Podcasts about tech talks daily

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.

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.

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.

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.

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.

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.

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      

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.

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.

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.

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.

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 Valiance, about enterprise AI ROI, outcome-based consulting, build versus buy decisions, proprietary data, ontologies, and governance. Valiance 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.

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.

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/    

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/

Why do AI agents and applications look impressive in demos but struggle when companies try to deploy them in production? In this episode of Tech Talks Daily, I speak with Nikunj Bajaj, co-founder and CEO of TrueFoundry, about why enterprise AI has become a systems problem, what companies need to move AI from proof of concept to production, and how better infrastructure can improve reliability, governance, security, observability, and cost control. Before founding TrueFoundry, Nikunj worked at Meta on conversational AI systems serving more than a billion users and contributed to the company's internal machine learning platforms. He explains how developers at Meta could concentrate on solving business problems while infrastructure handled logging, monitoring, deployment, and governance by default. In many enterprises, the same journey from an AI idea to a production application can still take weeks or months. Nikunj argues that increasingly capable AI models are not necessarily the biggest barrier to enterprise adoption. The harder challenge is building reliable systems around them. Companies need to know what happens when a model becomes unavailable, how an agent is behaving, which data it can access, how much it is costing, when a human should intervene, and whether there is a kill switch when something goes wrong. We discuss why AI proofs of concept often fail when exposed to real users. Controlled demonstrations rarely reproduce production conditions such as unexpected prompts, malicious actors, heavy workloads, model outages, latency, and dependencies between multiple components. Even when individual parts of a system perform reliably, combining them can create failure rates that businesses cannot accept for mission-critical workflows. The conversation also examines the infrastructure required as companies introduce multiple AI models and agents. Nikunj explains the roles of model gateways, MCP gateways, and agent gateways, and how bringing these components together through an AI gateway can give enterprises a control plane for observing and governing AI traffic. Cost is another major challenge. Nikunj explains why sending every request to the most powerful model can waste significant amounts of money when smaller or cheaper models could produce comparable results for simpler tasks. Intelligent model routing can help companies balance quality, latency, availability, and price. He shares how organizations using this approach have reduced model costs by as much as 75 to 80 percent in some production environments. We also discuss what reliable multi-agent systems require in practice. Companies need clearly defined boundaries for what agents can do, escalation routes to other agents or people, safeguards against infinite agent loops, and complete audit trails of interactions and decisions. For CIOs, CTOs, AI engineering teams, platform leaders, and companies trying to move generative AI and agentic AI into production, this conversation provides a practical guide to the infrastructure decisions that determine whether AI applications remain impressive prototypes or become reliable business systems. The next stage of enterprise AI will not be defined by models alone. Companies that can connect, observe, govern, secure, and control their AI applications while managing costs will be better positioned to turn experimentation into dependable production systems.

What if the biggest barrier to better customer service isn't how quickly employees work, but how much time they lose coordinating with everyone else? In this episode of Tech Talks Daily, I speak with Kevin Yang, Head of AI at Front, about why customer conversations are becoming a valuable source of business intelligence, how AI can improve work across entire teams rather than simply making individuals faster, and the hidden coordination costs affecting customer operations. Kevin brings a unique perspective to the conversation. Before joining Front following its acquisition of his AI voice-of-customer company, Syllable, he spent 15 years as an entrepreneur. While building an office food delivery business, he experienced firsthand how customer conversations could reveal problems that traditional surveys and dashboards failed to identify. By analyzing customer feedback at scale, his team could connect specific issues directly to retention, account growth, and referrals. Today, AI makes it possible for companies to analyze enormous volumes of customer conversations and turn unstructured feedback into intelligence that can inform decisions across product development, sales, marketing, and customer success. Kevin shares how Front analyzes conversations to understand why deals are lost, why customers leave, and which topics are associated with higher sales conversion rates. The result is a feedback loop that helps companies direct product investment toward problems customers genuinely care about while giving sales and marketing teams a clearer understanding of the conversations that influence buying decisions. But the episode also challenges the assumption that giving every employee an AI assistant will transform productivity. Front's Coordination Tax research found that teams can spend almost three hours coordinating work for every hour spent solving customer problems. When a single customer request requires input from sales, finance, support, operations, or external systems, employees can lose time to emails, Slack messages, meetings, handoffs, and information searches. Kevin explains why making one person faster does little to solve this problem if the rest of the workflow remains fragmented. The bigger opportunity is to use AI across end-to-end processes, automatically handling research and analysis while allowing people to concentrate on work requiring judgment, empathy, relationships, and human decision-making. We also discuss the growing use of AI agents in customer operations and why governance becomes harder as companies move from experimenting with one agent to managing many. Kevin outlines the need to measure whether agents follow processes correctly, understand customer satisfaction, identify where failures occur, and continuously improve the knowledge and guidance available to AI systems. For business and technology leaders considering where to apply AI, Kevin offers a practical starting point. Map the work your teams perform into three categories: tasks AI can automate, tasks AI can support with human review, and tasks that should remain human. This helps companies focus investment where AI performs well rather than forcing automation into customer interactions that depend on empathy, context, and relationships. For anyone responsible for customer experience, AI strategy, operations, or digital transformation, this conversation provides practical ideas for turning customer conversations into business intelligence, reducing coordination friction, designing better workflows, and introducing AI agents with greater visibility and oversight. The opportunity is not simply to make individuals work faster. It is to redesign how work moves across the organization so employees spend less time coordinating and more time solving the problems that matter to customers.

What happens when your next customer is represented by an AI agent that can research products, compare prices, evaluate suppliers, negotiate terms, and make purchasing decisions? In this episode of Tech Talks Daily, I speak with Ian Kahn, Partner and Customer and Commercial Excellence Platform Leader at PwC, about the rise of the Intelligent Customer Edge and why companies need to rethink how they sell, market, price, serve customers, and compete as artificial intelligence changes the buying process. Much of the enterprise AI conversation has focused on helping employees become more productive. Ian argues that this overlooks a much bigger change already taking place. Customers are using AI to research products, compare alternatives, evaluate pricing, and make decisions. In some consumer and business markets, AI agents are already being given permission to make routine purchases. Companies are no longer selling only to people. They increasingly need to serve customers whose AI agents expect accurate product information, transparent pricing, availability, service history, and performance data that can be discovered, verified, and understood by machines. This creates a serious problem for companies operating with fragmented front offices. Marketing, sales, pricing, commerce, and customer service have traditionally operated as separate functions, each with its own technology, data, processes, incentives, and performance measures. Customers do not experience companies through those internal structures. They expect consistent information and relevant experiences across the entire relationship. Ian explains why adding AI to each department independently will not solve this problem. Companies risk making existing processes faster without improving the customer experience or business performance. Instead, he argues that leaders need to reconsider the operating model behind the entire customer journey. The Intelligent Customer Edge is PwC's approach to bringing these commercial functions together into a connected system centered on the customer. Powered by proprietary company data and AI, the system can continuously learn from customer interactions, support real-time decisions, and help companies respond to changing customer needs. We also discuss the idea of the commercial brain and why proprietary data could become one of the most valuable competitive advantages available to companies adopting AI. Most businesses already possess customer records, transaction histories, operational information, market signals, service interactions, and other data their competitors cannot access. Yet much of that information remains fragmented across systems and departments. Ian explains how connecting these sources can create an intelligence layer that informs pricing decisions, marketing activity, sales opportunities, service interactions, and the moments that matter throughout the customer relationship. For CEOs, chief customer officers, marketing leaders, sales executives, CIOs, and technology teams, the conversation offers an important lesson about AI transformation. The companies achieving meaningful results are not starting with the technology. They begin with customer outcomes and redesign the work, decisions, workflows, and operating models required to achieve them. Human judgment remains an important part of that model. AI can process large amounts of information, identify patterns, provide recommendations, and handle routine tasks consistently. People continue to bring judgment, creativity, empathy, relationship-building, and strategic decision-making to customer interactions where trust and context matter. Ian argues that the goal is not to choose between people and AI. Companies need to design customer systems that use the strengths of both, determining where automation can improve speed and consistency and where people can create greater customer and commercial value. Trust, governance, explainability, and accountability also become more important as AI agents are given greater authority. Rather than treating guardrails as barriers to adoption, Ian explains why companies should design controls into AI-enabled customer processes from the beginning. The conversation also examines the cost of waiting. Customers are already adopting AI, and businesses that continue relying on fragmented front-office operations risk falling behind competitors capable of responding faster, providing better information, and creating more relevant customer experiences. Ian offers practical advice for companies deciding where to begin. Start with the customer journey. Understand how customer behavior is changing, identify where friction exists, determine how AI could improve the experience, and establish clear measures for customer outcomes and business value before investing heavily in new technology. For business and technology leaders under pressure to deliver growth, improve margins, control costs, and demonstrate returns from AI investment, this conversation provides a practical framework for redesigning the front office, using proprietary data more effectively, preparing for AI agents as buyers, and creating better customer experiences. Your customers are already using AI. Some AI agents are already making purchasing decisions. The question for companies is whether their customer systems, data, commercial models, and operating structures are ready to compete for business when the buyer on the other side of the transaction is no longer always human.

