Tech Talks Daily

Épisodes

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  Turning Warehouse Blind Spots Into Real Time Intelligence With Dexory 04.08.2026 28min

  What happens when a warehouse management system believes stock is present, but nobody can find it on the warehouse floor? In this episode of Tech Talks Daily, I speak with Oana Jinga, co-founder of Dexory, who oversees the company's commercial strategy and product roadmap. Dexory has developed autonomous mobile robots capable of scanning inventory at heights of up to 18 meters while creating a continuously updated digital view of warehouse operations. The company says its robots have scanned one billion locations across 12 countries. Its customers include Maersk, DHL, Samsung, GE Appliances, Stellantis, GXO Logistics, and C.H. Robinson. However, the real story goes beyond the size of the robot or the number of locations scanned. It concerns what businesses can do once they have accurate information about their physical operations. Oana explains why warehouses often become data blind spots. Businesses usually know what entered the facility and what eventually left, but stock movements, damaged items, misplaced pallets, and inefficient use of space can remain difficult to track between those events. Dexory's robots scan approximately 10,000 to 12,000 pallet locations per hour. Oana recalls one customer discovering around £1.5 million in stock it had considered lost or written off. Other scans have revealed repeated pallet movements and potential opportunities to recover around 10% of warehouse capacity through better organization. We also discuss why visibility alone does not create business value. Dexory initially gave users large volumes of information, only to discover that extensive lists of problems could overwhelm warehouse teams. Its platform now prioritizes the actions requiring attention, helping users concentrate on a manageable number of issues each day. Oana explains why physical AI faces different challenges from software operating entirely within digital systems. Warehouses change constantly as people, vehicles, stock, temporary obstacles, damaged areas, and local working practices alter the environment. Robots and AI systems therefore require current physical data rather than relying on an old floor plan or assumptions recorded in another system. For companies considering warehouse robotics, Oana recommends starting with the operational problem. Leaders should observe how work happens, speak with employees about bottlenecks, define the result they want, and appoint an internal owner responsible for adoption. A robot sent to collect stock from an empty or incorrect location cannot complete its task, regardless of how capable its software may be. We also consider the future of warehouse work. Oana argues that robots can remove repetitive inventory walks and manual counting, allowing employees to interpret data, investigate problems, and improve operations. She also shares her experience as one of the few women in robotics a decade ago and explains why visible female role models can make the sector feel accessible to a wider group of people. Could physical AI help your warehouse teams make better decisions, or would inaccurate data and unclear ownership prevent the technology from delivering value? Listen to the episode and share your thoughts with me.
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  How Infobip Uses AI Companions to Keep Sports Fans Coming Back 03.08.2026 26min

  What can Formula One and football teach businesses about building customer relationships that continue long after a single event? In this episode of Tech Talks Daily, I speak with Ben Lewis, Vice President of Marketing at Infobip, about the company's work with AI-powered sports companions and what those experiences can teach customer experience leaders in every industry. Ben explains how Infobip worked with TGR Haas F1 Team to create RaceMate, an AI companion available through WhatsApp and Apple Messages for Business. Fans can access team information, driver histories, race schedules, trivia, personalized content and interactive experiences without downloading another application. We also discuss PitchMate, Infobip's conversational companion for global football fans. It remembers a fan's preferred team, can deliver personalized schedules and match information, and supports quizzes and other interactive features across an extended tournament. For me, one of the most useful lessons is the decision to meet fans inside messaging channels they already use. We have all downloaded an application for a conference, flight or one-off event, used it for several days and then forgotten it exists. RaceMate and PitchMate allow the conversation to remain available in the same place someone would message a friend. Ben also explains why Infobip measures success through returning users, conversation duration and the number of interactions rather than relying solely on clicks. TGR Haas F1 Team is currently using RaceMate to grow its fan community and provide useful content rather than constantly pushing merchandise. The same thinking can apply far beyond sports. We discuss travel companies contacting customers during unresolved claims, healthcare providers sending poorly timed automated messages and brands promoting products without recognizing that a customer is already involved in a dispute. Connected data can help prevent these disjointed experiences. Our conversation closes with practical advice for businesses adopting agentic AI. Ben recommends connecting customer data with campaigns, testing carefully, establishing guardrails and defining when an AI agent should transfer a conversation to a person. Are businesses investing too much in new customer applications when the better experience could already live inside WhatsApp, RCS or Apple Messages for Business? Please share your thoughts with me.
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  How Technology Can End the Late Payment Crisis Costing UK Businesses £11 Billion 02.08.2026 22min

  Late payments have become so common that many businesses simply accept them as part of commercial life. But should they? In this episode of Tech Talks Daily, I speak with Pat Bermingham, founder and CEO of Adflex, about why late payments continue to cost the UK economy an estimated £11 billion every year, why thousands of businesses fail because of cash flow pressures, and how technology could help change payment behavior rather than simply respond to it. Pat argues that late payments are rarely an administrative accident. In many industries they have become an informal financing mechanism, allowing larger organizations to protect their own cash flow while placing increasing financial pressure on smaller suppliers. Construction is one example, but the challenge extends across many sectors where long supply chains and uneven bargaining power make delayed payments the norm rather than the exception. We discuss why new government proposals to strengthen payment regulations represent progress, while also examining why legislation alone cannot solve a structural problem that has developed over decades. Instead, Pat believes technology can play a much bigger role. He explains how virtual commercial cards and Straight Through Processing (STP) allow buyers to access extended finance while suppliers receive payment far more quickly, without introducing additional friction into the payment process. Rather than forcing suppliers to accept card payments directly, the technology automates the process behind the scenes while improving reconciliation, increasing visibility and supporting healthier cash flow across the supply chain. The conversation also explores why many organizations still rely on fragmented payment systems created through years of acquisitions and disconnected technologies. Modernizing payment infrastructure can reduce delays, improve operational efficiency and help businesses build stronger supplier relationships rather than treating late payment as a normal business practice. Pat also shares how an earlier career as a music producer shaped his thinking about technology. Watching digital innovation transform music production helped him recognize how technology can simplify complex processes while also creating new business models that challenge established industries. For finance leaders, procurement teams, CIOs and business owners, this episode provides practical insights into improving cash flow, strengthening supplier relationships, modernizing payment processes and preparing for a future where prompt payment becomes both a commercial advantage and an increasing regulatory expectation. Changing payment legislation is important. Changing payment behavior is what will ultimately strengthen businesses, protect suppliers and create more resilient supply chains.
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  AI, Value Creation and the Future of Business: Why Automation Is Only the Beginning 02.08.2026 28min

