Tech Talks Daily

Neil C. Hughes

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

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

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

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

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

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

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

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

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Today · 18 min
NetSuite Founder on the End of ERP and What Comes Next
What if your ERP system came to you through a conversation, instead of asking you to learn its menus before you could get anything done? Recording at SuiteWorld in Las Vegas, I welcomed Evan Goldberg, founder and executive vice president of NetSuite at Oracle, back to the podcast. We last spoke two years earlier about the company's origins. This time, the discussion centres on his description of AI as the UI and what that could mean for the people using business software every day. Evan describes an ERP experience in which users explain what they want in natural language. One example is adding a dashboard metric by describing the information it should show. If the result is not quite right, the user can explain what needs to change. For me, the useful question is whether that makes software easier to fit around a person's work and how reliably it understands the request. We discuss the knowledge behind those answers. Evan describes SuiteSkills as a way to bring together information about NetSuite and the procedures businesses use within it. He connects that work with consistent answers through NetSuite Next and through external tools using the AI connector service. His ambition is to support the models and ways of working customers choose, while making the information returned from NetSuite dependable. It is a product direction he describes, rather than an independently tested guarantee that every model will behave identically. The conversation then moves from answers to actions. Evan points to order approvals, accruals, and agents looking for exceptions that deserve attention. He argues for beginning with a process people already want to improve and deciding which parts can run with human approval or greater autonomy. The appropriate balance will depend on the company and the work involved. A conversational interface does not remove the need to decide where authority belongs. Personalisation is another part of adoption. Evan's view is that people are more likely to keep using an AI feature when they can explain how they want it to work. The dashboard example makes that concrete, but it also raises a wider question about how companies maintain consistent procedures while allowing people to adapt their tools. We also discuss smaller businesses and the opportunity to access capabilities without building a large specialist team. Evan describes a startup offering and his belief that the same AI models can help companies of different sizes. The practical considerations still include eligibility, the work a team needs to accomplish, and the controls around its business information. For partners, he sees an opportunity to make industry expertise available through reusable skills. A specialist's knowledge of a particular niche could inform how Ask Oracle helps customers, while the partner spends time developing that expertise further. It is an interesting view of how advice and software configuration may change as AI becomes part of delivery. I also ask about software pricing. If agents complete tasks that once required someone to log in, what happens to the traditional per-user model? Evan acknowledges the question but offers a counterpoint: easier access to information and personal agents could encourage people to remain users. He does not announce a pricing change, leaving us with a business-model question that is still developing. We finish with an unexpected customer conversation at Disneyland, which Evan uses to illustrate interest from everyday business users. It is a memorable anecdote rather than a measure of adoption, but it brings the discussion back to the people who ultimately decide whether these features earn a place in their working day. I would love to hear your thoughts, so please share your experiences. If AI becomes the interface to your business systems, what would you want it to understand before you let it act on your behalf?
Today · 26 min
Giving Enterprise AI Agents the Business Context They Need With SAP
What needs to happen before AI agents can reliably complete work across the systems that run a business? In this episode of Tech Talks Daily, I welcome back Irfan Khan, Chief Product Officer for SAP Data & Analytics, to discuss the distance between a useful AI pilot and a dependable operational workflow. Our conversation begins with the autonomous enterprise and what that means for teams working across finance, procurement, supply chain and HR. Irfan explains why agents need a shared understanding of business activity, including the state of purchase orders and inventory, before coordination can produce a useful result. We discuss the data quality, permissions and governance questions that become harder to manage as deployments grow. Irfan also considers how leaders should measure AI performance. Generating additional content or consuming additional tokens tells us something about activity, but leaders still need to understand the effect on productivity, accuracy and the cost of completing work. He shares how SAP has approached developer experimentation and why teams need to become deliberate about which tasks benefit from AI consumption. The conversation also covers company memory, an initiative Irfan describes as a way to bring institutional process knowledge into agent workflows. We discuss how employee trust depends on the task, why order entry requires a different level of precision from a forecast, and how open architecture can preserve choices as models and infrastructure develop. Irfan then explains SAP's approach to connecting SAP and non-SAP data through SAP Business Data Cloud and the wider SAP Business AI Platform. For leaders assessing the next stage of their AI programs, the discussion offers practical questions about context, access, cost and the business outcomes they want to improve. You can find the resources discussed in the accompanying show notes at TechTalksNetwork.com. Where is your organization finding the biggest obstacle to operational AI, and what would help you move forward?
