove than technical access. Justin recommends connecting data projects with revenue growth, cost reduction, or risk management. He also points to usage evidence within data platforms. If a data product is accessed frequently by leaders or operational teams, and the cost of producing it is known, those signals can help a company evaluate whether the investment is serving a recurring need.

Architecture economics also influence where workloads belong. Justin favors S3-compatible object storage and open formats such as Parquet and Apache Iceberg when companies assemble data in a lake architecture. His argument is that open storage can reduce cost while allowing customers to choose among query engines rather than binding the information to one provider.

That advice does not mean every workload belongs in one format or location. Some data will remain in warehouses, operational databases, regional systems, and on-premises infrastructure. Federation can provide access across those environments, while open formats create additional choice for the information that can be consolidated economically.

The human side of architecture receives equal attention. Justin says ownership, incentives, and internal politics affect data quality because centralized teams may lack the domain knowledge held by the business unit that produced the information. Treating data as a product gives an organization a way to assign responsibility for quality, maintenance, adoption, and feedback.

Named ownership changes the conversation. A successful data product can be recognized and improved because people know who created it. A weak product can receive feedback from its internal users. Distributed control can also allow teams closest to the data to apply their knowledge while the wider company accesses it through common governance.

Governance becomes especially important when AI agents can query enterprise information directly. Justin says access controls must operate beneath the agent rather than relying on the model to decide what a user should see. Row-level and column-level permissions, data masking, and query auditing can determine which records are available and provide a record of what the system retrieved.

Security and economics are also contributing to renewed interest in running some AI workloads on-premises. Justin says businesses may prefer open-weight models on owned hardware when scale improves the economics or when confidential data cannot comfortably leave the corporate firewall. He expects cloud and on-premises capabilities to coexist rather than one replacing the other.

For employees, conversational access could change the role of traditional business intelligence. Justin expects standard KPI dashboards to remain useful, but believes many custom reports and one-off dashboards could be replaced by interactive models that answer questions directly. Analysts and engineers may spend less time responding to requests and additional time improving trusted data products and governance.

The opportunity is faster access to answers. The risk is allowing speed to outrun security, quality, or accountability. A federated architecture can connect distributed systems, but it still requires companies to know who owns the data, which users can access it, how results are audited, and whether the outcome supports revenue, cost, or risk goals.

Should enterprises keep pursuing one central source of truth, or accept distributed data as a permanent condition and build governed AI around it? Listen to the episode and share your thoughts.

How should a business measure AI success when employee adoption tells leaders very little about revenue, savings, risk, or better decisions?

In this episode of Tech Talks Daily, I speak with Thomas Robinson, better known as T-Rob, who recently moved from Chief Operating Officer to CEO of Domino Data Lab. After ten years inside the company, he has seen enterprise AI move through several phases, from specialist data science projects to generative AI tools available across the workforce.

T-Rob argues that businesses have become too focused on the technology itself. Generative AI has attracted attention because almost anyone can use it, but an individual productivity tool is very different from an AI system making decisions about mortgages, clinical trials, financial markets, or defense operations.

As the potential value of a decision rises, so does the financial, regulatory, and operational risk. That is why T-Rob believes governance should be built alongside AI development rather than added after a system has been completed.

He compares the process with constructing a building. Engineers do not wait until the work is finished before checking whether it has been designed and assembled correctly. Reviews happen throughout construction. Domino applies the same principle to AI through policy controls, production monitoring, tracing, and continued human oversight.

We also discuss why companies should avoid beginning with a fashionable tool and searching for somewhere to use it. T-Rob recommends starting with the company's primary business measures and working backward. A pharmaceutical business may examine the number of promising therapies entering its pipeline, revenue, and risk. The appropriate AI system can then be designed around those outcomes.

That system may combine large language models with computer vision, statistical models, rules, and company data. T-Rob believes the assumption that every business problem requires the latest frontier model can waste money and produce weaker results.

People remain a major part of the equation. T-Rob has seen companies reduce headcount in anticipation of AI replacing employees before the technology was ready. He argues that domain experts become more valuable because they understand the business history, operating environment, exceptions, and consequences that a model may miss.

The conversation also considers model independence and AI sovereignty. Many enterprises became dependent on a single cloud provider by building their own technology on proprietary services. T-Rob believes businesses should avoid repeating that decision with AI models. Open systems can allow companies to replace models as prices, capabilities, regulations, and operational needs change.

For organizations handling sensitive intellectual property, sovereignty also raises questions about what information leaves the business when employees prompt external models. T-Rob describes the risk of enterprise knowledge being absorbed into future model development, even when information has been anonymized.

Perhaps his strongest argument concerns measurement. He calls consumption and adoption terrible measures of success because they mainly reveal cost. Giving every employee an AI tool does not mean the entire company becomes proportionally more productive. Real return comes from improving the business processes that generate revenue, reduce expense, control risk, or support better decisions.

Are businesses ready to stop measuring AI by logins and start measuring what it changes inside the company?

Listen to the episode and share your thoughts.

What happens when artificial intelligence moves beyond helping marketers create content and begins making decisions on their behalf?

Recorded at Forge 2026 in Las Vegas, I speak with Astha Malik, Chief Business Officer at Braze, about how AI is changing customer engagement and what marketers should retain control over as more operational work is handed to software.

Astha explains why the long-standing promise of genuine one-to-one personalization has been so difficult to deliver and why she believes AI can finally help brands move beyond broad segments toward individual decisioning. We discuss Decisioning Studio Go, where AI can optimize content, timing, and frequency for different customers, while marketers continue to define the objectives and brand boundaries within which the system operates.

But greater automation creates new questions. If AI can generate more campaigns and messages, does marketing simply become noisier? Astha talks openly about the danger of "AI slop" and why using the same models and tools can make brands increasingly forgettable.

We also discuss Agentic Standards and the idea of AI checking the work of other AI systems before campaigns reach customers. Astha argues that organizations need controls around agents in much the same way they already have quality processes around human teams.

Our conversation also moves beyond marketing into the changing enterprise software interface. Operator Connect allows Braze capabilities to be accessed through environments such as ChatGPT, Claude, and Microsoft Copilot, raising questions about whether employees will increasingly interact with business systems through AI assistants rather than traditional applications.

Finally, we examine how organizations can prove AI is creating measurable value, why some businesses remain trapped in experimentation, and Astha's advice for leaders overwhelmed by the pace of change.

Her recommendation is simple: start experimenting rather than waiting for certainty.

As AI takes on more decision-making and execution, which parts of marketing should remain firmly in human hands? Listen to the conversation and share your thoughts.

What happens when attackers can discover and exploit a weakness faster than your organization can patch it?

Recorded at Barracuda TechSummit 2026 in Alpbach, Austria, this conversation features Arve Kjoelen, CISO at Barracuda. Arve is responsible for protecting Barracuda's systems, environment, and code, which makes him the person answering the familiar question of who checks the checker.

Our conversation begins with the collapse in response time. Security teams once had hours or days to investigate suspicious activity. Arve explains why they may now have minutes or seconds, while vulnerabilities can move from disclosure to exploitation within days. Traditional weekly scans and handoffs to patching teams struggle when attackers operate at machine speed.

Arve offers a useful framework for understanding security posture through threats, exposures, assets, and controls. Technology changes constantly, but these categories give leaders a way to assess risk without chasing every new term. He also explains why reducing attack surface can begin with basic questions. Does a system need to be accessible from the internet? Does a web server also need remote management exposed? Does a midsize business benefit from spreading its workloads across every major cloud provider?

We examine the difficult balance surrounding AI adoption. Blocking every new tool can prevent employees from benefiting from useful technology, but allowing unrestricted adoption creates new exposure. Arve argues for deliberate choices and guidance that reduce risk without stopping progress.

The conversation also addresses AI-guided remediation. Barracuda uses AI internally to identify vulnerabilities, but Arve is cautious about fully automated fixes. An AI system may identify a problem and suggest a solution, while a human remains responsible for judging whether the proposed action could damage a production environment. Faster decisions are valuable only when organizations understand the consequences.

Arve also considers how entry-level technology roles may change as AI performs more coding and analysis. His view is that people will need to understand how to work with AI, evaluate its output, and carry an idea from design through secure implementation. The role changes, but the demand for human judgment remains.

We finish with model sovereignty, data trust, and provider dependency. If a security capability relies on one AI model, leaders need to know whether they can move to an alternative if access, performance, pricing, or policy changes. Arve also explains why Barracuda is preparing to support both open and closed models while the market develops. Where should your organization use AI to accelerate defense, and which security decisions should remain firmly under human control? Listen to the full conversation and share your thoughts with me.

What if every market research project could continue contributing to business decisions after its original question had been answered?

In this episode of Tech Talks Daily, I'm joined by Phil Ahad, Managing Director of Data at Cint, to discuss why he believes companies should move away from disposable research. For decades, the familiar model has been straightforward. A business asks a question, commissions a study, receives the answer and begins again when the next question appears. Phil argues that this process wastes useful information and repeatedly asks people for details that may already be available.

His alternative is an always-on human data engine that allows new studies to build on previous research. Existing responses can be combined with first-party information, third-party sources, transactional records and behavioral signals. Phil says this can help organizations answer new questions faster while reducing the burden placed on respondents.

That burden matters because survey fatigue is often misunderstood. Phil does not believe people have stopped wanting to share opinions. The problem is the experience. Customers are repeatedly asked long batteries of familiar questions, often after everyday transactions, because the structure of data collection has changed remarkably little since paper surveys. If researchers already know much of the background, they can ask fewer questions and focus on the reasons behind a person's decision.

We also examine synthetic data, a term Phil openly dislikes, and the growing use of AI personas or digital twins. At one end of the spectrum, a model might add 200 modeled responses to an 800-person study so researchers can work with a sample of 1,000. Phil says this extends an existing data set rather than creating genuinely new insight. At the other end, a company may create a digital representation of a person from survey responses, purchasing patterns, mobile activity and other signals, then ask that representation new questions.

The opportunity is faster research with less repeated questioning. The risk is believing the model knows a person better than the evidence allows. Phil says the industry must test how much information is required to predict an answer with an acceptable level of confidence. He expects progress to come from repeated comparison and validation rather than a single certification method or technical shortcut.

For business leaders, this makes transparency as important as speed. Before relying on AI-augmented research for a major decision, they need to understand where the original data came from, how modeled responses were created, how performance was tested and where human judgment remains involved. Phil also notes that strong decisions rarely rely on a single input. Companies bring together research, customer records, benchmarks and other sources before deciding what to do.

Cint's ambition, as Phil describes it, is to turn recurring tracking studies into a continuing source of insight. He says roughly one million people pass through the company's ecosystem each day, giving Cint an asset that can be combined with increasingly accessible technology. The larger challenge is making useful sense of growing data volumes at business speed.

Could continuous research help your organization ask people fewer, better questions, or would AI-generated responses introduce uncertainty that outweighs the speed gained? Listen to the episode and share your thoughts with me.

Why do increasingly capable AI models struggle to produce reliable answers inside large organizations?

In this episode of Tech Talks Daily, I speak with Misti Vogt, SVP of Engagement at Orange Logic. Her career spans military intelligence, data science, and enterprise content technology, and she also teaches in the DAM and AI program at Rutgers University. That combination gives her an unusually practical perspective on how machines interpret information and why business meaning cannot be assumed.

Misti argues that enterprise AI reliability depends on the context surrounding company data. A model may be technically impressive, but it needs to understand relationships, rules, metadata, rights, and intent. Without that layer, it reasons over information originally organized for people rather than machines. The result may sound convincing while remaining disconnected from the way the business defines accuracy, trust, and permitted use.

We discuss three forms of context. Static context reflects accumulated knowledge. Transactional context develops through projects and outside information. Semantic context helps systems interpret meaning and relationships across large collections of information. Misti compares this with human conversation. When an answer misses the point, we add information until the other person understands what we mean.

Digital asset management sits at the center of this discussion because DAM platforms already organize master data, metadata, transactional data, governance, relationships, and usage rights. Misti believes those structures can give AI applications a stronger business foundation. She also argues that content should become self-aware, carrying information about when it was created, how it was produced, its intended audience, where it has appeared, and how it has performed.

Natural language search provides a useful example of why this matters. An employee might ask for creative assets suited to a campaign and welcome a broad set of suggestions. The same employee may then ask for assets licensed for the United Kingdom and United States, with print and web rights for the next 12 months and no use in another campaign during the previous six months. That second request carries business consequences, so the system needs deterministic rules alongside creative choice.

Misti also shares an Orange Logic customer example involving a conglomerate with several brands. The company consolidated seven platforms, including three DAM deployments and local storage. Orange Logic then supported shared governance across the group while preserving autonomy for individual brands. Misti says the early results include time savings, improved efficiency, richer metadata collection, and lower costs, although no quantified figures were provided in the recording.

The wider question is whether businesses are spending enough time on the information surrounding their content before expanding AI use. Could better metadata, rights management, and business logic produce greater value than another round of model upgrades?

Listen to the conversation and share your thoughts with me.

What happens when employees begin using AI before their organization has prepared the data, training, controls and measurement required to support them?

In this episode of Tech Talks Daily, returning guest Denis O'Shea, CEO of Mobile Mentor, joins me to discuss the 2026 Endpoint Ecosystem Study. The research surveyed 2,500 workers across the United States, United Kingdom, New Zealand and Australia to understand how employees experience their devices, applications, sign-in processes, support systems and workplace AI.

The findings show a gap between access and useful adoption. According to the study figures discussed in our conversation, only 29 percent of employees say AI provides regular or indispensable value in their work, while 48 percent report receiving no AI training or do not know whether training exists.

Denis says the differences become sharper by sector. Finance has made greater progress with company-wide and role-specific training, while half of the healthcare and government employees surveyed reported receiving no AI training.

The generational picture is equally complicated. Denis says Gen Z workers are adopting AI faster than other age groups, but they are also the group most likely to work around company policies when approved tools create friction.

If employees cannot complete a task through the sanctioned route, some will use personal accounts and upload company information to public models. The same workers may also need greater support during onboarding, challenging the assumption that digital familiarity automatically means workplace technology fluency.

Denis also shares Mobile Mentor's own mistakes. The company deployed Microsoft Copilot to roughly two-thirds of its workforce, ran competitions and encouraged experimentation. When the board asked whether the investment was working, Denis realized he had no dependable answer. The team had not defined use cases, assigned licenses according to the work being done or established a reliable way to measure returns. A subsequent scan found 33,000 sensitive data assets that Denis says were overexposed or shared too widely.

Those lessons became what Denis calls the five foundations of AI success. Organizations should define each use case, secure the relevant data, provide training for that use case, build agents around the work and measure the outcome repeatedly. He recommends treating deployments as experiments. If a use case cannot demonstrate a return within three months, the licenses can be reassigned and tested elsewhere.

We also discuss passwordless access, the cost of AI tokens and services, and the operational work required to govern growing numbers of agents. Denis believes data, agents and spending will become three immediate management challenges. Each agent will need an identity, appropriate permissions, an owner and a retirement process, while finance and technology leaders will need a clear view of licenses, tokens, API calls and platform consumption.

One final lesson reaches beyond AI. Denis says organizations that automated password resets, patching and device provisioning have released technology staff to address newer priorities.

Businesses still handling those tasks manually may struggle to find the time needed for data preparation and agent governance. Does your AI strategy begin with another license purchase, or with a defined problem, prepared data and a measurable result? Listen to the episode and share your thoughts with me.

Can security teams defend an organization when attackers are using AI to research targets, personalize messages, identify weaknesses, and launch campaigns at a scale no human team can match?

I returned to Alpbach, Austria, for Barracuda TechSummit 26 and caught up with Neal Bradbury one year after our conversation about being secure today and ready tomorrow. A lot has happened since then. Agentic AI has become a boardroom subject, employee AI use has spread across businesses, and attackers have gained access to tools that lower the cost and expertise required to launch sophisticated campaigns.

Neal explains why Barracuda has continued with the unified platform strategy introduced at last year's event. In his view, AI creates additional exposure across identities, applications, email, and data, but it does not make every existing security control obsolete. The immediate requirement is to connect information across these areas and accelerate how quickly security teams can interpret and act upon it.

We discuss Barracuda ONE, its Barracuda IQ intelligence engine, the Bailey assistant, Integrated Email Protection, and the recently announced Barracuda AI Data Security offering. Neal also explains why the acquisition of Evo Security adds identity protection at a time when businesses must secure human users, service accounts, and AI agents.

One customer example shows why connected telemetry matters. According to Neal, Barracuda's team investigated an attempted wire fraud worth almost a quarter of a million dollars. No single product could see the complete attack. Information from email, network activity, and identity systems had to be combined before the team could understand what was happening.

The conversation also examines shadow AI. Employees are already placing workplace information into chatbots and using tools outside approved systems. Neal argues that attempting to ban every tool will send that behavior further out of view. Organizations first need to understand which services are being used, educate employees about the information they can share, and guide them toward approved options.

Attackers may have gained the early advantage from AI, but Neal says defenders are catching up through automation. Work that previously took around 45 minutes can now be completed in under a minute inside Barracuda's agentic SOC. The aim is to correlate signals, remove repetitive analyst work, and present fewer alerts with better context. Human judgment remains part of the process when accountability and empathy matter.

Do you agree that AI favors the side that automates most, or could excessive automation create another security weakness? Share your thoughts.

What does a security operations center need when attacks are arriving at a speed and volume that human analysts cannot manage alone?

