The Tech Blog Writer Podcast

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/

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

How quickly should an AI investment begin producing measurable business results? In this episode, I speak with Monica Kumar, Executive Vice President and Chief Marketing Officer at Extreme Networks, about the growing pressure on technology leaders to prove that AI investments are producing financial and operational value. The conversation draws on Extreme Networks' State of AI for Networking 2026 report, based on a global survey of 200 C-level executives and vice presidents of IT. The findings suggest that enterprise AI has entered a far less forgiving phase. Experimentation continues, but executives increasingly want evidence that deployments are reducing costs, improving productivity or creating better user experiences. The most striking result is the speed now expected. Some 57 percent of respondents said they expect measurable AI impact within weeks or sooner, compared with 16 percent in the previous year. Projects that once might have received six or 12 months to demonstrate value may now face questions within 30 or 60 days. Monica explains why these demands are changing which AI projects receive attention. Leaders are looking for use cases connected with existing operational problems, where results can be measured and communicated clearly. This makes enterprise networking an interesting test case. AI workloads depend on network compute, bandwidth, availability and access to current data. According to the research, 92 percent of respondents said AI is increasing demands on network compute and bandwidth. A fragmented or outdated network may therefore limit the performance of the AI applications running across it. The network can also provide an early opportunity to show what AI produces in practice. Monica discusses performance monitoring, predictive analysis, troubleshooting, compliance checks, capacity planning and security. These are repetitive, data-heavy activities where improvements can be measured in time saved, fewer support tickets and better service availability. A case from Middlesbrough College brings those claims into focus. The college reports that firmware tracking fell from as much as five hours each week to approximately five minutes, while the time spent troubleshooting decreased by around 90 percent. For a small network team, the value comes from giving people additional capacity without asking them to monitor every device or event manually. We also discuss why AI capabilities work better when embedded within normal business systems rather than added as another standalone tool. If an AI service remains outside the daily workflow, employees must move between platforms, transfer information and interpret the result themselves. Integrated AI can monitor the network, identify anomalies, recommend action and automate routine work within the environment where the team already operates. Monica also warns that the quality of AI depends heavily on its data. Before investing in another model or application, businesses need accurate, current and well-managed information. They must also examine whether their network has the capacity to support additional workloads and whether employees understand how to use AI responsibly. If executives expect AI results within weeks, are businesses selecting the right use cases, or simply imposing unrealistic deadlines on complicated technology programs? Listen to the episode and share your thoughts with me.

Could the disaster recovery plan designed to protect your company make a ransomware incident even worse? In this episode, I speak with Darren Thomson, Vice President and Chief Technology Officer for EMEA at Commvault, about Resilience Operations, commonly known as ResOps, and why cyber recovery now requires security, infrastructure, identity and data teams to work from one coordinated plan. Darren argues that many companies are accepting a difficult reality. Even with considerable investment in prevention and detection, a breach may eventually succeed. That does not make cybersecurity controls any less necessary, but it means recovery can no longer be treated as a secondary activity managed by another department. The problem is that security operations and infrastructure teams have traditionally worked toward different objectives. Security specialists concentrate on identifying and stopping threats. Infrastructure teams protect data, maintain backups and restore systems after outages. During a cyberattack, a successful recovery requires both sets of expertise. A backup administrator may be able to restore data quickly, but a forensic specialist must establish whether that data is clean. Without that confirmation, the company risks restoring malware and restarting the incident. Darren explains why a conventional disaster recovery plan may be particularly dangerous during ransomware. These plans were commonly designed for physical failures such as a lost data center. Data would be copied from one location to another so operations could continue. If the source data is infected, however, fast replication can carry the malware into the recovery environment. This is where ResOps enters the discussion. Darren describes it as an operating model rather than a product. It combines established practices from security and infrastructure management into a continuous program for testing, learning and improving recovery. Individual technology projects may come from the program, but resilience itself never reaches a final completion date. AI adds pressure on both sides. Criminals can use it to create faster and more effective attacks, while defenders can use machine learning to inspect large volumes of information, detect patterns and identify the newest clean recovery point. Companies must also protect AI systems as they would any other business application, including the models, data repositories and identities connected with them. Darren offers one practical starting point for CIOs and CISOs: Mean Time to Clean Recovery, or MTCR. This measures how long it takes to restore an application and its data with evidence that both are free from compromise. Before measuring MTCR, leaders must define their minimum viable company. These are the systems and services the business cannot operate without. Once that list exists, teams can test how long a verified clean recovery would take and replace assumptions with evidence. The initial answer may be uncomfortable. Teams may know how to restore an application without knowing whether the backup is clean. Security may know how to inspect the system but lack an established workflow with the recovery team. Darren sees those gaps as the starting point for a useful ResOps program because they provide everyone with a shared problem and a measurable objective. If your most important systems disappeared today, how long would it take to bring the minimum viable company back using verified clean data? Listen to the episode and share your answer with me.

Why can we track a meal traveling across town almost minute by minute, yet an international parcel can seemingly disappear between checkout and delivery? In this episode, I speak with Dieter Van Putte from Crossborder Global, a division of Bnode that provides global commerce and logistics services. Dieter oversees technology and operations across Landmark Global and Apple Express, giving him a close view of what happens when retailers attempt to sell and deliver products across international markets. Dieter explains why cross border delivery remains so difficult to track despite years of investment in supply chain technology. A domestic shipment may involve one main carrier, but an international order can pass between warehouses, road carriers, airports, airlines, customs authorities and final-mile delivery companies. Each participant may record events differently, operate at a different level of technical maturity or provide information at a different speed. We discuss why the customer rarely sees this complexity. They simply expect to know when their order will arrive, what duties and taxes they must pay, plus what will happen if the product needs to be returned. When retailers cannot provide those answers, the problem quickly becomes one of trust rather than logistics alone. Dieter also describes the less visible cost of manual reconciliation. Teams can spend hours checking carrier websites, comparing rates, reviewing spreadsheets, correcting customs information and translating inconsistent tracking events. Mistakes create further work through delayed parcels, billing disputes, customer inquiries and complaints. The direct labor cost matters, but the effect on repeat purchases and customer reviews may prove even more expensive. Our conversation then turns to the role of automation and AI. Dieter explains how technology can support product classification, customs documentation, landed-cost calculations and exception handling. We also examine logistics control towers, which combine data from multiple parties to provide an end-to-end view of an order. With enough reliable information, these systems can detect patterns, predict delays and recommend or initiate corrective action before the customer is affected. There is plenty of promise here, but good software cannot remove every customs rule, carrier handoff or data-quality problem. Retailers still need the right partners, accurate product information and a clear understanding of the promise they are making at checkout. Could better use of data finally make international delivery feel as dependable as domestic shipping, and what would need to change first? Listen to the conversation and share your thoughts with me.