Why are companies investing heavily in AI, analytics, and data platforms while business leaders still struggle to see what is happening across their operations quickly enough to make confident decisions? In this episode of Tech Talks Daily, I speak with Massimo Merlo, Vice President for UK, Iberia, and Italy at Elastic, about why the next stage of enterprise AI adoption will depend less on who deploys the most advanced models and more on which companies can give people and AI systems access to relevant, trusted, and secure information when decisions need to be made. Massimo describes the problem as a lack of decision-grade visibility. Most large companies are not short of data. They have spent decades building data platforms, analytics systems, dashboards, cloud infrastructure, and reporting tools. Yet information remains fragmented across departments and applications, insights arrive too late, and employees often struggle to find the small amount of information that matters among enormous volumes of data. The result is a growing gap between having information and being able to act on it. Massimo explains why simply adding an AI model to this environment does not solve the underlying problem. If an AI system is connected to fragmented, outdated, poorly governed, or irrelevant information, it can produce convincing answers without providing reliable business outcomes. The quality of an AI model matters, but the context available to that model increasingly determines whether AI becomes a useful business asset or an operational liability. This leads to one of the biggest technology conversations emerging around enterprise AI: context engineering. Massimo explains how context engineering provides AI systems with the relevant data, tools, permissions, organizational knowledge, and guardrails required to complete a task safely. Rather than sending ever-larger volumes of information to AI models, companies need infrastructure capable of retrieving the right information and making it available at the moment a person or software agent needs to act. Fraud detection provides a practical example. An AI agent evaluating a transaction needs more than access to a powerful model. It requires customer history, behavioral patterns, company risk thresholds, permissions, compliance requirements, and the ability to recognize activity that falls outside normal behavior. Without that context, the system could block legitimate customers or approve fraudulent transactions while presenting its decision with complete confidence. We also discuss why digitally mature companies can still struggle with real-time decision-making. Massimo shares lessons from Elastic's work with organizations including Reed, the Met Office, and Rightmove, explaining why having sophisticated technology systems does not automatically make a company context mature. Information can still remain trapped between applications, teams, and databases, preventing employees and AI agents from seeing the complete picture when it matters. The conversation challenges another long-standing enterprise technology habit: adding more dashboards. Massimo explains why dashboards often provide visibility into what has already happened without helping people decide what to do next. Companies can continue adding reporting layers while employees become overwhelmed by information and remain unable to identify the actions that will improve customer experience, productivity, security, or business performance. A healthcare example demonstrates what becomes possible when companies solve this problem. Massimo shares how CogStack at King's College Hospital brought together unstructured patient information during the COVID-19 pandemic and made it searchable using natural language processing. Clinicians could find relevant information without waiting for technical teams to build new queries or systems, helping medical professionals access information when patient decisions needed to be made. For CEOs, CIOs, CTOs, data leaders, and technology teams trying to improve AI ROI, Massimo offers practical advice on where to begin. Do not start with another model, tool, or dashboard. Start with a business decision or workflow that is currently too slow, unreliable, or difficult to execute. Identify what information that decision requires, where the data is stored, who or what system needs access to it, which permissions should apply, and where information currently becomes delayed or disconnected. That process can reveal the visibility gaps preventing companies from turning their existing data and AI investments into measurable results. We also examine why search and retrieval are becoming infrastructure concerns for companies introducing AI agents. As software agents begin making recommendations and taking actions across business systems, their performance will depend on whether they can securely retrieve relevant information at scale. For business and technology leaders facing pressure to demonstrate returns from AI investment, this conversation provides a practical framework for improving enterprise search, context engineering, AI agent reliability, real-time operational visibility, and decision-making. The companies that gain the greatest value from AI may not be those collecting the most data or deploying the most models. They will be the companies capable of finding what matters, understanding its context, and getting trusted information to people and AI systems quickly enough to act on it. That is where better visibility can become better decisions, stronger productivity, and business growth.

Why are companies spending heavily on AI tools while struggling to show meaningful improvements in productivity, revenue, or business performance? In this episode of Tech Talks Daily, I speak with Matt Cloke, Chief Technology Officer at Endava, about what it takes to become an AI-native business, why deploying thousands of AI licenses does not amount to an AI transformation, and how companies can move from experimentation to measurable business outcomes. Matt has played a central role in Endava's own adoption of artificial intelligence and the development of Dava.Flow, the company's methodology for applying AI throughout the technology delivery lifecycle. With more than 11,000 employees and clients operating across multiple industries, Endava has treated itself as "client zero," testing AI internally before advising other companies about how to introduce it across their operations. Matt shares the story of a CEO who proudly told him that his company had completed its AI transformation after purchasing 10,000 licenses for an AI tool. Twelve months later, the business had seen little return on its investment and returned for help understanding what becoming AI-native actually required. The story captures one of the biggest problems with enterprise AI adoption today: buying technology is easy, but changing how people think about problems, redesign workflows, and create business value is much harder. We discuss why Matt believes becoming AI-native is primarily a mindset. Rather than treating AI as another application added to the technology stack, employees should become curious about where AI can improve existing processes, remove unnecessary work, and create new ways of delivering value. Matt also explains his idea that AI works best when it becomes invisible. Instead of requiring employees to constantly interact with chatbots and standalone AI applications, software agents can operate inside existing workflows, monitor information, prepare responses, identify problems, and bring people into the process when human judgment is required. His own use of AI agents provides a practical example. While attending meetings that prevented him from monitoring email for several days, Matt used agents to review incoming messages, redirect requests, identify urgent communications, and prepare draft responses. Rather than handing complete control to automation, he determined which actions required approval and where AI could operate independently. This leads to a wider discussion about human oversight and accountability. Matt argues that managing AI agents may increasingly resemble managing teams. Leaders do not inspect every decision made by every employee, but they establish responsibilities, controls, escalation points, and circumstances where intervention is required. Companies introducing agentic AI need similar approaches to supervision. We also examine two mistakes Matt frequently sees companies make. The first is treating AI adoption as a software rollout, buying tools for employees and expecting productivity gains to appear automatically. The second is creating centralized AI centers of excellence and expecting a small group of specialists to determine how every department should use the technology. Matt argues that employees closest to business processes are often best placed to identify opportunities for improvement. At Endava, the legal team runs monthly AI hackathons to redesign its own workflows, supported by technology specialists but led by people who understand the work itself. For companies operating in payments, financial services, and other regulated industries, the conversation turns to reliability, auditability, traceability, and risk. Matt explains how Dava.Flow allows companies to translate regulatory requirements and operational controls into policies that AI systems must follow and demonstrate throughout the delivery process. Rather than searching for a single killer AI application, Matt recommends examining end-to-end business workflows. Companies can map how information moves between employees, departments, and systems, identify unnecessary handoffs and manual processes, and determine where AI agents can improve speed, cost, and performance without replacing entire technology platforms. Leadership is another major theme throughout the episode. Matt believes the companies that achieve meaningful results from AI will be led by executives who personally use the technology, understand its capabilities, and demonstrate the behaviors they expect from their workforce. He shares how Endava brought senior leaders from legal, technology, people, and other business functions together to build software agents themselves. The experience changed how executives thought about technology investments, including one leader realizing that an existing vendor contract might no longer be necessary because the company could build the required capability internally. For CIOs, CTOs, technology leaders, and business executives under pressure to demonstrate returns from AI investment, this conversation provides practical lessons on becoming AI-native, redesigning workflows, managing software agents, maintaining human accountability, operating AI in regulated industries, and moving beyond technology adoption toward measurable business value. The companies that succeed with AI may not be those buying the most tools or making the biggest announcements. They will be the ones whose leaders understand the technology, whose employees rethink how work gets done, and whose AI systems quietly become part of everyday business operations.

What happens when AI makes employees more productive today but gradually weakens the expertise companies will depend on tomorrow? In this episode of Tech Talks Daily, I speak with Dr. Margaret Cunningham, VP of Security and AI Strategy and Field CISO at Darktrace, about cognitive tech debt, the growing risk that companies are gaining short-term efficiency from AI while unintentionally weakening critical thinking, technical expertise, problem-solving ability, and human judgment. Margaret brings a rare combination of experience to this conversation. With a PhD in Applied Experimental Psychology and a career spanning behavioral science, cybersecurity, privacy, human-centered security, and AI strategy, she examines technology adoption through the lens of how people actually think, learn, develop expertise, and make decisions. She explains cognitive tech debt by comparing it with the technical debt familiar to software teams. Companies can introduce technology quickly and enjoy immediate improvements in speed and output, only to discover weaknesses underneath those gains later. With AI, the debt may accumulate in people. Employees can appear highly productive while outsourcing the difficult cognitive work required to build judgment, recognize patterns, understand failures, and develop genuine expertise. We discuss emerging evidence that over-reliance on AI is already affecting professional skills. Software engineers may become less capable of diagnosing problems in code they did not create themselves. Medical professionals can lose decision-making capabilities when they become dependent on automated systems. Across knowledge work, deep reading and sustained concentration are increasingly being replaced by summarization, generation, and superficial review. Margaret describes the current period as the "bridge years," when AI systems are becoming increasingly capable but people still need to maintain the expertise required to recognize mistakes, question recommendations, recover from failures, and understand when automation should not be trusted. Companies cannot safely abandon human skills before technology can reliably perform those responsibilities without supervision. The conversation also challenges one of the most repeated promises surrounding enterprise AI adoption: that automation will remove routine work and allow employees to concentrate on higher-value activities. Margaret argues that companies have done a poor job of defining which tasks people genuinely want to give up and which skills they need to preserve. Some of the repetitive, slow, and difficult work being automated may be exactly where people develop pattern recognition, creativity, and professional judgment. This creates a serious challenge for cybersecurity teams and other high-stakes professions. If employees become reviewers of AI-generated outputs rather than practitioners developing expertise through experience, where will the next generation of senior engineers, security analysts, doctors, researchers, and technical specialists come from? Margaret explains why leaders need to understand which AI techniques are being used for different business problems rather than treating every form of artificial intelligence as interchangeable. Large language models, machine learning systems, behavioral analytics, and other technologies have different strengths and limitations. Knowing what questions to ask requires domain expertise, creating a difficult paradox for companies that may be automating away the very experience needed to govern these systems responsibly. We also examine the human consequences of AI adoption. Technical specialists who enjoy solving difficult problems can lose motivation when meaningful work is replaced by reviewing machine-generated outputs. Companies may struggle to understand who owns decisions made through collaboration between humans and AI, while younger employees could lose access to the experiences that previously helped people progress from beginners to experts. Margaret offers practical advice for business and technology leaders deciding how quickly to introduce AI across their workforce. Companies can identify the skills they need to preserve, create opportunities for employees to practice difficult cognitive work, use simulations and training to maintain expertise, ask teams which aspects of their jobs give them purpose, and resist pressure to automate every task simply because the technology exists. The message is not anti-AI. Margaret sees enormous potential for artificial intelligence in scientific research, cybersecurity, productivity, and solving difficult problems. But realizing those benefits requires a more intentional relationship between people and machines. For business leaders, CISOs, technology teams, AI practitioners, and anyone concerned about the future of human expertise, this conversation provides a practical framework for recognizing cognitive tech debt, deciding what should and should not be automated, preserving critical thinking skills, and building healthier forms of human-AI collaboration. AI can make people faster. The bigger question is whether companies can capture those productivity gains without losing the human capabilities they will need when the technology gets something wrong.