  Most AI conversations begin with productivity. Joanna Pachnik thinks that's the wrong place to start. In this episode of Tech Talks Daily, I speak with Joanna Pachnik, founder of Blueclip, about why AI is changing far more than the speed of work. It's changing how businesses create value, what customers are willing to pay for, and what competitive advantage will look like over the next decade. Drawing on her experience leading global supply chain transformation projects at Ernst & Young and Mars before founding Blueclip, Joanna argues that knowledge is becoming increasingly accessible through AI. Research, analysis and reports that once took weeks and cost hundreds of thousands of dollars can now be produced in hours. That doesn't eliminate the need for expertise. It changes what expertise is worth. Rather than paying for information alone, organizations increasingly want implementation, measurable outcomes and practical experience that AI cannot easily replicate. Joanna explains why unique industry knowledge, benchmarking, practical experience and genuine human relationships may become more valuable as AI becomes more capable. We also discuss why so many enterprise AI initiatives struggle to deliver meaningful results. Joanna believes the technology is rarely the biggest obstacle. The real problem is poor data, undocumented processes and organizations trying to automate before building the foundations AI depends upon. Her advice is simple: prepare your data, document your processes, create a company knowledge layer, then introduce AI one use case at a time. The conversation also explores why AI should be viewed as a business transformation initiative rather than an automation project. Instead of accelerating existing processes, companies should ask whether those processes should exist at all. AI creates an opportunity to redesign how organizations operate, continuously improve decision-making and move people toward higher-value work. We also examine the importance of human oversight. Joanna believes AI should begin with people reviewing and guiding its outputs before gradually taking on more responsibility in carefully selected scenarios. Human accountability remains essential, particularly when AI supports material business decisions. For business leaders navigating AI strategy, digital transformation and enterprise innovation, this conversation offers practical advice on creating long-term value instead of chasing short-term AI hype. It explains why the companies that succeed will not necessarily be those using the most AI, but those prepared to rethink how they create value, organize knowledge and redesign their businesses around new possibilities. The future belongs to organizations that see AI as more than another productivity tool. It belongs to those willing to transform how they work, how they serve customers and how they create lasting business value.
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  Preparing 911 for AI Satellite Calls and Cloud Infrastructure With Intrado 01.08.2026 32min

  What happens behind the scenes when you dial 911, and is the infrastructure ready for AI, satellite messaging, video, and precise location data? In this episode of Tech Talks Daily, I'm joined by John Snapp, VP of Technology at Intrado. John has spent around 23 years working with cellular, location, and 911 technologies. He explains how a mobile emergency call is located, routed through a dedicated network, and directed to the appropriate Public Safety Answering Point. We discuss where AI can provide practical support inside emergency communications. Translation can help telecommunicators understand callers without waiting for an interpreter. Real-time transcription can capture details and suggest established procedures. AI voice agents can also handle suitable nonemergency inquiries, giving trained staff additional time for calls where lives may be at risk. John is clear that emotional emergency calls still demand human understanding and authority. AI can supply information, identify possible synthetic voices, and reduce administrative work, but trained telecommunicators remain responsible for interpreting the situation and directing the response. Our conversation also examines the infrastructure beneath these capabilities. Legacy 911 networks were designed largely for voice and limited amounts of data. Next Generation 911 introduces IP connectivity capable of carrying text, images, video, and richer location information. John explains how this foundation has made satellite texting possible and why similar capabilities were far slower to introduce using older networks. Moving to NG911 creates its own problems. Different vendors can comply with the same technical standard while implementing it differently. Calls may also need to move between modern and legacy call centers, making interoperability testing between jurisdictions a major part of deployment. We also consider cloud resilience, local survivability, connectivity diversity, telephony denial of service attacks, AI generated swatting calls, and the danger of adopting automation before establishing governance. John recommends starting with lower-risk areas such as quality assurance and nonemergency calls, communicating openly about AI use, and expanding only after teams understand the operational impact. As emergency communications become richer and increasingly connected, how should public safety agencies balance faster innovation with the reliability and human judgment every caller depends on? Listen to the episode and share your thoughts with me.
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  AI-Powered Cyberattacks Are Coming for Your Printers. Is Your Business Ready? 01.08.2026 32min

  When organizations review their cybersecurity posture, printers are rarely the first systems that come to mind. Yet they often account for around 20% of network endpoints while receiving, storing, processing, and transmitting sensitive business information every day. In this episode of Tech Talks Daily, I welcome back Jim LaRoe, CEO of Symphion, to discuss why printers and other connected IoT devices have become one of the most overlooked areas of enterprise cybersecurity and why AI-powered attacks are raising the stakes for organizations that continue to ignore them. Jim explains how many businesses continue to treat printers as simple office equipment rather than Linux-based network devices with privileged access to email systems, file servers, identity services, and critical business workflows. Because responsibility for these devices often sits between procurement, managed print providers, IT operations, and security teams, they can easily fall outside normal cybersecurity processes. We discuss how the threat landscape has changed over the past year as AI enables attackers to automate reconnaissance, credential theft, lateral movement, and ransomware deployment. Jim explains why organizations adopting Zero Trust principles also need to rethink how they secure and manage connected endpoints that have traditionally been overlooked. The conversation also explores certificate lifecycle management, cyber hygiene, firmware management, endpoint visibility, and why unsupported devices can introduce unnecessary risk into modern enterprise environments. For organizations managing hundreds or even thousands of printers across multiple locations, Jim explains why protecting these endpoints does not need to create additional operational burden. Instead, security should become an ongoing operational program that continuously monitors devices, detects configuration drift, applies security controls, and helps organizations maintain compliance without disrupting critical business workflows. We also discuss the governance challenge many organizations face. Before companies can reduce risk, someone needs to own it. That means establishing accountability, assigning budget, understanding which devices exist across the business, and recognizing that printers and IoT devices deserve the same attention as servers, laptops, and other managed endpoints. If you're responsible for cybersecurity, IT infrastructure, risk management, or digital transformation, this episode offers practical advice on protecting forgotten endpoints, strengthening Zero Trust strategies, improving endpoint visibility, and reducing the hidden risks that AI-powered attackers are increasingly looking to exploit. Sometimes the biggest cybersecurity vulnerability isn't the system you forgot to patch. It's the one you forgot was connected in the first place.
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  How BOLTS Technologies Brings Crypto Agility to Blockchain Security 31.07.2026 37min