Do you need to understand code before you can build a technology company or lead a team of developers? Five years after our previous conversation, I welcome Sophia Matveeva back to the podcast. Sophia is the founder of Tech for Non-Techies, where she helps founders and business professionals understand how technology products are created, tested, managed and turned into commercial ventures. A great deal has changed since we last spoke. Generative AI tools can now turn a written description into a working prototype within hours. For someone who has spent years believing a lack of coding experience disqualified them from building a technology business, that removes a significant barrier. Sophia believes this is the best time yet to be a non-technical founder, although her reasoning goes beyond AI-assisted coding. Research into billion-dollar technology companies shows that non-technical founders now make up a much larger share of founding teams than they did a decade ago. Many of these businesses sell technology to other companies, where commercial knowledge, customer relationships and an understanding of industry problems matter enormously. We discuss where AI belongs in the founder journey. Sophia recommends using tools such as Lovable or Replit to create a simple test product, show it to potential customers and learn whether people would use or pay for the idea. This allows founders to test their assumptions before committing substantial money to development. The boundary appears when that prototype becomes a real product. Once software stores customer information, processes payments or supports a commercial service, security and technical architecture cannot be treated as optional details. Sophia argues that professional developers are still needed to inspect the code, prepare the product for production and address problems a non-technical founder may not know exist. Her point is simple. AI can help a founder reach the testing stage sooner and at a lower cost. It cannot tell someone with no engineering experience whether the generated code is safe, maintainable or ready to support paying customers. Sophia also shares what she learned from managing her first development team. After raising investment, she attempted to compensate for her technical insecurity by taking a coding course and becoming involved in work she did not fully understand. The result was micromanagement, constant interruptions and frustrated developers. A better approach begins with business priorities. Founders should explain what customers want, ask developers about effort and tradeoffs, agree on what will be delivered during the next work cycle, then give the team the space required to complete it. They should also allow time for technical debt, the less visible maintenance work that prevents hurried development from creating larger problems later. We finish with advice for any business leader who wants greater technology fluency. Sophia recommends joining product meetings, contributing customer knowledge and building relationships with technical colleagues who want to understand the commercial side of the company. Neither side needs to become the other. They need enough shared language to make better decisions together. If AI has removed the cost of testing many technology ideas, what is stopping you from finding out whether yours could work? Listen to the episode, try Sophia's exercise and share your experience with me.

What happens when an AI agent begins influencing business decisions without fully understanding the systems, processes and dependencies behind them? In this episode, I speak with Bert van der Zwan, CEO of Bizzdesign, about the gap between enterprise AI expectations and the results many companies are seeing in practice. Bert has spent more than 25 years in software and SaaS leadership, including executive roles at Webex, Bynder, Twinfield and Unit4. Bert offers a candid assessment of the current AI market. He believes AI will have a lasting effect on businesses and society, but argues that expectations for near-term financial returns have become inflated. Many companies are spending money on tools and experimentation without reducing costs, consolidating software or producing new revenue. That does not mean experimentation is a mistake. Bert sees it as a necessary stage. The harder question is how companies move from a growing collection of pilots to AI capabilities that can operate dependably inside the business. One barrier is fragmented organizational context. Large enterprises have often grown through a combination of internal expansion and acquisitions, leaving behind disconnected applications, inconsistent data definitions and processes that cross several departments. An AI system working with only part of that picture may make a fast decision, but that does not make it a good decision. Bert argues that AI needs an authoritative view of how the enterprise works. Systems, processes, ownership, dependencies, approval status and policy restrictions must be visible and consistently defined. Without that shared context, AI may reproduce existing silos or make them worse. We also discuss the risks boards and technology leaders should consider as AI agents become involved in operational decisions. These include unreliable data, unclear accountability, legal exposure, weak governance and an incomplete view of the process being changed. Human oversight remains necessary, particularly when an automated decision could affect customers, employees or major investments. Bert then introduces the idea of "bespoke from the cloud." Traditional SaaS products were built around largely standardized interfaces and workflows. AI-assisted development could make software far easier to personalize around individual customers and use cases. This may give users greater control, but it could also challenge long-term software contracts and the economics that have supported the SaaS market. For leaders trying to connect AI spending with business results, Bert recommends beginning with visibility and a clearly defined outcome. Every initiative should be judged by whether it reduces costs, increases revenue or shortens the time required to deliver value. If AI depends on understanding how a company actually works, have businesses invested enough in creating that shared understanding before adding agents to their operations? Listen to the episode and share your thoughts with me.

Can you still trust an incoming phone call when AI can imitate a familiar voice, personalize the conversation and target information specifically to you? In this episode, I speak with Alex Quilici, CEO of YouMail, about how artificial intelligence is changing phone fraud and why the personal devices carried by employees are becoming part of the corporate attack surface. Alex explains how YouMail uses data from its consumer call-protection service to identify scam behavior, understand the type of fraud taking place and connect those patterns with the phone numbers involved. Advances in large language models have improved this analysis, but the same technology is also helping criminals build far more convincing campaigns. Generic robocalls are being replaced by personalized conversations designed to extract information, impersonate trusted people and manipulate victims. Fraudsters can use AI throughout the attack chain, from identifying targets and analyzing stolen data to generating dialogue and adapting an approach during the call. Alex argues that attackers have adopted these capabilities faster than many defenders expected because successful fraud produces an immediate financial return. The conversation also examines why voice biometrics can no longer be treated as sufficient proof of identity. As voice-cloning tools improve, companies may need to combine multiple forms of authentication and move sensitive communications into trusted applications. A call received through a banking app, for example, could give the customer greater confidence that the caller really represents their bank. For businesses, the risk extends beyond company-managed technology. Attackers can identify where someone works, learn about their role and contact them through a personal phone that may sit outside corporate monitoring. An employee's private number can therefore provide another route into the business through impersonation and social engineering. Alex also makes a persuasive case for collecting less personal data. Personalization can improve a service, but every additional piece of information becomes something an attacker might obtain during a breach. His advice is to identify the minimum information needed to deliver the intended experience rather than gathering data simply because it may prove useful later. Despite the seriousness of the threat, Alex offers evidence that coordinated action can produce results. He has seen brand-impersonation campaigns reduced from tens of millions of calls each month to around 100,000 through monitoring, disruption and cooperation between businesses and telecommunications providers. If AI is making fraudulent calls harder to recognize, should businesses stop treating the telephone network as a trusted communication channel by default? Listen to the episode and share your thoughts with me.