#3768
Yesterday · 28 min
Putting Engineering Judgment to Work With AI and Southworks
When AI makes writing code faster, how do you decide whether your team is building the right thing? In this episode of Tech Talks Daily, I speak with Johnny Halife, CTO at Southworks, about what happens to software engineering when code generation becomes easier but decisions still require experience. The pressure to deliver faster is familiar. The harder question is whether teams have the review capacity, testing processes, and deployment infrastructure to turn that extra output into useful software. Johnny describes a different way of directing AI coding agents. Engineers need to specify the outcome, define what finished work should look like, and provide checks that can show whether it meets the required standard. Constantly telling an assistant what to do next can keep a person busy without giving the system enough responsibility to complete a coherent piece of work. We discuss how to create tasks that are small enough to review and substantial enough to delegate. We also talk about context. Giving an AI system access to institutional knowledge sounds sensible, but adding every document to every conversation creates its own problems. Johnny describes borrowing from open source contribution practices, including README files, contribution guidelines, and records of architectural decisions. His approach combines an explanation of why the system works as it does with directions for finding additional information when needed. It is a useful way to think about onboarding an agent without expecting it to absorb the entire history of a business at once. The phrase human in the loop comes up frequently in AI discussions, so I ask what it should mean in practice. Johnny describes checks before work begins and a review of test results and other evidence at the end, supported by progress updates along the way. The person responsible needs to set expectations and assess the outcome. Being available for an alert is only one part of that responsibility. An unnamed streaming platform project provides a practical example. Johnny explains how Southworks developed the supporting process in stages, improving documentation, tests, and the infrastructure around the developer workflow. He reports a tenfold increase across requests, changes, and delivered value, although we do not have an independently verified measure or a detailed breakdown. His broader observation is that the same improvements made the environment easier for human engineers to understand and work in. Finally, we discuss hiring and professional development. Johnny values software and cloud knowledge, curiosity, clear writing, time management, and the judgment to choose between competing possibilities. AI can make experimentation quicker, but someone still needs to decide which experiment deserves attention and when to stop. I would love to hear how this compares with your own experience, so please share your thoughts. Where does work now wait in your engineering team, and how are you helping people make better decisions about what to build?
#3767
Monday · 20 min
Understanding the Infrastructure Behind Stablecoin Payments With Noah
What happens behind the scenes when a cross-border payment uses stablecoins, and how much of that should the customer need to understand? In this episode of Tech Talks Daily, I speak with Shah Ramezani, co-founder and CEO of Noah, about the infrastructure that connects stablecoins to business payments. The discussion goes beyond the transfer itself to the practical work required to move between currencies, choose a route, verify identities, and reconcile the result. Shah starts with the friction he sees in correspondent banking and the opportunity for a different settlement layer. He explains how stablecoins can move quickly between participants and then connect to local payment methods at either end. His example of a payment from the United States to Argentina helps explain the choices involved, including whether the recipient wants local currency or a dollar-denominated stablecoin. It is an illustrative scenario rather than a named customer case or a universal comparison of costs and delivery times. I ask what makes that experience work technically. Shah breaks the process into liquidity, routing, compliance, settlement, and reconciliation. The available partner, customer type, destination, and pricing can all affect how a payment is handled. Noah's role, as he describes it, is to bring those components together so partners can connect without building every part themselves. Identity is another part of the conversation. Shah discusses know-your-customer and know-your-business checks, including arrangements in which partners rely on checks performed by Noah and receive the information they need. His account makes clear that reducing repeated document requests requires coordination among the organizations involved. It also raises questions about who is responsible for each step and how that responsibility changes across markets. One idea I found most interesting is that users may encounter stablecoin infrastructure without choosing to hold or handle stablecoins themselves. Shah describes payments in which the technology operates behind a familiar interface. For a business considering that approach, the useful comparison is the full payment journey, including conversion, local payout, support, and each participant's obligations. We also discuss banks' continuing role. Shah rejects the idea that they disappear and instead describes a different distribution of responsibilities across payment networks and financial institutions. The conversation gives listeners a way to consider how new settlement technology can coexist with established banking relationships. Trust and regulation remain part of that assessment. Shah argues that stronger regulation provides reassurance, while acknowledging the difficulty of serving customers across jurisdictions with different reporting obligations, identity requirements, and fraud patterns. His confidence in the market's direction is his perspective. It should not be read as a guarantee that every stablecoin, provider, or transaction carries the same protections. Finally, Shah discusses Noah's plans to broaden its capabilities and invest in product development and hiring. He describes an opportunity to make the payment experience simpler and less complex for the user. For me, the accompanying question is how providers make their responsibilities clear when the underlying process spans several organizations. I would love to hear your thoughts, so please share your experiences. When you assess a new payment route, how do you weigh speed and cost against clarity about responsibility, access to funds, and help when something goes wrong?
#3766
Sunday · 34 min
Designing ERP Access Governance Before Go Live With Pathlock
What happens when the pressure to complete an ERP transformation leaves access governance waiting until after go-live? In this episode of Tech Talks Daily, I speak with Chris Radkowski, GRC Expert and Advisor at Pathlock, about the business consequences of access decisions. Chris previously spent 17 years at SAP and brings experience across access control, governance and security. We discuss why ERP risk belongs in transformation planning, with examples that connect system availability to manufacturing, incoming deliveries and the processes that keep a business operating. The conversation considers privileged access inside business applications. An identity does not need to administer a server to hold permissions that affect sensitive information, supplier records or financial transactions. Chris explains why the combinations of entitlements assigned to a person deserve attention, and how that assessment becomes more complicated as processes extend across several systems. AI agents and automated workflows add to the challenge when they can carry out transactions quickly or operate through credentials associated with a user. We also discuss the limits of scheduled access reviews. Chris describes the work involved in checking whether an inappropriate permission was used, including the manual effort that can arise when teams need to investigate historical transactions. He argues for combining access information with activity and transaction data, so teams can examine an unexpected pattern and initiate a review rather than waiting for the next certification cycle. For leaders planning an ERP or AI transformation, Chris recommends considering role design, access assignment, certification and emergency access early in the program. We discuss how changes between SAP ECC and S/4HANA can affect the permissions and transactions on which existing controls depend. Our conversation offers practical questions about who can act, what they actually do and how the organization responds when operations are under pressure. Which access decisions would you want resolved before your next go-live, and where are the gaps today? Please share your thoughts with me.