  What happens to digital asset ownership when the cryptography proving that ownership can no longer be trusted? In this episode of Tech Talks Daily, I speak with Yoon Auh, cofounder of BOLTS Technologies, about quantum computing, blockchain security, and the need for crypto agility. Yoon brings an unusual perspective to the subject. Before moving into applied cryptography, he spent years building and operating high performance trading systems at firms including Credit Suisse, Goldman Sachs, Geode Capital, and Magnetar Capital. Yoon explains that blockchain ownership ultimately depends on digital signatures and public keys. Most major blockchain systems use variants of elliptic curve cryptography because it has historically offered speed, compact signatures, and dependable protection. However, sufficiently powerful quantum computers could eventually challenge the mathematics supporting that protection. The risk does not begin when such a quantum computer arrives. Yoon describes how attackers can collect encrypted traffic today, store it, and attempt to decrypt it later. This creates an immediate concern for governments, financial institutions, and businesses holding information that must remain private for many years. We also discuss QFlex, the post quantum ready API developed by BOLTS Technologies. The company describes its approach as cryptographic logistics, allowing different cryptographic methods to be selected at the transaction level. Yoon argues that a small payment and a multimillion dollar asset transfer should not automatically receive identical protection, particularly when stronger cryptography may require additional processing, storage, and cost. Another concern is uncertainty around the available post quantum algorithms. Yoon explains that cryptographic methods can survive years of examination before a weakness is discovered. His argument is that organizations need the ability to change algorithms quickly if one becomes vulnerable, rather than making a permanent choice and hoping it survives every new attack. The conversation also examines digital asset sovereignty. Who decides how a transaction is protected: the platform, the protocol, or the asset holder? BOLTS Technologies believes that choice should return to the holder, while QFlex aims to provide that control without hard forks, network downtime, or protocol changes. The interview also covers the company's research background and its pilot work with the Canton Foundation. Yoon closes with a lesson from his trading career. Backup and failover exercises often failed because they were treated as occasional events. His advice is to make exceptional processes routine, ensuring that the organization has already practiced changing systems before the moment arrives when it has no other option. Should digital asset holders control the cryptography protecting every transaction, or should platforms continue making that decision for them? Listen to the episode and share your thoughts with me.
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  Moving From AI Pilots to Production With Boomi 30.07.2026 25min

  What prevents a successful AI experiment from becoming a dependable production system that delivers measurable business value? In this episode of Tech Talks Daily, I speak with Ed Macosky, Chief Product and Technology Officer at Boomi, about AI pilot purgatory, integration, governance, model selection, token costs, and the technical skills businesses may need as adoption grows. Ed leads Boomi's product and engineering teams while also using AI tools inside his own organization. That gives him a view from both sides: creating technology for enterprise customers and applying it within active product development workflows. He believes many AI pilots begin with the wrong question. Teams become interested in the latest model or feature before defining the business problem they want to solve. The experiment may work during a demonstration, then fail when it encounters real data, access controls, security policies, and production systems. Placing company information inside a data lake and adding a language model does not automatically create a business application. The system must access current data reliably, respect employee permissions, connect with existing applications, and operate within governance rules that security teams can approve. Ed recommends beginning with a defined business opportunity and establishing the access required to support it. Existing APIs can already provide authentication, permissions, and governance. MCP can offer another route into enterprise systems, but those connections still require security, monitoring, and management. Team alignment also matters. An AI center may be racing to test models while an integration center concentrates on a different set of priorities. When those groups fail to coordinate, the pilot lacks the connectivity and automation required to become part of a production workflow. The discussion then turns toward fragmentation. Every technology wave produces new vendors, frameworks, and specialist tools. Early experimentation benefits from variety, but mature companies can eventually find themselves maintaining a complicated collection of products held together with custom code and, occasionally, the digital equivalent of duct tape. Ed does not recommend placing every function with one provider. He does argue for enough consolidation and abstraction to prevent experimentation from creating years of technology debt. Governance and observability should also work horizontally across different environments, including platforms such as SAP, Salesforce, and several AI model providers. That becomes increasingly important as businesses introduce autonomous agents. Leaders need to know which agents exist, what systems they can access, what actions they can take, and how each decision is recorded. AI gateways and agent control towers can provide a wider view across otherwise separate technology environments. Ed also introduces the idea of the frontier engineer. A prompt engineer concentrates on communicating effectively with a model. A frontier engineer understands how the model works, including its logic, mathematics, algorithms, and suitability for different workloads. He does not believe every company needs a large team of these specialists. However, he argues that enterprises need at least one person capable of assessing vendor claims and deciding whether a frontier model, specialist model, or open weight model fits a particular workload. Cost creates another reason to examine model selection. Sending every employee request or agent task to the most capable frontier model can become expensive. Some repeatable workloads may run on open weight models inside the company's cloud or hardware environment, giving finance teams greater cost certainty. Boomi is developing Boomi Prompt to route requests according to their complexity and requirements. A simple factual request might go directly to an API. A forecasting task may use a smaller model. A difficult analytical request could be sent to a frontier model. Ed uses the weather as a helpful example. Retrieving next Tuesday's forecast does not require a language model when a public weather API can return the answer directly. Asking a model to perform every form of automation wastes tokens, computing power, energy, and money. The episode closes with practical advice for CIOs. Avoid starting with a broad objective such as agentifying the entire business. Choose a department, identify a small number of tasks, define the expected return, and work backward. Once the team proves value and understands the operating requirements, it can repeat the process elsewhere. Could intelligent routing, stronger integration, and clearer business outcomes finally move enterprise AI beyond pilot purgatory? Listen to the episode and share your thoughts with me.
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  Moving From AI Experiments to Autonomous Operations With Dynatrace 30.07.2026 28min