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.

What if one of the biggest obstacles to digital transformation isn't your technology stack, but the agreements connecting it all together? Recorded live at Docusign Momentum in London, this episode continues my conversations from the show floor by looking at one of the most overlooked challenges facing modern organisations. Companies have spent years investing in CRM platforms, ERP systems, HR software and cloud infrastructure, yet many of the agreements linking those systems together still rely on manual processes, email chains and static documents. Joining me is Stéphane Barberet, President of EMEA at Docusign. Having spent more than three decades helping organisations across Europe use technology to improve the way they work, Stéphane shares why he believes agreements have become one of the biggest blind spots in enterprise transformation and how AI is beginning to change that. We discuss why organisations are starting to view agreements as business intelligence rather than administrative paperwork, where businesses unknowingly lose value after contracts have been signed, and why removing friction from everyday workflows often delivers greater returns than simply introducing another AI tool. Stéphane also explains why organisations across financial services, healthcare, manufacturing and many other industries are all asking the same questions about AI, how leaders should approach adoption without trying to automate everything at once, and why measurable business outcomes matter far more than launching ambitious AI programmes. Throughout our conversation, we also explore how executives should measure success, what separates organisations making genuine progress from those still experimenting, and why the future of AI may be one where the technology becomes almost invisible, quietly improving the way businesses operate every day. After spending the day speaking with customers, executives and attendees at Momentum, one message kept coming back to me. The organisations creating the greatest value from AI aren't chasing the latest trend. They're solving meaningful business problems, building trust and helping their people spend more time on work that truly matters. Where do you see the biggest opportunities to remove friction from the way your organisation works? I'd love to hear your thoughts after listening and continue the conversation.  

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.

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.

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 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 if the most valuable business intelligence in your organisation has been hiding inside your contracts all along? Recorded live at Docusign Momentum in London, this episode explores why enterprise AI is at an inflection point. After years of focusing on what AI can create, the conversation is shifting towards how AI can help organisations understand the information they already have, remove friction from everyday work, and make better business decisions. Joining me is Allan Thygesen, CEO of Docusign. While many people still associate Docusign with electronic signatures, Allan explains why the company's vision has expanded far beyond that single moment in the agreement process.  Every business runs on agreements, from customer contracts and supplier relationships to employee onboarding and partnerships. The real opportunity, he argues, is transforming those agreements from static documents into business intelligence that helps organisations improve performance, reduce risk and uncover value that has often remained hidden for years. We discuss why decades of historical agreements are becoming increasingly valuable in the age of AI, how organisations are using agreement intelligence to shorten sales cycles from days to hours, and why understanding the context behind agreements can be just as important as the agreements themselves. Allan also shares his perspective on the rise of agentic AI, explaining why trust, governance and compliance will ultimately determine how quickly organisations allow AI to take on greater responsibility. Rather than viewing AI as another standalone tool, he explains why the future lies in connecting trusted data and workflows across the systems businesses already use every day. Throughout our conversation, we also explore why Docusign continues to build an open ecosystem through partnerships with companies including Anthropic, Harvey, Legora, OpenAI, Google, Salesforce, SAP and Thomson Reuters, enabling organisations to work with agreement intelligence wherever work already happens. After spending the day at Momentum hearing from customers like Harvey, Legora, Experian and AON, executives and attendees, one theme kept emerging. The organisations creating the greatest value from AI are not necessarily adopting the most AI. They're using it to remove friction, improve decisions and help people focus on work that genuinely benefits the business. Does your organisation still think of contracts as administrative files, or are they becoming a strategic source of business intelligence? I'd love to hear your thoughts after listening and continue the conversation.  

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.

What happens when an industry managing more than $150 trillion is still held back by decades-old systems, manual work, disconnected data, and highly paid experts spending hours on tasks AI could complete in seconds? In this episode, I speak with Markus Ruetimann, a member of the Experion Technologies Advisory Board and former Global Chief Operating Officer with more than three decades of experience in institutional asset management, alongside Siraj Alimohamed, Global Head of Data and AI at Experion Technologies. We begin with a simple question. What does an asset manager actually do with our pensions, savings, and investments every day? Markus takes us through the investment process, from research and stock selection to portfolio construction, trading, settlement, performance analysis, and regulatory reporting. Along the way, we examine where time, money, and expertise are being lost. Siraj then explains composable AI through one of the clearest analogies I have heard. Think of building with Lego bricks rather than creating every solution from scratch. Companies can create reusable AI agents for research, risk monitoring, compliance, portfolio analysis, trade execution, and reporting, all operating on a shared data and governance foundation. We discuss how this model can change the economics of AI adoption. Siraj shares examples of AI reading hundreds of broker reports in seconds, freeing hundreds of analyst hours, reducing portfolio review cycles from days to hours, improving trade execution quality, identifying settlement risks before trades fail, and accelerating regulatory reporting. The conversation also tackles one of the most common reasons companies delay AI projects: "our data isn't ready." Siraj argues that waiting for perfect data can become an excuse for inaction. His advice is to identify two or three measurable use cases, prove their value within weeks, and use those results to build confidence and secure further investment. But technology is only part of the story. Markus explains why AI adoption in asset management is also a cultural and organizational challenge. Companies must decide which processes to automate, which to support with AI, and where human judgment must remain firmly in control. The message from both guests is refreshingly practical. Start small, start with a real business problem, connect AI systems through a common data foundation, and give skilled people more time to make better decisions. Can composable AI help asset managers respond faster, reduce costs, improve investor returns, and make better use of human expertise, or will legacy systems and cultural resistance continue to slow progress? Listen to the conversation and share your thoughts.

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.