#3765
Saturday · 19 min
Losing the Endpoint Recovery Gap With IGEL
What happens when an organization restores its systems after an incident but employees still cannot connect to them? In this episode of Tech Talks Daily, I speak with Sterling Wilson, Global Field CTO for Business Continuity and Disaster Recovery at IGEL, about the endpoint recovery gap. Sterling explains why a laptop that is encrypted, unstable or being preserved for investigation can prevent work from resuming even when applications and infrastructure are available. We discuss why continuity planning needs to account for the device people use to reach those services. The conversation examines IGEL Emergency Mode, an administrator-led capability for supported IGEL Dual Boot-enabled endpoints. Sterling describes moving those devices into IGEL OS so employees can reach resources IT has approved while security teams continue investigating the Windows environment. He emphasizes that this is an alternative access path, rather than Windows repair, threat detection or a replacement for the wider incident response process. I ask how organizations should prioritize employees and services during a disruption. Sterling recommends making those decisions around business functions and dependencies rather than seniority, with the priorities established before a crisis. We also discuss who has authority to initiate a transition, whether people can still sign in and reach their applications, and how a team returns devices to its chosen operating state afterward. Sterling draws a distinction between a recovery time objective and the time it actually takes people to resume work. That brings the discussion back to testing, including scenarios where management systems, connectivity or other dependencies may be unavailable. For leaders assessing business resilience, the conversation offers a way to look beyond infrastructure status and examine whether the organization can deliver its services again. When did your team last test recovery from an employee's perspective, and what did it reveal? Please share your thoughts with me.
#3764
Friday · 25 min
Understanding Tokenized Shares and Ownership Rights With Equiniti
What does an investor actually own when a share becomes a token, and who keeps the official record accurate? In this episode of Tech Talks Daily, I speak with Dan Kramer, Chief Executive Officer of Equiniti, about the infrastructure behind tokenized securities. While much of the discussion focuses on trading venues and settlement, Dan brings the conversation back to the record of ownership and the responsibilities of a transfer agent, or registrar in the UK. He explains why issuers want a consistent register rather than separate versions of ownership across different systems. We discuss the difference between shares issued by a company and tokens created by a third party to represent an interest in an underlying asset. Dan explains why voting rights, information rights and exposure to another organization deserve attention when assessing a tokenized product. We also consider how existing market infrastructure connects to this work and why the technology itself cannot answer every question about rights and responsibilities. The conversation then turns to AI in shareholder services. I ask Dan about accountability when software assists with proxy voting, and we discuss the importance of being able to understand how decisions were reached. He also shares Equiniti's approach to employee training and its ambition to make interactions with its systems available through natural language by the end of 2027. This is a future target, rather than a description of capabilities already available across the business. Dan also discusses the planned Bullish acquisition and how the companies intend to bring together issuer relationships and blockchain infrastructure. We close with a practical example of a customer portal moving from a Monday discussion to a Thursday delivery, illustrating his view of how development is changing. For leaders assessing tokenization, the conversation offers useful questions about ownership, controls and the experience shareholders should receive. Which questions would your organization need answered before considering tokenized shares? Please share your thoughts with me.
#3763
Thursday · 28 min
Improving Specialty Care Access With AI and Leap
What good is a new treatment if patients still struggle to get the approvals, appointments, and support needed to receive it? In this episode of Tech Talks Daily, I speak with Amir Sharif, president and co-founder of Leap, about the operational infrastructure behind specialty care. While AI in drug discovery attracts attention, the journey from a prescription to treatment often depends on disconnected organizations, incomplete information, and manual coordination. Amir describes how Leap combines drug sourcing, distribution, care coordination, and employer benefit arrangements to address some of those gaps. His interest in patient experience grew through work helping build healthcare businesses including Care/of and Tend. Those experiences led him to question why everyday expectations around scheduling, pricing, and customer support can be so difficult to meet in healthcare. Specialty therapies bring another level of complexity because they can require clinical administration, recurring appointments, insurance authorization, and expensive medication procurement. We look at what happens after a physician writes a prescription. Approval may depend on the patient's health plan, supporting medical information, an administering provider, and a permitted site of care. Coordinating those pieces can fall to the patient, even while they are dealing with an illness. Amir explains why access depends on the administrative process as well as the treatment itself. Drug pricing provides another part of the picture. Under a buy and bill arrangement, a provider purchases a drug, administers it, and bills for it. Amir says the margin added to the acquisition cost can range from 40 percent to 400 percent. These are his reported figures rather than a finding independently established in this episode. He describes Leap's model as passing its drug acquisition cost to the employer or plan sponsor and coordinating administration through home infusion or partner clinical sites, depending on patient preference and clinical requirements. The technology discussion becomes particularly practical when Amir describes the data behind identifying eligible patients. Leap receives feeds containing diagnosis codes, claims, and prior authorization information. Its data engine normalizes those feeds so the team can act on signals that a patient is receiving, or may soon be prescribed, a therapy within the program's scope. This is a care coordination process. Amir repeatedly says Leap does not replace the prescriber or take control of clinical treatment decisions. He also describes an average patient encounter involving over 20 coordination touchpoints. Many external parties still communicate by email or fax rather than through software interfaces. Leap uses agentic workflows to support appointment scheduling, shipment tracking, and medical order processing. According to Amir, those workflows remain supervised by care teams, and patients speak with a person when they contact the service. For business leaders assessing AI returns, his account of model costs is especially useful. Some workflows the team considered two quarters earlier would have cost tens or hundreds of dollars per run in token consumption, he says. As models become cheaper and more capable, different operational uses become viable. The interview does not provide a measured before and after cost comparison, so the lesson is about evaluating changing economics rather than declaring a proven return for every workflow. Leap reports a patient Net Promoter Score of 92, with surveys sent after every encounter. Amir describes this as evidence of patient satisfaction, alongside feedback gathered by care teams. Satisfaction is useful, but it should not be confused with proof of improved clinical outcomes or faster treatment across every patient. Employers can request an impact analysis comparing historical claims with the proposed cost of Leap's service. Where would you begin when assessing specialty care, the drug price, the time patients lose, or the coordination needed to keep treatment on track? Listen to the conversation and share your thoughts with me.