  What must happen before a business can trust AI agents to detect and resolve operational problems without waiting for human intervention? In this episode of Tech Talks Daily, I speak with Josh Clay, Regional Vice President of Solution Engineering for Dynatrace in the UK, about autonomous operations, AI observability, fragmented telemetry, business outcomes, and the growing pressure to control token and data costs. Josh has spent much of his 11 years at Dynatrace discussing the road toward autonomous operations. The earliest version involved reducing the time organizations spent inside IT war rooms. He remembers calls with 30 or 40 people attempting to establish which team was responsible for an incident. He jokingly calls this the "mean time to innocence." Modern observability reduced many of those investigations from several hours to between 30 and 60 minutes. Agentic AI creates the possibility of going further by identifying a problem, understanding its cause, and resolving it before the customer experience is affected. That ambition also introduces risk. Josh cites Dynatrace research showing that 52% of respondents view security, privacy, and compliance concerns as barriers to AI adoption. He believes many organizations still lack full observability across their existing technology environments, making autonomous agents harder to supervise. Josh shares a warning from Alex Hibbert of Storia Group: AI can amplify existing technology problems. If telemetry is fragmented, data quality is poor, or teams cannot see how services depend on one another, adding autonomous agents may increase the speed and scale of the resulting failure. Trust therefore depends on visibility. Josh describes observability as a control plane for agentic AI because it can show what an agent is doing, why it made a decision, and what happened afterward. Defined guardrails and real time information can give leaders confidence without asking them to surrender control blindly. The adoption figures show how early this work remains. Josh says 50% of businesses have AI operating in limited production use cases, often performing one isolated task. Only 23% describe their deployments as connected across the wider organization. We discuss how observability has progressed beyond technical monitoring. An airport can measure whether technology changes improve e-gate availability and passenger processing times. A bank can examine whether application performance affects mortgage completion rates. These connections allow leaders to measure AI through business results rather than relying entirely on response times and infrastructure metrics. Reliable agents also need suitable data. Dynatrace says AI agents operating with deterministic data can work 12 times more accurately and three times faster while using two and a half times fewer tokens. These remain company findings, but they demonstrate why context and causality can affect cost as well as reliability. Fragmented telemetry creates another barrier. Logs may sit in one platform, front end monitoring in another, and metrics or traces somewhere else. Attempting to reconstruct every relationship for an AI agent can become expensive and difficult. Josh recommends bringing observability data into a connected environment where relationships between services, cloud resources, traces, metrics, and logs are already understood. He also warns against collecting information simply because it exists. Data hoarding increases ingestion costs and can introduce personal information into systems without a clear business need. The conversation then moves toward AI FinOps. Leaders want to know what agents cost, how many tokens they consume, and whether those costs produce a measurable return. Josh describes a Dynatrace proof of concept that identified potential annual savings just below £250,000 within one small environment. That example reinforces a recurring concern. Organizations are racing to place AI into production, then moving to the next project without reviewing whether the previous environment is appropriately sized or financially efficient. Josh hopes companies will develop a more pragmatic view of AI as another enterprise tool. That means establishing agreed methods for deployment, monitoring, cost management, incident response, and measuring business results. Could observability provide the confidence businesses need to move from isolated AI experiments toward autonomous operations? Listen to the episode and share your thoughts with me.
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  How Saviynt Zuma Secures AI Agents With Zero Trust 29.07.2026 34min

  How can businesses secure AI agents that read sensitive information, update systems and communicate with other agents on behalf of employees? In this episode of Tech Talks Daily, I speak with Sachin Nayyar, founder and CEO of Saviynt, about AI agent identity security and the controls businesses need before autonomous systems enter production. Saviynt manages over 100 million identities for over 700 customers. Sachin explains how enterprise identity has expanded beyond employees to include partners, applications, machines and autonomous AI agents. An AI agent creates a different access problem because it is both an identity and an application. It can receive permissions, access information and perform actions, but its behavior can also be governed through software while it is being developed and while it is operating. Sachin uses an HR copilot to demonstrate why context matters. Two employees can ask the same question about salaries but should receive different answers based on their roles, locations and applicable policies. Those decisions must be evaluated while the request is being processed without creating delays that make the system unusable. The risk grows when an agent crosses from one technology environment into another. An agent built within Microsoft may need to access Salesforce, ServiceNow, Jira or another external system. Sachin warns businesses never to solve this problem by giving an agent a permanent administrative account. We discuss Zuma, Saviynt's identity security platform for AI agents and non-human identities. Sachin describes a four-part framework beginning with agent discovery and a central registry. Every agent should then receive one accountable human owner, temporary access for its assigned task and policy enforcement while it acts. Ownership becomes especially important when an employee leaves. Saviynt's approach begins an automated reassignment process and blocks actions if an agent attempts to operate without a current owner. The relevant security team can then investigate before allowing further activity. Sachin also explains why identity controls should enter the development process rather than being added after deployment. Saviynt is working with LangChain and other agent development platforms to make identity policies available while AI agents are being built. The conversation also covers Saviynt's partnership with Zscaler. Zscaler provides inline enforcement, while Saviynt contributes identity information about the agent, its owner, existing permissions and expected behavior. Sachin closes with an optimistic argument. Because businesses can place security controls into the code and enforce them while agents act, AI workloads may eventually become better governed than traditional human access. Could every AI agent inside your business be traced to one accountable owner, one approved purpose and a limited set of temporary permissions? Please share your thoughts with me.
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  Running Enterprise Computer Vision on CPUs With Ultralytics YOLO26 29.07.2026 25min