I recorded this episode with Adam Khan, VP of Global Security Operations and AI Security at Barracuda, during the 20th anniversary of the Barracuda Tech Summit in Alpbach, Austria.

Adam has spent over 25 years in technology and security. When we last spoke at the event, he used Home Alone and soccer to make complex security topics easier to understand. This year, expectations were high, and he arrived with Formula One.

The comparison begins with what spectators see. Attention naturally falls on the car and driver. Behind them sits a much larger operation involving engineers, strategists, mechanics, simulations, telemetry, and rapid decisions. Adam believes modern security works in a similar way. Customers want to run their businesses, while a largely unseen combination of analysts, threat intelligence, automation, and AI works behind them.

That operating model has developed into what Barracuda calls the Agentic SOC. AI agents follow the same playbooks analysts use when examining endpoints, malware connections, artifacts, threat intelligence, identity behavior, and other signals. They can complete repeatable investigative work quickly and consistently across volumes that have grown from hundreds of alerts to thousands or millions.

Adam says Barracuda now has hundreds of agents with hundreds of individual skills. These agents can support the process from triage and intelligence gathering through correlation and response. When the system has high confidence that an ordinary user account has been compromised, it may disable that account. If the incident involves an administrative account capable of locking down an entire customer environment, a person must confirm the action.

This matters because an AI system can misclassify an event or reach a conclusion that requires additional context. Adam describes feedback mechanisms through which people review decisions, identify mistakes, and feed those findings back into the system. Barracuda also records an audit trail of the actions and queries performed by its agents.

One of the most surprising details concerns employment. While headlines frequently associate AI with reducing headcount, Adam says his team has doubled since adopting it. Analysts previously occupied with repetitive investigation have moved into threat hunting, model development, prompt engineering, and attack and defense exercises. The team is also attacking its own systems so that its agents can learn from emerging techniques before a genuine incident occurs.

The audience saw this operating model during Adam's keynote. Attendees used their phones to launch controlled business email compromise, QR-code phishing, and ransomware scenarios against Barracuda's attack and defense environment. The platform then analyzed and blocked the activity while the audience watched.

Adam says approximately 395 attacks were initiated during the demonstration and all were successfully blocked. That result comes from a controlled Barracuda demonstration rather than an independent test, but it gave attendees a rare view of the speed required during an active incident.

We also discuss how Barracuda Managed XDR uses behavior and telemetry across email, endpoints, cloud services, identities, networks, and other technology. An employee traveling with a familiar laptop and phone should not create the same response as an unknown device attempting an unusual login. Historical patterns, device identifiers, signatures, and location data can help reduce unnecessary alerts while highlighting activity that deserves attention.

For Adam, the purpose of AI is to increase the speed and reach of security experts rather than remove them. People determine strategy, examine high-consequence decisions, test systems, and remain accountable for customer outcomes.

Could the Agentic SOC give security teams the speed they need without surrendering the judgment and accountability customers expect? Listen to the episode and share your thoughts.

How should security leaders respond when AI-powered attacks compress detection and response windows from minutes to seconds?

At Barracuda TechSummit 26 in Alpbach, Austria, I spoke with Rohit Ghai, Chief Executive Officer at Barracuda. The conversation took place exactly one year after Rohit joined the company, giving us an opportunity to discuss what brought him to Barracuda, what he inherited, and how his first year has influenced his plans for the business.

Rohit explains that Barracuda's focus on smaller and resource-constrained organizations was an important reason he accepted the role. A cyber incident can threaten the survival of a smaller company, particularly when it has a lean IT team and limited access to specialist security knowledge. For these businesses, Rohit argues that AI-supported and increasingly autonomous security is a practical requirement.

We discuss Barracuda's platform strategy and why genuine integration must extend beyond a shared interface. Rohit compares loosely connected product portfolios to supermarkets. Customers may find it easier to purchase several products from one supplier, but that commercial convenience does not mean the products share data or produce a coordinated response.

Traditional tool sprawl forced analysts to interpret information across several screens. Agent sprawl could introduce systems that act independently, disagree with one another, or take conflicting actions. Rohit believes security platforms must connect information across email, identity, applications, data, and infrastructure so they can reason across the complete attack sequence.

Identity is another major part of the discussion. Barracuda's acquisition of Evo Security addresses privileged access for managed service providers and smaller businesses. Rohit expects machine and non-human identities to greatly outnumber human users, raising questions about excessive privileges and how organizations grant temporary access to autonomous agents.

We also discuss the economics behind AI security. Rohit explains how Barracuda is adapting its value reports to account for token consumption as well as staffing, software, and security outcomes. Barracuda absorbs the direct token costs associated with its own AI capabilities and selects different models according to the task, an approach intended to keep its products affordable for smaller customers and MSPs.

Rohit is cautious about calls for the AI industry to slow development. Coordinating a worldwide slowdown between companies and countries would be extremely difficult. He also argues that cyber defenders cannot pause while attackers continue using widely available models to improve their campaigns.

The conversation ends with a wider leadership question. Rohit believes intelligence will become widely available, making empathy and trust more valuable. AI can generate an answer, but customers, partners, and security professionals must decide whether they trust the organization acting upon it.

Will connected AI security platforms reduce complexity, or could autonomous agents introduce a new form of operational risk?

Share your thoughts.

What happens when enterprises spend trillions of dollars on AI but the systems underneath it still cannot provide the context those models need to make reliable decisions?

In this episode of Tech Talks Daily, I reconnect with Christian Monberg, CTO at Zeta Global, to examine what separates AI experimentation from production systems that organizations can actually trust.

Our previous conversation focused on how businesses could use AI to scale marketing without losing the human connection with customers. This time, we move deeper into the technology underneath those experiences.

Christian explains why disconnected tools and fragmented data remain barriers to AI adoption, and why Zeta rebuilt its data architecture using Palantir Foundry. We discuss the role of context graphs in connecting customer identity, business objectives, previous decisions, campaign history and outcomes so AI systems can understand more than isolated pieces of information.

We also examine one of the biggest questions surrounding agentic AI: when should businesses allow an AI agent to take action?

Christian shares what enterprises need around explainability, permissions, observability and learning loops before AI systems can safely move from recommendation to execution.

With global AI spending expected to reach $2.59 trillion in 2026, the conversation ultimately comes back to a simple question: how can technology leaders prove that their AI investments are producing measurable business value?

Why is employee AI use rising so quickly while measurable business value remains difficult for many organizations to find?

In this episode of Tech Talks Daily, I speak with David Martin, Senior Partner and Global Leader of People and Organization at BCG, about the firm's fourth annual AI and workforce report. The research surveyed 11,749 employees across 14 countries and points to a growing divide between companies that distribute AI tools and companies that give people a clear plan for changing how work gets done.

BCG reports that 74 percent of frontline and nonmanagerial employees now use AI regularly, an increase of 23 percentage points from the previous year. Adoption, however, is only part of the story. The report says 71 percent of employees receive little or no guidance about what to do with the time AI frees, while over half are not redirecting that capacity into strategic work.

David describes one of the research findings that best captures the problem. Companies where employees understand the strategic direction but rate their AI tools poorly can realize greater value than organizations with strong tools and limited strategic clarity. Better technology helps, but its impact remains small when employees do not understand which business problem they are solving or how the operating model should change.

The report connects clearer strategy with a roughly 25 percentage point increase in measurable business impact when companies redesign workflows from end to end or create new business models. BCG says strong tools without that clarity produce an improvement of roughly five percentage points. Companies that redesign workflows also outperform tool-only adopters by 23 percentage points on measurable business impact, 22 points on time saved, and 20 points on job satisfaction.

David explains what redesign looks like in practice. Giving software engineers stronger coding tools may improve part of a development task, but keeping the overall product lifecycle unchanged limits the result. A deeper redesign considers how research, product management, engineering, and decision-making operate together, then changes roles and processes around the capability of AI. The objective is a better business outcome rather than a faster version of the same work.

This distinction also explains why promising pilots fail when companies attempt to expand them. A pilot can prove that a model works inside a controlled environment. Wider deployment tests whether the organization surrounding that model works. David cites BCG research indicating that 70 percent of the factors determining whether AI scales with a return relate to people, organization, and process. Talent, operating models, cross-functional teamwork, incentives, learning, and leadership all become part of the result.

Measurement must also move beyond adoption. David argues that the final metrics remain familiar business outcomes such as conversion, competitive win rate, price realization, cycle time, and inventory performance. A pilot can use controlled comparison to test whether AI changes one of those outcomes. The missing management step is often accountability. If several executives share ownership but nobody is responsible for the return, the investment can continue without a clear test of success.

There is a case for broad experimentation because it can build familiarity and surface ideas. David warns that hundreds of isolated use cases can also fragment investment, increase risk, and save small amounts of individual time without producing company-level value. His preferred balance combines focused governance with structured opportunities such as hackathons, where employees contribute ideas but the organization selects which ones receive investment.

The workforce findings add an important human dimension. BCG says 67 percent of regular AI users report higher job satisfaction, while 41 percent also report higher cognitive load. David connects that tension with the effort required to assign work to agents, evaluate quality, and keep those agents operating. He refers to separate BCG research called AI Brain Fry, which found productivity rising as employees managed additional agents until a limiting point. In that research, productivity fell when workers moved beyond managing three agents.

Training remains another stubborn problem. The report says 72 percent of employees believe AI has changed skill expectations, and nearly half say their role is moving toward directing and managing AI. Only 36 percent feel they have received enough training, a figure David says has not improved despite new learning programs. His recommendation is in-context training that brings AI into daily work, followed by peer discussion about what worked, what failed, and how behavior should change.

The purpose of recovered time may be the most revealing management question of all. David says employees report saving an average of around eight hours a week, but many use that time to perform additional versions of the same tasks. That can become demoralizing if greater output benefits the company without giving employees room for learning, infrastructure improvement, experimentation, or new product work. Leaders need to explain where the capacity should go and why.

Clear communication also reduces fear. Automation targets introduced without an explanation of strategy can leave employees assuming that efficiency is a code word for job loss. When leaders explain whether AI is intended to improve customer experience, create growth, reduce cost, or change the business model, employees have a better basis for understanding what is expected of them.

If strategic clarity is producing greater value than better tools, should the next AI investment begin with another platform or with a decision about how the work itself must change? Listen to the episode and share your thoughts.

Useful Links

What will customers remember about a business when the voice answering their questions becomes the main expression of its identity?

In this episode of Tech Talks Daily, I speak with Ruth Zive, who leads marketing at Voices, about the business and human questions surrounding AI voice. Voices is an enterprise marketplace and platform where companies can find professional voice actors, license AI voices with consent, and source custom voice data for training models. The company says its global talent network includes millions of performers and has served brands including Microsoft, Shopify, and Cisco.

For years, much of the AI voice debate focused on whether synthetic speech could sound convincingly human. Ruth believes the commercial conversation has moved toward provenance, brand integrity, permission, and usage rights. A voice can sound polished while exposing a company to reputational damage if the performer did not understand the use, the license is unclear, or the same generic voice appears in a competitor's customer experience.

Voices research cited during the interview found that 79 percent of business leaders believe inauthentic AI voices could damage brand perception. Ruth also says almost half of enterprise decision makers regard tone and emotional expression as the most important vocal factor in authenticity. That matters when a customer is frustrated, confused, or asking for help. A voice that sounds human but responds without suitable emotion can weaken trust at the exact moment a company needs to earn it.

Ruth describes responsible licensing through three ideas: compensation, control, and consent. The performer should understand how the voice will be used, retain an agreed degree of control, and receive payment that reflects the commercial use. Those decisions need to appear in the contracting, licensing, and entitlements before a model is trained or placed in front of customers.

We also consider the economic effect on professional performers. AI voice can change existing work, but Ruth argues that it can also create additional assignments when licenses are written carefully. An actor might provide the voice for an in-car assistant while continuing to record commercials in unrelated categories. Other opportunities include contact center experiences and the creation of specialized voice data used to train models. The positive case depends on clear boundaries and fair commercial terms rather than unlimited reuse.

The brand question may become even larger as customers move from websites and typed interfaces toward spoken conversations. Ruth points to BMW's careful selection of voices based on customer profile, tone, language, accent, and how each performer sounded inside the vehicle cabin. Her advice is to treat a voice as a long-term brand asset, test it in the setting where customers will hear it, and confirm that the company has the required rights before deployment.

Voice AI offers companies a more natural customer experience and gives performers access to new forms of paid work. It also raises difficult questions about disclosure, ownership, exclusivity, and trust.

Should every company now have a formal policy for choosing, licensing, and governing the voice that speaks on its behalf? Listen to the conversation and share your thoughts with me.

Can ideas developed thousands of years ago help leaders make better decisions about AI, data and digital transformation today?

In this episode of Tech Talks Daily, I speak with Alfonso Asensio, author of Digital Wisdom: Leading Transformation With the Sophia Factor and head of data measurement for global clients at Google in Tokyo. Alfonso has spent his career working across data, digital business and global client leadership, but his latest work looks beyond technical capability. He asks what changes when organizations bring sound judgment, ethical reasoning and human purpose into the decisions that shape digital change.

We begin with Sophia, the classical Greek idea of wisdom. Alfonso argues that modern business often treats wisdom as another word for knowledge, even though the older idea also included practical intelligence and judgment. Technical expertise can tell a company whether a system can be built. Wisdom asks why it should be built, who benefits and what consequences may follow. That distinction matters when businesses feel pressure to adopt AI because competitors are doing the same.

Alfonso shares the story of a large company with the resources, talent and urgency to pursue an ambitious digital program. When he asked what the business was trying to achieve, the answer became a list of fashionable technologies. AI-driven customer activity, blockchain supply chains and data optimization had become substitutes for a clear objective. His conclusion was that the company was attempting to build its future on buzzwords. Sometimes the better decision is to remain analog in a particular process if that choice serves customers and employees better.

We also consider Socratic thinking in organizations where boards and investors expect certainty. For Socrates, confusion was a stage in learning rather than a failure of leadership. Alfonso believes leaders can use probing questions to expose assumptions and contradictions before a technology plan becomes expensive. Admitting uncertainty can be difficult, but false certainty can send a business confidently in the wrong direction.

Heraclitus offers another useful comparison. Technology resembles a river that never stops moving, while employees need stability and meaning. Alfonso argues that leaders should create stable banks around that flow through a consistent capacity for improvement, adaptation and long-term thinking. The tools will continue to change, but organizations can reduce exhaustion when people understand the purpose behind that change and have a reliable way to respond.

One of the most memorable parts of our conversation compares large language models with the Oracle of Delphi. Ancient leaders sought answers from an institution whose workings they could not fully see. Modern users can receive equally confident guidance from AI systems without knowing which data, assumptions or commercial interests influenced the response. Alfonso's point is not that machines are mystical. It is that people need discernment, source awareness and judgment when an answer arrives with authority.

We then turn to Epicurus and the idea of ataraxia, or freedom from anxiety. In a business setting, Alfonso connects this with reducing unnecessary friction, decision fatigue and overload. Systems should be reliable, suited to the organization and valuable to employees as well as customers. Governance sometimes requires deliberately adding friction before investment, so teams can pressure test assumptions and ask whether people will be served by a tool or forced to serve it.

Alfonso closes with two questions for any leader considering a major AI decision. What is our identity as an organization, and are we acting ethically? He uses Blockbuster as an example of a company that defined itself through videotape rental rather than entertainment. A clear identity can help a business choose technology that supports its purpose. The ethical check then asks whether transparency, consent and accountability are present, or whether data and algorithms are being used to manipulate people.

Are organizations giving themselves enough time to ask why an AI system should exist before asking how quickly it can be deployed? Listen to the conversation, then share your thoughts with me.

Why is launching a consumer product easier than ever while turning it into a profitable global brand remains so difficult?

In this episode of Tech Talks Daily, I speak with Omer Kaplan, co-founder and CEO of ZyG, about the operational gap between creating a product and building a durable ecommerce business around it. Omer previously helped build ironSource into an $11 billion public company before its acquisition by Unity. He explains how recognizing the move from desktop to mobile helped shape that company's growth and why the ability to adapt quickly matters even more when AI capabilities are changing every week.

ZyG is building what it describes as an operating system for ecommerce scale. It combines AI agents with experienced human specialists to manage the work surrounding a consumer product, including the online store, creative production, advertising, retention, customer support, analytics, and other commercial operations. The brand retains its product, identity, and intellectual property, while ZyG operates the connected scale engine and is assessed by the resulting performance.

Omer argues that existing routes solve only part of the problem. A marketplace can provide distribution, but a young brand may disappear among thousands of competitors. A commerce platform can make it easy to open a store, but the store alone does not create demand, coordinate marketing, or build customer loyalty. Agencies and software products can fill individual gaps, yet their data, incentives, and messages often remain separated.

We discuss ZyG's approach to what Omer calls scale market fit. Its team creates the store, campaigns, and brand assets with agentic systems, then tests them with real paid traffic and real customer behavior. Omer says each test includes about $10,000 in media spending and that ZyG has completed over 100 tests. Cost of acquisition, predicted customer value, category benchmarks, and expected performance at higher volumes are combined into a score intended to show whether a brand can grow in the US market. He says the full process can be completed in about a week, compared with a far longer manual exercise before current AI capabilities.

The conversation also examines the move from software as a product toward outcomes as a service. ZyG's consumption-based model takes a percentage of the revenue it manages. Omer is careful to distinguish accountability from assuming every commercial risk. His point is that one party should own the end-to-end result, removing the familiar cycle in which creative, advertising, and retention providers blame one another when growth stalls.