How is AI changing the way we choose hotels, and what does that mean for the businesses trying to deliver unforgettable guest experiences? In this episode, I speak with Frank Pitsikalis, Senior Vice President of Product Strategy and Chief Marketing Officer at Agilysys, about how AI is changing hospitality from the very first search through to every interaction a guest has during their stay. Travel planning is already changing. More travelers are turning to AI assistants instead of traditional search engines or online travel agencies, asking detailed questions about destinations, dining, activities, and experiences. That creates both an opportunity and a challenge for hotels. If AI is recommending experiences rather than simply rooms, hospitality businesses need technology behind the scenes that can actually deliver on those promises. Frank explains why connected systems have become the foundation for modern hospitality. For many years, hotels invested in individual systems for reservations, restaurants, spas, golf courses, and guest services. While each application may have worked well on its own, they often failed to share information in meaningful ways. As AI becomes more capable, those disconnected systems become increasingly difficult to work around. We also explore how leading hospitality brands are changing the way they measure success. Rather than focusing solely on room occupancy, many are now looking at total guest spend across dining, wellness, entertainment, and other services. Frank explains how better use of guest data helps properties create more personalized experiences while also improving operational performance and profitability. Another fascinating part of our discussion centers on personalization. We talk about what genuine one-to-one guest experiences look like when AI helps staff understand preferences, dietary requirements, previous visits, and even customer sentiment before every interaction. Rather than replacing hospitality professionals, AI is giving them the information they need to create memorable experiences that feel thoughtful and genuinely personal. Throughout our conversation, Frank returns to a simple but powerful idea. Technology works best when it disappears into the background, allowing people to focus on serving people. AI may be changing how guests discover, book, and experience travel, but hospitality will always be judged by how people are made to feel. If you work in hospitality, customer experience, or enterprise technology, this conversation offers a practical look at how connected data, AI, and operational excellence are coming together to redefine the guest journey. As AI becomes part of every stage of travel, is your business building the connected experiences that today's guests increasingly expect?

What does responsible AI actually look like once you move beyond the headlines and start deploying it inside highly regulated businesses? In this episode, I speak with Richa Kaul, Founder and CEO of Complyance, about one of the biggest challenges facing enterprise AI today: building systems that people can trust. As companies race to adopt AI across every part of the business, governance, risk management, and compliance are no longer back-office functions. They are becoming central to every conversation about innovation. Richa shares the personal experiences that inspired her to build Complyance, from her work in public sector technology and legal AI to her long-standing passion for data privacy. We discuss why trust has become one of the defining themes of enterprise AI and why businesses must think beyond their own AI initiatives to also understand the risks introduced by third-party vendors. One of the most interesting parts of our conversation focuses on the difference between compliance and risk. Rather than viewing compliance as a box-ticking exercise or a cost center, Richa explains why AI has brought risk discussions directly into the boardroom. Business leaders are now asking deeper questions about how customer data is handled, how AI decisions are governed, and what safeguards need to exist before new technologies are deployed at scale. We also explore how AI is changing governance itself. Traditional compliance has often relied on manual reviews and simple pass-or-fail checks, but Complyance is applying agentic AI to introduce greater context and human-like judgment into governance workflows. Richa explains how that approach is helping reduce manual effort while allowing teams to focus on higher-value risk decisions rather than repetitive administrative work. Our conversation also covers practical advice for companies introducing AI into regulated environments. From evaluating third-party vendors and defining acceptable risk thresholds to adopting emerging AI standards and maintaining transparency throughout the process, Richa offers thoughtful guidance for leaders trying to balance innovation with accountability. Along the way, we also discuss Complyance's recent $20 million Series A investment led by Google Ventures and what that recognition means for the company's mission to modernize governance, risk, and compliance with AI. If your business is investing in AI while trying to strengthen trust, transparency, and responsible innovation, this episode offers a timely look at how governance is evolving alongside the technology itself. As AI becomes embedded into more business processes, how is your company building trust while still giving teams the freedom to innovate?

What if the biggest obstacle to successful AI adoption isn't the technology at all, but the state of your data and the way work gets done inside your business? In this episode, I speak with Jim Spignardo, Director of Cloud Strategy & AI Enablement at ProArch, about why so many AI initiatives struggle to deliver lasting value and what business leaders should be doing before deploying the next AI tool. After more than 25 years working across networking, cloud, cybersecurity, and enterprise technology, Jim has seen plenty of technology trends come and go. His perspective on AI is refreshingly grounded in experience rather than headlines. Instead of focusing on the latest models or features, he explains why data governance, business processes, and user adoption remain the biggest factors in determining whether AI succeeds or fails. One of the topics that stood out for me was Jim's definition of AI enablement. Rather than viewing AI as another application to deploy, he argues that real value comes from embedding AI into everyday workflows and helping people rethink how work is performed. That means identifying repetitive tasks, improving decision making, and creating measurable outcomes that executives can clearly understand. We also discuss why many businesses are carrying years of technical debt into their AI initiatives. Poor data quality, outdated processes, and unclear ownership can all limit the effectiveness of AI, regardless of how advanced the underlying technology may be. Jim explains why companies that invest time in cleaning and governing their data today will be far better positioned to build reliable AI systems tomorrow. Another fascinating part of our conversation focuses on ProArch's own AI adoption journey with Microsoft 365 Copilot. Rather than attempting a company-wide rollout overnight, Jim describes a phased approach built around real use cases, structured training, internal champions, and measurable success. It's a practical roadmap that many technology leaders could adapt inside their own businesses. We also tackle one of the biggest concerns surrounding AI: jobs. Jim believes AI should be viewed as a way to augment people rather than replace them, allowing employees to spend less time on repetitive administrative work and more time applying creativity, expertise, and critical thinking where it delivers the greatest business value. If you're responsible for technology strategy, cloud transformation, or AI adoption, this conversation offers practical advice on avoiding common mistakes while building a stronger foundation for long-term success. How prepared is your business for enterprise AI, and have you addressed the data, governance, and cultural challenges before expecting AI to deliver measurable results?