  What becomes possible when enterprise computer vision no longer depends on expensive GPU infrastructure? In this episode of Tech Talks Daily, I speak with Glenn Jocher, founder and CEO of Ultralytics, about YOLO26, CPU inference, edge AI, open vocabulary vision, deployment economics, and the practical work required to move computer vision from a promising pilot into production. Glenn's route into AI began inside the U.S. intelligence community. He worked with the National Geospatial Intelligence Agency and Defense Intelligence Agency on particle physics applications, attempting to detect and track antineutrinos. Antineutrinos are extraordinarily difficult to detect because they pass through almost everything. Glenn describes them as the perfect spy. While searching for better detection methods, he discovered that computer vision researchers were solving similar problems with images. His original attempt to transfer those techniques into particle physics did not succeed. However, the work introduced him to a field where the technology could create a visible effect on everyday life. That led him toward open source development and eventually the YOLO models for object detection, classification, segmentation, and tracking. Glenn believes computer vision research has historically placed too much attention on small gains in accuracy while overlooking deployment economics. A model can perform impressively inside a laboratory and still remain unsuitable for a factory, warehouse, store, vehicle, drone, or medical environment. Price, latency, power consumption, data privacy, and deployment speed can determine whether the technology is commercially useful. This led Glenn and Ultralytics toward smaller models capable of running close to where images and video are generated. YOLO26 continues that approach with architectural changes designed specifically for CPU inference. Glenn says the model can process camera streams in real time at 30 frames per second and run across Intel CPUs, AMD CPUs, and lower power devices such as Raspberry Pi computers. This matters because specialist GPUs can increase the equipment cost and power requirements of a computer vision project. Running inference on existing CPUs or edge hardware can make deployment economically possible across larger numbers of cameras and locations. The scale already involved is difficult to comprehend. Glenn says Ultralytics models now process approximately three billion inference jobs each day, equivalent to around 30,000 every second. These jobs include images, videos, and collections of images being analyzed to detect, segment, or track objects. He attributes the platform's maturity to thousands of mistakes and bugs corrected through a rapid feedback cycle. New models are released, users report problems and request features, and the team incorporates that information into later versions. We also discuss the respective roles of cloud and edge infrastructure. Glenn sees cloud platforms continuing to provide the computing power required for training, while computer vision inference often belongs at the edge. Local processing can reduce latency, control operating costs, and keep sensitive video or medical information closer to where it was created. The smallest YOLO model is approximately three megabytes, according to Glenn. That allows it to reach mobile phones, vehicles, drones, battery powered devices, and other environments where a large language model would be impractical. Open vocabulary vision provides another development. Traditional object detection models are trained to recognize a fixed collection of objects. If a model learns to detect dogs and the user later wants it to detect cats, retraining can cause it to forget earlier knowledge unless both categories appear in the new training data. Glenn explains how promptable models can identify common everyday objects from text or visual instructions without additional training. A user could request a person wearing a blue shirt and white shoes, for example, and the system could search an image for that description. That flexibility could benefit businesses whose requirements change regularly. It reduces the need to create and label a new data set every time the company wants the model to recognize another common object. The range of current applications is already extensive. Glenn describes YOLO being used across robotics, parking, industrial safety, PPE detection, warehouses, aviation, security, traffic management, food quality, and manufacturing. Some of his favorite examples involve environmental problems. One company uses YOLO with underwater vehicles to identify and recover plastic from the ocean. Other applications detect smoke and fire early enough to support forest fire response. For leaders considering computer vision, Glenn recommends beginning with a defined problem and measurable outcome. A manufacturing company may want to reduce defects, but it still needs labeled examples showing the model what acceptable and defective products look like. He advises testing the idea through a limited pilot, measuring the return, and expanding only when the evidence supports further investment. Computer vision has become easier to deploy, but practical problems involving data, cameras, integration, reliability, and operating conditions still separate a demonstration from a production system. Could CPU inference and open vocabulary models make computer vision practical for processes your organization previously considered too expensive? Listen to the episode and share your thoughts with me. Useful Links   Ultralytics website Ultralytics Platform      
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  How Equifax Connects AI Data and Human Support in Government Services 28.07.2026 26min

  What can private companies learn from government caseworkers about adopting automation and using data more effectively? In this episode of Tech Talks Daily, I speak with David Turner, General Manager and Senior Vice President of Government Services at Equifax Workforce Solutions, about public sector automation, data modernization, and the people responsible for delivering social services. The conversation begins with a surprising finding from an Equifax Government Services study of over 500 U.S. government employees. Every respondent expected efficiency to improve during the following year, while 95% believed automation would free time for higher value, human centered work. David explains why government employees may be more receptive to modernization than many people assume. Caseworkers operate under rising demand, staffing pressures, changing policy, and limited budgets. When technology removes repetitive administration or supplies information faster, they can see an immediate connection between the tool and the person waiting for support. We discuss how the meaning of automation has changed inside social services. A few years ago, it might have meant entering information into an online portal. Today, integrated connections can search data sources behind the scenes and return verified information during the caseworker's existing process. David describes continuous evaluation, which can identify when circumstances within a caseload have changed. Instead of searching every case for a possible update, a worker can direct attention toward the people whose income, address, or employment data indicates that further review may be needed. Income verification provides another example. Equifax says it can return income information in under one second, helping prevent the delays created when an applicant must leave the process to find a document. Those pauses matter when an eligibility decision already involves several stages and numerous external systems. The gig economy makes this work harder. Applicants may receive income from employment, contract work, digital platforms, and several side projects. Agencies need access to a fuller income picture without sending people back toward paper forms and manual verification. David also shares what Equifax has learned through its Day in the Life program. The team spends time with caseworkers to understand their processes, policy restrictions, and information constraints. He believes public sector leadership can remain closely connected to employees in the field because many agency executives previously performed those roles themselves. AI introduces further possibilities. David discusses how data and AI could eventually identify signs that someone who has left an assistance program may be experiencing financial difficulty again. Community groups, food banks, or other services could potentially offer support before that person returns to crisis. Such a system would also require careful decisions around consent, privacy, accuracy, and responsibility. The central lesson is refreshingly human. Technology creates value when it removes administrative pauses and gives experienced caseworkers additional time to understand someone's circumstances. Could public sector automation teach private companies how to connect technology investment with human outcomes? Listen to the episode and share your thoughts with me.
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  How Ensono is Building AI Resilience Beyond a Single Model 27.07.2026 28min