#3762
October 21 · 27 min
Keeping the Human Voice in AI Translation With DeepL
What happens to a good idea when the person who understands the problem best cannot express it confidently in the language used by the rest of the team? In this episode of Tech Talks Daily, I speak with Gonçalo Gaiolas, Chief Product Officer at DeepL, about real time voice translation and the work required to make a multilingual conversation feel natural. Our discussion moves from the business cost of language barriers to the technical choices behind speaker identity, tone, speed, and the handling of sensitive meeting data. Gonçalo begins with a cost that is difficult to place on a spreadsheet. An employee may understand a local market, a product, or a customer need but struggle to contribute in the company's shared language. The organization can miss that knowledge even when the person is present in the meeting. Customers also tell DeepL that language barriers affect the time needed to make decisions and understand nuance. These are experiences he describes, rather than independently measured results presented in the interview. Live translation introduces a demanding set of constraints. People pause, interrupt, change direction, and build meaning at different speeds across languages. A system needs enough context to produce an accurate translation while responding quickly enough to remain useful. Waiting can interrupt the flow, but responding too early can lose the meaning of the sentence. Tone creates another challenge because urgency, hesitation, and a question can carry information that the translated words alone do not capture. We discuss DeepL's approach to retaining the characteristics of individual speakers. Gonçalo describes separating speakers and carrying elements of voice, rhythm, and emotion into the translated audio. He also explains that some original audio remains audible at a lower level. His aim is to help listeners follow who is speaking while understanding the translated conversation. He says users have options around retaining vocal identity, acknowledging that people may feel differently about using that capability. The enterprise adoption discussion is equally practical. Gonçalo describes a desktop app intended to provide a consistent experience across Zoom, Microsoft Teams, and Google Meet. A global workforce may use one platform internally and encounter several others when speaking with customers and partners. For him, working across those environments is part of the product's usefulness, alongside the quality of the translation itself. Accuracy remains a recurring theme. Gonçalo describes testing with real users and evaluating whether the result works in the customer's context. He gives the example of an engineering department whose acronyms and specialist terms can confuse a model. DeepL's customization capabilities, he says, let customers supply terminology and guidance about how language should be handled. The example offers a useful reminder that a fluent sentence can still misinterpret the way a particular team speaks. We also discuss procurement and governance. Meeting audio can contain sensitive business information, so buyers need to understand technical architecture, policies, and how their data is processed. Gonçalo says DeepL does not use customer data to train its own models. The interview does not supply the contractual scope, retention periods, or detailed controls behind that statement. Those remain questions for buyers to check against the service and agreement they intend to use. Examples range from conversations between parents and teachers to contact centers, large events, and use alongside human interpreters. Gonçalo describes customer demand that extends beyond the scenarios the team originally expected. I also ask about translating podcasts, which leads to a discussion of possible uses outside live meetings. We keep that possibility separate from a confirmed podcast production service or a promise about this show's availability in other languages. I'd welcome your thoughts. Where could voice translation help someone contribute knowledge your organization is currently missing, and what would you need to test before relying on it?
#3761
October 20 · 13 min
Making Industrial Work Safer With Ascend Robotics
What if the most useful industrial robots are the ones helping experienced people continue working in safer conditions? Recording from the show floor at IFS Unleashed 2026 in Orlando, I spoke with David Askey, CEO of Ascend Robotics, about construction and maintenance machines working in spaces that are difficult or dangerous for people to reach. We discussed the gap between a robotics demonstration and equipment that can perform reliably at a customer site, where conditions rarely match the original plans. David shares a ship painting example in which he reports work completed three to four times faster than with typical human crews, alongside approximately twenty five percent material savings. He also describes more consistent coating thickness and explains why people continue to monitor and direct the work. A separate example involves cleaning and coating a factory ceiling during a data center conversion, where live electrical infrastructure added to the difficulty of the job. One of the most interesting parts of our conversation concerns the people operating these machines. David describes hiring experienced workers from painting crews as robot operators and working with unions as the equipment enters the market. For workers affected by the physical demands of construction, operating a robot can provide a way to keep contributing their knowledge from a different working environment. We also discuss how robots adapt to buildings as they actually exist and how operators help them decide what to paint and what to avoid. David explains how AI tools have accelerated his team's software development and why connecting the data collected by robots to enterprise systems is a separate challenge. He describes IFS as a bridge between work happening in the field and the information management teams need. For anyone considering robotics, the conversation offers practical questions about reliability, safety, integration and the outcomes worth measuring. Where could robots support your workforce, and what would you need to see before putting them to work? Please share your thoughts with me.