What happens to blockchain networks, digital assets, and the wider internet when quantum computers become powerful enough to break the cryptography protecting them? In this episode of Tech Talks Daily, I speak with Bruno Martins, Chief Technology Officer of the Algorand Foundation, about what quantum computing means for blockchain security, why post-quantum cryptography is becoming a technology priority, and how enterprises should evaluate blockchain infrastructure for payments, digital assets, identity, and other business applications. Bruno brings experience from across several major blockchain ecosystems, including Consensys and IOHK, alongside a background in applied cryptography, key management systems, enterprise blockchain development, and software engineering. His perspective provides a useful view of how the blockchain industry has changed from experimental projects and speculative use cases toward platforms expected to support real financial transactions and business operations. We begin with the quantum threat itself. Bruno explains why the cryptographic systems protecting blockchains, financial infrastructure, communications, messaging platforms, and much of the internet could eventually become vulnerable to sufficiently powerful quantum computers.  While the exact timeline remains uncertain, he argues that waiting for a cryptographically relevant quantum computer to arrive before beginning migration would leave companies with too little time to update infrastructure, applications, wallets, accounts, and user behavior. The conversation examines why post-quantum security is not simply a future technology problem. Large digital ecosystems can take months or years to migrate, and businesses need time to understand their cryptographic dependencies, introduce new standards, educate users, and build systems capable of adopting new security methods without disrupting existing operations. Bruno shares how Algorand has been working on post-quantum security for several years, including the deployment of Falcon signatures for state proofs and plans to introduce quantum-resistant account types and additional protections across consensus and network communications. We discuss why cryptographic agility may be more important than simply replacing existing cryptography with newer algorithms that have not yet experienced decades of testing in real-world systems. This leads to one of the most valuable technical lessons in the episode. Moving directly from classical cryptography to post-quantum cryptography introduces its own risks because newer cryptographic methods may later reveal weaknesses. Bruno explains why hybrid approaches, where digital assets and accounts can be protected by both established and quantum-resistant cryptography, could provide a more responsible path for institutions managing long-lived systems and valuable assets. We also examine how enterprises should evaluate blockchain platforms. With thousands of networks competing for developers, users, and institutional adoption, Bruno argues that businesses need to look beyond market attention and transaction speed. Throughput, decentralization, security, programmability, finality, operational risk, and the ability to trust the state of a ledger all influence whether blockchain infrastructure is suitable for real business operations. Payments provide a practical example. Companies issuing payment products backed by stablecoins need confidence that transactions are final and cannot later be reorganized or reversed by the underlying network. Bruno explains why instant finality can reduce operational uncertainty and risk for companies building financial applications on public blockchain infrastructure. The conversation also turns to AI agents and agentic commerce. If autonomous software agents begin negotiating, purchasing services, exchanging value, and conducting transactions with other agents, they will need payment rails, identity systems, trusted counterparties, and ways to establish ownership and accountability. Bruno explains why stablecoins, digital identity, decentralized finance, and blockchain infrastructure could become increasingly relevant as AI systems begin participating directly in economic activity. Throughout the episode, Bruno offers a balanced assessment of the blockchain industry itself. He discusses the problems created by technical fragmentation, competing standards, thousands of networks, and ecosystem tribalism. Greater cooperation between blockchain communities, particularly around wallets, hardware, cryptographic standards, and post-quantum security, could make it easier for enterprises and developers to build applications that work across ecosystems. For technology leaders, security professionals, blockchain developers, and anyone responsible for long-lived digital infrastructure, this conversation provides a practical introduction to quantum threats, post-quantum cryptography, cryptographic agility, blockchain finality, stablecoins, and the technical questions companies should ask before choosing distributed infrastructure. The quantum threat may not arrive tomorrow, but migrating complex systems takes time. The companies and technology platforms preparing today will be in a much stronger position to protect digital assets, maintain trust, and continue operating when current cryptographic standards eventually need to change.

Why do companies continue spending heavily on cybersecurity technology while human behavior, poor governance, and skills shortages leave them exposed to attacks? In this episode of Tech Talks Daily, I speak with Phil Chapman, Cybersecurity Subject Matter Expert at Firebrand Training, about what more than two decades in the Royal Air Force, signals intelligence, counterterrorism, threat intelligence, and cybersecurity education taught him about defending companies in an increasingly complex threat environment. Phil's career provides a fascinating perspective on how intelligence skills developed in military and national security environments can be applied to modern cyber defense. After 23 years in the RAF, including work supporting organizations such as GCHQ and the NSA, training intelligence analysts, and working in counterterrorism, Phil moved into technology training and cybersecurity education. Today, he helps companies understand their cybersecurity training needs while supporting people building careers in an industry that continues to need new talent. A major theme throughout our conversation is Phil's belief that cybersecurity is fundamentally about people. Technology matters, but expensive security products cannot compensate for employees who do not recognize threats, executives who misunderstand their responsibilities, or companies that treat security awareness as an annual compliance exercise. Phil explains threat intelligence in practical business terms, examining the relationship between threats, vulnerabilities, business assets, and risk. We discuss why insiders remain one of the biggest security concerns facing companies, including malicious employees and the far more common problem of accidental actions such as clicking phishing links, sharing sensitive information, or sending data to the wrong recipient. The arrival of generative AI is making these problems harder to manage. Phil discusses how criminals are using AI to create more convincing phishing campaigns, deepfakes, social engineering attacks, and other forms of cybercrime. At the same time, employees are introducing new risks by using AI tools without understanding what happens to company data or whether appropriate policies and controls are in place. But this episode is also about opportunity. Phil challenges the stereotype that cybersecurity careers are only for highly technical people sitting behind multiple screens writing code. He explains the different career paths available across cybersecurity engineering, threat intelligence, incident response, security operations, governance, risk, compliance, and analysis, and why skills from customer service, the military, data analysis, writing, communications, and other professions can transfer successfully into cyber roles. For anyone considering a career change or trying to enter the technology industry, Phil offers practical advice on where to begin. Rather than chasing advanced certifications or trying to become an ethical hacker immediately, he recommends building a strong foundation, understanding networks and operating systems, staying current with the news, developing analytical thinking, and remaining curious about how criminals adapt world events and new technologies to create attacks. We also discuss cybersecurity apprenticeships and why alternative routes into technology careers could help companies develop talent while giving people of different ages and professional backgrounds access to an industry they may previously have considered out of reach. Finally, Phil explains why cybersecurity professionals cannot focus only on today's threats. AI is already changing both attack and defense strategies, while quantum computing is forcing companies to examine cryptography, data protection, and long-term security planning. His message to business leaders and technology professionals is clear: buying more technology will not solve every security problem. Companies need informed leadership, better governance, continuous learning, practical training, and people who understand how threats evolve. This conversation offers business leaders a clearer understanding of cyber risk, provides technology teams with practical ideas for improving security awareness, and offers anyone considering a cybersecurity career a realistic view of the opportunities, skills, and pathways available through training and apprenticeships.

What if companies rushing to deploy AI agents are overlooking the basic problem that much of their business data is still trapped inside PDFs, emails, attachments, spreadsheets, and paper documents? In this episode of Tech Talks Daily, I speak with Sylvestre Dupont, co-founder and CEO of Parseur, about why successful AI adoption begins with making business data usable, why traditional automation can often outperform more sophisticated AI systems, and how he built a profitable global technology company with six employees across six countries without venture capital funding. Sylvestre introduces the concept of data liquidity, the ability to move information from the documents and systems where it is trapped into the applications, workflows, and AI systems that can put it to work. Companies may have years of valuable operational data, but if that information remains buried inside what Sylvestre calls "digital concrete," even the most advanced AI models will struggle to produce useful results. The conversation examines why structured data extraction has become increasingly important as companies invest in AI agents, copilots, and automated workflows. Sylvestre explains that better models alone cannot compensate for incomplete, inaccessible, or poorly structured information. Before businesses can expect AI to automate complex processes or support better decisions, they need reliable ways to collect, structure, and move data between systems. We also challenge the assumption that every business problem now requires an AI solution. Sylvestre explains why AI should be treated as one tool among many and why deterministic automation remains the better option for repetitive processes where accuracy, consistency, and explainability matter. Parseur itself combines AI-powered document processing with template-based extraction and traditional workflow automation, using each approach where it performs best. Drawing on Parseur's experience processing more than 100 million documents annually, Sylvestre describes the different stages companies move through as they mature their automation strategies. Some begin by manually uploading documents and downloading extracted data. Others automate document ingestion and connect information directly to accounting platforms, CRM systems, and other business applications. The most advanced companies add exception handling and human review processes for situations where automation cannot reliably complete the task. Data privacy and security are another major part of the discussion. Sylvestre shares the questions technology leaders should ask before sending sensitive company information to AI-powered platforms, including where data is stored and processed, whether customer information is used to train AI models, how deletion requests are handled, and whether vendors genuinely understand the regulations and security standards they claim to follow. For founders and bootstrapped entrepreneurs, Sylvestre also shares an alternative perspective on building technology companies. Parseur has remained profitable, globally distributed, and customer-funded rather than pursuing the venture capital model of rapid expansion. Sylvestre explains why he prefers customers to determine the company's priorities, how asynchronous communication supports a team operating across multiple time zones, and why building a sustainable business can offer founders greater control over product decisions and company culture. This conversation offers practical lessons for technology leaders deciding where AI belongs in their operations, operations teams trying to reduce repetitive manual work, and founders questioning whether venture capital is the only route to building a successful global software company. The message throughout the episode is simple: AI can be extremely useful, but companies still need reliable data, appropriate technology choices, strong privacy practices, and well-designed business processes. Sometimes the smartest technology strategy begins by solving the boring problems first.