Omer also shares why he returned to startup life after ironSource. Music, travel, and family offered appealing alternatives, but he saw the current technology cycle as a rare period for creating enduring companies. His advice to founders is to pursue large, complicated problems that general-purpose AI cannot easily reduce to a single feature.

ZyG recently announced a $60 million Series A led by Accel, following a $58 million seed round two months earlier. Can its combination of AI agents, human expertise, real-world testing, and commercial accountability provide the missing infrastructure for the next generation of consumer brands? Listen to the conversation and share your thoughts with me.

Why do pet owners so often learn the limits of their insurance when an animal is already ill and the veterinary bill is growing? That trust problem sits at the center of my conversation with Hedda Båverud Olsson, co-founder and CEO of Lassie, a pet insurer that combines coverage with preventative health guidance, rewards, activity tracking, and AI-assisted claims.

Hedda's reason for starting Lassie is personal. Her mother is a veterinarian, and Hedda grew up around healthy pets without fully appreciating how much fear and financial pressure many owners experience. After working at McKinsey and EQT, she became absorbed by an idea she describes as putting her mother in every owner's pocket. The aim was to help people understand risks earlier and make better daily choices, rather than waiting until an animal needed treatment.

The claims process shows where AI can offer immediate value. Lassie has developed a system called Bark Office that scans an invoice, reads each line, identifies whether the treatment relates to illness or an accident, checks the policy, and decides whether enough information is available. Hedda says that when the system is confident, the money can reach the customer in around six minutes. She reports that approximately 65 percent of claims in Germany follow that route.

Automation has limits, especially when a blurry receipt, missing diagnosis code, incomplete journal, unusually expensive treatment, or uncertain policy detail prevents a reliable decision. Those cases can prompt a request for further information or move to a human reviewer. Hedda says customers receive a line-by-line explanation of what was and was not covered, with the option to dispute a result and request another review. She reports an error rate below 2 percent for automated claims and compares it with what she describes as a 5 percent human error rate across insurance. Those are Lassie's figures, but the operating principle is useful across many regulated services: automate clear cases, explain the result, and give uncertain or sensitive cases to a person.

We also consider why an insurer should have a role when nothing has gone wrong. Hedda says over 90 percent of Lassie customers use its app and roughly a quarter use it daily. Owners can watch health videos, complete quizzes, follow life-stage guidance, record activity, and earn rewards that can reduce their insurance price. Advice changes according to breed, age, and season, covering subjects such as weight, joint health, toxic foods, nail trimming, and ticks.

Lassie also works with Tractive, allowing customers to connect a tracker and bring activity data into the app. Hedda explains that Lassie customers can receive a tracker while paying the Tractive subscription, and existing Tractive users can connect their current device. Owners who do not want a tracker can record activity manually. The feature gives the company another regular point of contact while helping customers follow their pet's routine.

That daily relationship has commercial consequences. Hedda says regular app use supports customer loyalty, reduces churn, and raises lifetime value. Preventative actions may also support lower prices for owners. The opportunity is to make insurance useful before a claim, although firms must avoid turning care advice and rewards into confusing conditions or allowing gamification to distract from clear coverage.

The conversation moves to the UK, where the supplied briefing estimates that around 20 million pets remain uninsured. Hedda believes culture and distrust may outweigh price alone, comparing the UK with Sweden, where she says approximately 90 percent of dogs and 50 to 60 percent of cats are insured despite higher prices. She also argues that established insurers have been slowed by old systems and disconnected technology, making simple onboarding, mobile service, and automated claims harder to deliver.

For Lassie, the test is knowing where automation improves the experience and where it would make a difficult moment worse. Customers may welcome an administrative claim completed in minutes, but few want to speak with a bot when a pet is seriously ill or dying. That distinction between speed and empathy may be the most useful lesson for any business automating emotionally sensitive work. Can insurance become something customers value every day without losing the clarity and human care they need during a crisis? Listen to the episode and share your thoughts with me.

How much of construction's cost, delay, and waste begins with information that fails to survive the journey from design to delivery? That question runs through my conversation with Julian Geiger, Chief AI Officer at Nemetschek Group, as we look at the practical role of AI across architecture, engineering, construction, and operations.

Julian describes what he calls the industry's 90, 40, 20 problem. According to the figures he shares, 90 percent of projects are over budget or over time, the built world accounts for roughly 40 percent of global carbon emissions, and around 20 percent of material is wasted. His argument is that many poor outcomes start as information and decision problems. Each project phase may work reasonably well on its own, but handovers can strip away context. A building information model becomes a PDF, a PDF becomes an email, and a decision may never be recorded against the object it changed.

We discuss how AI, building information modeling, and digital twins can identify missing information, scope gaps, clashes, and design choices before they become expensive construction site problems. Julian shares Nemetschek's work bringing Firmus AI into Bluebeam to review two dimensional drawings, then explains the broader goal of feeding lessons from construction back into design and engineering tools. The commercial promise is easy to understand. Finding a mistake while a wall exists only in software costs far less than finding it after workers and materials are waiting on site.

The conversation also moves beyond the assumption that every task needs the largest available model. Julian sets out a four tier approach. Deterministic calculations such as structural math should remain deterministic. Stable, high volume checks may be handled by conventional rules. Smaller domain models can classify objects, retrieve data, and interpret geometry close to the source. Frontier models earn their place when the work involves ambiguity, reasoning across documents, or several dependent steps. His test is refreshingly practical: use the least expensive method that is reliably right and fast enough for the person waiting on the answer.

That discipline matters when finance teams ask for proof. Time saved on drawing reviews or tender preparation can be measured quickly, while reductions in rework or missed issues require a longer data series. Julian also notes a familiar problem for enterprise AI programs. If a firm never established a baseline, it becomes difficult to show what improved. Usage can indicate that people find a tool useful, but adoption alone does not settle the return on investment question.

Data sovereignty adds another layer. Construction files can include valuable designs, commercial information, and details tied to national infrastructure. We discuss where the data is stored, who processes it, which jurisdiction applies, and whether customer material is used for model training. Julian argues for separating genuine intellectual property from routine usage data, then matching controls to the sensitivity of each project rather than treating every data set as identical.

Finally, we consider people. In an industry facing a skills shortage, removing junior roles creates a future shortage of experienced professionals. Julian sees AI as a way to shorten the apprenticeship period and reduce repetitive documentation, while preserving a clear line of accountability: AI proposes and a qualified human decides. Could that model help construction professionals spend more of their time on judgment, design, and better buildings, and where should the industry draw the line? Listen to the episode and share your thoughts with me

Why do businesses replace the AI model when the failure may have started somewhere else entirely?

In this episode of Tech Talks Daily, I speak with Richard Shaw, Technology General Manager for Databricks in the UK and Ireland. Richard leads the field engineering organization that works closely with customers on data and AI problems, giving him a practical view of what happens when promising agentic AI projects meet production workloads.

Richard argues that the model often receives the blame because it is the most visible part of the system. The actual fault may come from stale data, missing business context, inconsistent permissions, an unsuccessful tool call, or another point in the workflow. Replacing the model before tracing the request from start to finish can recreate the same problem in a new place. This is why lineage, end-to-end tracing, and continuous evaluation matter once an agent moves beyond a controlled pilot.

We discuss what a production-readiness rehearsal should include. Richard recommends realistic data, realistic user volumes, unauthorized requests, ambiguous questions, failed tool calls, and tests of what the agent should refuse to do. Teams also need agreed standards for quality, security, cost, and auditability, along with a clear decision about which actions an agent can complete independently and where a person must review or approve the result.

The conversation also looks at model choice and infrastructure cost. Richard believes the strongest test is performance on the organization's actual work rather than a benchmark leaderboard. A frontier model may suit complex reasoning, while a smaller or open-weight model may perform routine extraction or classification at a lower cost. Access policies, observability, and spend controls need to remain consistent as those model choices change.

Conversational analytics creates another governance challenge. Databricks customers such as Virgin Atlantic and Repsol are using natural-language tools to make company data easier for employees to question. Richard says wider access should preserve existing permissions, ownership, definitions, and lineage. An answer becomes far more useful when the user can see where it came from and which team owns the information behind it.

We also cover the boundary between historical analytical data and fast operational workloads. Richard describes how Databricks positions the lakehouse for broad enterprise context and Lakebase for immediate reads and writes, such as updating an account, placing an order, or storing agent memory, while keeping both connected to a common data and governance base.

Are companies ready to trace and test the whole AI workflow, or are too many treating the model as both the hero and the culprit? Listen to the episode and share your thoughts with me.

How can businesses turn growing investment in AI infrastructure, cloud capacity and devices into outcomes that employees, customers and finance teams can actually measure?

In this episode of Tech Talks Daily, I speak with Neil Sawyer, who manages HP's business across Europe, the Middle East and Africa. Neil works with companies across one of HP's largest global regions as they move from AI experimentation into wider deployment, making him well placed to discuss what happens when early enthusiasm encounters cost, security, governance and the realities of the workforce.

We begin with the gap between building AI capacity and applying it to a business problem. Data centers, models and powerful devices provide options, but the investment only becomes useful when a company identifies the workflow it wants to improve. Neil argues that leaders should begin with the outcome, understand where AI can remove friction and decide how they will measure productivity, employee experience and business performance before buying another layer of technology.

That raises a difficult question about productivity. If AI helps someone complete a task faster, does the organization use that saved time to improve the work, develop new ideas and give employees room to think, or does it simply add another task to the queue? I share my own experience as a business of one, where every efficiency gain has a habit of becoming extra output rather than a Wednesday afternoon at the cinema. Neil compares the current moment with earlier periods of industrial change and makes the case that automation should release people from repetitive administration so they can contribute creativity, judgment and higher value work.

We also discuss why AI costs are becoming a boardroom issue. Token based services and agentic systems can produce growing and unpredictable bills as adoption spreads across a company. Neil explains why every workload does not need the same model or environment. Large language queries may benefit from cloud capacity, while sensitive data, company specific information and some recurring tasks may be better suited to local or on device processing. The decision affects cost, responsiveness, privacy, security, data sovereignty and environmental impact.

Neil describes HP's view of hybrid AI, including devices with neural processing units and Z by HP Boost, which can connect available workstation GPU resources. He also explains why device refresh decisions should reflect workforce personas. A data scientist, account manager and office administrator may work for the same company, yet their computing needs can be very different. Mapping technology to the employee's role can help a business spend with greater discipline while giving people the performance they need.

The conversation also covers governance and measurement. Informal use of public AI services can be difficult to see, assess or manage. Neil recommends giving employees an approved AI toolkit, using enterprise services that provide telemetry and examining how technology availability and performance affect the employee experience. Adoption figures can show that a tool is being used, but they do not prove that it is improving an outcome.

We finish with a practical checklist for leaders. Define the outcome, identify the workflow, determine where each workload should run, calculate the cost, agree the measures of success and put clear controls around data, privacy and cybersecurity. That approach gives cloud and device based AI distinct jobs within the same business strategy.

How is your organization deciding which AI workloads belong in the cloud, which should run closer to the employee, and whether the investment is producing measurable value? Share your thoughts with me.

What could your team achieve if a document that previously took two hours could be created in eight minutes?

In this episode of Tech Talks Daily, I speak with Oskar Konstantyner, Chief Product Officer at Templafy, about the rapid adoption of AI agents across enterprise document workflows and what those productivity gains mean for knowledge workers.

According to Templafy's proprietary usage data, AI agent adoption among its enterprise users grew from virtually zero in October 2025 to 53% by June 2026. Its analysis found that documents created without agents had a median completion time of two hours and an average of 5.6 hours across 16,000 sessions. With AI agents, the median fell to eight minutes and the average to 27 minutes across 14,000 sessions.

The most common documents included pitch decks, company communications, sales materials, and product roadmaps. However, Oskar cautions against treating speed as the final measure of AI productivity.

We discuss an accounting firm that could not respond to thousands of tenders because it lacked the capacity to create enough proposals. Faster document production could allow that business to participate in additional opportunities while applying its knowledge about what makes a winning submission. The benefit comes from increased commercial capacity and stronger results, rather than counting recovered hours alone.

Oskar also explains what happens during those eight minutes. AI can locate relevant information, find approved content, recommend a presentation structure, apply previous lessons, and complete much of the production work. Humans remain responsible for original thinking, client judgment, factual accuracy, and final approval. In many cases, the agent may produce 60% to 90% of the document, but the beginning and end of the process remain human-led.

The conversation also considers the growing volume of generic AI documents. A business already has approved slides, company descriptions, brand assets, legal statements, and sales messages. Regenerating all that material wastes tokens and risks inconsistency. Oskar argues that agents should determine when existing content should be reused, when rules should be applied, and when something genuinely new needs to be created.

We also discuss why AI adoption improves when agents appear inside PowerPoint, Claude, OpenAI, and Copilot. Most employees are unlikely to abandon familiar workflows every time another AI application arrives.

Is your company measuring AI success through minutes saved, or through the additional business those minutes make possible? Listen to the conversation and share your thoughts with me.

What happens when an AI system gives a customer the wrong price, misrepresents a product, or recommends a competitor using outdated information?

In this episode of Tech Talks Daily, I speak with Alex Sherman, co-founder and CEO of Bluefish AI, about the growing influence of AI-generated answers on brand reputation, product discovery, and purchasing decisions.

Search once gave companies a reasonably visible path between a customer's question and the websites informing the answer. AI changes that relationship. Systems such as ChatGPT, Gemini, Rufus, and other assistants combine information from many sources into a single response. Customers may never visit the original pages, read the supporting evidence, or know which source carried the greatest influence.

Alex explains that inaccurate AI answers are not always extraordinary hallucinations. Models learn from an internet filled with conflicting product descriptions, outdated specifications, opinionated reviews, creator videos, and content generated by other AI systems. Bluefish says its monitoring has found inaccuracies or misleading portrayals in roughly 10% to 20% of the AI responses it tracks.

A company may publish a concise product page containing a few hundred words, while a customer writes a lengthy Reddit post describing why they love or hate the same product. The longer and more detailed source may give an AI model material it can use across many customer questions, even when that source presents an extreme or unbalanced view. Bluefish also reports finding small YouTube creators carrying considerable influence over how models describe certain brand attributes.

This creates a new responsibility for marketing teams. Visibility alone is no longer enough. Businesses need to understand whether they appear in AI answers, how positively they are presented, whether the facts are accurate, which sources influence the response, and whether that exposure contributes to a sale.

Alex describes how Bluefish's AI Accuracy tool monitors model responses, flags potential errors, identifies the cited source, examines the content behind it, and helps a company determine what action could correct the result. The source may be an external article, but the problem could also come from the company's own website. A model might confuse this year's device with last year's version because the distinction between their specifications was unclear.

Correction is only part of the process. Brands must measure whether new content changes the AI response and whether the revised answer cites the information they supplied. This turns AI accuracy into an ongoing measurement discipline rather than an occasional reputation exercise.

The stakes rise further with agentic commerce. Alex believes companies will increasingly serve two audiences: the person buying the product and the AI agent researching or acting on that person's behalf. Marketing teams will need to understand what agents read, how they evaluate choices, and why a recommendation resulted in a purchase or a lost customer.

Our conversation ends with a wider concern about convenience and choice. Alex compares AI discovery with opening Netflix and accepting the options placed on the first screen. AI can make research faster and easier, but relying on synthesized answers may weaken our willingness to search beyond what an algorithm selects for us.

I'd love to hear your thoughts, so how much influence should AI have over what consumers discover, compare, and ultimately buy?

What happens when an AI assistant becomes the first place a potential customer learns about your company, but the answer it provides is inaccurate, outdated, or influenced by a competitor?

In this episode of Tech Talks Daily, I speak with Justin Seibert, founder and president of DOM, also known as Direct Online Marketing, about generative engine optimization, AI search visibility, and the brand narratives being assembled by ChatGPT, Claude, Google AI, and other answer engines.

Justin has worked in digital marketing since 2001 and founded DOM in 2006. He has watched search marketing develop from the early days of measurable clicks and online leads into a system where an AI assistant may answer the customer's question before that person visits a company website.

We discuss why appearing in AI search results tells only part of the story. Companies must also understand what the system says about them, whether the information is accurate, and which sources influenced the answer. Reviews, Reddit discussions, press coverage, social media, newsletters, company websites, and third-party platforms can all contribute to the narrative.

Justin recommends separating prompts into four groups: branded searches, competitor searches, top-of-funnel informational questions, and bottom-of-funnel transactional questions. Each category requires different measurements. Branded prompts reveal sentiment and accuracy, while transactional prompts show whether the company reaches the shortlist presented to a motivated buyer.

He also explains why companies should define the niche in which they want to become the preferred recommendation. A broad insurance company may struggle to dominate every AI conversation, for example, but it could establish authority among a specific age group, product category, or geographic market.

According to Justin, visitors arriving through AI referrals can convert at rates two to four times higher than traditional search visitors. He believes these buyers often arrive better informed, with a shorter list of options and a stronger intention to make a decision. That makes exclusion from an AI-generated shortlist a serious commercial risk.

We also consider what happens when paid placements become common inside AI experiences. Justin argues that companies building organic authority today may retain an advantage as AI advertising becomes increasingly crowded and expensive.

Finally, Justin offers a practical audit any leader can perform. Log out, use a private browser, check from relevant countries, compare the company with its competitors, ask the AI why it produced its answer, and inspect the sources it cites.

Have you checked what AI assistants say about your company, and would a potential customer trust the story they find? Listen to the conversation and share your thoughts with me.