#3760
October 19 · 14 min
IFS Unleashed: How Digital Workers Are Becoming Digital Teams
What happens when individual AI agents begin working together as digital teams, and how much more can businesses achieve when automation moves beyond a single task? Recording live at IFS Unleashed 2026 in Orlando, Neil C. Hughes reconnects with Somya Kapoor, CEO and Co-founder of IFS Loops, to discuss how digital workers have developed since their previous conversation in New York. Last year, the focus was on individual digital workers performing specific tasks. Now, IFS is bringing several workers together to support wider business processes, with early customer deployments providing evidence of what these systems can achieve. Somya explains why the initial approach centered on low-risk, high-impact workflows rather than attempting to automate complex processes all at once. She shares how some customers who began with a single digital worker have expanded to two or three, creating opportunities to automate more work across procurement, asset management, and field service. The conversation also examines customer examples from IFS Unleashed. Somya discusses KLM's reported efficiency improvements, the potential annual savings associated with invoice processing at Eight Group, and how Kodiak Gas is using a digital worker to make spare-parts identification and purchasing significantly faster. These examples provide an interesting perspective on enterprise AI at a time when many organizations are still struggling to move beyond pilot projects. Rather than beginning with large transformation programs, Somya describes an approach built around focused deployments, measurable results, and the opportunity to expand when the initial benefits become clear. But the technology is only part of the challenge. Somya also explains why change management can become a greater obstacle than building the agents themselves, particularly when employees and business leaders are unfamiliar with allowing digital workers to perform operational tasks. As AI agents become increasingly capable of working together, will organizations find greater success by starting small and building confidence through results? And could digital teams provide a more practical route from AI experimentation to everyday business value? Listen to the conversation and share your thoughts on where digital workers could make the greatest difference in your organization.
#3759
October 19 · 12 min
IFS Unleashed: AVEVA on Why Industrial AI Is More About Data Than Algorithms
What if the biggest obstacle to industrial AI isn't the technology itself, but the data businesses are trying to use with it? And how much could manufacturers and industrial operators achieve if they could connect information from across the entire life of their assets? Recording live at IFS Unleashed 2026 in Orlando, Neil C. Hughes speaks with Shawn Gregerson, VP APM & MES Business Development at AVEVA, about how industrial companies are using AI, operational data, and predictive maintenance to improve reliability and make more informed business decisions. Shawn explains why industrial AI is increasingly becoming a data challenge rather than an algorithmic one. Although AI models and analytical techniques have developed significantly, many organizations still struggle with disconnected information spread across engineering, operations, maintenance, and asset management systems. The conversation examines how AVEVA is bringing these different sources together through its industrial knowledge graph technology, creating a more reliable foundation for AI applications that need to understand the relationships between assets, processes, and operational conditions. They also discuss the growing financial pressures facing industrial organizations. Shawn highlights the increasing cost of downtime and the challenge of maintaining assets that are operating beyond their original design life. With AVEVA and IFS working together on Continuous Asset Decision Intelligence, or CADi, the discussion turns to connecting asset planning, design, operations, maintenance, and optimization so that decisions can be informed by real operating conditions. Shawn also shares where industrial AI is already delivering results, including predictive maintenance systems that monitor equipment health, identify early warning signs, and help maintenance teams determine what action to take before failures occur. Other applications include predictive quality, manufacturing throughput, and the use of generative AI to make industrial information easier to access. Looking beyond individual applications, they consider how connected industrial data could support agentic workflows and enable businesses to automate more of the tasks that employees currently perform manually. As industrial organizations face pressure to improve efficiency while extending the life of existing equipment, could connecting their operational data deliver greater value than investing in increasingly sophisticated AI models alone? Listen to the conversation and share your thoughts on how industrial AI is changing maintenance, asset management, and operational decision-making.
#3759
October 18 · 29 min
Making Enterprise Data Ready for AI With The Modern Data Company
Could your enterprise data support a reliable report and still leave an AI agent unsure what the numbers mean? In this episode of Tech Talks Daily, I speak with Saurabh Gupta, CEO of The Modern Data Company, about the difference between being analytics ready and AI ready. Saurabh's background includes data strategy and governance work at Thoughtworks, Washington DC government, and international organizations. Our conversation focuses on the information around data that machines need when they begin consuming it directly. A familiar reporting process can depend on human knowledge that never becomes part of the underlying dataset. Saurabh describes a system-of-record report exported to Excel, adjusted, and then used for compliance reporting. The spreadsheet and the original source now tell different stories. If an AI system encounters multiple copies, it needs a way to understand which version is appropriate and why. We discuss how context includes quality, lineage, transformations, meaning, usage, and permissions. Those details often live across separate tools and teams. Bringing them together is part of the challenge of making data useful to agents, rather than simply making it available. The distinction becomes clearer through a furniture company's date fields. Saurabh describes 14 fields that were tagged similarly but carried different business meanings. A warehouse, an ecommerce team, and a delivery team can use a label in different ways. An answer based on the wrong interpretation may look convincing while failing to address the question. Governance also needs to reach the point of consumption. Saurabh explains why one person may be allowed to see a name, address, and date of birth while another needs the same fields masked. His approach is to connect those permissions with how the data is used, including when a machine becomes the consumer. We then discuss DataOS, which The Modern Data Company describes as a platform for data products. Saurabh argues for starting with the outcome a user wants, then working backward to the necessary sources and controls. He explains how the company brings data from different locations into that process, while acknowledging that moving or centralizing data can still be appropriate where latency requires it. The interview also addresses measurement. Saurabh clarifies the company's 90% claim as faster use-case delivery or time saved. It is not a 90% increase in adoption. He describes tracking data interactions to identify stale processing and report schedules that do not reflect actual use. The company also reports potential reductions in data ownership costs, but the interview does not provide enough detail to treat those figures as a universal return. For CIOs and CDOs, his practical recommendation is to begin with a business problem and implement incrementally. A clearer view of which data people consume can help teams decide what to process, what to retire, and which investments serve a useful outcome. We also discuss how AI may change the division of work between consumers, analysts, and engineers. Saurabh's forecast is a strong one. The broader issue is how organizations preserve business interpretation and accountability as additional reporting tasks move into automated systems. The resources supplied after the interview include the company website, the Modern Data 101 practitioner community, and The Modern Data Report 2026. They offer destinations for listeners who want to continue learning about the team's approach. What would make your data genuinely ready for AI, and where do definitions, permissions, or inconsistent copies still create uncertainty? Please listen and share your thoughts.