  What happens when an AI experiment becomes a production service that your employees, customers, and daily operations depend upon? In this episode of Tech Talks Daily, I speak with Brian Klingbeil, Chief Strategy Officer at Ensono, about AI infrastructure resilience, operational dependency, FinOps, legacy modernization, and the growing pressure to prove that enterprise AI investments are producing meaningful returns. Brian has been speaking with major enterprises through Ensono's Executive Advisory Council. Three years ago, many participants were experimenting with proofs of concept. Today, they are being asked to present AI projects that are already in production, approaching production, or demonstrating a clear return through productivity, lower risk, service quality, or financial results. That progression creates a new problem. When an AI model begins supporting product delivery, customer service, logistics, software development, or internal operations, it becomes part of the company's operating infrastructure. Leaders must then ask familiar IT questions about availability, monitoring, security, incident response, disaster recovery, ownership, and cost. Brian believes FinOps often provides the first warning. Token consumption can be difficult for CFOs and business leaders to interpret, particularly when hundreds of agents are operating across different models. Ensono's internal platform has produced around 1,000 agents, prompting questions about which are effective, which are expensive, and who should carry the cost. We discuss why chargeback and showback could change employee behavior. When AI spending is absorbed by a central corporate budget, teams may have little reason to question whether an expensive model is suitable for a routine task. When the cost reaches their departmental budget, the decision can look very different. Architecture also matters. Brian recommends systems that are loosely coupled and tightly integrated. Companies should be able to replace a model, provider, FinOps tool, or service as the market changes, while still connecting each component closely enough to deliver useful business outcomes. That creates a genuine tradeoff. Providers such as Microsoft, Amazon, Google, OpenAI, and Anthropic can offer specialist capabilities that businesses may want to use. Avoiding every provider specific feature can limit what the technology delivers, while becoming too dependent on one provider can make future change expensive and disruptive. The conversation then turns toward legacy technology. Brian argues that many systems described as outdated still process airline reservations, banking transactions, insurance claims, government services, and other high volume workloads. Turning them off without suitable replacements would create far bigger problems than the word "legacy" suggests. AI can change the modernization decision. Ensono worked with Markerstudy Group to analyze six million lines of RPG code running on an IBM i platform. The resulting plan identified applications that should move elsewhere while preserving workloads that still benefited from the platform's reliability and transaction processing capabilities. Brian treats migration as one possible part of modernization. AI tools can document old code, support modern development environments, and allow younger developers to work with established platforms without immediately beginning a lengthy and expensive replacement program. We also discuss Ensono's use of AI operations. Brian says the company reduced mean time to repair by 50% while processing approximately 50,000 tickets each month. The example shows how AI value can be measured through service quality and operational performance rather than relying entirely on direct revenue. The result is a balanced conversation about moving quickly while building enough control to keep AI dependable. Organizations need space for experimentation, but production services also require ownership, budgets, recovery planning, and people who know what to do when something fails. If one AI model or provider disappeared tomorrow, how much of your business would stop working? Listen to the episode and share your thoughts with me.
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  How RedStone is Connecting Financial AI Agents to Verifiable Data 26.07.2026 25min

  What happens when an autonomous AI agent makes a financial decision using inaccurate, outdated, or poorly synchronized data? In this episode of Tech Talks Daily, I speak with Marcin Kaźmierczak, cofounder of RedStone Oracles and Credora Ratings, about why verifiable data is becoming so important to financial AI agents. RedStone originally developed its oracle infrastructure to supply smart contracts with reliable information from hundreds of sources. The same principles are now being applied as AI agents begin analyzing markets, recommending allocations, processing payments, and executing trades. Marcin explains what a blockchain oracle does and why smart contracts cannot independently access real world information. RedStone aggregates, cleans, and distributes financial data, including asset prices, liquidity, volatility, and market capitalization. According to Marcin, its network currently secures over $10 billion in total value locked and has operated for six years without a mispricing or downtime event. The discussion then moves into agent driven finance. AI agents can process information and act at considerable speed, but that speed introduces problems when the underlying information is delayed or the model hallucinates. Marcin describes the synchronization challenge created when an oracle updates every five seconds while an agent makes decisions every second. One example captures the risk. An AI trading agent reportedly generated $200,000 over three months before losing $250,000 in two transactions. Marcin explains how Credora Ratings can add financial risk context by rating assets and strategies from D to A. Companies can then instruct an agent to operate only within an approved risk range. We also discuss tokenized assets, the growing interest from major financial institutions, and why blockchain networks offer an attractive operating environment for autonomous finance. Marcin shares practical advice for leaders, including speaking with experienced implementers, testing agents in closed environments, identifying likely failure scenarios, and creating response policies before introducing real money. What evidence would you require before trusting an AI agent with a financial decision? Listen to the episode and share your answer with me.
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  How Craftable is Using AI to Protect Restaurant Margins and Human Hospitality 26.07.2026 28min

  What if a restaurant could identify a margin problem while the ingredients were still being unloaded, rather than discovering it several weeks later? In this episode of Tech Talks Daily, I speak with David Cantu, CEO of Craftable, about AI restaurant back-office technology, margin intelligence, inventory management, purchasing, invoice automation, and the continuing importance of human hospitality. David has spent decades working in restaurants and technology. He describes an industry dealing with staffing difficulties, rising leases, food inflation, lower traffic, and relentless margin pressure. David cites a National Restaurant Association study indicating that 40% of restaurateurs were not profitable during the previous year. Craftable connects purchasing, recipe management, inventory, accounting, sales data, and analytics. The company says its platform is used by over 10,000 restaurants, hotels, and venues. David argues that useful hospitality AI should automate the work that keeps managers, chefs, and operators away from guests. This includes producing sales forecasts, suggesting orders, planning preparation, identifying invoice anomalies, and comparing projected labor with actual requirements. The conversation becomes particularly practical when David describes a restaurant receiving a ribeye that has increased in price by 20%. If the dish is a popular, high-margin menu item, that vendor increase can quickly reduce its profitability. Craftable's Invoice AI can detect the change when the invoice is received. The operator can then consider running a higher-priced chef's special, promoting another steak, reviewing the menu price, or adjusting future orders. Waiting until month-end reconciliation would explain the lost margin but leave no opportunity to recover it during service. We also discuss the difference between AI intelligence and operator knowledge. AI can examine large volumes of information, but an experienced restaurateur understands the atmosphere, the team, the guests, and what is happening at that particular moment. David believes AI recommendations need to show their work. Managers should be able to inspect how a sales forecast, suggested order, staffing plan, or trend was calculated. Transparency helps people assess the recommendation without forcing them to search through another large analytics report. Craftable is also testing how to measure whether a recommended action produced a result. If a manager coaches a server with unusually high complimentary items or promotes a menu category with falling attachment rates, the platform can examine whether that action affected sales or margins. David is skeptical of hospitality AI added as a promotional layer without being built into the daily workflow. At industry conferences, he has seen vendors attach language models to existing products while offering little operational value. His hope for restaurant AI is highly human. He wants technology reducing administrative pressure while employees welcome guests, serve meals, develop their teams, and create the warm experiences that define hospitality. Which restaurant decision could protect profitability if the operator received the right information before the next service began? Listen to the episode and share your thoughts with me.
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  How Valliance Fixes the Enterprise AI ROI Problem 25.07.2026 30min