Can electricity grids built for an earlier era support AI data centers, expanding manufacturing, electric vehicles, severe weather, and rising customer expectations at the same time?

In this episode of Tech Talks Daily, I speak with Mark Hollis, utility executive advisor at SAP, about the pressures reshaping the utility industry and the practical choices available to leaders today. Mark spent over 15 years at Duke Energy before moving to SAP, where his work gives him visibility into utility organizations across North America.

Mark describes a combination of load growth, disruption, long construction timelines, and regulation. Data centers are receiving much of the attention because of the electricity required by AI, but he argues that they are only part of the story. Manufacturing is returning to parts of North America, transport and heating are becoming increasingly electrified, and utilities must prepare for wildfires, hurricanes, winter storms, and other events that affect generation and delivery.

The obvious response is to produce additional electricity, but every option comes with physical and commercial limits. Wind and solar contribute to the generation mix, although output depends on conditions. Small modular nuclear reactors could support future demand, but commercial deployment takes time. Batteries can store electricity and release it later, but they do not generate the power they hold. Customer programs can also reduce pressure at busy periods, including arrangements that allow a utility to adjust connected thermostats by a few degrees.

This makes modernization a portfolio of decisions rather than a single bet. Utilities must decide how to divide capital among generation, transmission, resilience, customer systems, and new technology. The people who understand existing processes are often the same employees needed to design and implement replacements. At the same time, information technology and operational technology are becoming increasingly connected, forcing companies to reconsider how business functions share information and how technology decisions support outcomes across the enterprise.

AI creates another tension because it contributes to electricity demand while also offering utilities new ways to work. Mark says most utilities he meets are cautiously optimistic. Their questions include where to begin, whether the value is proven, how long adoption will take, and whether poor data must be fixed before useful work can start. He warns against choosing the hardest problem first or judging a business process by the forgiving standards people accept from consumer AI tools.

His advice begins with the business problem. Automating an inefficient process can make it more expensive and harder to correct. Utilities should define what they need to improve and why, establish connected data with the right business context, set clear quality requirements, and retain human review where errors could affect customers, safety, finance, or regulatory obligations.

Mark brings this to life with several utility AI use cases. AI could summarize customer interactions across field service and contact center systems, allowing the next employee to understand what happened previously. It could review billing exceptions during unusually hot or cold periods and support earlier customer communications when consumption is likely to produce a much higher bill.

He also discusses using AI to summarize lengthy rate-case rulings before approved changes enter billing systems. During outages, an AI system could help dispatch crews by considering skills, equipment, parts, certifications, location, safety, and customers with medical needs. A human dispatcher could then review and approve the recommendation rather than building the complete schedule manually.

The opportunity is real, but so are the limits. Utilities operate regulated infrastructure where reliability, safety, auditability, and public trust cannot be treated as optional features. Where should the industry begin, and which use case offers the right combination of low effort, meaningful impact, and manageable risk? Listen to the conversation and share your thoughts with me.

What happens when an AI system moves beyond recommending the next sales action and begins running a connected revenue workflow?

In this episode of Tech Talks Daily, I speak with Abhijit Mitra, CEO of Outreach, about the operational work required to turn agentic AI into measurable revenue outcomes. Abhijit argues that adding another AI tool can create extra complexity when customer data remains fragmented and applications cannot share context. The starting point is the business process: what problem is being solved, which data supports it, what agents may do, and where human judgment remains necessary.

We discuss the difference between a recommendation and an autonomous action. Revenue teams may begin with supervised spot checks while an agent researches accounts, identifies prospects, drafts messages, and runs targeted campaigns. Once the data and results earn confidence, parts of that process can operate continuously. Multi-step work adds another requirement because the output of one agent must become useful input for the next. Research, outreach, coaching, forecasting, and expansion cannot deliver their full value as isolated tasks.

Context runs through the entire conversation. Abhijit describes the customer history, product usage, prior interactions, industry signals, buyer priorities, and organizational memory that can turn a generic model response into a commercially useful action. He says access to a frontier model alone does not create a revenue platform because each business still needs its own context layer and controls.

We also discuss Outreach's MCP Server and Client, which allow agents to receive information from surrounding systems and return their work to platforms such as Salesforce Agentforce, OpenAI, Anthropic, and Microsoft. That interoperability creates a governance question. Abhijit's advice is to give an agent the same roles, permissions, and data access as the person or team it supports. If the platform cannot control access at that level, he advises companies to wait.

The conversation then turns to the changing software model. Abhijit describes a move from assigning SaaS licenses to employees toward deploying agents with particular skills and a defined capacity. That makes workflow design and measurement increasingly important. He recommends establishing a baseline before rollout and tracking revenue against cost through indicators such as win rate, deal size, quota attainment, pipeline movement, forecast accuracy, and seller productivity.

Can revenue teams use agents to remove administrative work while protecting customer trust and keeping relationship building human? Listen to the episode and share your thoughts with me.

What would happen if specialty insurance underwriters and risk capital providers could work from the same timely, detailed information?

In this episode of Tech Talks Daily, I speak with Jeff Radke, CEO and cofounder of Accelerant, about the infrastructure behind specialty insurance and why his team chose to rebuild it around a data-driven risk exchange. Jeff has spent decades in reinsurance broking, reinsurance underwriting, and specialty insurance across New York, Bermuda, and London. That experience gave him a direct view of a process he describes as expensive, slow, and supported by weak data flows.

Jeff explains that Accelerant backs independent underwriting specialists who focus on narrow areas of risk, from pickleball courts to New York brownstones. These teams need the regulatory ability to issue policies and the capital required to support them. Accelerant connects those needs through a shared platform that routes risks to insurance companies and distributes them across a group of capital providers.

The economic argument is striking. Jeff says the traditional chain can consume about 40 cents of each premium dollar in expenses and overhead. Accelerant instead seeks portfolio-level solutions across a diverse book of business, reducing repeated negotiations and transfers between intermediaries. He also addresses the tradeoff created by concentrating information in one exchange, including the need to protect data and cash flows when participants depend on a shared platform.

Data quality sits at the center of the conversation. Jeff says older policy administration systems often retain only eight to twelve exposure characteristics for each policy, while Accelerant captures over 60 on average. That fuller record gives underwriters and capital providers more information when selecting risk, reviewing performance, or investigating a problem.

We also discuss why smaller underwriting organizations may hold an advantage over established insurers. New teams can begin with data at the center of their operating model, while larger companies must change processes built around older systems. Jeff argues that the biggest barrier is often mindset rather than budget.

AI has a defined role in that model. Accelerant uses agentic AI to organize varied incoming data, identify products whose performance needs attention, support portfolio construction, and improve internal operations. Jeff draws a firm boundary around responsibility: underwriters remain accountable for underwriting outcomes. His father's advice captures the principle neatly: do not blame the wheelbarrow; responsibility belongs to the person driving it.

Could shared data and lower operating expense return more value to policyholders while preserving human judgment? Listen to the conversation and share your thoughts with me.

What if the vehicles already traveling through our towns and cities could report road damage before a pothole becomes dangerous and expensive?

In this episode of Tech Talks Daily, I speak with Jonathan Selbie, CEO of Stockholm-based Univrses, about using computer vision and vehicle sensor data to give road authorities a much clearer picture of the infrastructure they manage. Jonathan's career has taken him from Formula One engineering at Red Bull Racing to unmanned aircraft and autonomous navigation, before bringing those lessons into automotive AI and road monitoring.

Univrses can work with cameras installed by vehicle manufacturers or retrofit cameras and processors to vehicles already operating around a city. Waste collection trucks and taxis can continue their normal routes while gathering information about surface damage, obscured traffic signs, roadworks and deteriorating road markings. The video is processed on the vehicle, and authorities receive mapped findings and recommended actions rather than hours of footage.

Jonathan explains that some Swedish cities moved from road condition surveys every five years to updates every two weeks. According to the figures discussed in our conversation, one council reduced its pothole count from around 3,000 to 900 in six months. He also says repairing damage at an early stage can cost up to 15 times less than waiting for it to become a major pothole. That changes road maintenance from an expensive reaction into a regular process based on current evidence.

We also discuss whether road infrastructure is ready for autonomous vehicles. Waymo uses a broad mix of cameras, radar and lidar alongside detailed maps, while Wayve is pursuing an approach designed to adapt to changing roads without relying on the same level of pre-mapping. Jonathan explains why faded lane markings can reduce the performance of driver-assistance systems, creating a useful feedback loop in which vehicles rely on roads and also provide data to maintain them.

The conversation also covers Pirelli's 30 percent investment in Univrses and the combination of connected tire data with forward-facing cameras. A tire can feel the road surface while a camera sees what lies ahead, giving vehicles and road operators different views of the same conditions. Jonathan also addresses privacy, explaining that Univrses detects and blurs faces and license plates before deleting the original imagery.

This is a practical example of AI producing value through existing fleets, frequent data and earlier decisions rather than another expensive technology project searching for a problem. Could the cars, taxis and service vehicles already using our roads become part of the infrastructure maintenance system, and would you be comfortable with that if privacy protections were clear? Please share your thoughts.

Why can an AI pilot produce an impressive result and still fail to create measurable value for the business?

In this episode of Tech Talks Daily, I speak with Jitendra "Jit" Putcha, chief operating officer at Tredence, about what the company calls the last mile of AI. This is the gap between generating an insight and making sure it reaches the person, process, and decision where it can produce a useful result.

Jit argues that many companies are facing an execution problem rather than a shortage of technology. Models are widely available, and teams can build demonstrations at remarkable speed. The harder task is redesigning a complete workflow so that employees can use AI without leaving one system, checking another, and manually carrying information between the two. Trust, explainability, governance, and continuous evaluation also become harder once a pilot moves from a small group into everyday enterprise operations.

We discuss why resistance from employees should not be dismissed as stubbornness. People are trying to understand what AI means for their role, judgment, and future. Jit recommends translating the program into a practical question: how will this make somebody's Monday morning better? He describes human and AI agent teams, along with workshop-based learning that allows employees to solve real problems, test the tools, and understand where human judgment remains necessary.

The conversation then turns to measurement. Jit challenges technology teams to move away from vanity measures such as the number of models built or code interactions recorded. Instead, he recommends examining margin improvement, loss reduction, cycle time, conversion, customer satisfaction, and other measures already understood by the business.

Jit supports the argument with several customer examples. He says one retail workflow reduced analyst effort by 70 percent, while a manufacturing supply chain platform reportedly produced $10 million in first-year savings. He also describes a supermarket forecasting program that reportedly produced close to $200 million in value and replenishment match rates above 90 percent, along with another modernization program associated with a reported $100 million loss reduction. These are Tredence customer examples shared by Jit during the interview and should be presented as attributed company claims.

We also discuss an AI-native operating model built around three layers: foundation, intelligence, and experience. Data infrastructure and governance support the foundation, intelligence turns data into decisions, and the experience layer brings those decisions into human workflows. Jit adds five supporting elements covering human and agent teams, execution rhythm, business measures, the technology ecosystem, and company culture.

His final advice is refreshingly practical. Escape the demo trap, prepare the whole organization for deployment and ongoing operation, consider an internal marketplace for reusable agents, and give the supposedly boring work a larger role. Data hygiene, evaluations, governance, change management, and runbooks help AI continue producing value after the launch presentation has ended.

Is your company measuring the number of AI projects it has created, or the business outcomes those projects have changed? Listen to the conversation and share your thoughts with me.

What happens when a one-hour conversation with a financial advisor creates an entire day of paperwork behind the scenes?

In this episode of Tech Talks Daily, I speak with Hardy Michel, Co-Founder of Marloo, about the administrative load limiting how many clients financial advisors can support. Hardy previously helped build retail investing platforms in New Zealand and the UK, where he saw people gain easier access to investments while personal financial advice remained harder to obtain.

Before building Marloo, Hardy and his co-founders spent months inside financial advice firms. They interviewed managing directors, compliance leaders, support teams and advisors, then worked beside them as they moved between inboxes, planning tools, client records and compliance systems. This "go slow to go fast" approach helped the team map the complete advice process before deciding where software could remove friction.

Hardy says a 60-minute client meeting can produce 10 to 14 hours of follow-up work. An advisor may need to document the discussion, demonstrate why the advice was suitable, complete product research and cash-flow modeling, record fees and disclosures, and prepare a client-facing report that can run to dozens of pages. According to Hardy, the cost and time involved have left some advisors unable to accept new clients for several years.

Marloo began as a specialist meeting assistant because note-taking is frequent, painful and driven by regulation. Hardy explains how transcripts created a current source of client context that was often absent from static records. The company then expanded into the work that follows a meeting, including advice documents and presentations, with the longer-term aim of becoming a central working environment for an advice firm.

We also discuss the trust required when AI handles personal and financial information. Hardy describes Marloo's zero-data-retention arrangements for certain model APIs and the security information it provides to firms. He argues that specialist systems need to demonstrate how client data is handled and give advisors language they can use to explain recording and transcription to clients.

Adoption is another major theme. Hardy recommends a focused two-week trial with three to five likely power users, a defined goal and a clear measure of value. Rather than relying on a successful demonstration, firms should examine whether advisors continue using the product and are prepared to recommend it to colleagues.

The strongest business outcome may be what advisors choose to do with the time returned to them. Hardy says some Marloo users have increased client meeting frequency from once or twice a year to five or six times. Should AI in financial advice be measured by the volume of cases completed, the quality of client relationships, or a combination of both? Listen to the episode and share your thoughts with me.

What does it take to move from giving employees AI tools to rebuilding how an organization gets work done?

In this episode of Tech Talks Daily, I speak with Oren Levitzky, VP of R&D at Fiverr. Oren has spent ten years at the company, progressing from backend engineer through a series of leadership roles before taking responsibility for Fiverr's AI program.

That experience gives him a valuable view of AI adoption from inside a global technology marketplace. He has watched engineering teams move from using ChatGPT as a conversational assistant to GitHub Copilot for code completion, Cursor for context-aware development, and an internal agent ecosystem containing Fiverr's code, data, and organizational knowledge.

Oren explains that adding AI to an existing workflow produced useful gains, but it did not completely change how people worked. Becoming AI native required Fiverr to create a dedicated team of engineers, designers, and product managers responsible for building agents around company context and helping employees adopt new working practices.

Fiverr reports that this approach has made some development work three to five times faster. Repetitive coding and design tasks can be passed to agents, allowing employees to concentrate on decisions, validation, and accountability. However, Oren is clear that manual code review remains necessary when AI-generated changes could introduce bugs or destructive operations.

We also discuss what AI fluency means for hiring. Fiverr has redesigned parts of its engineering recruitment process so candidates can use their preferred AI tools to build an application during the interview. Oren says around 80 percent of the assessment focuses on how candidates work with AI, communicate instructions, make decisions, verify changes, and demonstrate that they understand the resulting code.

This creates opportunities for people who can combine technical knowledge with AI fluency, but it also introduces a serious learning problem. Junior engineers may produce work at a speed previously associated with experienced developers without acquiring the knowledge needed to spot errors or question poor recommendations.

Oren argues that regular workshops, practical education, self-directed learning, and continued hands-on work are needed to prevent that loss of understanding. His advice applies to leaders too. Remaining close to the work makes it easier to recognize where AI succeeds, where it struggles, and what employees need from management.

Beyond Fiverr's internal engineering teams, we consider how AI is affecting the global freelance workforce. Businesses increasingly want people who can take an AI-generated draft and turn it into secure, accountable, production-ready work. Oren points to AI video production as one example where independent creators can produce work that previously required a larger studio, while retaining the judgment and creativity customers value.

For leaders hoping to make agentic AI part of daily operations, Oren recommends dedicated resources, structured education, employees who constantly seek better ways to work, and clear measurement. Releasing another tool will achieve little when habits, incentives, and expectations remain unchanged.

As employers place greater value on people who can direct, question, and verify AI, how should we prepare today's workforce without weakening the knowledge tomorrow's experts will need? Listen to the episode and share your thoughts with me.

How much control would you hand to an AI agent when the result is a real flight, a real hotel, and a meeting you cannot afford to miss?

In this episode of Tech Talks Daily, I speak with Evan Konwiser, Chief Product and Strategy Officer at American Express Global Business Travel, about the role AI can play across search, booking, disruption support, expense management, and the wider managed travel experience.

Evan begins with a problem many travelers already recognize. Buying a ticket has become far harder than choosing a departure time and airline. Travelers now face different cabins, fare types, seats, amenities, loyalty benefits, corporate policies, and payment rules. Amex GBT and Ipsos research referenced during the interview also found that four in ten Gen Z business travelers consider arranging work trips too difficult. The challenge for a travel platform is to reduce that complexity while respecting the policies of the employer and the preferences of the person taking the trip.

That is where AI becomes promising, but the consequences of failure are unusually tangible. A wrong answer in a chat window is irritating. A travel tool that sends someone to a closed location or recommends a train that does not stop at the required station can damage confidence immediately. Evan describes trust as the deciding factor and argues that business travel may have an advantage over leisure travel because a managed travel provider already knows the traveler's profile, company policy, payment method, and authority to book.

We discuss what Evan calls trusted transaction authority. Agentic workflows can help arrange a trip, but most travelers still want to confirm the final booking. Disruption may become one of the first situations in which people accept greater autonomy. If a flight is canceled and time is short, an agent could reserve a suitable alternative, provided the action can be reversed and the traveler can reach a human advisor whenever needed.