What separates the founders who build category-defining companies from the thousands of startups that never make it through the venture capital funnel? In this episode of Tech Talks Daily, I speak with Keval Desai, founder and General Partner of Shakti, an early-stage venture capital firm investing in AI and space technology companies from inception. Drawing on his experience backing companies including Canva, The RealReal, and Gatik, Keval shares how he evaluates founders before the rest of the market recognizes their potential and why the venture capital industry needs to confront some uncomfortable truths about startup funding and successful exits. Keval introduces Shakti's "toothbrush" investment philosophy, an idea he first encountered through Larry Page at Google. The principle is simple: can a product or service become something used frequently by millions or even billions of people? He explains why this question helps investors distinguish impressive technology from businesses capable of creating lasting value, particularly at a time when thousands of AI startups are competing for capital and attention. But identifying a large market is only part of the equation. Keval shares three characteristics he has observed in exceptional founders. They can describe a future that others cannot yet see, attract talented people before they have money or resources, and execute at a speed that continually surprises those around them. His stories from meeting Canva co-founder Melanie Perkins and The RealReal founder Julie Wainwright offer a rare look at what investors can learn from founders at the earliest stages of company building. We also discuss Keval's thesis that AI is taking the economy into a new Imagination Era. As AI becomes increasingly capable of handling specialized tasks such as coding, analysis, and production, he believes human value will move toward imagination, judgment, taste, and the ability to combine technologies into products and services people actually want. For founders, employees, and business leaders, this raises important questions about education, careers, and what it means to build a company as access to technical capabilities becomes dramatically cheaper. Keval also compares the arrival of open-source AI models such as DeepSeek to the role Linux played in the development of the commercial internet. He explains why falling inference costs could lower barriers to building AI companies and create opportunities for a new generation of startups, while also examining what this could mean for today's dominant AI companies and the industry's economics. The conversation then turns to one of the biggest problems facing venture capital. The number of startups receiving funding has grown dramatically, yet the number of technology companies reaching public markets has remained relatively static. Keval explains why venture capital can scale dollars but cannot simply manufacture more category leaders, and why founders need to decide early whether venture capital is actually the right source of funding for the business they want to build. We also examine the commercial opportunities emerging from space technology. Keval believes the SpaceX IPO could play a similar role for space commerce to Amazon's IPO for e-commerce, by demonstrating viable business models and encouraging entrepreneurs to build new companies in communications, energy, manufacturing, infrastructure, robotics, and services beyond Earth. Finally, Keval offers an optimistic counterargument to fears that AI will leave younger workers without meaningful careers. He explains why he believes Gen Z's status as the first AI-native generation could become an advantage, why technical careers are changing rather than disappearing, and why the ability to apply AI to problems across healthcare, manufacturing, agriculture, finance, and other industries could create opportunities far beyond Silicon Valley. This conversation offers founders a practical framework for evaluating ideas, choosing investors, understanding venture economics, and building companies in the age of AI. It also provides investors and technology leaders with a broader perspective on open-source AI, space commerce, the future of work, and where the next generation of category-defining companies could come from.

What happens when your customers stop searching through pages of Google results and start asking ChatGPT, Claude, and other AI platforms which companies they should trust? In this episode of Tech Talks Daily, I speak with Kathleen Lucente, founder and CEO of Red Fan Communications, about zero-click search, AI-mediated discovery, and why producing more content is unlikely to solve the growing challenge of brand visibility in AI-generated answers. Kathleen argues that content is what a company says about itself, while authority is built through what credible third parties say about it. As buyers increasingly use large language models to research companies, compare vendors, and make purchasing decisions, earned media, analyst relations, customer reviews, executive visibility, original research, and consistent brand messaging are becoming increasingly important signals of trust. But building brand authority cannot be completed in a few weeks or solved by purchasing another AI visibility tool. Kathleen explains why companies appearing prominently in AI-generated answers often earned that position through years of reputation building. We discuss her seven-part framework for measuring brand authority across earned media, company recognition, reviews, entity coherence, content authority, social authority, and technical readiness, as well as how marketing leaders can identify where their companies are falling behind competitors. The conversation also examines what the rise of generative engine optimization, answer engine optimization, and AI search means for traditional SEO and content marketing strategies. Kathleen explains why SEO still matters but can no longer carry the entire burden of brand discovery, and why marketing, communications, sales, customer success, and executive leadership must work together to build the credibility signals that influence both people and AI systems. We also discuss how companies can measure reputation and connect communications programs to tangible business outcomes. Kathleen shares examples of original research and earned media opening doors to new customers, generating conversations with major publications, and creating commercial opportunities that traditional sales efforts had struggled to reach. Drawing on more than 30 years of experience helping B2B technology companies through IPOs, acquisitions, funding rounds, and periods of rapid growth, Kathleen explains why reputation often acts as invisible insurance for a business. Companies may not recognize its value until a deal, crisis, leadership change, or major transaction puts trust under pressure. Finally, Kathleen shares practical advice for B2B technology leaders who want their companies to become trusted authorities in the age of AI search. From auditing how your brand appears across multiple sources to refreshing customer reviews, developing credible executive voices, strengthening analyst relationships, and creating original data that journalists and industry leaders want to reference, this conversation offers a practical roadmap for companies trying to become visible in AI-generated answers. Is your company still trying to win the AI search battle by producing more content, or are you investing in the reputation and third-party credibility that will influence how both people and AI systems perceive your brand? Share your thoughts with me.

Have you ever wondered whether the biggest competitive advantage in wealth management is no longer investment performance alone, but the ability to turn information into action faster than everyone else? In this episode of Tech Talks Daily, I welcome Bob Pisani, Chief Technology Officer at Addepar, a platform that helps investment professionals manage and analyze more than $9 trillion in assets globally. Our conversation explores why modern wealth management has become a technology challenge just as much as a financial one, and why firms that continue relying on fragmented legacy systems risk falling behind in an industry where speed, data quality, and client expectations are changing faster than ever. Bob explains how wealth advisors have historically spent far too much of their day moving between disconnected systems, stitching together spreadsheets, and trying to answer client questions using incomplete information. While that may once have been acceptable, today's investors expect near real-time visibility into their portfolios, along with personalized guidance that reflects rapidly changing market conditions. That changing expectation places an enormous premium on time, making technology one of an advisor's most valuable assets. Our discussion explores why successful AI initiatives begin long before deploying a model. Data quality, governance, and creating a trusted source of truth remain the foundations that determine whether AI produces reliable insights or simply accelerates poor decisions. Bob shares how Addepar approaches this challenge by bringing together fragmented financial data, standardizing it across hundreds of custodians, and creating the conditions where AI can produce meaningful, actionable intelligence rather than more noise. We also look at practical examples of AI already improving advisor productivity today. From summarizing portfolio performance and analyzing complex alternative investment documents to introducing intelligent agents that reduce operational workload, Bob explains how AI is freeing experienced professionals to spend less time gathering information and more time building trusted client relationships. One of my favorite moments in our conversation comes when we discuss predictive intelligence. Instead of waiting for advisors to search for answers, AI is beginning to surface opportunities, risks, and client conversations before anyone even knows which questions to ask. That represents a fundamental change in how financial advice can be delivered, moving from reactive reporting toward proactive guidance that is grounded in trusted data. We also address one of the biggest questions surrounding AI in financial services. Will technology replace human advisors? Bob offers a thoughtful perspective, arguing that while AI can automate repetitive work and accelerate decision-making, qualities such as judgment, precision, trust, and human relationships remain impossible to automate. Those are the characteristics clients ultimately value most when making important financial decisions. As our conversation draws to a close, Bob shares why he believes the gap between firms embracing AI and those delaying modernization will widen rapidly. The organizations investing today in clean data, modern platforms, and AI-ready operations will be better positioned to serve clients, attract talent, and compete in an increasingly fast-moving market. Can wealth management continue to rely on yesterday's technology in an AI-driven world? And if time has become the industry's most valuable asset, how is your business making the most of it? I'd love to hear your thoughts after listening.

Have you ever stopped to think about what really powers the AI revolution? While the conversation often focuses on the latest models, chips, and applications, the real story may lie in something far less visible: the energy systems and digital architecture that make it all possible. In this episode of Tech Talks Daily, I welcome Sadiq Syed, Senior Vice President of Digital Energy Software at Schneider Electric, to discuss why the future of electrification depends as much on software as it does on hardware. As demand for AI continues to grow at an extraordinary pace, data centers are consuming increasing amounts of electricity, putting pressure on aging grids and exposing the limitations of traditional approaches to energy management. During our conversation, Sadiq explains why electrification alone cannot deliver global decarbonization goals. Without intelligent software capable of monitoring, predicting, and optimizing energy usage, businesses risk wasting valuable resources while struggling to meet rising demand. We discuss why AI may ultimately become the technology that helps solve the energy challenges it has helped create, using continuous analytics and predictive intelligence to improve efficiency across complex environments. We also examine the growing regulatory pressure. With more than a thousand energy-related regulations introduced around the world in recent years, compliance has become part of everyday operations rather than an occasional reporting exercise. Sadiq explains why organizations should stop viewing compliance as an administrative burden and instead see it as an opportunity to build trust, strengthen resilience, and improve operational performance. Another area we explore is digital resilience. Whether supporting hospitals, pharmaceutical manufacturers, or mission-critical data centers, modern infrastructure depends on uninterrupted operations. Sadiq shares why cybersecurity, predictive maintenance, unified operational visibility, and connected digital platforms are becoming central to maintaining uptime while helping organizations make better use of limited energy resources. The conversation also turns to people. As experienced engineers retire and younger generations enter the workforce with very different expectations, organizations face an urgent challenge: modernizing the tools they provide. We discuss how intuitive digital platforms can reduce complexity, shorten training time, attract the next generation of technical talent, and make daily operations easier to manage. Throughout our discussion, one message remains consistent. The future of sustainable infrastructure is built on the combination of electrification, automation, and intelligent software. From AI-enabled operational insights to connected energy management platforms, technology is becoming the foundation that allows businesses to balance performance, sustainability, regulatory requirements, and resilience in an increasingly unpredictable world. Is the biggest challenge facing AI actually an energy challenge? And if software is becoming the foundation for modern electrification, how prepared is your organization for what comes next? I'd love to hear your thoughts after listening.

What does meaningful engagement look like in today's classroom? And as AI becomes part of everyday teaching, how can technology help teachers inspire curiosity without adding even more pressure to an already demanding profession? In this episode of Tech Talks Daily, I'm joined by Jon Neale, Growth Director for UK and Ireland at Kahoot!, and one of the UK's most respected voices in education technology. With more than a decade in the classroom before moving into EdTech, Jon brings the perspective of someone who understands both the realities teachers face every day and the opportunities that technology can create when it is used with purpose. Our conversation begins by exploring why student engagement has become more challenging in a world filled with digital distractions. Jon explains why gamification should never be confused with entertainment and how thoughtfully designed learning experiences can encourage participation, collaboration, and confidence without turning education into a competition. We also discuss how Kahoot! has evolved far beyond the classroom quiz that many people know. Today's platform helps teachers create interactive lessons, collaborative learning experiences, and personalised activities that support learners across schools, higher education, workplace learning, and professional development. AI is another major focus of our discussion. Jon shares practical examples of how teachers are using AI to enhance existing lesson materials, generate engaging classroom activities, personalise learning, and identify where students may need extra support before small learning gaps become bigger challenges. Rather than replacing educators, AI is helping teachers spend more time doing what drew them into the profession in the first place: teaching. We also explore one of the biggest challenges facing education today, teacher workload. Jon explains why successful technology adoption starts with confidence rather than features, and why professional development plays such an important role in helping educators choose the right tool for the right situation instead of feeling overwhelmed by an endless stream of new platforms. Whether you're a teacher, school leader, learning and development professional, or simply interested in how AI is changing education, this episode offers practical insight into how technology can create richer learning experiences while keeping people firmly at the centre of education. As classrooms continue to change, what do you think will have the greatest impact on learning: AI itself, or the teachers who know how to use it well?