#3557
October 17 · 22 min
Moving Marketing From Static Rules to AI Agents With JustAI
What happens when marketing software starts learning from customer behavior instead of following a campaign rule that someone wrote weeks ago? In this episode of Tech Talks Daily, I speak with Neha Mittal, co-founder and CEO of JustAI, about AI agents, reinforcement learning, and a different approach to personalization. Neha spent over a decade working on growth and retention at companies including Twitter and Pinterest. Her experience repeatedly brought her back to the same problem: teams were running sequential experiments while the interests and behavior of their users kept changing. Neha describes how a series of small A/B tests could consume weeks without producing a durable answer. Changing a button from red to blue might teach a team something, but the larger opportunity was to keep experimenting and learning across many decisions. Seeing similar problems at different companies helped shape the idea behind JustAI. We discuss why she considers marketing a promising environment for autonomous agents. It involves a high volume of decisions, frequent feedback, and experiments that can often be assessed quickly. Opens, purchases, and ignored messages can all contribute to learning. Neha contrasts those conditions with higher-consequence fields such as medicine and law, while explaining that strategy and judgment remain responsibilities for people. The conversation also separates generating content from choosing an action. A language model can help produce an email. The reinforcement learning approach Neha describes aims to learn which decision supports longer-term customer value. The question becomes what message, timing, or channel makes sense for a particular person in the current moment, rather than which static segment or campaign they belong to. JustAI divides the work across Strategy, Creative, Decisioning, and Data agents. Neha explains how opportunities feed into messaging, how the decisioning system learns from responses, and how the data agent returns insights to the process. Marketers can review customer-facing creative, provide brand guidance, and adjust strategy when the company's products or priorities change. Her hypermarketer vision raises another question about how teams grow. Neha imagines one marketer having the capacity traditionally associated with a much larger team, with agents handling repetitive execution and experimentation. She presents this as a direction for the business rather than a demonstrated productivity ratio for every customer. The marketer still needs to translate business goals into instructions and judge whether the results make sense. We bring the discussion back to current examples. Neha describes Coursera and ClickUp delegating the creation and selection of messaging variants. She also discusses the importance of human review for Better's financial-services communications. These examples show why the level of autonomy should depend on the task and the consequences of getting it wrong. The supplied booking notes report over $100 million in customer revenue influenced and a $17 million funding round. Those company claims provide background, but the interview does not establish a revenue attribution method, incremental lift, or a complete return-on-investment calculation. The practical value of the conversation lies in understanding what work is being delegated and what people continue to own. For me, the interesting issue is how to create a feedback loop that improves relevance without letting operational speed run ahead of customer trust or business judgment. Which marketing decisions would you delegate to AI agents today, and what would you want to review before a message reaches a customer? Please listen and share your thoughts. 5. Suggested URL
#3756
October 16 · 30 min
Building a Better Buying Experience With AI and 1mind
Is your company using AI to improve the buying experience, or to carry out the same processes faster? In this episode of Tech Talks Daily, I speak with Jonathan Kvarfordt, known as Coach K, about business strategy, go-to-market AI, and the relationship between people and automated systems. Jonathan introduces himself as VP of Marketing at 1mind and brings experience training revenue leaders and teams to assess where AI can support their work. We begin with his framework for optimizing, amplifying, or reinventing a process. The point is to recognize the difference between making an existing task easier and considering whether the task should be done differently. Jonathan also acknowledges that organizations have different levels of readiness. Changing everything at once is not a practical answer for every business. The conversation becomes especially useful when we discuss measurement. Rather than beginning with licenses or prompt activity, Jonathan recommends identifying the business outcome and working backward. Is the goal improved quota attainment, a higher win rate, reduced churn, or another result? What is preventing it today? Sometimes the answer concerns process or people rather than AI. We then turn to the customer journey. Jonathan describes how 1mind aims to help buyers get product information, receive demonstrations, and answer technical questions before moving into a conversation with a person. His argument is that a website visitor may already be researching a specific decision, so an experience built only around awareness and meeting bookings can leave their immediate questions unanswered. The practical question is how to support that buyer at the moment they need help. Jonathan talks about website assistance, sales engineering, product guidance, and customer success. He sees opportunities to provide information when a team cannot staff every interaction, while giving people additional context when they take over. We also discuss the human side of AI adoption. Jonathan describes the continued value of emotional intelligence, relationships, and strategy. He challenges the assumption that every buyer always wants to speak with a person, but also recognizes that providing facts is different from understanding someone's motivations. The opportunity depends on the individual, the task, and the trust they place in the experience. A comparison between two companies using the same outbound tool brings the discussion back to operational reality. In Jonathan's account, one company had clearer customer definitions, messaging, and data. The other used a broad target and expected the technology to compensate. His lesson is to examine what the system receives before concluding that another purchase will solve the problem. For leaders assessing AI value, we talk about conversion rates, selling time, and the parts of a buyer's journey a system can support. Establishing a baseline matters. A team cannot confidently claim improved selling time if it never measured how much time people had to sell in the first place. The interview also contains an inconsistent percentage claim about hallucinations. I have kept those figures out of the promotional package pending clarification. Reliability needs evidence, and the discussion does not provide a benchmark methodology or enough detail to establish a performance guarantee. We close with Jonathan's advice to remain willing to learn and reconsider familiar habits. He is candid about feeling overwhelmed by the pace of change, which makes the conversation useful for people who are experimenting without feeling they have every answer. Where does your buying journey create unnecessary effort, and how would you assess whether AI made the experience better for the customer? Please listen and share your thoughts.