What if the biggest advantage of building a global team has very little to do with reducing costs? In today's episode, I speak with Nicolas Bivero, CEO of Penbrothers, about what it really takes to build high-performing distributed teams in an AI-driven world. Having spent decades working across Asia and helping businesses build remote teams in the Philippines, Nicolas offers a refreshingly practical perspective on hiring, culture, leadership, and why execution matters far more than simply moving work offshore. Too often, discussions around remote hiring focus on labor arbitrage. Nicolas explains why that way of thinking misses the bigger opportunity. We explore how access to outstanding talent, around-the-clock operations, business resilience, and long-term team building are changing the way companies think about global workforces. Rather than treating remote employees as external resources, he shares why successful businesses invest in making them a genuine part of the company from day one. Our conversation also examines why the first 90 to 180 days are so important when bringing new people into a business. Nicolas explains how structured onboarding, clear expectations, regular communication, and cultural understanding create stronger relationships and better outcomes for both employers and employees. We also discuss the role his Hypercare framework plays in helping bridge the gap between companies and their distributed teams, particularly when leaders are hiring internationally for the first time. Culture is another major theme throughout our discussion. We talk about why company culture cannot be left to chance when people are working across different countries and time zones. Nicolas shares practical examples of how businesses can build trust, create a sense of belonging, and strengthen employee engagement, even when teams rarely meet face to face. Of course, no conversation about the future of work would be complete without discussing AI. Nicolas offers an honest assessment of how AI is changing hiring, productivity, and the skills companies now value. Rather than viewing AI purely as a replacement for people, he explains how it is helping employees become more capable while also creating demand for entirely new roles. At the same time, he acknowledges that businesses are still learning where AI fits best and why there is no universal playbook yet. If you're leading a growing business, managing distributed teams, or wondering how AI is changing global hiring, this episode is packed with practical lessons from someone who has spent years helping companies build successful international teams. What lessons have you learned about building culture, trust, and performance across distributed teams, and how is AI changing the way your business approaches hiring and collaboration?

Have public sector organizations reached the point where AI is no longer an experiment but an operational necessity? In this episode, I welcome Carmen Taglienti, CTO of Insight Public Sector, to discuss why AI is moving beyond chatbots and pilots to become part of the everyday workflows that support government services. As budgets tighten and expectations continue to rise, public sector teams are under pressure to deliver more for citizens without increasing resources. Carmen explains why this moment represents a turning point and how AI is helping agencies rethink the way they operate. We talk about the rise of agentic AI and why the next generation of AI is focused on completing real work rather than simply answering questions. Carmen shares how governments are beginning to automate permitting, citizen inquiries, service requests, and internal processes while keeping people involved where oversight and accountability remain essential. Our conversation also looks at why trust remains one of the biggest barriers to AI adoption. Whether it is employees using AI in the workplace or citizens interacting with AI-powered public services, confidence must be earned through transparency, governance, and consistent outcomes. Carmen explains why operational security, AI governance, and established frameworks are becoming as important as the technology itself. Drawing on her experience teaching AI and cybersecurity at Northeastern University and leading AI strategy programs at Wake Forest University, Carmen also shares how higher education is preparing students for a workplace where AI will become part of almost every role. Rather than avoiding these tools, she argues that the next generation needs practical experience using them responsibly to solve real business problems. We also discuss why smaller, specialized AI deployments may prove more effective than large agency-wide platforms, how procurement teams are adapting to a rapidly changing AI market, and what successful AI adoption actually looks like inside public sector organizations. If you've been wondering what AI adoption really looks like beyond the headlines, this conversation offers practical insight into how governments are balancing innovation, compliance, security, and citizen trust while building services for the future. How do you see AI changing the relationship between governments and the citizens they serve over the next few years? I'd love to hear your thoughts.

What keeps the digital world running safely behind the scenes, and why are so many organizations only now discovering that one of their most important security foundations is becoming increasingly difficult to manage? In this episode, I sit down with Lakshmi Hanspal, Chief Trust Officer at DigiCert, to demystify Public Key Infrastructure (PKI) and explain why it quietly powers almost every secure digital interaction we rely on each day. From online banking and cloud services to connected devices, APIs and AI systems, PKI is the technology that verifies identities, protects communications and helps ensure the information we exchange can be trusted. Our conversation is inspired by DigiCert's latest PKI Modernization research, which surveyed senior IT and security leaders to understand how organisations are managing digital trust in an increasingly connected world. The findings reveal a growing gap between awareness and execution. While most leaders recognize the importance of digital trust, only 34% report having full visibility into their digital certificates, leaving many vulnerable to outages, operational disruptions, and unnecessary risk. Lakshmi explains why certificate management has become so fragmented over the years, how machine identities now outnumber human identities by a significant margin, and why AI is adding another layer of complexity as autonomous systems communicate at unprecedented scale. We discuss why spreadsheets and disconnected management tools are no longer sufficient, and why many organizations are moving towards centralized PKI management with greater automation and policy enforcement. We also look ahead to two challenges that are rapidly moving up the boardroom agenda. The first is establishing trust in AI-generated content through stronger verification, digital signatures, and cryptographic provenance. The second is preparing for a future in which quantum computing could eventually undermine today's encryption standards, making cryptographic agility an important business capability rather than simply a technical consideration. If you've ever wondered what lies behind the secure websites, applications, and digital services you use every day, this conversation offers a practical introduction to one of the internet's most important foundations while explaining why modernizing digital trust has become a business priority rather than simply an IT project. How prepared is your organization for the next generation of digital trust? I'd love to hear your thoughts and whether PKI modernization is already part of your security strategy.

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?

What happens when the biggest AI security risk isn't the technology itself, but the people using it? In this episode, I sit down with Cormac Whelan, CEO of Nitro Software, to discuss why organizations need to rethink their approach to AI adoption. With research showing that 68% of C-suite executives are bypassing approved AI tools in favor of their own, we explore how an "ask forgiveness, not permission" culture is creating new security and compliance challenges for businesses around the world. Cormac shares why successful AI adoption begins with business outcomes rather than the latest model or headline-grabbing announcement. Drawing on his experience leading Nitro and previously building an AI company acquired by Apple, he explains why AI should be an enabler rather than the destination, and why organizations that focus on trust, transparency, and practical business value will ultimately pull ahead. Our conversation also looks at why documents, contracts, PDFs, and e-signatures have become some of the most overlooked parts of the enterprise AI conversation. As AI systems increasingly interact with sensitive business information, protecting document workflows is becoming just as important as securing networks and endpoints. We also discuss how European privacy standards are becoming a competitive advantage rather than simply another compliance requirement, how to separate genuine AI innovation from expensive security theater, and why AI should quietly improve the way people work instead of becoming the center of attention. If you're trying to balance AI innovation with security, governance, and business value, this conversation offers practical advice without getting lost in the hype. After listening, I'd love to hear your thoughts. Is your organization focusing on outcomes first, or is it still chasing the latest AI headline?