Evan also describes how AI can identify possible disruption before it happens, prepare alternative routes, and carry the context of a digital conversation to an experienced travel counselor. This matters because automation and human service do not have to operate as separate experiences. Travelers may begin in a self-service channel, move to a person when the situation becomes complicated, and expect the context to follow them.

Expense management provides another practical example. Evan believes much of the manual expense report could eventually disappear as trip data, receipts, virtual cards, risk controls, and exception handling work together behind the scenes. He describes guest travelers, contractors, recruits, and event attendees receiving controlled virtual payment cards so ordinary travel spending can be processed automatically while unusual purchases are blocked or reviewed.

We also look at bringing travel assistance into tools such as Microsoft Teams. The potential benefit goes beyond convenience. An enterprise assistant may already understand a traveler's calendar and meeting commitments, allowing the booking experience to exclude flights that arrive too late. That context may help employees make better choices while increasing policy compliance, although it also raises questions about data access, responsibility, and how results should be measured.

Evan argues that companies should assess AI supported travel at both the program and traveler levels. Time to book and cost matter, but so do satisfaction, policy fit, channel choice, human support, and the quality of the trip itself. He also acknowledges that early agentic chat workflows can take longer than established booking tools, a useful reminder that novelty and improvement are not the same thing.

Would you allow an AI agent to rebook a canceled flight automatically if you could reverse its decision, or would you always want to approve the change first? Listen to the episode and share your thoughts with me.

How can data center developers meet soaring demand for AI capacity without locking billions of dollars into buildings that may no longer fit tomorrow's workloads?

In this episode of Tech Talks Daily, I speak with Steve Conner, president of EdgeCore Digital Infrastructure, about the decisions sitting beneath the rapid expansion of AI infrastructure. Steve has worked in and around the data center sector since 1998, including the dot-com era and the later growth of cloud computing. That history gives him a measured view of the current rush to build large facilities quickly.

Steve argues that AI has intensified what he calls shiny object syndrome. The opportunity is large, but training, inference, and cloud workloads do not all ask the same things of a building. Rack density, floor loading, cooling, electrical design, available space, network distance, and proximity to cloud regions can all affect whether a campus can adapt when customer requirements change.

We discuss why EdgeCore has chosen to preserve flexibility in its facilities. A training-focused building might be made smaller because dense racks require less floor space, but future inference workloads may need a wider footprint. EdgeCore therefore accepts additional space in some designs, reinforces floors for heavier equipment, and enables liquid cooling even when a lower-density workload may not need it immediately. Steve presents those decisions as insurance against expensive retrofits or stranded capacity.

The conversation also examines EdgeCore's recently secured $1.5 billion in financing. Steve says the covered buildings were fully leased and designed to support mixed cloud and AI workloads. For him, the financing reflects continuing demand both inside established cloud regions and in surrounding markets, while the mixed-use design gives the customer options as requirements develop. These figures and interpretations remain EdgeCore's account of the investment.

Site selection is another major part of the equation. Power availability receives much of the attention, but Steve adds network distance, workforce availability, long-term political support, and relationships with utilities and local authorities. He describes looking beyond crowded locations such as Ashburn while remaining close enough to established cloud regions to support different use cases.

For me, the most valuable part of the discussion concerns communities. Steve says developers should begin meeting local leaders and understanding local needs before purchasing land. EdgeCore's examples include support for chambers of commerce, first responders, hospitals, fire services, and workforce development. His argument is that a company cannot simply purchase goodwill after construction begins. It has to be present early and demonstrate that the relationship runs both ways.

We also address public concerns about water, emissions, energy demand, and jobs. Steve argues that many modern data centers use cooling systems that do not consume water for routine cooling, though his comments apply to the facilities and designs he knows and should not be generalized to every data center. He also notes that AI facilities consume substantial power while arguing that developer-funded transmission upgrades can benefit other users of the grid.

The episode closes with a wonderfully plain analogy. Steve describes the data center as the plate rather than the meal. The infrastructure serves whatever workload the customer needs, which is precisely why the plate must be designed for a menu that keeps changing.

Are developers doing enough to prepare AI data centers for changing workloads while earning the confidence of the communities around them? Listen to the episode and share your thoughts with me.

What does an AI agent need before it can carry out useful work across the systems that actually run a business?

In this episode of Tech Talks Daily, I speak with Daniel Chilcott, Managing Director and co-founder of Flowgear, about the integration infrastructure behind agentic AI, product-led growth, and enterprise automation. Daniel's career began with a ZX Spectrum and a job as the first software developer inside a small business.

The company built custom software and a CRM, but customers repeatedly needed that software connected with accounting, ERP, and other operational systems. Building every connection by hand convinced him there had to be a better approach. He created an on-premises integration product in 2007, then co-founded Flowgear in 2010 as a cloud service.

The conversation shows how much the market has changed. In Flowgear's early years, Daniel had to explain why integration software belonged in the cloud. Today, roughly half of the company's customers are in the United States, and the larger question is how AI changes the way people build integrations.

Traditional platform vendors often supplied templates or starter packs. Those templates offered a useful starting point, but Daniel says they could create the illusion of a finished solution when every customer still had different processes, rules, and systems.

Generative AI offers another route. A user can describe the integration they need, and an agent can create and test the workflow, identify problems, and revise the design. That makes a product-led model more practical because customers can reach a result without first becoming specialists in the platform. Flowgear still supports a visual designer, but Daniel says many customers increasingly build outside the product interface because the integration is part of a wider application or business outcome.

This matters because much of the information needed for knowledge work sits behind APIs in ERP, CRM, warehouse management, and other line-of-business software. Reading a document from cloud storage is useful, but an agent becomes far more capable when it can work with operational records and complete an approved action.

Flowgear's Builder MCP server is intended to bring that capability into the AI chat or development environment where the user already works. A person can ask for an application, and the agent can create the supporting integration without requiring that person to construct every workflow manually.

Daniel is equally clear about the limits. Some business processes contain what he calls irreducible complexity. They carry unusual rules, historic decisions, exceptions, and dependencies that cannot be removed by a cleaner interface or a better model.

Flowgear therefore continues to rely on solution architects who can connect the customer's operational knowledge with the technical workflow. An experienced specialist may identify the question nobody thought to ask because they have seen the failure pattern before.

We also discuss the decision to rebuild Flowgear's platform. Daniel estimates that less than five percent of the code from five years ago remains in the current product. The rewrite was difficult, but its timing allowed the company to support generative and agentic AI from the start instead of attaching those capabilities to an older architecture. He describes it as feeling like a startup again, accompanied by the less glamorous work of testing failure modes and making the product dependable.

The most human example comes from a customer that used a call center for weekly product reorders. Flowgear helped automate the routine transaction through WhatsApp, allowing the same employees to spend their time on better conversations with customer accounts. It is a useful test for automation: does it merely reduce minutes, or does it create room for more valuable work?

Where does your organization need stronger integration before AI agents can become useful across everyday operations? Listen to the episode and share your thoughts with me.

Would employees use AI differently if a practical project could earn them a 2 to 4 percent salary increase?

In this episode of Tech Talks Daily, I welcome back Rytis Lauris, CEO and co-founder of Omnisend. We last spoke in December 2022, before generative AI became part of almost every technology and workplace conversation. This time, we examine why so many company AI projects attract attention during a demonstration but never become part of the work people do each day.

Rytis calls this the "beautiful junk" trap. A prototype can look impressive, yet employees return to the old process when the agent makes mistakes, lacks context, or requires extra effort. He believes prompting is partly a delegation skill. People must define the result they want, supply enough context, and review the output. Managers face an unusual tension because employees often perform best with room to use their judgment, while AI agents require much tighter instructions.

Another problem is the way organizations treat implementation. A traditional CRM project begins with mature software, installation, training, and a defined handover. An AI agent may begin with inconsistent results and improve only through continued use, evaluation, and correction. Rytis argues that businesses must treat agents as products that require ongoing ownership rather than projects that end after launch.

Omnisend's response began with broad access to AI tools. The company then created recurring AI days when most employees canceled meetings and spent time experimenting. Accountants, lawyers, and other teams began building their own tools rather than waiting for developers.

Rytis shares an accounting agent that checks whether employees have supplied reimbursement documents, sends reminders, and asks a person to intervene when repeated requests fail. He also describes a legal-review agent that examines new AI tools and assigns a green, yellow, or red status. Green tools can be used without further review, yellow decisions go to legal counsel, and red tools are rejected.

Omnisend is now formalizing this approach by offering salary increases of between 2 and 4 percent. Rytis says individual contributors must demonstrate that AI is saving time on repetitive, low-value work. Employees can qualify by building a useful tool, helping colleagues create one, or becoming an effective adopter. Managers are also assessed on whether their teams are using AI to reduce time spent on routine tasks.

The policy creates a genuine debate. Financial rewards can give employees permission and motivation to change established habits, but they could also encourage people to automate work simply because a reward is available. Rytis says Omnisend has not identified cheating or harmful behavior so far. Decisions are decentralized to managers, which gives teams flexibility but also places considerable responsibility on management judgment. He notes that Omnisend has approximately 260 employees, a scale that may make this approach easier to oversee than it would be inside a much larger enterprise.

The conversation includes an example of a recurring agent designed to identify and recover failed customer payments. It assesses risk signals, detects failures, contacts customers through several channels, brings account managers into the process when needed, and reports results through a dashboard. The goal is to remove friction for Omnisend and customers rather than adding an AI layer with no clear result.

Rytis also offers a candid account of customer-support automation. He says AI now handles around 40 percent of Omnisend's support tickets. When the system launched two years earlier, customer satisfaction was almost three times lower than the human team's result. Two employees worked continuously on training the agent, and Rytis says human and AI customer-satisfaction levels are now comparable. The figures are Omnisend results shared by Rytis during the interview and should remain attributed to him.

A separately recorded section also covers AI inside the Omnisend product. Rytis describes recommendations that identify possible improvements in marketing automations, natural-language segment creation, and MCP connections that let customers work through ChatGPT or Claude before sending campaigns through Omnisend. He says these capabilities have received the strongest customer usage among the company's AI work.

This is an honest discussion about incentives, experimentation, uncomfortable tradeoffs, and the patience required to turn an unreliable agent into a dependable colleague. Would a salary increase encourage meaningful AI adoption inside your organization, and who should decide whether the result deserves the reward? Listen to the conversation and share your thoughts with me.

How do you give sports fans deeper insight into a live match without covering the action with statistics they never asked for?

Two years ago, I spoke with Patrick Mostboeck in episode 2788, How Sportradar Are Revolutionizing Sports with AI. Patrick returns to Tech Talks Daily as Senior Vice President of Fan Engagement at Sportradar for a timely conversation during the U.S. Open about how AI and real-time sports data are changing the way fans follow a match.

We begin with the move from scores, schedules, and basic statistics to thousands of data points that can describe what is happening on the court or field. Patrick explains that the value comes from context. In tennis, ball position, shot type and player movement can reveal patterns around fatigue, court positioning and momentum that may be difficult to see from the television picture alone. AI can process those signals quickly enough to help broadcasters and digital services explain why a match may be changing.

That creates an obvious temptation to show everything. Patrick is candid about the lesson Sportradar has learned from putting products in front of users: less is often more. A product team may want to display every feature it has built, while the fan simply wants to understand the action. The technology works best when the improvement feels natural and the viewer does not have to fight through a stream of graphics.

We also consider the second screen. Many of us now watch sport with a phone or tablet nearby, checking other scores, following another match or looking for an explanation of a moment we have just seen. Patrick argues that media companies and rights holders can support those habits by offering different routes into the same event. A first time tennis viewer may need immediate context, while a fan who has watched the sport for 25 years may want deeper performance data. Personalization can serve both groups without taking away the shared experience of live sport.

Patrick explains how Sportradar's 4Sight combines 3D data visualization, contextual data, and real-time insight inside live streams. The aim is to identify relevant moments and provide a clear narrative rather than add graphics for their own sake. He also describes official sports data as infrastructure that rights holders can actively develop and commercialize across media, advertising, coaching analytics and other services.

For organizations wondering where to begin, Patrick offers a practical sequence. Start with the fans and identify the information they value before and during an event. Assess the quality and history of the data already available. Then speak with partners who understand how that information can support useful products and sustainable commercial models.

We close by discussing prediction. Better data can help systems model possible outcomes for fans, media teams and coaches, but sport still retains the uncertainty that makes it worth watching. Will predictive insight deepen our appreciation of the action, or could too much information remove some of its magic? Listen to the conversation and share your thoughts with me.

What if your website already supports post-quantum cryptography, but nobody inside your organization knows how, why, or which provider controls it?

I speak with David Warburton, Director of F5 Labs Threat Research, about new F5 research examining post-quantum cryptography across the world's top one million websites. According to the research discussed in our conversation, 54% now support PQC. It is an encouraging sign that preparations for future quantum threats are entering mainstream infrastructure.

That figure is also easy to misread. David explains that much of the adoption comes from cloud and CDN providers enabling hybrid post-quantum protection for their customers. A smaller business could therefore appear better prepared than a large enterprise simply because its provider activated the technology automatically. However, that customer may have little understanding of the chosen cipher, the protection applied elsewhere, or the dependencies created around a small number of technology companies.

David says the adoption rate looks very different when major CDN providers are removed from the data. This raises an important question about whether businesses are developing their own post-quantum security capabilities or temporarily benefiting from decisions made on their behalf.

We discuss why current deployments combine established cryptography with newer post-quantum algorithms. This hybrid approach protects compatibility while browsers, APIs, operational technology, IoT devices, and older enterprise systems catch up. It also carries performance costs through larger cryptographic material and increased network traffic. David argues that crypto agility matters because organizations need the ability to change algorithms, certificates, and encryption methods as threats develop.

The conversation also moves beyond encrypted traffic. Harvest now, decrypt later attacks involve collecting sensitive information today so it can potentially be decrypted when capable quantum computers arrive. David believes authentication and digital identity could create an even greater concern. A quantum computer able to produce valid certificates could potentially impersonate trusted websites, signed software, devices, or firmware.

Legacy infrastructure remains one of the largest barriers. F5 Labs found that roughly one in ten leading websites lacked TLS 1.3 support, preventing them from supporting current hybrid PQC connections. David also explains why Germany and France may trail countries including the US, UK, Australia, Ukraine, and Singapore, despite strong national policies. Factors include digital sovereignty concerns and the concentration of older manufacturing and operational systems.

For leaders beginning this work, David recommends speaking with suppliers, establishing internal ownership, reviewing business continuity plans, and creating a cryptographic bill of materials covering certificates, algorithms, libraries, applications, and devices.

I'd love to hear your thoughts, so does your organization know where its cryptography lives and who controls its post-quantum readiness?

How can governments and public-service organizations adopt AI quickly while protecting the people affected by their decisions?

In this episode of Tech Talks Daily, I speak with Holly Ellis, AWS Director for UK, International Organizations and Germany Public Sector Technology. Holly has worked on both sides of public-sector technology, with previous roles in local and central government before joining Amazon. She now leads teams supporting customers across education, healthcare, nonprofit organizations, local government and central government.

We discuss why public-sector technology adoption depends on a wider system of governance, procurement, regulation, culture and skills. Holly cites AWS research with Strand Partners showing that half of UK public-sector organizations identify shortages in AI and digital skills as their main adoption challenge, up from 46 percent in the prior year. Over the same period, reported AI adoption rose from 52 percent to 64 percent. Her point is simple: greater adoption creates demand for a larger number of people with deeper knowledge.

Holly also explains what responsible speed looks like when AI supports services involving education, healthcare or national institutions. Her approach is to think big, start small and scale fast, containing the effect of failure while teams build confidence. University clearing offers one example. Several universities used Amazon Connect during A-level results, with one institution handling up to three times the call volume of its previous system and confirming a four-figure number of student places in one day.

The conversation then turns to safeguards. Holly argues that leaders must define organization-wide protections while engineers remain responsible for the systems they build. Depending on the consequence, those protections may include human review, observability measures and tightly scoped permissions for AI agents. At the Ministry of Justice, AWS Transform processed 24,000 lines of code during an initial nine-hour pass and completed a second pass in two hours. Human review took about 20 hours, compared with an estimated nine months for manual modernization.

We also consider legacy technology, digital sovereignty and the difficulty of measuring AI outcomes. Holly describes sovereignty in practical terms as control, transparency and optionality. She advises leaders to define the outcomes they intend to measure before selecting initiatives, then build upon work that demonstrates the strongest returns. According to the AWS research discussed, organizations redesigning workflows and decision-making with AI reported average efficiency gains of 68 percent, compared with 40 percent among basic users.

The wider lesson is that responsible public-sector AI depends on technical choices, people, governance and evidence working together. Can public services become faster and more responsive while retaining the safeguards and public confidence they require? Listen to the conversation and share your thoughts with me.

What happens when an AI system moves beyond generating answers and begins influencing machinery, maintenance schedules, technician dispatch, and safety?

In this episode of Tech Talks Daily, I speak with Bob De Caux, Chief AI Officer at IFS, about moving industrial AI from promising pilots into dependable production deployments.

Bob explains why access to advanced models is no longer the main obstacle. Successful enterprise AI depends on understanding the processes, operational logic, metadata, and boundaries surrounding each decision. An AI system ordering a replacement bearing for a wind turbine must meet a very different standard from one generating a nursery rhyme.

We hear how IFS customer Kodiak Gas is using a digital worker to support material replenishment. According to Bob, the company projects approximately $3 million in annual return and 90,000 hours returned to technicians for higher-value work.