  Why do so many enterprise AI initiatives begin with impressive demonstrations but struggle to produce measurable business value? In this episode of Tech Talks Daily, I speak with Dom Selvon, CTO and value partner at Valliance, about enterprise AI ROI, outcome-based consulting, build versus buy decisions, proprietary data, ontologies, and governance. Valliance is an AI-native consultancy that charges against client outcomes rather than hours worked. Dom explains why his "value partner" title is deliberate. The company begins by identifying the financial or operational result a client wants and connects its own compensation with achieving that result. Dom argues that many AI initiatives begin without a clear definition of success. The pressure to adopt AI is real, but companies frequently select technology before agreeing on the business problem, desired outcome, or measurement. He identifies three recurring mistakes. The first is framing the project around AI rather than the business need. The second is failing to establish a metric and baseline before work begins. The third is using a consulting model that rewards billable time without connecting payment to the client's result. We also discuss how generative AI is changing traditional build versus buy decisions. Companies historically bought software because custom development was slow, expensive, and difficult to maintain. Coding agents can now reduce the time and cost required to create software for specific internal needs. Dom does not believe SaaS will simply disappear. However, vendors selling convenience, workflow wrappers, or integration glue face new competition from customers who can create similar capabilities themselves. He argues that stronger SaaS positions will depend on assets a model cannot easily regenerate, including proprietary data, networks, regulatory standing, and deep workflow adoption. This leads to a wider discussion about competitive advantage. When companies have access to similar models, generated code begins to converge. Dom believes lasting differentiation comes from company data, institutional knowledge, connected systems, employee experience, and the semantic context surrounding that information. Dom explains why ontologies matter to enterprise AI. Raw data tells an agent what is stored in a particular field. An ontology describes the customers, orders, contracts, payments, relationships, and business rules represented by that data. This context allows people and agents to reason about information in a way that reflects how the company actually works. Governance also needs to be designed from the beginning. Dom argues that security, permissions, accountability, and compliance allow successful pilots to expand without forcing the business to rebuild everything later. How can leaders tell when AI is genuinely being adopted? Dom offers a surprisingly simple signal: people stop talking about AI. The technology becomes part of ordinary Monday morning work, and employees focus on completing the task rather than explaining the tool. Has your company defined the business result, measurement, proprietary context, and governance required to turn AI enthusiasm into operational value? Listen to the episode and share your thoughts with me.
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  How Genesys Cloud Helped StepChange Cut Misrouted Calls by 60 Percent 25.07.2026 23min

  What does a modern contact center need to deliver when the person reaching out may already feel anxious, embarrassed, and unsure where to turn? In this episode of Tech Talks Daily, I speak with Chris Lovell, service delivery lead for StepChange Debt Charity's contact center and product owner for its Genesys Cloud platform. StepChange supports hundreds of thousands of people facing financial hardship each year. Chris explains that approximately 40% of its clients receive Universal Credit, over 60% rent their homes, and many are dealing with an additional vulnerability alongside debt. That context makes the first interaction especially important. People may have delayed asking for support while their financial position became harder to manage. A failed call, long queue, unnecessary transfer, or request to repeat their story can increase stress at the moment they need reassurance and practical help. Before adopting Genesys Cloud, StepChange relied on fragmented contact center technology that experienced regular technical problems and outages. The charity also lacked detailed insight into why clients were making contact at different stages of their journey. People could enter the wrong queue, wait to speak with an advisor, and then discover they needed another team. The technology also affected employees. Chris says frontline colleagues eventually stopped proposing improvements because they did not believe the existing platform could support them. StepChange migrated to Genesys Cloud in four weeks through a three-phase delivery. The team began with lower-risk services, increased the size and complexity during the second phase, and moved the core debt advice operation during the third. Chris says the migration was completed without downtime. The initial objective was to reproduce the existing service on a stable cloud platform before introducing further capabilities. This sequencing gave the team time to correct early issues and adjust training before the largest group of advisors moved across. We discuss how improved intent capture and routing helped one team reduce misrouted calls by 60%. Clients reached the right advisor sooner, avoided repeated explanations, and could move toward a suitable debt solution faster. Advisors also began conversations in the right place instead of apologizing for delays or correcting the journey. Chris argues that contact center success cannot be judged through efficiency alone. StepChange examines whether clients understand their options, complete the advice journey, activate a sustainable plan, and continue toward becoming debt free. The circumstances remain difficult for many clients. Chris says approximately 28% are still in a negative budget after receiving advice, with an average monthly shortfall of around £600. Some conversations require time, empathy, and experienced human support. Around 85% of StepChange advice journeys now happen online. Digital access can offer privacy and flexibility, while advisors remain available for the emotional and complicated moments where a person needs reassurance. We also consider future plans for WhatsApp, web messaging, connected journeys, and AI-powered advisor support. Chris advises leaders to begin with genuine customer behavior rather than selecting a technology and searching for somewhere to use it. How can your contact center remove unnecessary effort while preserving the conversations where people most need to feel heard? Listen to the episode and share your thoughts with me.
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  Beyond AI Pilots: What Valiantys and Mercedes Can Teach Enterprise Leaders 24.07.2026 30min

  What does it really take to build an AI-ready enterprise when your data is fragmented, teams operate in silos, and years of technology decisions have created complexity that no large language model can magically fix? In this episode of Tech Talks Daily, I speak with Raymon Ohmori, Senior Principal Software Engineer at Valiantys, and Jiecheng Dong, Senior Software Engineer at Valiantys, about the work that goes into enterprise AI adoption and why successful AI transformation begins long before companies deploy agents, copilots, or autonomous workflows. Using Valiantys' work with Mercedes as a case study, Raymon and Jiecheng explain how modernizing software delivery and connecting data across teams can create the foundation required for AI systems to deliver meaningful business value. We discuss why fragmented data, organizational silos, poor governance, and unclear business problems continue to prevent many companies from moving beyond AI pilots. The conversation examines what it means to become AI-ready in practice. Jiecheng explains why enterprises need performant, structured, and queryable data rather than simply feeding huge volumes of information into large language models. Raymon shares why businesses must begin with real problems, stakeholder needs, and clearly defined outcomes to justify the cost of AI and successfully move projects into production. We also discuss the growing role of agentic AI and autonomous workflows in software engineering. How should engineering teams prepare AI agents to become active participants in software development? What tools, context, permissions, governance, and observability do these systems need to operate effectively? And why might treating an AI agent more like a new colleague than another software tool help teams think differently about deployment? Raymon and Jiecheng also share their perspectives on AI-assisted software development and developer productivity. As AI becomes increasingly capable of writing code, the role of the software engineer is shifting toward architecture, system design, requirements gathering, trade-off evaluation, and translating business needs into technical specifications. We also discuss the challenge facing junior developers and why companies still need to create pathways for new engineering talent. Finally, we examine the practical steps CIOs, CTOs, and engineering leaders can take today to build more connected, AI-enabled enterprises. From improving data ownership and governance to identifying costly problems that AI can realistically solve, this conversation offers a practical guide for companies trying to move from AI experimentation to production systems that deliver measurable value. Where is your company on its AI journey? Are fragmented data, organizational silos, and unclear business problems preventing your AI projects from reaching production, or have you found effective ways to turn experimentation into measurable results? Share your thoughts with me.   Useful Links   Valiantys Website: https://www.valiantys.com/  Valiantys LinkedIn: https://www.linkedin.com/company/valiantys/    
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  AI in HR: Dayforce on Why Governance Helps Companies Move Faster 23.07.2026 33min

