Tech Talks Daily

Neil C. Hughes

If every company is now a tech company and digital transformation is a journey rather than a destination, how do you keep up with the relentless pace of technological change?

Every day, Tech Talks Daily brings you insights from the brightest minds in tech, business, and innovation, breaking down complex ideas into clear, actionable takeaways.

Hosted by Neil C. Hughes, Tech Talks Daily explores how emerging technologies such as AI, cybersecurity, cloud computing, fintech, quantum computing, Web3, and more are shaping industries and solving real-world challenges in modern businesses.

Through candid conversations with industry leaders, CEOs, Fortune 500 executives, startup founders, and even the occasional celebrity, Tech Talks Daily uncovers the trends driving digital transformation and the strategies behind successful tech adoption. But this isn't just about buzzwords.

We go beyond the hype to demystify the biggest tech trends and determine their real-world impact. From cybersecurity and blockchain to AI sovereignty, robotics, and post-quantum cryptography, we explore the measurable difference these innovations can make.

Whether improving security, enhancing customer experiences, or driving business growth, we also investigate the ROI of cutting-edge tech projects, asking the tough questions about what works, what doesn't, and how businesses can maximize their investments.

Whether you're a business leader, IT professional, or simply curious about technology's role in our lives, you'll find engaging discussions that challenge perspectives, share diverse viewpoints, and spark new ideas.

New episodes are released daily, 365 days a year, breaking down complex ideas into clear, actionable takeaways around technology and the future of business.

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#3718
Today · 28 min
Building a Faster Specialty Insurance Market With Accelerant
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.
#3717
Yesterday · 30 min
Turning AI Pilots Into Measurable Business Value With Tredence
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" Putchea, 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.
#3715
Yesterday · 26 min
Finding Potholes Before They Form With AI and Univrses
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.
#3713
Monday · 32 min
Building AI Around the Financial Advice Workflow With Marloo
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.
#3712
Sunday · 24 min
Becoming AI Native Without Losing Human Judgment With Fiverr
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.
#3712
Sunday · 30 min
Building Trust in AI Powered Business Travel With Amex GBT
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.
#3710
Saturday · 26 min
Building Flexible AI Data Centers With EdgeCore
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.
#3710
Saturday · 26 min
Connecting Enterprise Systems for Agentic Work With Flowgear
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.
#3709
Friday · 35 min
Turning AI Experiments Into Everyday Work With Omnisend
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.
#3708
Thursday · 28 min
Turning Real Time Sports Data Into Better Fan Experiences With Sportradar
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.
#3707
September 2 · 25 min
Preparing for Post Quantum Security With F5 Labs
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?
#3706
September 1 · 22 min
Building Responsible AI for Public Services With AWS
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.
#3705
September 1 · 28 min
Making Industrial AI Deliver Real Operational Value With IFS
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.
#3704
August 31 · 22 min
Building AI Data Readiness With Kiteworks
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.
#3702
August 30 · 29 min
Testing the Blast Radius of Agentic AI With NTT DATA
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.
#3702
August 29 · 25 min
Building Legal Accountability for AI Agents With Norm AI
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.
#3701
August 28 · 22 min
Moving Enterprise AI From Hype to Accountable Results With Freshworks
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.
#3700
August 27 · 25 min
Building Infrastructure That Can Govern AI Agents With Broadcom
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?
#3699
August 27 · 27 min
Turning Rising AI Cloud Costs Into Business Value With Unravel Data
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.
#3698
August 26 · 29 min
Testing AI That Never Stops Changing With UL Solutions
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.

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