Our conversation also covers AI sovereignty. Bob argues that sovereignty means retaining control over data, decisions, providers, and the ability to keep operating under changing circumstances. He compares the technology layer to a duck paddling furiously beneath calm water. Models may change rapidly, while the operational application above them must remain stable, tested, and auditable.

We discuss staged autonomy as a way to earn worker confidence, beginning with manual questions, progressing to recommendations, and granting greater authority only after consistent performance. Bob also explains why agents need identities, permissions, defined roles, separation of duties, sponsors, and complete audit trails.

Accountability remains with the organization deploying the system. In an industrial environment, an agent can produce a harmful action rather than an inaccurate answer. Even after 999 successful decisions, the thousandth can carry catastrophic consequences.

Is your organization measuring AI through pilot counts, or through uptime, cost, technician capacity, turnaround time, and safety? Listen to the conversation and share your thoughts with me.

Could your organization produce a complete record of everything its AI systems accessed, sent, or shared within one business day?

In this episode of Tech Talks Daily, I welcome Tim Freestone, Chief Strategy Officer at Kiteworks, back to the podcast for his third appearance. Our conversation centers on the company's 2026 Data Security and Compliance Risk Annual Survey and the difference between buying security technology and being able to demonstrate that sensitive data is properly controlled.

According to the Kiteworks research supplied for this interview, 80 percent of surveyed organizations experienced at least one security or AI related incident during the previous 12 months. Half could not produce a complete AI data access audit record within one business day. The strongest group recorded an average readiness score of 46 out of 100, while organizations with weaker security and AI governance averaged eight. Even the higher score leaves considerable room for improvement.

Tim argues that technology spending can produce a larger version of the same exposure when a company lacks the people, ownership, and operating model needed to manage what it has purchased. Network, cloud, and infrastructure security still matter, but the business ultimately needs to understand what is happening at the data layer. Which identities can access a system? What actions can they take? Which records can they read, change, send, or share?

We discuss why this has become harder as employees create large numbers of AI agents. A company may have 1,000 people and tens of thousands of nonhuman identities, each requiring permissions and oversight. Tim describes three connected control planes covering identity, actions, and data. Together, they offer leaders a practical way to assess whether an agent can reach information it should never see or perform an action it was never meant to take.

The conversation also examines audit evidence. Tim says businesses should map regulated data types to the controls governing their use and then connect those controls with reporting. Without that connection, answering an auditor may require months of work, large consulting bills, and teams manually assembling records from disconnected systems.

Ownership remains difficult because security, compliance, infrastructure, and data governance teams often work separately. Tim's view is that the CEO must orchestrate responsibility when the board is asking AI to produce higher productivity while the same systems create new data risk. That position may feel demanding, but it exposes an issue many leadership teams still need to settle: who owns the consequences when an AI agent exposes or transforms sensitive information?

For board members, Tim offers two direct tests. Ask for a clear account of the company's data controls, then ask who is responsible for the associated risk. If those answers require a long explanation or several departments pointing at one another, the readiness score may matter less than the inability to demonstrate control.

How quickly could your organization show who or what touched sensitive data, and who would be accountable if the record were incomplete? Listen to the episode and share your thoughts.

What happens when an AI agent follows your documented process perfectly, but that process bears little resemblance to how decisions are actually made?

In this episode, I speak with Bill Wilson, Executive Head of Data and AI Solutions at NTT DATA UK&I. Bill oversees AI globally for NTT DATA's public sector work, giving him a close view of how governments are using AI while trying to manage risk, accountability, public confidence, and constrained resources.

Bill offers a refreshingly practical test for any proposed AI system: is it competent, and what is the worst thing that could go wrong? He describes this potential consequence as the system's "blast radius." An AI assistant helping somebody understand a grant application presents a very different level of risk from an agent making decisions that affect employment, justice, taxation, or access to public services.

We also discuss why companies can make a mistake before deploying their first agent. Automating an inefficient process simply allows the organization to perform the wrong work faster. Bill argues that teams should examine complete workflows, identify where several AI capabilities could produce a measurable result, and remain prepared to redesign the process as they learn.

Another major problem is tacit knowledge. Employees frequently make decisions using experience that was never written down. An agent trained solely on formal documentation may therefore understand the official process while missing how the work gets done in practice. Bill explains how targeted questions, behavioral traces, feedback, and supervised learning could capture some of that reasoning.

Public sector AI provides several useful examples. Bill discusses systems that process volumes of information beyond human capacity, emergency response work in Tennessee, and case management applications that gather information before a human reviews it. In these situations, AI can reduce administrative work and waiting times while leaving consequential decisions with people.

But human approval alone provides no guarantee. If employees lose direct experience of the work, they may eventually approve whatever the system recommends. Bill compares this with airline pilots maintaining manual flying skills and describes how known test cases can reveal when reviewers are becoming overly trusting.

For CIOs deciding which AI pilots should reach production, the advice is equally direct: choose work with measurable returns, group related use cases where their combined effect can be seen, learn from a varied set of deployments, and avoid building something a software provider is about to include in an existing product.

As AI agents gain access to external information, internal data, and operational tools, how should your organization decide what they may do alone and when a person must intervene? Listen to the conversation and share your thoughts with me.

Who carries responsibility when an AI agent begins reviewing contracts, applying regulatory rules or making commercial decisions on behalf of an organization?

In this episode of Tech Talks Daily, I speak with John Nay, founder and CEO of Norm Ai, about Agentic Law and the attempt to redesign legal work around AI agents, legal engineers and experienced attorneys. John has worked across AI, law and public policy for approximately 14 years. His early research adapted neural network methods to legal and government text before large language models became a commercial force.

The company information supplied for this episode states that Norm Ai recently raised $120 million in Series C funding at a $1.2 billion valuation, bringing total funding to over $260 million. Norm also says organizations representing over $30 trillion in assets under management use its technology for legal and compliance work. Those figures provide useful context for the scale of interest, while our conversation concentrates on how the model works and where responsibility remains human.

John describes Norm Ai as automating the first pass of legal and compliance tasks. One example involves an in house team using an agent to review communications against relevant rules before a person finalizes the decision. Another involves Norm Law receiving transaction documents, assigning the first analysis to AI agents and then presenting the output to an experienced attorney. The attorney decides what happens next, communicates with the client and supervises anything leaving the firm.

That division of labor matters because legal reasoning contains several layers. Some work can be handled through deterministic rules. Frontier models can then apply guidance and precedent to new situations. Human judgment remains responsible for high stakes advice and the review of agent output. John also stresses that the model is not making an isolated request to a generic system. Legal judgment is embedded in the way agents are designed, tested and called before live matters enter the workflow.

We discuss legal engineering as the bridge between software and professional practice. Norm's legal engineers are trained attorneys who spend much of their time building, testing and validating agents. They work with practicing lawyers, clients and AI engineers to translate preferences, policies and matter specific requirements into systems that can operate within real workflows.

Pricing is another part of the model. Norm Law prices selected matters around outcomes rather than hours. John acknowledges the limits. Some complex work can be scoped with enough confidence for a fixed price, while highly unpredictable litigation is much harder to price upfront. The opportunity is to give AI the incentive to examine additional documents and identify inconsistencies without increasing a client's bill for every human hour.

The conversation then moves to the proposed Delaware AI Company initiative. John describes it as a regulatory sandbox for a legal entity managed by an AI agent while humans remain involved in its creation and supervision. His argument is that autonomous agents will take increasingly consequential economic actions, so policymakers must decide whether those activities happen within a recognized legal order or outside it. The proposal is designed to test questions around liability, disclosure, capitalization and government oversight before any broader model is adopted.

John also believes companies deploying agents today should consider supervisory AI. If an operational agent can act faster and at greater volume than a person, a human team may be unable to inspect every decision. A second agent can evaluate the first against laws, regulations and company policies, with people retaining authority over exceptions and consequential outcomes.

Does adding a supervisory agent create stronger accountability, or does it introduce another system whose reasoning must also be tested and questioned? Listen to the episode and share your thoughts with me.

Has enterprise AI finally reached the point where impressive demonstrations are no longer enough?

In this episode, I speak with Murali Swaminathan, CTO at Freshworks, about the growing pressure on AI investments to deliver measurable business value. Murali has over 30 years of enterprise software experience, including roles at ServiceNow and CA, and now leads engineering and architecture teams at Freshworks.

Murali believes the AI hype cycle is being replaced by an accountability cycle. Buyers want to understand reliability, governance, total cost of ownership, traceability, and the return generated by every deployment. They also want the ability to audit decisions, override outcomes, and use feedback to improve performance.

Productivity alone provides an incomplete measure. Within service operations, companies can examine time to resolution, the volume of repetitive work automated, the number of issues completed without human intervention, and the quality of the employee's experience.

Murali describes the difference between service-level agreements and experience-level agreements. Resolving a ticket within two minutes means very little if the employee's problem remains. The better question is whether AI completed the workflow and restored the person's ability to work.

We also discuss why mid-market and agile enterprises provide a demanding test for AI. These companies have complex requirements but cannot absorb lengthy implementation programs, unclear pricing, or failed experiments. Murali recommends beginning with a limited process, measuring the result, establishing whether it can be repeated, and expanding only after it has proved reliable.

Architecture plays an important role. Murali argues that ease of use begins beneath the interface. Configuration-led platforms can be upgraded as new capabilities arrive, while heavily customized systems can leave companies trapped on older releases.

Autonomous service operations do not require removing people from every process. Murali uses the example of a printer incident. AI can read the ticket, classify the problem, route it to IT or facilities, and apply an automated fix when a trusted process exists. People retain responsibility for unusual, uncertain, or higher-risk decisions.

Scaling this model requires cloud infrastructure that respects regional data residency, privacy, encryption, routing, and audit requirements. AI requests and diagnostic logs must remain within the correct geographic and regulatory boundaries.

The conversation concludes with engineering skills. AI coding tools can generate software quickly, but engineers must understand architecture, usability, testing, and customer requirements. Companies also need rules determining which code can be reviewed by AI and which changes require human approval.

Is your company measuring whether AI genuinely improves service operations, or is it counting deployments and calling that progress? Listen to the episode and share your thoughts with me.

What happens when an organization writes careful AI governance policies but its infrastructure cannot enforce any of them?

In this episode of Tech Talks Daily, I speak with Sabina Anja, Chief Technologist at Broadcom within the VMware Cloud Foundation division, about the infrastructure controls required as AI agents move from generating answers to accessing data, calling APIs, modifying systems, and triggering work.

Sabina brings experience from both sides of enterprise technology. She remembers cabling networks, dealing with unstable infrastructure, and receiving those weekend calls when downtime had already upset the business. That background informs her belief that ambitious AI programs cannot succeed without stable, observable, and enforceable infrastructure beneath them.

Many organizations are repeating a familiar pattern. Business teams adopt AI services before IT has established visibility, ownership, or control. The terminology may have changed from shadow IT to shadow AI, but the management problem remains. Sabina argues that CIOs first need an inventory of agents, nonhuman identities, data access, processes, and accountable owners.

The risk becomes greater because agents behave differently from people. They operate across multiple systems at machine speed and can perform repeated actions without appreciating the wider business outcome. An agent does not need malicious intent to cause disruption. Excessive permissions, flat networks, inconsistent access rules, and years of deferred infrastructure work can give it plenty of opportunities.

Sabina recommends brokered access rather than direct access, alongside dedicated virtual machines or namespaces, microsegmentation, lateral security, east-west policy controls, and tamper-evident logging. Organizations also need to define which data an agent can view, modify, or move, especially when sovereignty and regulatory requirements apply.

One of Sabina's most memorable ideas is to treat an AI agent like a superhuman contractor. It should have a defined purpose, a named manager, a clear access specification, an activity record, and an end date. Additional permissions should be earned through evidence of reliable behavior rather than granted on the first day.

She also warns about agent debt. AI systems are developing rapidly, so an agent created today may become outdated within months. Sabina recommends assuming that many agents will expire after six to nine months rather than allowing forgotten systems and permissions to accumulate indefinitely.

For CIOs wanting an immediate test, her advice is straightforward. Create an inventory of nonhuman identities with production access. Then select one agent and examine every part of the infrastructure it attempted to reach. The question is not simply whether the application produced the expected result. Leaders should ask whether the agent entered systems, networks, or data stores that nobody expected it to access.

We also challenge the familiar claim that AI agents will take everybody's jobs. Sabina sees an opportunity to remove repetitive tasks and give technology professionals new skills, although she warns that agents may behave like teenagers armed with infrastructure permissions. They may not take your job, but they could become remarkably good at testing your patience.

I'd love to hear your thoughts. Does your organization know how many AI agents have production access and who is accountable for each one?

What does a rising cloud bill actually tell you about the value your business is creating?

Eight years after our first conversation, I welcome Kunal, co-founder and CEO of Unravel Data, back to Tech Talks Daily. We compare the data infrastructure he was optimizing during the Hadoop era with today's enterprise stacks built around Databricks, Snowflake, BigQuery, AI pipelines, and autonomous agents.

Kunal says Unravel Data has analyzed over 10 billion workloads across hundreds of enterprises. From that work, he argues that data platforms and infrastructure can account for up to 60% of cloud spending at some global businesses, while 30% to 40% of data platform spending may produce no business value. These are company claims, but they frame a problem many technology and finance leaders will recognize. The cloud bill arrives after thousands of individual engineering decisions have already been made.

We discuss where cloud waste hides, including oversized clusters, hot storage holding cold data, abandoned pipelines, inefficient queries, duplicate datasets, and development jobs consuming production-level resources. The people creating those workloads seldom see the price attached to their decisions, leaving technology leaders with an aggregated bill that explains what was purchased but not why it was needed.

AI adds another complication. Humans create workloads at human speed, while agents can generate queries, launch infrastructure, and consume tokens around the clock. An agent is designed to complete its task, not worry about whether a single query costs $5 or $5,000. Kunal argues that machine-speed consumption cannot be governed through monthly human reviews.

We also discuss the difference between cost cutting and cost optimization, why aggressive reductions can damage performance and reliability, and how FinOps must connect cost with business outcomes. Kunal explains why leaders should measure cost per pipeline, model, agent, successful run, customer report, and business result.

Finally, we consider the benefits and risks of autonomous data platform optimization. Kunal describes autonomy as a dial, with bounded, reversible, and validated actions earning wider authority as trust develops.

Does your cloud bill show healthy growth, or is expensive waste hiding behind the headline number? Share your thoughts with me.

How can an independent safety evaluation remain meaningful when the AI inside a product may change after its next update?

In this episode of Tech Talks Daily, I speak with Dr. Robert Slone, Senior Vice President, Chief Scientist, and Innovation Officer at UL Solutions. Robert has spent almost 30 years leading science, research, product development, and innovation teams. He now helps guide UL Solutions' scientific work across safety, security, and sustainability.

Many listeners will recognize the UL Mark without knowing what happens behind it. Robert explains how UL Solutions tests products to their limits, which can involve setting them on fire, finding their breaking points, inspecting manufacturing facilities, and determining whether they meet defined safety requirements.

That work began over 130 years ago when electricity was introducing unfamiliar risks. Today, the same broad question applies to artificial intelligence: how can society benefit from a new technology while understanding and managing the harm it could cause?

The need is becoming increasingly visible as AI moves into healthcare, transportation, manufacturing, financial services, infrastructure, and consumer products. Robert recalls being approached about evaluating an AI-enabled teddy bear capable of talking with children. It is a memorable example of how decisions made inside an AI model can reach directly into everyday life.

Robert organizes AI product safety around three pillars. The technical pillar considers robustness, risk management, functional safety, and whether the system performs its intended purpose. The ethical pillar includes fairness, bias, privacy, transparency, and explainability. Governance covers data management, product updates, accountability, and the complete operating life of the system.

We also discuss one of the hardest problems in AI certification. Traditional products and software can be evaluated against a defined version, but AI systems may be updated, retrained, or affected by changing data. Robert explains why meaningful safety assurance requires version-specific testing, annual reviews, disclosure of significant changes, and eventually telemetry capable of identifying problems much closer to real time.

For business leaders buying AI, the conversation provides a practical vendor checklist. Where did the training data come from? How was performance measured? What are the system's known limitations? How were privacy and bias assessed? Who takes responsibility if its behavior changes?

Independent testing cannot promise that an evolving product will remain safe forever. It can provide evidence about the version evaluated, expose gaps, establish accountability, and create a process for monitoring future changes.

What proof would you demand before allowing an AI product to influence an employee, patient, customer, or child? Listen to the episode and share your thoughts with me.

Could allowing patients to choose their own appointment times help reduce missed visits and shorten NHS waiting lists?

In this episode of Tech Talks Daily, I speak with Alison MacDonald, European Lead and Senior Vice President at Nordic Global. Alison brings an unusual combination of clinical and technology experience as a registered nurse who moved into digital health over 15 years ago.

Her career began in community nursing, where she was asked to lead an electronic health record project because colleagues thought she was good with computers. What initially appeared to be a simple exercise in converting paper forms into digital records encouraged her to question whether healthcare could redesign the process rather than copy it onto a screen.

We discuss Nordic's work with Cambridge University Hospitals NHS Foundation Trust on patient self-scheduling and automated earlier appointment offers. Before the program, missed outpatient appointments were removing valuable clinical capacity while administrative teams spent time calling patients and rearranging bookings.

Cambridge introduced self-scheduling through Epic MyChart, allowing patients to select appointment times through the patient portal. Alison says the DNA rate fell from 5% to 2.2% during the program.