  What happens when AI moves beyond writing emails and summarizing meetings and starts influencing who gets hired, promoted, and paid? In this episode of Tech Talks Daily, I speak with David Lloyd, Chief AI Officer at Dayforce, about why HR is becoming one of the highest-stakes environments for artificial intelligence and how companies can introduce AI while protecting employee data, maintaining human accountability, and preparing for growing regulatory scrutiny. HR systems contain some of the most sensitive information companies hold, from salaries and performance records to benefits and personal data. At the same time, AI is increasingly being introduced across recruitment, workforce management, compensation, performance, and employee experience. David explains why this combination creates enormous opportunities but also places greater responsibility on employers to understand how AI systems operate and how decisions are made. A major theme throughout our conversation is the role of AI governance. David challenges the assumption that governance slows innovation, arguing that the right processes can help companies evaluate AI ideas quickly while reducing the risk of introducing systems that lack appropriate data, transparency, or regulatory safeguards. Dayforce recently achieved ISO/IEC 42001 certification for AI management systems and NIST AI Risk Management Framework attestation. David explains what independent validation means in practice and why companies evaluating AI vendors should ask for evidence of how systems are governed, tested, monitored, and audited. We also discuss the principle of "AI by choice." David argues that CIOs and HR leaders should never discover that a new AI capability has suddenly been activated across hundreds of employees without their knowledge. Companies need visibility into where AI is being used, what data employees can provide to models, and whether customer information is being used to train external AI systems. The conversation examines AI literacy and why HR leaders need to become comfortable with the technology themselves before guiding employees through changes to jobs and working practices. Employees are already experimenting with AI, sometimes through personal tools outside approved company systems. Rather than ignoring this behavior, David explains why companies should provide safe environments where people can learn while establishing clear rules around sensitive data. Human accountability remains central as AI takes on more responsibility. David discusses why people using AI should remain accountable for its outputs and why human oversight matters when technology influences decisions involving recruitment, compensation, performance, and careers. For CEOs, CHROs, CIOs, HR technology leaders, and anyone responsible for enterprise AI, this conversation provides practical guidance on responsible AI adoption, employee data, AI bias, model monitoring, regulatory compliance, vendor selection, and building AI governance that can stand up to scrutiny. The lesson is that governance does not have to be a brake on AI adoption. Done well, it can give companies the structure and confidence to move faster, make better decisions about where AI belongs, and continue using the technology when regulators, employees, customers, and boards start asking harder questions. https://www.dayforce.com/ https://www.linkedin.com/company/dayforce/ https://www.linkedin.com/in/dtlloyd/
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  How IBM Is Turning Agentic AI Into Measurable Business Value 22.07.2026 26min

  What separates an impressive agentic AI demonstration from a deployment that produces measurable business value across an entire company? In this episode, I speak with Frank Theisen, Vice President of IBM Technology across Europe, the Middle East and Africa, about how businesses can move AI agents beyond isolated pilots and into the processes where work actually happens. Frank believes the conversation has changed considerably. Most large companies are deploying some form of AI, yet many still struggle to demonstrate a significant commercial return. The difference comes from connecting AI with end-to-end business processes rather than creating another assistant that sits outside the systems employees use every day. IBM has attempted to prove this internally through its "client zero" approach, using its own technology across human resources, IT, procurement, sales and software development before taking those practices to customers. The company reports that AI, automation and hybrid cloud have contributed to $4.5 billion in productivity gains over three years. Frank explains how IBM's AskHR service handles common employee inquiries and helps managers complete administrative tasks without learning how to operate several separate enterprise applications. IBM reports that AI now resolves 94 percent of common HR requests automatically, while similar work is taking place across IT support and procurement. The discussion then turns to orchestration. As companies acquire agents from multiple software providers, the problem becomes far larger than creating individual assistants. Businesses need to understand how agents communicate, which systems they can access, what identities they use and who remains accountable for their actions. Frank expects the number of applications, agents and non-human identities to grow rapidly. Without orchestration and governance, companies risk recreating the same application sprawl they have spent years attempting to reduce, this time with software capable of making decisions and generating additional code. Data presents another barrier. Publicly trained models rarely contain the proprietary information that gives a company its commercial advantage. That information remains distributed across databases, applications, mainframes and cloud services. Frank argues that enterprises need a governed, federated way to bring AI to their data without repeatedly copying everything into another repository. We also discuss digital sovereignty across Europe and the Middle East. Frank describes sovereignty as a matter of control across data, operations and technology. Companies need to decide which workloads require isolation, which regulations apply and where dependence on one provider could limit their future choices. Wimbledon provides a timely example of these principles in practice. IBM Bob helped modernize the tournament's digital platform by mapping and migrating approximately 15,000 articles, videos, photographs and related metadata. IBM says work that would traditionally require four or five specialists over several months was completed by one engineer within four weeks, with the assets themselves extracted in 47 minutes. Frank closes with three practical priorities. Understand where AI could affect the business, determine how successful use cases can be automated across complete processes, then address security, governance and provider dependence before expanding them. If your company already has dozens of AI pilots, should the next investment create another agent or coordinate the ones you already have? Listen to the episode and share your thoughts with me.