#3754
October 15 · 48 min
Turning Building Data Into Better Decisions With Willow
What would change if your building could help explain what needs attention, why it matters, and whether the action you took actually worked? In this episode of Tech Talks Daily, I welcome Bert Van Hoof, CEO of Willow, back to the show to discuss operational AI for buildings and infrastructure. Bert spent about 20 years at Microsoft, where his work included launching Azure Digital Twins. He now works with environments such as hospitals, airports, universities, corporate campuses, and data centers, where the condition of a physical space can directly affect the work happening inside it. We begin with a problem familiar to many facilities teams. Buildings already have sensors, management systems, meters, maintenance records, and dashboards. Having access to information does not necessarily help an operator decide what to do next. Bert describes operational AI as a way to move through a sequence of questions: what is happening, why is it happening, what might happen next, what should we do, and did the action produce the expected result? Context gives those questions meaning. A temperature reading alone cannot tell us whether a room is operating correctly. We need to know the room's purpose, occupancy, equipment, weather conditions, and recent maintenance history. Bert explains how digital twins and knowledge graphs connect those relationships so that a system can assess the situation rather than interpret a sensor value in isolation. The discussion then turns to predictive maintenance. A probability of failure may be useful to an analyst, but a technician also needs to understand the consequence, urgency, and next step. Bert describes how condition-based maintenance can support equipment decisions and how earlier detection can reduce the disruption caused by refrigeration problems or water leaks. These are practical examples of the types of outcomes Willow targets, rather than a claim that every customer will achieve the same result. There is a human side to the conversation too. Bert describes operators opening numerous browser tabs each morning to assemble a picture of their buildings. Daily briefings and natural-language questions could reduce that search effort, helping teams focus their attention on the issues that deserve it. We also discuss the relationship between occupancy, ventilation, energy consumption, comfort, and maintenance. Bert describes Willow's Active Control and the use of schedules and occupancy signals to help buildings respond to how spaces are being used. His argument is that these decisions should account for the whole environment. Saving energy in isolation can create different problems if comfort, reliability, or safety is overlooked. Trust runs through the interview. A poor recommendation in a hospital has different consequences from an awkward paragraph in a document. Bert argues for explainable recommendations, operational evidence, defined limits, and verification after an action. He also sees experienced facilities engineers as part of the solution, with responsibility for setting objectives and judging trade-offs. For me, the larger question is how building operations connect with the organization's purpose. A hospital needs spaces and equipment ready for patient care. An airport needs to account for passengers, flights, gates, and changing demand. Understanding the environment means understanding what it needs to support. Where would operational AI make the most useful difference in your buildings, and what evidence would you want before allowing it to take action? Please listen and share your thoughts.
#3753
October 14 · 25 min
Why Old Leadership Playbooks Fail in the AI Era With Pantheon
How much damage can a proven leadership playbook cause when it is applied to the wrong company? Experience can give executives confidence, but Sameer Kazi argues that confidence becomes dangerous when it replaces diagnosis. In this episode of Tech Talks Daily, I welcome Sameer Kazi, CEO of the Pantheon Platform, back to the show for a conversation about copy-and-paste management, first-principles thinking, AI adoption, and the value of listening before making changes. Pantheon supports major websites and provides DevOps tools for agencies and enterprise web teams, placing Sameer close to the changing relationship between SaaS, web development, and agentic website creation. Sameer describes a familiar mistake in technology companies. A new executive arrives with a formula that worked elsewhere, hires people from a previous team, and assumes the same inputs will produce the same growth. In the early SaaS era, for example, a company might have treated its sales efficiency ratio as a direct signal to hire another quota-bearing sales representative. Product-led growth, changing buying behavior, and different forms of product market fit have weakened that assumption. Adding salespeople does not automatically create demand. His alternative begins with product market fit and the way buyers discover, evaluate, and purchase a product. From there, leaders can study the mechanics across marketing, sales, customer success, support, and product development. Sameer argues that rapidly growing AI businesses do not come with a settled operating manual, so executives need curiosity and evidence instead of a formula copied from the past. Customer feedback provides one source of that evidence. Pantheon runs a public Slack channel where customers and partners speak directly with the company. Sameer says those conversations can reveal customer experience problems and, in some cases, signal a platform slowdown before observability tools detect it. A small piece of feedback may not represent every customer, but it can give an experienced leader a reason to investigate immediately. We also discuss AI return on investment and the growing cost of adding token consumption to an organization's existing workforce and infrastructure. Sameer does not claim to have one universal metric. Instead, he recommends looking at customer reactions to AI across sales, support, and internal processes. If AI makes a process faster for the company but worse for the buyer, customer evidence should reveal the problem. The conversation then turns to data ownership. Sameer compares the hesitation companies once felt about moving data into the cloud with the speed at which some now provide business data to frontier AI labs. He expects small language models and open-weight models to give companies additional choices, including the option to run systems on their own hardware and isolate proprietary information where appropriate. One of the strongest leadership examples comes from Sameer's arrival at Pantheon. He promised to speak with every employee and spent four months holding small roundtables. The format began with introductions and favorite movies before moving to a direct invitation: tell me what works, what needs attention, and what should be addressed first. Sameer describes the result as a treasure trove of information from people closest to customers, prospects, products, and daily operations. That approach also challenged the lazy assumption that long-serving employees represent outdated thinking. Sameer found experienced practitioners who had built agencies, developed large fleets of websites, and worked directly with customers. Recognizing that knowledge can strengthen commitment, while dismissing it can leave employees disconnected and encourage quiet quitting. Can leaders keep the principles that made them successful while treating every familiar method as a hypothesis to be tested? Listen to the episode and share how your organization balances executive experience with customer evidence and employee knowledge.