What if the biggest weakness in cybersecurity isn't the technology, but the way defenders communicate with each other? Cybercriminals openly exchange techniques, vulnerabilities, and attack methods, constantly learning from one another. Meanwhile, many organizations remain reluctant to share details of breaches, investigations, or lessons learned. In this episode, I sit down with Lee Sult, Chief Investigator at Binalyze, to discuss why that imbalance is creating an advantage for attackers and what the industry can do to change it. Drawing on almost two decades in digital forensics and incident response, including work with Palantir, law enforcement agencies, and government organizations, Lee explains why cybersecurity should learn from industries such as aviation and emergency services, where every major incident becomes an opportunity for everyone to improve. We discuss why incident response needs to move beyond reacting to alerts, how proactive threat hunting can reduce attacker dwell time, and why repeatable investigation processes are becoming just as important as the security tools themselves. We also explore the growing influence of AI, not because it is making attackers dramatically smarter, but because it is lowering the barrier to entry and increasing the volume of attacks security teams must handle. Lee shares why automation is becoming essential for investigators, how organizations can move from hours to minutes when responding to threats, and why cybersecurity is steadily becoming a boardroom issue rather than simply an IT concern. If cybersecurity is truly an information war, what would happen if defenders became just as collaborative as the attackers they face every day? After listening, I'd love to hear your thoughts. Should organizations be more open about cyber incidents if it helps strengthen security for everyone?

What happens when a consulting company decides that adding AI to existing workflows isn't enough? In this episode, Mal Vivek, CEO and co-founder of zeb, joins me to discuss the launch of Substrate, an AI-native operating architecture that challenges many assumptions about enterprise consulting, software delivery, and AI adoption. Rather than layering AI onto legacy processes, zeb made the bold decision to scrap its own operating model and rebuild the company from the ground up with AI at its core. The result is Substrate, a system designed to learn from every project it completes, continuously improving through a Plan, Execute, Evaluate operating loop while helping organizations move from experimentation to measurable business outcomes. Our conversation goes far beyond another AI product announcement. Mal explains why so many organizations remain trapped in what she calls "pilot purgatory," investing heavily in AI without producing measurable returns. We discuss why treating AI as an assistant often limits its potential, and why businesses may need to rethink their organizational structures, workflows, and even leadership models if they want AI to become part of their operational foundation. We also explore one of the most talked-about aspects of zeb's business model: a 100 percent outcome guarantee. Instead of charging for time or software licenses, zeb only gets paid when agreed business outcomes have been delivered. That raises interesting questions about accountability, risk, and whether the traditional consulting model still makes sense in an era where AI can dramatically compress delivery timelines. Mal also shares why zeb gives customers ownership of their own version of the Substrate engine instead of locking them into a traditional SaaS subscription, how AI changes the relationship between technology vendors and their customers, and why she believes future organizations will become flatter, faster, and increasingly focused on builders rather than management layers. If you're a technology leader trying to move beyond AI proofs of concept, or a business executive searching for a practical path to measurable AI value, this conversation offers plenty of fresh thinking on what AI-native organizations could look like over the next few years. Can businesses continue adapting yesterday's operating models for tomorrow's technology, or is it time to rebuild from the ground up? I'd love to hear where you stand after listening to this episode.

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?

How prepared are businesses for a new wave of attacks targeting the apps, APIs, and AI systems now powering digital growth? In this episode, I speak with Richard Meeus from Akamai Technologies about the latest findings from Akamai's State of the Internet report, with a focus on apps, APIs, and DDoS activity across EMEA. Richard explains why APIs have become such an attractive target for attackers, especially as AI adoption accelerates. We discuss the sharp rise in API abuse, the growing use of automation to industrialize attacks, and why many organizations still lack visibility into the APIs exposing sensitive data. We also examine the rise in layer 7 DDoS attacks, how attackers are combining multiple techniques to distract defenders, and why sectors such as retail and manufacturing are facing growing pressure. Richard also shares his view on the geopolitical forces shaping DDoS activity and why hacktivist groups continue to use these attacks as a public statement. Another major theme is the security risk around AI chatbots. As more organizations deploy chatbots to improve customer service, Richard explains how overly helpful AI systems can expose data, respond to prompt injection attempts, or create new blind spots if the right controls are missing. But this conversation is not all about risk. Richard also explains why AI can help defenders strengthen visibility, improve testing, analyze logs faster, and support more proactive security strategies. So, as businesses race to adopt AI and modern digital services, are they paying enough attention to the APIs and infrastructure sitting underneath it all? Share your thoughts.

What happens when AI agents start talking to customers more often than humans do? In this episode, I'm joined by Robert Gerstmann, co-founder and Chief Evangelist at Sinch, to discuss what may be the biggest shift in business communications since SMS transformed how organizations connected with customers. As someone who helped build Sinch from a bootstrapped startup into a global communications platform serving enterprises worldwide, Robert offers a unique perspective on where customer engagement is heading next. Our conversation traces the evolution of business communications from the early days of SMS and feature phones to today's world of conversational AI, rich messaging, voice agents, and intelligent engagement platforms. Robert explains why communication networks are evolving from simple connectivity infrastructure into intelligent conversation platforms that support increasingly sophisticated customer interactions. We explore Sinch's prediction that AI agents could increase conversation volumes by three to five times across industries and discuss what that means for businesses that are already struggling with fragmented customer data, disconnected systems, and rising customer expectations. Robert shares why many organizations remain unprepared for the scale and complexity of AI-powered interactions and why getting the data foundation right is becoming a business priority. The conversation also examines the surprising resurgence of voice. While messaging has dominated much of the discussion of the digital customer experience in recent years, Robert explains why AI is giving voice a new purpose. As voice interactions become more natural, contextual, and responsive, organizations are beginning to see voice not as a legacy channel but as a vital part of delivering high-quality customer experiences. We also discuss the growing importance of trust, authentication, and verified communications in an era of deepfakes, spoofed identities, and synthetic content. Robert explains how technologies such as RCS and verified messaging are helping organizations build confidence while creating richer and more engaging customer experiences. Along the way, we explore why personalization at scale is becoming the new battleground for customer attention, why relevance now matters more than volume, and how businesses can avoid becoming digital noise in increasingly crowded inboxes. If you're interested in AI, customer experience, communications technology, or understanding how businesses will engage customers in the years ahead, this conversation offers valuable insights from someone helping shape the future of global communications. What role do you think AI-powered conversations will play in the future of customer engagement?