According to the supplied results, over 20,000 patients successfully scheduled their own appointments and over 3,000 accepted offers to attend earlier when cancellations created availability. Patients moved appointments forward by an average of 16 days.

Forty percent of accepted earlier appointments occurred within seven days of the offer, while 7% took place on the same or following day. Administrative teams also saved an estimated ten minutes for every self-booked appointment.

Alison explains why patient control can improve attendance. People can choose times that work around employment, caring responsibilities, travel, and family life instead of receiving a fixed appointment through a letter or telephone call. Patients can also cancel or reschedule without waiting for somebody to answer the phone.

The operational lesson goes beyond appointment booking. Healthcare systems may be able to recover existing capacity by examining missed appointments, theater scheduling, waiting list processes, pre-visit questionnaires, and patient communications before concluding that every problem requires additional staff or facilities.

We also discuss where AI is producing practical results in healthcare. Alison points to medical imaging, emergency department triage, waiting list management, clinical documentation, and workforce deployment. She warns against discussing AI as one generic solution because each application requires a defined use case, suitable data, workable processes, governance, and staff adoption.

Ambient clinical documentation offers one example. An AI scribe can record a consultation, prepare a structured note, and pass it to the clinician for review and correction. Alison cites an NHS evaluation reporting a 23.5% increase in direct patient interaction time and an 8.2% reduction in appointment length.

Interoperability remains another major challenge. Healthcare journeys cross hospitals, primary care, community services, and specialist providers that may use different systems or a mixture of electronic and paper records. Even basic differences, such as one organization measuring pain on a five-point scale and another using ten points, can prevent reliable comparison.

Alison recommends agreeing on common data standards, defining the minimum patient information required during care transitions, including interoperability requirements in procurement, and avoiding bespoke integrations that make future information sharing harder.

Digital access also requires balance. Online services can improve convenience, but healthcare providers must retain appropriate alternatives for patients who lack digital skills, connectivity, confidence, or access.

Could your healthcare organization improve patient access and staff capacity by redesigning one familiar process before purchasing another large technology platform? Listen to the episode and share your thoughts with me.

What would change if a customer could return three days later through a different channel and continue the same conversation without repeating a single detail?

In this episode of Tech Talks Daily, I speak with Paul Adams, Chief Product Officer at Fin, the company previously known as Intercom. Paul has spent almost 13 years with the business and provides a candid account of how it abandoned its previous roadmap, placed a company-wide bet on AI, and rebuilt its products and working practices around AI agents.

Our conversation begins with Fin's move from a customer service agent toward what Paul calls a single customer agent. The idea is that customers do not care whether their request belongs to sales, service, or customer success. They want the company to understand their situation and help them complete the task.

Paul explains how AI agents can bring customer history, company knowledge, operational data, and business goals into the same conversation. This could allow an agent to resolve an issue, support a purchase, recognize a valuable customer, or transfer the conversation to a human without losing the context already provided.

We also examine the economics behind poor customer service. Many companies are not ignoring customers through a lack of concern. They are receiving volumes of requests that cannot economically be handled by adding people alone. Paul says some Fin customers are resolving between 70 and 90 percent of customer queries through AI. Rather than seeing entire teams disappear, he is observing employees move into customer success, knowledge management, AI supervision, and higher-touch services.

The episode also provides an unusually candid account of what it took to rebuild an established SaaS company around AI. Paul describes the process as brutal. Strategies were discarded, familiar processes were removed, and some people decided the new direction was not for them. His advice is to prioritize speed, place smaller experiments in front of real customers, and learn from evidence rather than waiting for every internal condition to become perfect.

Paul also recalls working on early versions of mobile YouTube and Gmail when colleagues questioned whether anyone would watch video or answer email on a phone. Those stories provide a timely warning about judging new technology by its early limitations.

Could AI agents finally give customers continuity across sales, service, and support, or will internal company structures remain the greater obstacle? Listen to the conversation and share your thoughts with me.

Can organizations trust an AI recommendation when they cannot understand the evidence, relationships, and previous decisions behind it?

In this episode of Tech Talks Daily, I welcome back Jim Webber, Chief Scientist at Neo4j, to discuss the company's acquisition of GraphAware and its move from graph database provider to graph intelligence platform.

GraphAware has worked with Neo4j for many years and developed Hume, an intelligence analysis platform used to connect and examine complex information. Bringing the two companies together gives Neo4j a direct role in applications serving police forces, governments, intelligence agencies, and other organizations handling connected data.

Jim explains why context has become one of the biggest requirements for dependable AI. Enterprises already possess enormous volumes of data, but facts alone provide endpoints rather than the complete path leading to a decision.

An agent needs to understand the knowledge available, the conversation taking place, and the record of previous decisions. It also needs to know which actions produced good outcomes and which produced poor ones.

Jim compares these information layers to SimCity. Each can be viewed separately, but their greater value appears when they are combined. Knowledge, conversations, and decision traces can then help an agent understand why something happened and learn from the result.

This introduces an interesting lesson from scientific research. Positive outcomes are frequently published, while failed experiments receive less attention. An AI agent needs both. Recording the breadcrumbs behind good and bad decisions provides the material required to improve its future behavior.

We also discuss why large language models cannot understand every organization by themselves. Jim describes a model as a lossy compression of the internet. It can generate impressive natural language, but it does not automatically understand a company's policies, customers, history, evidence, or operating environment.

Retrieval augmented generation can introduce relevant organizational information into the process. Graph RAG adds relationships between facts, helping the system understand how people, events, products, accounts, and other entities connect.

According to research Jim references from the National Innovation Centre for Data, Graph RAG can improve accuracy while reducing costs by using fewer, higher-quality tokens.

Explainability becomes especially important when AI supports decisions across policing, cyber defense, taxation, intelligence, banking, and government. A fluent answer may sound authoritative while containing a serious technical mistake.

Jim shares an example from his own work where an agent confidently warned him about a "committed minority" inside a fault-tolerant computing protocol. The statement sounded plausible, but only a majority could commit within that protocol. Someone without Jim's technical knowledge might have accepted the recommendation and removed working code.

This leads us to human oversight. Jim argues that the correct level depends on the consequences of the action. Automating a routine banking process with monitoring and safeguards may improve the customer experience. Ordering someone's arrest based solely on an agent's conclusion demands human involvement.

We also consider digital sovereignty and why control over data has become a strategic concern for governments and large enterprises. Geopolitical instability, overseas technology dependencies, privacy requirements, and changing national policies are forcing leaders to ask where their data resides and whether they can retrieve or move it.

Jim explains how Neo4j intends to offer organizations flexibility over where their information is stored and how it is deployed. The discussion also examines the opportunity for Neo4j and Hume to provide an alternative within a market where Palantir has held a powerful position.

Looking ahead, Jim imagines intelligence analysts directing swarms of digital agents. Those agents could search data, connect evidence, identify relevant patterns, and present findings while humans retain responsibility for consequential decisions.

If AI can connect information at machine speed, how do we ensure the person making the final decision can inspect the evidence and challenge the conclusion? Listen to the episode and share your thoughts with me.

What does an AI agent need to understand about your business before you allow it to make decisions and take action without waiting for human approval?

In this episode of Tech Talks Daily, I speak with Kash Mehdi, Field CTO at Reltio, about the move from analytical AI that supports decisions to agentic AI that can execute them.

Kash argues that leaders should begin treating AI agents as a workforce rather than another collection of software tools. A digital workforce needs training, boundaries, oversight, trusted information, and clear permissions before it can act safely.

He uses the analogy of raising a puppy. When the puppy misbehaves, the problem may be inadequate training or poorly defined boundaries. AI agents present a similar leadership challenge. Organizations must ask what the agent has learned about the business and what authority it has been given.

We discuss why model selection may be receiving too much executive attention. Kash describes four components of an agentic system: the model, tools, data, and context. Models are improving rapidly and tools are increasingly available, but business context remains incomplete across many enterprises.

Data tells an agent a fact. Context helps it understand what the fact means within a particular customer relationship, geography, policy, or business process.

Kash illustrates the difference with a pizza order. The data may confirm that someone is logged in, the model can interpret the request, and a tool can place the order. Context tells the system that it is Friday night, the customer is watching television, and they usually order pineapple and cheese pizza.

The same principle becomes far more serious when an agent is dealing with medical equipment, supply chains, financial customers, or regulated information. It must understand which entities exist, how they relate, what information it may access, and which actions it has authority to complete.

Kash identifies three requirements for safer autonomy: a governed source of truth, a live feedback loop, and enforceable permission boundaries. Trust must be built into the data and operating rules before the agent acts because the familiar human review step may no longer exist.

We also discuss how governance changes when AI can execute decisions at machine speed. A poor decision made by one employee can usually be reviewed and corrected. A poor decision repeated automatically across thousands or millions of transactions can become a business incident before anyone intervenes.

Kash shares examples involving restaurant menu launches, medical equipment deliveries, and call center offers. Each depends on current information and the relationships connecting customers, products, suppliers, locations, and previous interactions.

For CIOs preparing today, Kash recommends building context around reusable entities rather than constructing an isolated data project for every AI use case. He points to Schneider Electric as an example where one unified foundation supported sales, shipping, operations, and marketing use cases.

The conversation ends with a warning about slow data. Autonomous agents need current context because information that arrives after a decision has been made may no longer carry much business value. Kash predicts that the half-life of enterprise data will become a board-level measure.

If a smarter agent can make a poor decision faster and with greater confidence, is your organization investing enough in the context, governance, and feedback needed to keep it on course? Listen to the conversation and share your thoughts with me.

How much of your technology stack is being held together by people swiveling between screens, copying information, and quietly compensating for systems that cannot communicate?

In this episode, I speak with John Foreman, Chief Product Officer at Clio, about what he calls the "swivel chair problem." John previously served as Chief Product Officer at Mailchimp and Podium, and now helps guide product development at a company seeking to support the complete operation of a law firm.

We discuss why legal professionals have moved from understandable caution around AI toward increasingly sophisticated daily use. John explains why concerns about client confidentiality, intellectual property, model training, and data access initially slowed adoption, as well as why lawyers are now helping set the pace for responsible professional AI use.

Our conversation also examines why disconnected technology stacks make AI appear far less capable. People can interpret information across documents, billing platforms, case management tools, email, and court systems. An AI system cannot perform the same work unless it receives the necessary context and access.

John also explains why the familiar chatbot may be the wrong interface for many jobs. Some AI tasks should happen quietly, while work involving legal filings and client records requires structured review, accountability, and human approval.

With lawyers spending an average of 62% of their time on nonbillable work, the immediate opportunity could include intake, billing, timekeeping, reviews, document processing, and filing. These lessons extend well beyond legal services.

Where is the swivel chair problem hiding inside your organization? Listen to the conversation and share your thoughts with me.

Could Making Tax Digital improve the way small businesses manage their finances, or will it become another administrative burden competing for an already crowded evening?

In this episode, I speak with Caroline Duong, Head of Business Admin at ANNA Money, about Making Tax Digital, quarterly reporting, AI bookkeeping, and the reality of running a small business when one person is often responsible for almost everything.

ANNA Money stands for Absolutely No Nonsense Admin. It is an AI-powered, app-based business account and financial admin service designed for small businesses, startups, freelancers, and sole traders in the UK. Its goal is to reduce the paperwork that regularly follows business owners home after the working day has supposedly ended.

Caroline explains that Making Tax Digital quarterly updates are reports to HMRC rather than full tax returns. The intention is to encourage people with self-employment or property income to maintain digital records throughout the year instead of rebuilding their finances from receipts shortly before a deadline.

Awareness remains a problem. Caroline says an estimated 864,000 people are expected to submit updates during the first year, while fewer than half had signed up at the time of recording. HMRC's softer first-year approach gives people time to adjust, but Caroline warns against waiting until penalties enter the system before changing established habits.

We also discuss what AI can do differently from traditional accounting software. Caroline offers a wonderfully simple example: a tire purchase may represent vehicle maintenance for one business and inventory for a car parts dealer. An AI system with enough business context can recognize that difference and categorize the transaction accordingly.

Caroline also explains why responsible automation still needs human confirmation. Software can learn about suppliers, customers, and regular expenses, but it must recognize when information is missing or a decision requires human judgment.

The conversation ends with two practical recommendations. Keep business and personal transactions separate, and begin tracking income and expenses early. Both can make quarterly reporting significantly easier and reduce the risk of being caught off guard later.

If AI can give business owners a few hours back each month, which administrative task should it take on first? Listen to the conversation and share your thoughts with me.

Who really controls your enterprise technology strategy: your organization or the vendors writing its software contracts?

In this episode of Tech Talks Daily, I speak with Tomás O'Leary, founder and CEO of Origina, about enterprise software vendor lock in, forced upgrades, subscription contracts, and the financial consequences of surrendering control over mission-critical systems.

Tomás founded Origina in Dublin after working within the enterprise software supply chain and questioning the value customers received from traditional support contracts. He saw organizations paying substantial annual fees while experiencing poor response times, constant pressure to change versions, and upgrades that produced limited business value.

He argues that the balance of power between technology buyers and suppliers has moved heavily toward the vendor. Companies that previously purchased perpetual software rights are increasingly being encouraged or forced toward subscription models, while complex contract terms and audit risks can make customers feel trapped.

Some Origina customers have described this behavior as a "digital mafia," while one Fortune 50 organization, according to Tomás, uses AI to assess whether suppliers could be acquired by vendors it considers predatory. That business then considers longer contracts as protection against future licensing changes.

However, leaving a vendor does not always require replacing the software. Tomás explains why perpetual software rights and independent support can give companies another option. A system that continues to perform its required business function may not need to be replaced simply because the original vendor has ended support or introduced a new commercial model.

We discuss how leaders should distinguish between technology that genuinely requires modernization and dependable systems of record that could continue operating securely. Payroll platforms, general ledgers, claims systems, and other back-office applications may not require constant reinvention if the business requirement remains stable.

Tomás also describes a European organization spending approximately €1 million annually on a software product. The company estimated that a vendor-required version change would cost €30 million. By moving to an alternative support arrangement, it expects to defer that expenditure while keeping the existing system operational. These figures are the organization's estimates, shared by Tomás during our conversation.

We also discuss centralized technology dependency, outages, software patching, AI-assisted development, and why some companies are returning to internally developed applications for operations they consider particularly important.

Tomás recommends that CIOs create a small team combining technical, procurement, contractual, and legal knowledge. This group should remain close to senior leadership and challenge assumptions before renewals, migrations, or major software changes are approved.

Is your organization modernizing because the business needs to change, or because a vendor has decided that time is up? Listen to the conversation and share your thoughts with me.

Why are companies preparing for agent-to-agent customer service when many customers still cannot get a chatbot to answer a straightforward question?

In this episode of Tech Talks Daily, I speak with Latané Conant, Chief Marketing Officer at Parloa, about the state of customer experience and what businesses must repair before agentic AI becomes another barrier between customers and support.

Parloa's State of Agentic CX report assessed 10,000 enterprise websites, 4,000 chat interactions, and 100 phone trees. According to the company's findings, fewer than 10% of the tested chat conversations achieved the customer's goal. Only 1% of enterprises demonstrated readiness for automated agent-to-agent interactions.

Those results raise a difficult question about years of customer experience investment. Businesses now have websites, chatbots, mobile applications, email, messaging, and phone systems, but customers frequently struggle to find help or complete the task that brought them there.

Latané argues that part of the problem comes from treating customer service primarily as a cost center. When the objective is reducing contact volume, organizations can unintentionally make themselves harder to reach. This overlooks the commercial and operational information contained within customer conversations.

Calls can reveal onboarding problems, unexpected product uses, recurring faults, and potential sales opportunities. Latané explains how analyzing service conversations can give marketing, product, operations, and executive teams a clearer picture of what customers are experiencing.

We also examine why so many chatbots reproduce the frustration of traditional phone trees. Although the interface looks conversational, the system underneath may still rely on rigid categories and predefined routes. Customers then find themselves trying different words or repeatedly requesting a human agent.

Latané describes a better agentic customer experience as being closer to talking with someone who already knows you. A personal AI agent could remember previous interactions, understand preferences, work across voice and text, and complete a request without making the customer repeat information.

That possibility also introduces questions about trust, permissions, personal information, and oversight. Latané discusses the need to monitor what AI agents are doing, identify when conversations move away from approved subjects, and use supporting agents to detect potentially harmful behavior.

Human involvement remains particularly important when a conversation involves distress, vulnerability, or emotional care. In Latané's roadside assistance example, AI can arrange a tow truck for a flat tire. If it detects signs that the caller is in distress, the conversation should move quickly to a person.

We finish by considering what this means for customer service employees. Latané believes experienced representatives and operations teams can become builders and managers of AI agents, applying their customer knowledge across a much larger digital workforce.

If the existing customer service front door is confusing and unwelcoming, should businesses repair that experience before inviting AI agents through it? Listen to the episode and share your thoughts with me.

What happens to tomorrow's leadership pipeline when employers automate the entry-level tasks through which beginners learn?

In this episode of Tech Talks Daily, I speak with Gary Flowers, Chief Information Officer for Transformation and Technology Services at Year Up United, about AI fluency, human skills, economic mobility, skills-first hiring, and the future of entry-level work.

Year Up United prepares young adults without bachelor's degrees for meaningful careers while helping employers reach skilled, career-ready talent. Gary says the organization has over 35,000 alumni working across companies ranging from the Fortune 1000 to the Fortune 50.