What if the biggest barrier to successful AI isn't the model itself, but the lack of context behind every decision your teams make? As AI agents become more capable, how do organisations ensure they understand the people, projects, documentation, and history that shape real work? In this episode of Tech Talks Daily, recorded at Team '26, I'm joined by Taroon Mandhana, CTO of AI and Teamwork at Atlassian. His responsibilities span engineering for products including Jira, Confluence, Loom, and Trello, alongside the company's AI strategy and the development of Rovo. Our conversation explores why Atlassian believes AI should become a teammate rather than simply another chatbot. Taroon explains why enterprise context has become one of the most valuable assets in the AI era. While today's foundation models continue to improve at an incredible pace, they still lack the organisational knowledge that human teams naturally accumulate over time. Atlassian's Teamwork Graph aims to bridge that gap by connecting people, projects, documentation, code, goals, and conversations into a living knowledge network that AI agents can use to produce more accurate, relevant outcomes. We also discuss why Atlassian has chosen an open approach, making its Teamwork Graph available through technologies such as MCP rather than limiting it to its own AI products. Taroon shares why interoperability will become increasingly important as businesses adopt multiple AI platforms and why organisations should be free to use the agents that best suit their needs without losing access to valuable business context. Another fascinating part of our conversation focuses on how Atlassian's own engineering teams are changing the way they build software. Smaller teams, tighter collaboration, AI-assisted development, and faster iteration cycles are allowing products to move from concept to release in weeks rather than months. Taroon explains how AI is changing both software development and the structure of engineering teams themselves. We also examine where AI should take ownership of work inside platforms like Jira, where human judgement remains essential, and why successful organisations are treating AI adoption as an ongoing product journey rather than a one-time technology deployment. If your business is looking beyond isolated AI experiments and wondering how to build AI into everyday work, this conversation offers valuable insight into the role context, openness, and organisational change will play in the next generation of enterprise software. As AI becomes part of every workflow, what do you think will become the real competitive advantage: better models, or better organisational knowledge?

What happens when customer service moves beyond answering questions and starts anticipating needs, coordinating actions, and resolving problems with AI? And what does that mean for the future of the contact center? In this episode of Tech Talks Daily, I'm joined by Jonathan Barouch, Vice President and General Manager of Contact Center at Zendesk, to discuss the company's vision for the next generation of customer experience following the integration of Local Measure into the Zendesk platform. Jonathan explains why the contact center is entering a new chapter, where AI is becoming part of every interaction rather than existing as a standalone feature. We discuss the announcements from Relate 2026 and how Zendesk is bringing together customer service, voice, automation, and AI to create a more connected experience for both customers and agents. A major part of our conversation focuses on the acquisition and integration of Local Measure. Jonathan shares why bringing enterprise voice capabilities into the Zendesk platform creates new opportunities for organisations looking to modernise their contact centres without adding unnecessary complexity. Rather than treating voice as a separate channel, Zendesk is building an experience where every customer interaction contributes to a complete understanding of the customer journey. We also discuss how AI is changing the day-to-day reality for contact center teams. Instead of replacing people, Jonathan explains how AI can remove repetitive work, surface the right information at the right time, and allow agents to spend more time solving problems that require empathy, judgement, and human conversation. Looking further ahead, we examine what the future of Contact Center as a Service could look like as AI agents become increasingly capable. Jonathan shares his perspective on how businesses should prepare for this shift, where automation fits alongside human expertise, and why success will depend on creating experiences that customers genuinely value rather than simply reducing costs. If your organisation is rethinking customer service, investing in AI, or planning the next stage of its contact center strategy, this conversation offers practical insight into where the industry is heading and what business leaders should be thinking about today. What role do you think AI should play in customer service? Where should businesses draw the line between automation and the human touch?

What happens when a company best known for garage door openers decides to compete with the biggest names in smart home technology? Can a business built on hardware reinvent itself as an AI-powered software company without losing the trust that made it successful in the first place? In this episode of Tech Talks Daily, I welcome Jeff Meredith, CEO of Chamberlain Group, to discuss one of the more fascinating business transformations happening in the technology sector. Chamberlain Group has spent more than 70 years building products that millions of homeowners rely on every day. Under Jeff's leadership, the company has expanded that heritage into intelligent access, creating a connected ecosystem through the myQ platform that now serves more than 15 million users worldwide. Jeff shares why leaving a successful career at Lenovo to join what many dismissed as "a garage door company" became the biggest leadership challenge of his career. Rather than following the comfortable route, he chose what he describes as the hardest path, helping reshape an established manufacturer into a technology business built around software, data, AI, and recurring customer relationships. Our conversation looks at what it really takes to attract software engineers, AI specialists, and data scientists into a company with an industrial heritage. Jeff explains why interesting problems often matter more than fashionable brands, and how Chamberlain Group's combination of trusted hardware, millions of existing customers, and ambitious software projects created an environment that appealed to top technical talent. We also spend time discussing leadership. Jeff believes the best leaders teach rather than direct, preferring to stand at the whiteboard alongside colleagues instead of issuing instructions from the corner office. He speaks openly about mistakes he made when joining the business, why vulnerability has strengthened trust across the organisation, and why admitting when you're wrong can become a strength rather than a weakness. Looking ahead, Jeff explains how AI could reshape intelligent access, moving beyond notifications to systems that understand patterns, recognise context, and help people secure their homes and businesses in smarter ways. Instead of viewing AI as technology searching for a purpose, he believes access control offers one of its most practical everyday applications. From leadership philosophy and organisational change to connected homes, AI, and the future of intelligent access, this conversation offers valuable lessons for anyone leading transformation inside an established business. What part of Jeff's story resonated most with you? Is the biggest challenge in business transformation changing the technology, or changing the mindset of the people building it?

Can organizations really call themselves AI-ready if their data foundations still have gaps? In this episode of Tech Talks Daily, I sit down with Dave Shuman, Chief Data Officer at Precisely, to discuss the findings from the company's latest State of Data Integrity and AI Readiness Report. Drawing on insights from more than 500 senior IT leaders across the US and Europe, Dave explains why many organizations are confident in their AI readiness while simultaneously identifying infrastructure, data quality, and governance as their biggest obstacles. Our conversation focuses on what Dave describes as the AI data integrity gap, the growing disconnect between ambitious AI initiatives and the quality, consistency, and context of the data powering them. We explore why successful AI projects often perform well in controlled pilot environments before struggling when deployed at scale, and why many organizations continue to underestimate the importance of data lineage, semantic layers, governance, and observability. Dave also shares why he believes data governance and AI governance should be treated as a single discipline rather than separate initiatives. We discuss how businesses can move beyond vanity metrics such as token usage and agent counts to focus on outcomes that genuinely matter, including revenue growth, cost reduction, customer experience, and risk management. As the conversation turns to the future of agentic AI, Dave offers a practical perspective on what autonomous systems will require of organizations and why trust in data will become increasingly important as AI assumes greater responsibility behind the scenes. If your organization is investing heavily in AI and looking for measurable business value, this episode offers a timely reminder that successful AI strategies begin long before the first model is deployed. They begin with data integrity. Based on Precisely's latest research, Dave explains why companies making progress are focusing less on the latest AI tools and more on laying the foundations that enable those tools to deliver reliable outcomes. What role does data integrity play in your organization's AI strategy, and are you confident your data is truly AI-ready?

Can technology and AI genuinely improve lives at scale, or are we still spending too much time talking about potential rather than outcomes? In this episode of Tech Talks Daily, I sit down with Hala Hanna, Executive Director of MIT Solve, as the organization marks its tenth anniversary. Over the last decade, MIT Solve has supported more than 500 innovators, helped solutions reach hundreds of millions of people worldwide, and connected founders with the funding, partnerships, and mentorship needed to turn ideas into lasting impact. Hala shares why the world is not suffering from a shortage of innovation. Instead, she argues that the real challenge is connecting talented problem-solvers with the resources and relationships that help ideas grow beyond the pilot stage. Drawing on lessons from nearly 30,000 applications and 100 innovation challenges, she explains why proximity to a problem often leads to better solutions and why founders with lived experience frequently outperform expectations. We also discuss the growing conversation around AI for good and how MIT Solve separates meaningful impact from marketing hype. Hala outlines the practical tests her team uses when evaluating AI-powered solutions and shares inspiring examples from healthcare, education, agriculture, and public services. From improving cancer diagnostics in underserved communities to digitizing centuries of public records and helping farmers access data through simple mobile devices, these stories show how technology can create tangible value when designed with people at the center. Another fascinating part of our conversation focuses on women in technology. With 64% of MIT Solve's supported teams led by women, Hala explains why this outcome is less about special treatment and more about removing barriers that have traditionally limited access to opportunity. We explore how open innovation challenges, diverse judging panels, and recognizing lived experience as expertise can help surface talent that conventional funding models often miss. Hala also offers a refreshing perspective on the future of AI, arguing that the next chapter should focus on inclusion, local relevance, and community ownership rather than simply building larger models and more infrastructure. Her examples of AI being used to preserve endangered languages and strengthen local sovereignty offer a powerful reminder that technology can support culture and identity as well as economic growth. If you've ever wondered what happens when innovation, purpose, and practical action come together, this conversation provides plenty of reasons for optimism. What role do you think technology should play in creating a fairer and more inclusive future?