What happens when a single lost network packet can disrupt hours of manufacturing? Recorded at Cisco Live, this episode features John Hoenemier, Director of Network Security Operations at GlobalFoundries, one of the world's leading semiconductor manufacturers. While most people think about microchips only when they buy a smartphone, laptop, car, or connected device, the reality is that modern life depends on a vast ecosystem of manufacturing facilities operating around the clock with extraordinary levels of precision. During our conversation, John explains why semiconductor manufacturing is one of the most demanding operational environments in the world. Production runs continuously throughout the year, and even minor disruptions can have significant consequences. In this environment, the network serves as the digital nervous system of the factory floor, connecting equipment, systems, data, and people in real time. We discuss the challenges of maintaining resilience in an environment where downtime is rarely an option. John shares how visibility, observability, security, and automation have become increasingly important as manufacturing operations grow more connected and more dependent on digital infrastructure. The conversation explores what happens when connectivity is interrupted and why reliability remains one of the most important measurements of success. We also examine the growing role of AI, operational intelligence, and unified management platforms. John explains why bringing together data from multiple systems is helping teams make faster decisions and why technologies such as Cisco Cloud Control are generating so much interest among infrastructure leaders. Along the way, we discuss cybersecurity, identity management, observability, and the unique realities of protecting highly distributed manufacturing environments. Despite operating in a very different industry, many of the challenges GlobalFoundries faces are remarkably familiar to technology leaders everywhere: balancing innovation with reliability, improving visibility, and finding ways to manage increasing complexity. What stood out most was the reminder that behind every AI application, cloud service, connected device, and modern technology platform sits a manufacturing ecosystem that must operate with extraordinary consistency and precision. As industries become increasingly connected, how important will resilient digital infrastructure become to the products and services we rely on every day?

What does it take to deliver reliable connectivity to a floating city carrying thousands of guests across the world's oceans? Recorded at Cisco Live, this episode features Diogo Almeida, Head of IT Infrastructure at AIDA Cruises, and Amine Belhad, IT Network Architect at AIDA Cruises. Together, they share what happens behind the scenes to keep one of Europe's leading cruise fleets connected while supporting everything from guest services and entertainment to restaurants, healthcare facilities, security systems, and operational technology. Most travelers think of a cruise ship as a vacation destination. The reality is far more complex. Each vessel operates like a self-contained city at sea, complete with data centers, wireless networks, hospitality systems, broadcast infrastructure, retail operations, medical facilities, and connectivity requirements that extend far beyond the ship itself. During our conversation, Diogo and Amine explained how guest expectations have evolved dramatically in recent years. Travelers now expect the same digital experiences they enjoy at home, whether that's streaming content, staying connected with family, accessing onboard services through mobile applications, or remaining productive while traveling. Meeting those expectations requires a resilient technology foundation capable of operating in one of the most challenging environments imaginable. We discuss the architecture supporting AIDA's fleet, the role of automation in managing complex environments, and how standardization has helped improve operational consistency across multiple ships. The conversation also explores how connectivity supports both guest experiences and business operations, from check-in processes and shore excursions to entertainment systems and day-to-day vessel management. Looking ahead, we examine how AI, predictive analytics, and greater visibility into infrastructure performance could help identify issues before they impact guests or crew. As digital services become increasingly important to the cruise experience, proactive operations are becoming just as important as connectivity itself. What stood out throughout this discussion was the scale of what happens behind the scenes. Most passengers never see the technology supporting their vacation, but it plays a role in almost every aspect of their experience from the moment they arrive at the terminal until they return home. As customer expectations continue to rise, how can organizations deliver increasingly connected experiences while operating in environments where reliability matters more than ever?

Recorded at Cisco Live, this episode features Mark Broderick, Senior Network Engineer at Room & Board. This furniture retailer has built its reputation around design expertise, long-term customer relationships, and personalized service. While many retailers focus on transactions, Room & Board takes a different approach, helping customers make decisions about products they may live with for years or even decades. During our conversation, Mark explains how technology is helping the company meet customers wherever they are. Whether someone visits a showroom, works with a designer remotely, or schedules a virtual consultation from home, the goal remains the same: deliver a consistent experience that feels personal, thoughtful, and easy. We discuss how customer expectations have changed in recent years and why retailers must support both physical and digital experiences without compromising quality. Mark shares how Room & Board expanded from thinking about dozens of retail locations to supporting a workforce that can effectively serve customers from almost anywhere. The conversation also explores the realities of running modern IT operations with a lean team. Mark explains how a small group of engineers supports networking, wireless connectivity, security, collaboration tools, and business operations across the company. We discuss the value of visibility, the role of automation, and why reducing operational complexity allows teams to spend more time supporting business outcomes rather than managing infrastructure. Looking ahead, we examine the growing role of AI in IT operations. Rather than viewing AI as a replacement for skilled professionals, Mark sees agentic technologies as digital coworkers that can help teams find information, handle routine tasks, and accelerate troubleshooting. The result is more time spent focusing on customers, employees, and strategic priorities. What stood out throughout this discussion was a simple philosophy: the best technology is often the technology nobody notices. When systems work as intended, customers can focus on designing their homes, employees can focus on helping them, and technology quietly does its job in the background. As customer expectations continue to evolve, how can organizations leverage technology to deliver better experiences without losing the human connection customers value most?

What happens when one of the world's largest enterprise software companies declares that it is no longer a software company, but an AI company? At SAP Sapphire, I caught up with James Bates, Head of Customer Advisory at SAP UK & Ireland, to discuss the company's vision for what it calls the Autonomous Enterprise and why this year's event felt different from any SAP conference before it. From standing-room-only AI sessions to bold declarations from SAP leadership, there was a clear sense that the conversation around AI has moved beyond experimentation and into the world of measurable business outcomes. In our conversation, James explained why so many organizations remain stuck in what he described as the experimentation phase of AI, despite years of investment and countless pilot projects. We explored why successful AI initiatives begin with business outcomes rather than technology choices and why data, governance, and process context have become the foundations of enterprise AI success. We also examined some of the standout announcements from Sapphire, including SAP's AI Agent Hub, the growing role of Joule as a new interface for work, and the company's expanding ecosystem of partnerships with organizations including Anthropic, NVIDIA, Microsoft, Google Cloud, Palantir, and Mistral. James shared why SAP believes the future lies in combining large language models with business context, process knowledge, and trusted enterprise data. The discussion also touched on real-world examples that demonstrate how AI agents are beginning to transform customer experiences, automate complex workflows, and support employees across finance, supply chain, and customer-facing operations. Rather than replacing people, James sees AI assistants and agents working alongside employees, removing repetitive tasks and helping teams focus on higher-value activities. We also explored the challenge many business leaders continue to wrestle with: how to balance autonomy with governance. As AI agents become more capable, maintaining visibility, accountability, and control becomes increasingly important. James shared why governance, trusted data, and strong business processes must remain at the center of every AI strategy. If you've been wondering whether enterprise AI is finally moving beyond the hype cycle and into meaningful business transformation, this conversation offers a fascinating perspective from the heart of SAP's AI strategy and its vision for the future of work. What role do you think AI agents will play inside your organization over the next few years? Share your thoughts.

