Tech Talks Daily

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.