What happens when software can be built and shipped faster than ever, but trust becomes the real challenge? In this episode of Tech Talks Daily, I sit down with Dean Hickman-Smith, Chief Revenue Officer at Testlio, to discuss why software quality has become a boardroom issue in the age of AI.  As organizations race to release new features, deploy AI-powered experiences, and automate development workflows, the question is no longer whether software ships successfully. The question is whether customers can trust what they receive. Dean explains why human testers remain an essential part of the software development process, even as automation and AI continue to advance. We explore the limitations of synthetic testing environments, the growing importance of cultural context and demographic representation, and why real-world user experiences often expose problems that automated systems miss. From voice interfaces and regional dialects to accessibility and personalization, the conversation highlights the growing complexity of delivering reliable digital experiences. We also discuss the rising business risks associated with poor software quality. While cybersecurity often dominates headlines, Dean argues that failed updates, inaccurate AI responses, poor customer experiences, and software outages can be equally damaging to brand reputation and customer loyalty. He shares insights from Testlio's work with global organizations and explains why human insight continues to complement AI-driven testing rather than compete with it. The conversation also looks ahead to a future where AI-generated code becomes increasingly common. Will software testing become fully automated, or will specialist human expertise become even more valuable? Dean offers his perspective on how AI, automation, and human judgment can work together to create better digital experiences while helping organizations avoid costly mistakes. If your organization is building AI-powered products, managing customer-facing applications, or trying to balance speed with quality, this episode offers practical insights into why software testing remains one of the most important parts of the development process. What role do you think humans will play in software testing as AI continues to advance? Share your thoughts.

What happens when a camera company starts thinking less about lenses and specifications and more about how people actually capture and share their lives? In this episode of Tech Talks Daily, I spoke with Max Richter from Insta360 about the company's journey from pioneering 360-degree cameras to building a much broader ecosystem of hardware, software, AI tools, and creator-focused workflows. While many people still associate Insta360 with immersive 360 content, the company has steadily expanded into action cameras, wearable cameras, webcams, creator tools, and enterprise applications that reach far beyond social media. Our conversation explored how Insta360's philosophy of "shoot first, frame later" challenged traditional assumptions about photography and video creation. Rather than worrying about angles, framing, or missing a moment, users can focus on the experience itself and decide later how they want to tell the story. That approach has helped shape products that are now used everywhere from family vacations and sports adventures to construction sites, virtual tours, education, and live broadcasting. We also discussed the growing role of artificial intelligence in the creative process. Instead of replacing creativity, Insta360 is using AI to remove many of the technical hurdles that often prevent people from sharing the content they capture. From automated editing and intelligent reframing to enhanced low-light performance and future cloud-based experiences, AI is becoming an important part of making professional-quality content creation accessible to a much wider audience. A major focus of our discussion was Luna, Insta360's new pocket gimbal camera developed in partnership with Leica. Max explained why this launch represents an important step for the company as it expands further into the creator market. Combining premium imaging capabilities, advanced stabilization, AI-powered features, and a highly portable design, Luna reflects Insta360's belief that creators increasingly care about the entire workflow, from capture through editing and publishing, rather than camera specifications alone. We also explored an increasingly common question: if modern smartphones are so capable, why would anyone need a dedicated camera? Max shared his perspective on why purpose-built devices still matter for travelers, vloggers, filmmakers, and everyday users who want a more immersive and intentional way to capture life's moments. From AI-powered storytelling and creator workflows to the future of wearable cameras and intelligent imaging, this conversation offers an interesting look at how one company is trying to shape the next chapter of visual content creation. How do you think AI will change the way we capture, edit, and share our stories over the next few years?

What happens when the technology keeping essential services running fails at the worst possible moment? When most people think about workplace technology, they picture laptops, smartphones, and office software. But for millions of workers maintaining power networks, repairing infrastructure, supporting emergency services, managing transport systems, and operating in remote environments, technology has a very different job to do. It has to work every single time, often in conditions where failure is simply not an option. In this episode of Tech Talks Daily, I speak with Alex Gittins from Getac about the changing world of field operations, rugged computing, and the growing role of Edge AI in supporting the deskless workforce. Alex explains why rugged technology is far more than placing a consumer device inside a protective case. From extreme temperatures and harsh weather to vibration, dust, poor connectivity, and demanding working environments, true rugged devices are engineered from the ground up to support people working where most technology struggles. We also discuss the often-overlooked reality that around 80% of the global workforce operates away from a desk. These workers are increasingly dependent on digital tools to receive work orders, access mapping systems, capture field data, complete inspections, and communicate with central teams in real time. The conversation also turns to Edge AI and its growing importance for frontline teams. Rather than relying on constant connectivity and cloud processing, Edge AI enables workers to access intelligence directly on their devices. Whether identifying damaged assets through image recognition, guiding inspections, reducing paperwork, or supporting faster decision-making, AI is becoming a practical tool for improving efficiency and safety in the field. Alex also shares how customer expectations are changing. Organisations are no longer buying devices in isolation. Instead, they are involving technology providers much earlier in the process to help design complete solutions that can support future operational requirements. From defence roots to modern field operations, this episode offers a fascinating look at the technology helping keep critical services running behind the scenes. How will AI, connectivity, and rugged computing continue to reshape the future of work for the billions of people who never sit behind a desk?

What happens when your AI ambitions collide with the reality of your infrastructure? Across boardrooms everywhere, agentic AI has quickly moved from experimental projects to strategic priority. The excitement is easy to understand. Business leaders see opportunities to automate workflows, improve decision-making, and increase productivity. Yet behind the headlines and product announcements sits a less visible challenge that many organizations are only beginning to understand. In this episode of Tech Talks Daily, I speak with Abby Strong, Chief Market Officer and Chief Customer Officer at Cribl, about the growing gap between AI ambition and operational readiness. Drawing on new research conducted with Harvard Business Review Analytic Services, Abby shares why so many organizations are struggling to move AI initiatives from pilot projects into production environments. The findings paint a fascinating picture. While almost every business leader surveyed views agentic AI as strategically important, only a small percentage believe they currently have both the strategy and infrastructure required to support it. At the heart of the challenge is data. As AI agents interact with systems, applications, and services, telemetry volumes are increasing at rates that many organizations never anticipated. In some cases, data volumes have doubled or tripled, creating unexpected infrastructure costs and operational complexity. Abby explains why telemetry, observability, and data management have become central to AI success. We discuss why AI systems are only as effective as the quality, accessibility, and context of the data available to them. She also shares real-world examples of how organizations are wrestling with growing infrastructure demands, rising costs, governance requirements, and the challenge of proving meaningful return on investment. Our conversation also examines the growing importance of visibility into AI activity. As enterprises deploy large language models and AI agents across their environments, security and observability teams are facing entirely new questions around monitoring, governance, compliance, and cost control. How do you establish a baseline when the technology itself is evolving so quickly? How do you maintain trust when AI systems generate vast numbers of automated queries and interactions? Abby offers a balanced perspective on what comes next. Rather than replacing existing systems overnight, many organizations are adding AI capabilities onto current workflows while gradually rethinking how work gets done. The result is a period of transition where businesses must support today's operations while preparing for a future that looks very different. If you're trying to understand why infrastructure readiness may become one of the biggest factors in AI success, this conversation provides valuable context. Are organizations focusing too much on AI models and not enough on the data foundations that support them? And what happens when the cost of AI adoption extends far beyond the AI tools themselves?

Have we become so used to data breaches that we no longer stop to think about what they actually mean for the people affected? In this episode of Tech Talks Daily, I speak with Simon Pamplin, CTO at Certes, about why cybercrime remains one of the biggest threats facing businesses and consumers alike. While headlines about ransomware attacks and data breaches appear almost every day, Simon argues that too many organizations are still treating cybersecurity as a technology problem rather than a business risk with real human consequences. Our conversation begins with a simple but powerful question. Why are so many companies still focused on protecting networks when attackers are really after the data itself? Simon explains why traditional perimeter-based security approaches are struggling in a world where information moves between cloud environments, devices, applications, and partners far beyond the boundaries organizations once controlled. We also discuss the personal cost of cybercrime. Behind every breach announcement are real people whose financial records, personal details, healthcare information, and digital identities may have been exposed. Simon shares why the impact often extends far beyond resetting a password, creating financial, emotional, and reputational consequences that can last for years. Another major theme is the growing concern about quantum computing and the rise of harvest-and-decrypt attacks. While fully realized quantum computing may still be in the future, cybercriminals are already collecting encrypted data with the expectation that future technology will eventually unlock it. Simon explains why businesses need to think about protecting sensitive information today rather than waiting for tomorrow's threats to become reality. The conversation also examines the growing pressure from regulations such as GDPR, DORA, and NIS2. With larger penalties and increased regulatory scrutiny, organizations are facing greater accountability for how they handle and protect customer information. Simon argues that trust has become one of the most valuable assets a business can possess and one of the easiest to lose. Of course, no cybersecurity discussion would be complete without addressing AI. We explore how AI is making attacks faster, cheaper, and more accessible while also creating opportunities for defenders. Simon shares his thoughts on why businesses must rethink long-held assumptions and prepare for a future in which cybercriminals can automate many techniques that once required significant expertise. Throughout our discussion, Simon returns to a consistent message. Attackers target data because it has value. Organizations that focus their efforts on protecting that data, wherever it travels, will be in a far stronger position than those relying solely on traditional defenses. If you are responsible for cybersecurity, risk management, compliance, or digital transformation, this episode offers a timely discussion of what businesses should prioritize as threats continue to evolve. Customer trust becomes harder to earn and easier to lose. When the next breach makes headlines, will it simply be another news story, or will it be a reminder that every piece of stolen data belongs to a real person whose life could be affected?