How do healthcare organizations continue innovating while protecting some of the most sensitive data in existence? Recorded at Cisco Live, this episode features Kevin Ludwig, Associate Vice President of Information Technology at XiFin, and Jeff Kronlage, CEO of CrossConnect Engineering.  Together, they provide a behind-the-scenes look at the technology infrastructure supporting healthcare billing, laboratories, pharmacies, and medical providers, where reliability, security, and automation all play a central role in keeping critical processes moving. During our conversation, Kevin explains how XiFin helps healthcare organizations process and validate complex billing workflows while reducing manual intervention wherever possible. With healthcare providers facing growing financial pressure, improving efficiency has become increasingly important, but never at the expense of security or data protection. We explore how cyber threats continue to shape decision-making across healthcare technology and why organizations are looking beyond traditional security architectures. Kevin and Jeff share their experience as one of the earliest adopters of Cisco's smart switching technology, discussing how distributed security models can simplify operations, reduce complexity, and help teams manage growing demands without constantly adding new layers of infrastructure. The conversation also examines AI adoption inside healthcare environments. While organizations are eager to benefit from automation and AI-powered capabilities, Kevin explains why guardrails, governance, and customer trust remain top priorities. We discuss the balance between innovation and risk, the importance of observability, and how teams are approaching AI with both enthusiasm and caution. Along the way, Jeff offers an insightful perspective on reducing decades of accumulated technical debt, challenging long-standing assumptions about network security, and creating simpler ways to manage increasingly complex environments. What emerges is a discussion about much more than technology. It's a conversation about trust, responsibility, and helping healthcare organizations deliver better outcomes through smarter systems and better operational decisions. If you're interested in healthcare technology, cybersecurity, AI adoption, or the future of enterprise infrastructure, this episode provides valuable insights from leaders working at the intersection of all four. As healthcare becomes increasingly connected and data-driven, how should organizations balance innovation, security, and trust?

What does it take to move from AI experimentation to real business impact? Recording during Google Cloud Summit London 2026 at Tobacco Dock, I had the opportunity to speak with Maureen Costello, Vice President for UKI and Sub-Saharan Africa at Google Cloud, about one of the biggest shifts currently taking place across technology and business. After years of discussion around generative AI, the focus is now turning toward agentic AI and how organizations can put these capabilities to work in practical, measurable ways. Maureen offered a fascinating view from the front line of AI adoption, sharing how businesses across financial services, retail, government, and other sectors are beginning to move beyond pilots and proof-of-concept projects. We discussed how AI is helping organizations improve customer experiences, increase productivity, strengthen decision-making, and create new opportunities for growth. From helping banks tackle financial crime and deliver smarter customer services to supporting government departments in modernizing public services, the conversation is filled with examples that bring the technology to life. We also explored why the UK is so well positioned for the next chapter of AI adoption. With world-class research, exceptional talent, and ambitious investment across both the public and private sectors, Maureen believes the UK has a genuine opportunity to remain at the forefront of AI innovation. She also explained why skills development, data readiness, security, governance, and trust will play such an important role as organizations begin introducing AI agents into everyday workflows. What I particularly enjoyed was discussing the human side of this transition. As AI becomes embedded into business operations, how should leaders prepare their teams? What separates organizations that achieve meaningful outcomes from those that struggle to move beyond the early excitement? And how can businesses strike the right balance between innovation, responsibility, and long-term value? Whether you're following the announcements from Google Cloud Summit London, building your own AI strategy, or simply trying to understand where this technology is heading next, this conversation offers valuable insight into one of the most talked-about topics in business today. What role do you think agentic AI will play inside your organization over the next 12 months, and are businesses finally moving from curiosity to meaningful adoption?

What does strategy look like when the technology industry seems to change every few months? Recorded at Cisco Live, this episode features Ammar Maraqa, Cisco's Chief Strategy Officer, whose role spans corporate strategy, mergers and acquisitions, venture investments, technology incubation, strategic partnerships, and long-term planning. Few people have a broader view of where the technology industry is heading and how companies can position themselves for what comes next. During our conversation, Ammar shares why he believes many organizations are thinking about AI the wrong way. Rather than viewing it as a productivity tool or cost-saving exercise, he argues that AI represents a much deeper shift in how work gets done, how organizations operate, and how leaders should think about growth. We explore Cisco's approach to strategy in an era defined by constant disruption, including why the company focuses on testing assumptions rather than repeatedly changing direction. Ammar also explains how Cisco uses a combination of building, acquiring, partnering, investing, and incubating to accelerate innovation and stay close to emerging technologies. The discussion also examines what Cisco learns from engaging with startups, entrepreneurs, venture investors, customers, and partners around the world. From advances in AI infrastructure and silicon to agent orchestration, observability, security, and enterprise adoption, Ammar shares the themes he believes deserve the closest attention from business leaders today. We also discuss one of the biggest challenges facing organizations: the growing gap between what AI is capable of and what companies are actually prepared to adopt. Ammar explains why infrastructure, data, security, workflow redesign, and organizational change remain essential ingredients for success, regardless of how powerful the underlying models become. Along the way, he offers insights into business model disruption, the future of enterprise software, and why some companies successfully reinvent themselves while others struggle to adapt. If you're interested in strategy, innovation, AI adoption, or the forces shaping the next decade of enterprise technology, this conversation provides a thoughtful perspective from someone helping guide one of the industry's most influential companies through a period of extraordinary change. How often does your organization challenge the assumptions behind its strategy, and would those assumptions still hold true if you were making them today?

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?