Gary challenges the assumption that younger workers will automatically understand AI because they grew up with technology. Access to a chatbot does not create workplace readiness. Young adults also need training, support, ethical awareness, judgment, communication, adaptability, and experience applying tools to real business problems.

He argues that AI may redefine entry-level work rather than eliminate it entirely. Candidates who understand how to work with AI may gain an advantage over those who do not, but employers must also reconsider which tasks beginners need to develop business knowledge and professional confidence.

We discuss the risk of another technology divide. AI could widen economic opportunity, but unequal access to tools, training, mentorship, and workplace experience could reinforce existing inequalities. Gary believes organizations must teach workers when AI should be used, rather than limiting training to what the technology can do.

Year Up United combines AI fluency with workplace and career readiness. Gary describes its 17 durable skills, six-month curriculum update cycle, close employer feedback loops, and participation as an inaugural host partner in Anthropic's Claude Corps Fellowship program.

For employers, one of the hardest decisions involves balancing immediate efficiency with future capability. Automating junior work may reduce costs today while weakening the pipeline of experienced professionals and leaders required later.

Gary recommends creating a culture of continuous learning, supplying employees with appropriate tools, building communities of practice, sharing successful use cases, and treating AI as a company-wide responsibility. He also distinguishes between AI as a workforce skill and AI as an organizational capability capable of changing how functions operate.

Can employers capture the value of AI while preserving the career pathways that allow inexperienced workers to become tomorrow's experts and leaders? Listen to the episode and share your thoughts.

Can an app approved by Apple or Google still expose your business to security, privacy, and governance risks?

In this episode of Tech Talks Daily, I welcome back Michael Covington, Vice President of Strategy at Jamf, for a conversation about the false confidence that can surround mobile security. Apple and Android provide strong protections, including app review processes, sandboxing, and device authenticity controls. However, Michael argues that a device being secure on day one does not mean it will remain secure throughout its working life.

One of the most interesting points from our conversation is that mobile malware represents only a small part of the problem. Michael says it appears on fewer than 1% of the devices Jamf protects. The wider concerns include vulnerable third-party libraries, aging app versions, excessive permissions, unsafe web connections, compromised identities, software supply chains, and AI functionality introduced without the company fully understanding how it handles data.

Michael also shares findings from Jamf analysis of corporate applications. According to the research he discusses, 95% of the apps examined contained at least one medium or higher severity vulnerability, 10% used vulnerable third-party libraries, and 96% included AI features. For security leaders, this creates a much broader question than whether an app contains malware. They need to understand what the app can access, where it communicates, how it handles data, and whether its capabilities comply with company policy.

We discuss why familiar advice about updates, passwords, and suspicious links continues to fail when employees are busy or working from mobile devices on the front line. Michael explains how automation, clearer deadlines, and access policies can reduce risk without placing every responsibility on the user.

The conversation also covers BYOD security and employee privacy. Modern Apple and Android controls can separate business information from personal apps, allowing employers to manage the work container without inventorying an employee's private digital life. Michael believes many organizations should reassess older BYOD programs that remain intrusive or unnecessarily restrictive.

Finally, we examine the visibility security teams need across device configuration, patch levels, apps, permissions, identity services, web activity, and AI tools. Michael's advice is to start by understanding how people work before introducing heavier controls that may encourage workarounds and shadow IT.

Does your organization know how its mobile risk changes after a device has been issued, or are you relying on the protection it had on day one? Listen to the conversation and share your thoughts with me.

How should finance leaders measure AI ROI when adoption has slowed and the cost of models, tokens and disconnected tools remains difficult to predict?

In this episode of Tech Talks Daily, I welcome Jeremy Ung, Chief Technology Officer at BlackLine, back to the podcast to discuss how businesses can move from finance AI experimentation to operational deployment.

Figures supplied for the interview show AI adoption in finance rising from 37% in 2023 to 58% in 2024, before moving only slightly to 59% in 2025. Jeremy argues that this apparent plateau reflects several pressures, including uncertainty around cost, regulatory requirements, auditability and the continuing debate over whether companies should build their own AI capabilities or purchase them through established platforms.

Token spending is part of the problem. Unlike traditional software costs, model usage can be difficult to predict and allocate. Finance leaders want to understand whether applying AI to a workflow will produce enough value to justify that uncertainty.

Jeremy believes companies should avoid creating artificial AI ROI metrics. The business measurements already exist. Is AI helping the company close its books faster? Is transaction matching becoming more accurate? Are collections improving? Is the work being completed faster or with fewer manual steps?

We discuss what operationalizing AI in finance looks like in practice. Many processes still require employees to contact vendors, collect information, reconcile data and coordinate with other departments. Traditional software struggled with the variation found in these workflows, while AI can adapt to different processes and communication requirements.

Accuracy and oversight remain necessary. Jeremy explains why companies need visibility into the prompts, reasoning, models, data, tools and permissions used by every AI agent. That information creates an operating record that finance teams, auditors and regulators can examine later.

His analogy with food labeling provides a useful way to understand AI auditability. Consumers can inspect ingredients, calories and sourcing information before buying food. Finance leaders should expect comparable information about the models and data involved when an agent performs financial work.

We also discuss the problem of fragmented data. Jeremy acknowledges the familiar rule of garbage in, garbage out, but argues that AI can help connect legacy platforms and mainframe systems that businesses previously found difficult to integrate.

The role of finance professionals will change as agents perform additional work. Employees may spend less time completing individual tasks and more time setting goals, reviewing results, approving actions and directing teams of agents.

Should CFOs continue buying additional AI tools, or concentrate on embedding existing investments into the financial workflows that determine business performance? Please share your thoughts with me.

What happens when an autonomous AI agent can complete thousands of actions before a traditional access review has even identified that something has gone wrong?

In this episode of Tech Talks Daily, I speak with Alex Bovee, CEO and co-founder of C1, about identity security, runtime governance, shadow AI, and the controls companies need as humans and agents begin working together.

Alex has spent much of his career in identity and security. He and his co-founder previously worked at Okta on zero trust products before creating C1 as an access control platform capable of operating at machine speed.

That requirement has become increasingly important as AI agents begin accessing company data, calling tools, using credentials, and taking actions across enterprise systems.

Alex describes agents as non-deterministic systems that can "reward-max." An agent may pursue its assigned objective so aggressively that it finds an unexpected or dangerous way to complete the task. It does not possess a moral compass or an intuitive understanding of what the company considers acceptable.

Traditional identity processes were created for people. A company might review access every 90 days or investigate a security issue after an event. That approach becomes inadequate when an agent can execute thousands of actions within minutes.

We discuss why identity is becoming a control plane for AI agents. Networks, data systems, and security tools all play important roles, but identity determines which resources an agent can access, which actions it can perform, and whether it acts independently or on behalf of a person.

Without a defined identity or delegated authorization model, an organization may struggle to connect an agent's behavior with a responsible owner, a limited mission, and enforceable permissions.

Alex explains the four connected capabilities inside C1's Agentic Control Plane.

The first concerns shadow AI discovery. Companies need visibility across cloud services, SaaS applications, endpoint agents, hosted agents, local MCP servers, and credentials stored throughout the environment.

This is particularly relevant because employees are downloading locally developed or "vibe-coded" MCP servers and running agent tools on their devices. These components can introduce software supply chain risks and expose local credentials.

The second capability covers credential security. C1 has introduced a post-quantum credential vault designed to protect secrets and inject them into authorized agent workflows without leaving credentials scattered across devices and applications.

The third area is runtime governance. Instead of reviewing behavior after an incident, organizations can evaluate an agent's actions against its assigned mission as they occur.

If an agent is authorized to complete one business task but begins exploiting an internal tool, contacting an unapproved service, or attempting to extract data, runtime controls can block the action or request human approval.

The fourth capability concerns agentic security intelligence. This uses information collected across identities, agents, permissions, credentials, and behavior to identify risks and support automated remediation.

We also discuss human accountability. Alex says emerging regulatory thinking recognizes the need for a responsible person behind an autonomous agent. That connection allows businesses to establish ownership, delegate authority, and determine who remains accountable for the agent's behavior.

The conversation then turns to the effect of AI on employees. Alex rejects the assumption that organizations will simply remove people as agents become more capable.

His preferred analogy is that people are moving from manually producing every artifact to building and supervising the factory. Employees provide the inputs, direct the agents, examine the outputs, and correct the process when necessary.

C1 has experienced this internally. Alex says its engineering team increased from roughly 150 weekly software merges to around 1,500, while engineering headcount grew by approximately 10% to 15%.

That productivity requires careful human review. Generating work faster does not remove the need to assess whether the output is accurate, secure, useful, and aligned with the original objective.

For CISOs and CIOs, the goal is to provide a governed path for AI adoption. A blanket prohibition may encourage employees to work around policy. Secure self-service access can give teams approved tools, defined permissions, and runtime protection.

If an AI agent can operate at machine speed, are your organization's identity controls capable of observing, authorizing, and stopping it at the same pace? Listen to the conversation and share your thoughts with me.

What evidence would convince you that an AI agent is ready to make decisions involving employment, money, healthcare, or legal rights?

In this episode of Tech Talks Daily, I speak with Vin Sharma, founder and CEO of Vijil, about the trust gap preventing many enterprise AI agents from progressing beyond proof of concept.

Vin has spent approximately 30 years building software across security, operating systems, open source, cloud computing, machine learning, and AI. His previous work includes leading engineering at Amazon SageMaker and helping develop 11 AWS AI services.

He argues that AI agents differ from conventional software because they combine autonomy with agency. They can interpret an objective, make decisions under ambiguous conditions, and take action. This raises a deeper question than whether an agent can complete a demonstration successfully: will it remain loyal to the interests of the person or business delegating the task?

Trust is also specific to the job. Vin uses a simple analogy. You may trust a gardener to care for your lawn, but that does not automatically make the same person suitable to babysit your child. An AI agent must therefore be evaluated within the context of its users, task, operating conditions, authority, and potential consequences.

Vin proposes testing three areas. Reliability asks whether the agent can perform its assigned task. Security examines whether it maintains its integrity when facing hostile or noisy conditions. Safety considers what happens when the agent fails and whether the resulting damage remains contained.

This evaluation cannot end when the agent enters production. Models, integrations, data, users, and external conditions change. An agent may drift away from its original purpose, which means businesses need continuous monitoring, testing, and updating across the full AI agent lifecycle.

We discuss how established security practices can be applied to this problem. Trusted execution environments, containment, least privilege, limited-duration access, and bounded models can reduce exposure. Smaller language models may also be better suited to narrow, high-risk tasks than a general model with broad permissions.

Vin offers a three-part framework for governance: personas, purpose, and policy. Personas describe the people and attackers who may interact with the agent. Purpose defines the legitimate task. Policy sets the boundaries between permitted and prohibited behavior.

For high-risk systems, his recommended starting position is that any action not explicitly permitted should be prohibited. A natural-language policy can then be converted into deterministic rules and controls governing the agent's behavior.

Vin's most direct advice concerns evidence. Vibes, demonstrations, and benchmark scores do not prove that an agent is safe for a particular business process. A CISO should expect a complete risk assessment, while a business owner should receive proof that the agent will serve the organization's interests.

His bridge analogy captures the issue perfectly. Engineers do not claim a bridge is safe because it looks impressive during a demonstration. They calculate load, tolerance, failure conditions, and provide test evidence. AI agents acting in consequential workflows deserve a comparable engineering discipline.

If an agent developer asked you to trust their system today, would they be able to provide evidence of reliability, security, safety, loyalty, and contained failure? Listen to the episode and share your thoughts with me.

What if employees could access sensitive business applications from personal phones without storing company data on those devices?

In this episode of Tech Talks Daily, I speak with Jared Shepard, CEO of Hypori, about virtual mobile infrastructure, BYOD security, employee privacy, zero trust, and the growing mobile threat created by AI.

Jared's personal story deserves attention in its own right. He describes himself as a former homeless high school dropout who joined the Army, discovered an aptitude for IT, and applied what he learned to difficult technology problems in Iraq and Afghanistan. That experience gave him a firsthand understanding of what people working at the edge need from secure communications.

The requirement that led to Hypori was unusually demanding. Users needed to obtain a phone from a local market, connect through a network assumed to be compromised, and access a protected enterprise environment without exposing sensitive information.

Hypori's answer is virtual mobile infrastructure. According to the company, applications and enterprise data remain inside a protected cloud environment while the user receives a streamed visual experience. Sensitive data is not stored on the physical phone, tablet, or laptop.

Jared explains why this differs from mobile device management. MDM attempts to secure, monitor, and control the endpoint. Hypori begins from the assumption that the endpoint may already be compromised. This can also protect employee privacy because the organization does not need visibility into the worker's personal device.

We discuss how this approach could help government, defense, healthcare, banking, and smaller businesses that cannot maintain the same mobile security resources as a large enterprise. However, virtual infrastructure does not remove every responsibility. Organizations still need strong identity controls, protected cloud environments, reliable connectivity, policy enforcement, and careful vendor assessment.

Jared also argues that AI is reducing the time between vulnerability discovery and exploitation. Security programs built around monthly patching may struggle when attack windows are measured in minutes.

The conversation closes with leadership, resilience, and mentorship. Jared explains why hard work alone does not guarantee success and why valuable lessons can come from investors, generals, colleagues, friends, or the janitor who has spent 20 years observing how an organization works.

Could virtual mobile infrastructure give employees secure access and personal privacy without forcing companies to control every device? Listen to the episode and share your thoughts.

Could your company be paying suppliers earlier than its competitors and unintentionally financing their advantage?

In this episode of Tech Talks Daily, I welcome back Oliver Belin, co-founder and CEO of Calculum. Our previous conversation took place around ten years ago when Oliver was working with the Marco Polo Network and blockchain was attracting attention across trade finance. His latest venture concentrates on working capital, payment terms, and the role of AI in supplier negotiations.

Oliver explains why working capital has moved higher on the agenda for procurement, treasury, and finance leaders. Companies can generate cash through sales, borrowing, inventory efficiency, faster customer collections, or changes to supplier payment terms. With borrowing costs higher and sales growth difficult in many markets, businesses are examining the cash already tied up within their operations.

The difficulty is that procurement teams usually know their own supplier data but lack reliable information about the terms those suppliers accept from other customers. Negotiating without market benchmarks can lead to blunt policies, such as extending every supplier to 90 days.

Oliver warns that indiscriminate extensions can create serious consequences. Smaller suppliers may experience cash flow pressure, increase their prices, reduce service, or direct capacity toward customers offering better terms. The buyer may improve its balance sheet while weakening an important part of its supply chain.

Calculum uses transactional benchmark data to compare existing payment terms with the wider market. According to Oliver, the platform can show how frequently a supplier appears in its dataset, which terms it accepts elsewhere, and the probability that it will agree to a proposed change.

AI and predictive analytics can then help companies concentrate on the suppliers where an adjustment would create the greatest financial impact and carry a higher probability of acceptance. This is particularly useful when an enterprise has tens of thousands of suppliers and procurement teams can only negotiate directly with a small proportion of them.

Oliver says Calculum typically identifies free cash flow opportunities equivalent to approximately 8% to 11% of the spend analyzed. The amount identified does not automatically become realized cash. Procurement teams need targets, internal ownership, supplier conversations, and financing options to turn recommendations into results.

He shares the example of an unnamed Fortune 500 pharmaceutical company that generated $227 million in free cash flow over 16 months. The program combined market-aligned payment terms with Supply Chain Finance, allowing participating suppliers to receive early payment in exchange for a discount based on the buyer's financial strength.

Another UK company with approximately 4,000 suppliers generated €3 million in free cash flow within two months. Oliver attributes the speed partly to knowing which suppliers to approach first rather than attempting a broad, manual campaign.

We also discuss supplier protection. Calculum identifies whether a business is a small or medium-sized enterprise, examines ultimate ownership, and considers financial strength. A financially vulnerable supplier may need early payment support rather than longer terms.

Oliver's wider point is that AI cannot create reliable benchmarks from nothing. Useful predictions require traceable transactional data, clear objectives, and people prepared to act. Could better payment term intelligence improve your cash position while creating fairer, better-informed supplier relationships? Listen to the episode and share your thoughts with me.

What if the chemistry created by neurological disease could help activate medicine precisely where it is needed?

In this episode of Tech Talks Daily, I speak with Sara Isbell, neuroscientist and co-founder of Enabled Therapeutics, about a proposed approach to one of medicine's most stubborn problems: delivering effective drugs to diseased brain tissue without exposing healthy areas to the same activity.

Sara explains how the blood-brain barrier prevents many promising compounds from reaching the brain. When drugs do enter, they may spread across healthy and diseased regions alike, creating a difficult balance between therapeutic benefit and unwanted effects.

We hear how an unexpected laboratory result led Sara and her co-founder to investigate whether pathological oxidative stress could convert a precursor molecule into a biologically active compound near the affected tissue. Sara describes this as pathology-gated therapeutic activation, where disease-associated chemistry provides the trigger that turns the medicine on.

This remains developing science. At the time of recording, Enabled Therapeutics was preparing its first peer-reviewed manuscript and seeking partners to support further studies. Sara explains why reproducible evidence, regulatory guidance, and careful laboratory validation must determine whether the hypothesis advances.

We also discuss how AI helps small biotechnology teams review literature, organize regulatory materials, connect ideas across scientific disciplines, and identify possible hypotheses. However, Sara offers an important reminder: AI can propose possibilities, but nature and experimental evidence decide what is true.

Could following one unexpected result eventually offer researchers another way to approach neurological disease? Listen to the conversation and share your thoughts with me.