Can businesses still rely on cybersecurity strategies that were designed for a very different threat environment? In this episode of Tech Talks Daily, I speak with Matt Knell from ESET about why many managed service providers and businesses are being forced to rethink what effective cybersecurity looks like in 2026. As cybercriminals become faster, more sophisticated, and increasingly powered by AI, many of the approaches that once provided reassurance are struggling to keep pace. Matt shares why the idea of "good enough" security is becoming increasingly difficult to defend. While endpoint protection remains an important part of any security strategy, he explains why technology alone is no longer enough. Organizations must continually review, update, and strengthen their defenses rather than assuming that yesterday's protections will be sufficient tomorrow. Our conversation explores the lasting impact of ransomware and the lessons businesses continue to learn from high-profile incidents. From major retailers to global manufacturers, attacks are creating operational disruption, financial losses, and reputational damage on a scale that few organizations would have imagined a decade ago. We also discuss one of the industry's most persistent challenges: the cybersecurity skills gap. Finding experienced security professionals remains difficult, while retaining talent has become equally challenging. Matt explains how managed detection and response services are helping MSPs extend their capabilities without having to build and maintain large security operations teams. AI naturally plays a major role in the discussion. While cybersecurity vendors use AI to improve threat detection and response, attackers are also leveraging the technology to accelerate and sophisticate phishing campaigns, social engineering, and other forms of cybercrime. Matt explains why businesses must remain realistic about both opportunities and risks. Another theme throughout the episode is the growing expectation that cybersecurity should be treated as a business issue rather than purely an IT concern. Regulations, cyber insurance requirements, supply chain scrutiny, and customer expectations are all increasing pressure on organizations to demonstrate stronger security practices and greater resilience. We also discuss ESET PRIVATE and why more organizations are seeking security services tailored to their specific operational needs. Rather than relying on a standard package, many businesses are looking for solutions that align with their industry requirements, compliance obligations, risk profile, and long-term objectives. Finally, Matt reflects on the conversations emerging from ESET's recent partner conference and shares his perspective on the topics shaping cybersecurity priorities for the coming year. AI, resilience, compliance, and business education continue to dominate discussions as organizations look for practical ways to strengthen their defenses. If you're an MSP, IT leader, business owner, or anyone responsible for protecting digital operations, this episode offers a timely look at the challenges facing organizations today and the steps many are taking to prepare for what comes next. Is your organization still relying on security strategies designed for yesterday's threats, or have you adapted to today's cyber risks?

Have you ever wondered whether the skills that build a company are the same skills needed to scale it? In today's episode of Tech Talks Daily, I sit down with Hadi Moussa, the newly appointed CEO of Oyster, the global employment platform helping businesses hire, pay, and support talent in more than 180 countries. The conversation comes at a fascinating moment for the company, following founder Tony Jamous' decision to step into the Executive Chairman role and hand over the CEO position from a place of strength rather than necessity. What makes this leadership transition particularly interesting is that it challenges many assumptions about founder succession. Rather than waiting for investor pressure, market turbulence, or burnout, Tony recognized that the next chapter of Oyster's growth required a different operational skill set. Hadi shares what he learned from a succession process that centered on mission alignment, alongside leadership assessments, case studies, and extensive feedback. We also explore Hadi's own journey from Lebanon to leadership positions at Facebook, Airbnb, Deliveroo, Coursera, and now Oyster. His personal experience of leaving home to pursue opportunity has given him a deep connection to Oyster's mission of making global employment accessible regardless of geography. The discussion moves beyond leadership transitions and into the future of work itself. As artificial intelligence reshapes hiring, productivity, and workforce structures, Hadi explains why he believes there is a real risk that AI could concentrate opportunity within a handful of established technology hubs. He shares Oyster's vision of using technology to more broadly distribute opportunity, enabling companies to access talent wherever it exists while maintaining trust, compliance, and human support. We also discuss what businesses continue to underestimate about managing distributed teams at scale. From culture and communication to trust and compliance, Hadi argues that remote work success requires far more than technology alone. Companies must be intentional about how they build relationships, create alignment, and support employees across borders and time zones. For founders and business leaders, this episode offers thoughtful lessons on self-awareness, leadership evolution, and knowing when a company's needs may outgrow the strengths that originally built it. It is a conversation about growth, opportunity, and the difficult decisions required to put mission ahead of personal attachment. How should leaders know when it is time to pass the baton, and can AI help create a more globally distributed future of work rather than concentrating opportunity in a few select places? Share your thoughts and join the conversation.

Are organizations investing enough in cybersecurity, or are they simply spending more money while falling further behind? In this episode of Tech Talks Daily, I speak with Martyn Ditchburn, CTO in Residence for EMEA at Zscaler, about the findings from the company's latest Ripple Effect Report and what it reveals about the growing gap between cybersecurity investment and true organizational resilience. Drawing on insights from more than 1,700 IT leaders across 14 countries, Martyn explains why many organizations are still struggling to adapt to a threat landscape that is evolving faster than their security strategies. While cyber resilience budgets continue to rise, many leaders admit their approach remains too inward-looking, leaving critical vulnerabilities across supply chains, cloud environments, third-party ecosystems, and emerging AI deployments. We explore why shadow AI is rapidly becoming the new shadow IT challenge, with employees adopting AI-powered tools faster than governance frameworks can keep pace. Martyn discusses how AI is quietly being embedded into countless business applications, creating visibility and security challenges that many organizations have yet to recognize fully. The conversation also examines the growing importance of supply chain resilience. As businesses become increasingly dependent on external providers, cloud platforms, and interconnected digital services, traditional security perimeters continue to disappear. Martyn shares why third-party risk remains one of the biggest blind spots in modern cybersecurity programs and how organizations can better understand their expanding attack surface. Agentic AI is another major focus of our discussion. As AI systems move beyond assisting users and begin taking autonomous actions, security teams face entirely new challenges around identity, governance, accountability, and risk management. Martyn explains why many organizations are racing ahead with adoption while still lacking the guardrails needed to manage these emerging technologies safely. We also discuss lessons from previous technology shifts, including cloud computing and shadow IT, and why history keeps repeating itself when innovation outpaces security planning. Martyn offers practical advice on limiting risk, reducing blast radius through segmentation, and treating AI agents as digital identities that require the same controls and oversight as human users. As organizations pursue AI-driven growth and competitive advantage, are they building resilience into their foundations or creating new risks they cannot yet see? And in a world where AI is becoming embedded in everything, how can security leaders stay ahead of threats that are evolving faster than ever before?

Can businesses embrace AI without surrendering control over their data, technology choices, and future direction? In this episode of Tech Talks Daily, I sit down with Sachin Agrawal, Managing Director of Zoho UK, to discuss one of the biggest challenges facing organizations today. As AI adoption accelerates, many leaders are finding themselves caught between the pressure to innovate and the responsibility to maintain trust, transparency, and control. Sachin shares his perspective on what separates successful AI adoption from costly experimentation. Drawing on his experience leading Zoho's growth in the UK, he explains why organizations achieving the best results are focusing on clearly defined business outcomes rather than chasing headlines or reacting to fear of missing out. We discuss how AI is already improving customer service, sales operations, application development, and decision-making, while also highlighting the importance of digital maturity as a foundation for meaningful AI success. A major theme throughout our conversation is the growing concern around black-box AI systems. Sachin explains why transparency, explainability, and contextual intelligence are becoming increasingly important for businesses operating in regulated environments. We explore how organizations can build trust by keeping AI close to the systems where their data already resides, thereby creating more auditable, accountable outcomes. The discussion also turns to digital sovereignty, a topic that has rapidly moved from technical teams into boardroom conversations. Sachin outlines the different dimensions of sovereignty, including data residency, infrastructure, model choice, intelligence ownership, and vendor flexibility. As geopolitical tensions, regulatory expectations, and concerns about technology concentration grow, organizations are taking a closer look at how dependent they want to become on a small number of technology providers. We also examine whether AI will strengthen the dominance of major technology firms or create new opportunities for diverse software providers. Sachin argues that while the largest players may own much of the underlying infrastructure, customers are increasingly focused on practical outcomes, transparency, and flexibility rather than simply choosing the biggest platform. Along the way, we discuss cloud fragmentation, governance, responsible AI adoption, data privacy, and the importance of challenging AI rather than unquestioningly trusting its outputs. Sachin offers practical advice for leaders who want to balance innovation with accountability while maintaining independence in an increasingly interconnected technology environment. As AI continues to reshape business software and digital operations, how can organizations remain agile without sacrificing control? And what role will digital sovereignty play in determining who succeeds in the next era of enterprise technology?

What happens when the weakest link in your technology supply chain becomes the entry point for a national security incident? In this episode of Tech Talks Daily, I welcome back Haydn Brooks, CEO and founder of Risk Ledger, to discuss why supply chain security has moved from an IT concern to a boardroom and government priority. As organizations race to adopt AI, connect more systems, and depend on increasingly complex ecosystems of vendors, partners, cloud providers, and third-party services, the attack surface continues to expand in ways many businesses still struggle to understand. Haydn explains why supply chains remain one of the largest blind spots in cybersecurity, despite years of warnings and a growing list of high-profile incidents. We explore how attackers increasingly target smaller suppliers that lack the resources and expertise of larger enterprises, using them as stepping stones to reach critical infrastructure, government agencies, and major corporations. The conversation also examines how AI is reshaping the risk equation. As organizations rapidly integrate AI tools, APIs, and third-party models into existing technology stacks, many are creating new forms of concentration risk. What happens when multiple services rely on the same AI provider? And how can businesses maintain visibility over technology dependencies that are constantly evolving? Haydn shares his perspective on why collaboration and information sharing have become far more common across the cybersecurity community, and why security leaders are beginning to recognize that defending against modern threats requires collective action rather than isolated efforts. We also discuss accountability, resilience, and why organizations must move beyond simply identifying risk and develop the ability to understand the impact of incidents when they occur. Along the way, Haydn offers practical advice for security leaders, explains why now is the time to reassess supply chain security strategies, and shares insights into Risk Ledger's international expansion as the company grows its presence in the United States. As AI accelerates innovation and organizations become increasingly interconnected, are businesses truly prepared for the risks that come with that progress? And could an overlooked supplier become the starting point for the next major cybersecurity crisis?

Have you ever stopped to think about the technology powering almost every text box you interact with online? Whether you're applying for a job, drafting a legal contract, publishing content, or updating a website, there's a good chance a rich text editor is quietly working behind the scenes. In this episode of Tech Talks Daily, I caught up with Fredrik Danielsson, Product Manager at TinyMCE, to discuss how one of the internet's most widely used editing platforms is evolving for the AI era. Frédéric shares the remarkable story behind TinyMCE, a tool that traces its roots back to the early days of the web and has played a role in creating much of the internet's human-generated content. From the days of hand-coded websites and Flash applications to today's AI-powered content workflows, we explore how the company has continually adapted to changing developer and user needs. Our conversation focuses on the launch of TinyMCE AI and why the company believes artificial intelligence belongs inside the content creation experience rather than in a separate chatbot window. We discuss the hidden productivity costs of constantly switching between applications, copying and pasting content between AI assistants and business tools, and why bringing AI directly into the editor creates a more natural and efficient workflow. We also examine the growing challenges around AI governance, content ownership, compliance, and accountability. As organizations race to adopt AI tools, how can they maintain visibility into which content was AI-assisted, who made changes, and how information flows through the business? Frédéric explains why features such as revision history, track changes, and audit trails may become increasingly important as regulations and expectations mature. Along the way, we discuss context-aware AI, model flexibility, developer experience, and the future of content creation. Frédéric also shares his thoughts on why AI adoption is becoming more natural for everyday users and what the next phase of AI-powered productivity could look like as these tools become deeply embedded in the software people already use. If AI is changing how we create, edit, review, and collaborate on content, what happens when the editor itself becomes the smartest participant in the room? And how will that reshape the way we work over the next few years?

Have you ever wondered why political campaigns can send millions of text messages but still struggle to have meaningful conversations with voters? In this episode of Tech Talks Daily, I sit down with Tom Carroll, Co-Founder of Convos, a startup rethinking how political campaigns communicate through SMS. While political texting has become a standard part of modern campaigning, Tom argues that the industry has spent years solving the problem of message delivery while largely ignoring what happens when voters actually respond. We explore how Convos is building a conversational SMS infrastructure that helps campaigns manage thousands of voter interactions simultaneously. Rather than focusing solely on message volume, the platform analyzes replies, identifies sentiment and alignment, prioritizes urgent conversations, and helps campaigns understand what voters are really talking about. Tom shares how this approach is helping campaigns move beyond one-way broadcasts and toward genuine engagement at scale. During our conversation, we discussed why traditional political texting often breaks down once campaigns begin receiving large volumes of replies, how AI-powered conversational systems can help manage those interactions responsibly, and why transparency remains essential when introducing AI into political communications. Tom also explains how Convos uses campaign-approved knowledge bases and multiple validation checks to reduce misinformation and maintain message consistency. We also examine the broader implications of conversational AI in politics, from voter education and turnout efforts to balancing automation with authenticity. Tom shares examples of how campaigns have used conversational SMS to answer voter questions, provide election information, and create opportunities for meaningful engagement without overwhelming campaign staff. As AI continues to influence how organizations communicate with large audiences, this conversation offers an interesting look at how technology can help people listen at scale rather than talk louder. What role should AI play in political engagement, and where should the line be drawn between helpful voter communication and automated persuasion? Share your thoughts and join the conversation.

What happens when one of the world's fastest-growing travel platforms decides the future of business travel will be built around AI from the ground up? In this episode of Tech Talks Daily, I sat down with Navan co-founder and CTO Ilan Twig to discuss how the company is reshaping travel, payments, and expense management through AI-native systems designed for the real world, not just polished demos. What immediately stood out during our conversation was Ilan's mix of technical obsession and relentless focus on user experience. This is someone who isolated himself for months to truly understand the mechanics of large language models before most companies had even worked out what ChatGPT meant for their business. That curiosity now powers Navan's AI strategy, where conversational interfaces are replacing what Ilan calls the old "forms and tables" model of software interaction. We explored how Navan's AI assistant, Ava, is already handling thousands of real-world travel support conversations every day, with customer satisfaction scores that rival those of human agents. During major disruption events like Storm Fern and the Heathrow airport fire, Ava scaled instantly, resolving huge volumes of customer requests without the delays and staffing nightmares that traditionally overwhelm travel providers. But this conversation goes much deeper than travel. Ilan shared his thoughts on why the software industry is moving toward conversational, context-aware interfaces, why most businesses still misunderstand what agentic AI actually means, and how Navan is building proprietary models trained on its own travel data to outperform larger, generic frontier models. We also discussed trust, hallucinations, AI supervision layers, and why companies must stop treating AI as a magic trick and start measuring it against hard business outcomes. There is also a fascinating human side to this episode. From building a company through market turbulence, investor skepticism, and geopolitical uncertainty, to challenging accepted thinking since his school days, Ilan's story reflects the mindset of someone who genuinely believes technology should solve real problems rather than create headlines. If you have been wondering where AI moves beyond hype and starts delivering measurable operational value, this conversation offers a rare look behind the curtain from someone building these systems at scale every single day. Useful Links Connect with Ilan Twig Learn more about Navan Check out blog posts by Navan Follow Navan on LinkedIn Visit our Sponsors Check out the Nordlayer Browser Learn more about Denodo Data Products  

What Happens When AI Starts Protecting the Power Grid Before Humans Even Spot the Problem? In this episode of Tech Talks Daily, I speak with Kaitlyn Albertoli, co-founder and CEO of Buzz Solutions, about how AI, drones, and computer vision are changing the way utilities inspect and maintain power infrastructure. As weather events become more frequent and energy demand continues to rise from EV adoption, renewable energy growth, and AI-driven data centers, utilities are under growing pressure to modernize systems that were built decades ago. Kaitlyn explains how utilities once relied on crews walking transmission lines with binoculars and handwritten notes before moving toward helicopter inspections and aerial imaging. Today, autonomous drones and aircraft can capture hundreds of thousands of inspection images every year. The real challenge now is turning that mountain of visual data into useful action before damaged equipment leads to outages, fires, or safety risks. We discuss how Buzz Solutions processes enormous image datasets in hours instead of weeks, helping utilities identify damaged insulators, corrosion, vegetation risks, and failing components before they become larger problems. We also talk about the people behind the infrastructure. Kaitlyn shares why AI should support frontline workers rather than replace them, especially as utilities face an estimated shortage of thousands of skilled linemen over the next several years. The conversation covers balancing false positives with missed detections, reducing operational data silos, and why partnerships with companies like Skydio and Esri are helping utilities connect inspection workflows more effectively. Kaitlyn also shares how Buzz Solutions is expanding into solar inspections, where AI can detect damaged or underperforming panels before warranties expire and energy production quietly drops over time. Alongside the technology discussion, she reflects on how competing in the 2012 U.S. Olympic Trials shaped the resilience and mindset she now brings to building a fast-growing AI company. From wildfire prevention and storm recovery to renewable energy operations and autonomous inspections, this episode looks at how AI is quietly becoming part of the infrastructure keeping modern society running. As utilities modernize aging systems under growing environmental and operational pressure, can AI help prevent the next major outage before it happens?

What happens when the cybersecurity industry stops debating whether agentic AI is a future problem and starts treating it as a present-day reality? In this episode of Tech Talks Daily, I sit down with Tim Freestone to unpack the biggest shift coming out of this year's RSA Conference. After attending RSA for more than two decades, Tim describes 2026 as the year the energy returned to the cybersecurity world, driven by one unavoidable topic: agentic AI. We explore why the conversation has rapidly evolved from curiosity to urgency, and why organizations are suddenly confronting an uncomfortable truth. AI agents are already operating inside businesses, often without visibility, governance, or control. Tim explains how shadow AI is spreading faster than many leadership teams realize, with employees experimenting with autonomous tools that connect directly to company data and external AI models. Our conversation also looks at the growing gap between visibility and control. Security teams may be discovering agents across their networks, but stopping risky behavior is an entirely different challenge. Tim argues that companies focusing purely on infrastructure are already falling behind, and that the real battleground is now the data layer itself. We discuss why data governance, audit trails, and access controls are becoming central to the future of cybersecurity strategy. Tim also shares his thoughts on state-sponsored AI threats, the rise of autonomous espionage operations, and why open-source AI models present a completely new level of risk for defenders. At the same time, he offers practical advice for IT and security leaders trying to figure out where to start amid the noise, complexity, and endless flood of new tools entering the market. If your organization is trying to understand how AI changes cybersecurity, governance, compliance, and risk management, this conversation offers a clear look at what security leaders are actually worried about right now, and why the next 12 months may redefine how companies think about protecting data altogether. Useful Links Connect with Tim Freestone Learn More About Kiteworks Data Security and Risk Report Kiteworks Substack Kiteworks LinkedIn Newsletter Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What if the cybersecurity industry has spent decades fighting the wrong battle? In this episode of Tech Talks Daily, I sat down with Benny Czarny, founder and CEO of OPSWAT, to discuss why he believes the traditional "detect and respond" model is no longer enough in a world where AI is accelerating cyber threats faster than security teams can react. Benny joined me to discuss his new book, Cybersecurity Upside Down, which combines personal stories from building OPSWAT with a bold argument for rethinking how organizations approach cyber defense altogether. His central belief is simple but provocative: detection-based security has trapped the industry in a losing cycle in which attackers need to succeed only once, while defenders are forced into a constant state of reaction. During our conversation, Benny explained how his thinking evolved after realizing that even layering dozens of antivirus engines and sandboxing technologies still failed to stop malicious files reliably. That realization ultimately pushed him toward a prevention-first philosophy built around Deep Content Disarm and Reconstruction, or CDR. Rather than trying to determine whether a file is malicious, the approach assumes files may already be dangerous and regenerates clean, safe versions before they ever reach users or systems. We also explored how generative AI is changing the cybersecurity landscape in ways many organizations still underestimate. Benny shared why AI is dramatically reducing the time required to create malware, weaponize exploits, and scale attacks, effectively giving even inexperienced attackers capabilities once reserved for nation states or advanced cybercriminal groups. He also raised concerns that AI data lakes could become contaminated with malicious content, creating entirely new risks for organizations rushing to deploy large language models without securing the data feeding them. One of the most fascinating aspects of the discussion was the psychology and culture within cybersecurity teams. Benny argued that the industry often celebrates visible incident response activity while undervaluing quiet prevention. In a world dominated by alerts, dashboards, and SOC metrics, truly preventing attacks can almost appear invisible, despite potentially delivering far greater security outcomes. We also talked about the sectors Benny believes are most exposed today, including energy, manufacturing, and critical infrastructure operators that still rely heavily on reactive security models while facing growing operational and regulatory complexity. He explained why some industries are advancing faster than others and why compliance mandates could become a major catalyst for broader prevention-first adoption. Beyond cybersecurity itself, this episode also offered a fascinating look into Benny's entrepreneurial journey, what he learned building OPSWAT over two decades, how AI helped him research and structure his book, and why he is now even producing a cybersecurity-focused TV series called Into the Breach, designed to make complex security concepts easier for wider audiences to understand. This conversation challenges many of the assumptions the cybersecurity industry has normalized for years. Whether you work in security, IT leadership, compliance, or want to understand how AI is reshaping digital risk, this episode offers a very different perspective on what modern cyber resilience could look like in practice.