#3752
October 13 · 27 min
Making Travel eSIMs Work the Moment You Land With Holafly
What matters most when you land in another country and your phone has no data? The cheapest plan may have looked like the obvious choice before departure, but priorities change quickly when you need a map, a translator, a ride to the hotel, or a way to tell your family you arrived safely. In this episode of Tech Talks Daily, I speak with Alfonso Mata Villa, Head of Strategy at Holafly, about the growth of travel eSIMs and why they have moved from a specialist product to a far easier alternative to physical SIM cards and traditional roaming. Alfonso explains that modern smartphones now make it possible to keep a normal number active while adding a separate data connection for an international trip. The result is a product that feels less like replacing a phone service and more like adding connectivity for a destination. We talk through the practical experience for someone using an eSIM for the first time, including installation, activation, turning off the home data connection, and avoiding an unexpected roaming bill. Alfonso also describes Holafly's Always On benefit, which the company says gives customers one gigabyte of data each month so they can access basic services after arrival before buying another plan. The apparent simplicity hides a demanding operating model. Holafly works across over 200 markets, where network quality, device behavior, carrier restrictions, hardware differences, and local regulation can all vary. Alfonso explains why providers need strong operator relationships, redundancy, and the ability to move traffic when coverage becomes unreliable. His argument is that travelers should never have to see the complexity required to make a connection work. That raises a useful question for anyone comparing travel eSIM providers. Price and data allowance matter, but what happens after the purchase? We discuss network reliability, data routing, latency, access to local services, privacy, and the speed of customer support when an installation or connection fails. Alfonso says Holafly treats multilingual support, unlimited data, transparent pricing, and reliable connectivity as parts of the product rather than extras. The conversation also looks at the changing relationship between eSIM companies and mobile operators. Travel eSIMs compete with traditional roaming, but they still depend on operator networks. Alfonso sees an increasingly connected model in which operators supply infrastructure while global providers package connectivity for travelers and distribute it through airlines, travel agencies, hotels, online travel platforms, and financial services. There is also a risk of leaving some travelers behind. Older phones may lack eSIM support, while carrier-locked devices and regional differences can create additional barriers. Alfonso argues that progress should be measured by whether the technology becomes simple enough for almost any traveler to use, rather than by technical sophistication alone. If mobile connectivity can become another digital part of planning a trip, will travel eSIMs finally retire the anxiety and surprise costs associated with international roaming? Listen to the episode and share your experiences with eSIMs, roaming charges, and staying connected abroad.
#3752
October 12 · 27 min
Who Controls Your Data in a Sovereign AI World With NetApp
Could your board explain where the organization's most valuable data resides, who can control it, and how quickly it could be recovered In this episode of Tech Talks Daily, I speak with Adam Gale, CTO for Cybersecurity and Artificial Intelligence at NetApp, about why digital sovereignty is moving from compliance teams into boardroom discussions. Adam argues that data has measurable business value, so decisions about its location, ownership, protection, and recoverability should be treated with the same seriousness as other financial risks. We discuss how the US CLOUD Act, Europe's sovereignty agenda, and wider geopolitical tensions are changing cloud decisions. Adam explains that sovereignty extends beyond physical data location. Hardware supply chains, software development, labor, access, operational control, and recovery all influence the level of sovereignty an organization may require. His advice is to treat sovereignty as a scale rather than impose the same controls on every workload. The conversation also turns to sovereign AI. Adam describes why businesses and governments want greater control over training data, intellectual property, model activity, and audit records. He explains how poisoned data can produce plausible but unsafe outputs, and why controlled AI environments, traceable logs, and immutable records matter when systems begin making consequential decisions. We then connect data visibility with business value. Adam shares how uncertainty over data location can affect a company acquisition and explains why insurers increasingly ask organizations to prove how information is protected and recovered. He also describes the growing role of behavior analytics, automated responses, immutable storage, and storage-level ransomware protection in limiting damage and restoring trusted data. If digital sovereignty begins with responsibility, control, and security, who owns those decisions in your organization today? Listen to the conversation and share your thoughts with me.
#3751
October 11 · 34 min
Moving Enterprise AI From Pilot to Production With Stelia
Why do promising AI pilots struggle when they become services that real customers depend on? In this episode of Tech Talks Daily, I'm joined by David Hughes, Chief Technology Officer at Stelia, to talk about the engineering decisions behind enterprise AI deployment. Dave's responsibilities cover networking, cloud infrastructure, and customer-facing products, giving him a view of what happens when an application meets the physical systems expected to support it. We begin with a question that can get lost in the excitement around a successful demonstration. What problem was the system designed to solve, and what happens when the number of users changes? Dave describes reviewing a proof of concept that had worked for one external customer, only to identify a messaging component he believed would become a bottleneck under heavier demand. His lesson is to understand the intended workload before treating a working prototype as a production service. The discussion also offers a balanced view of cloud computing. Dave praises elastic resources and programmatic control, while arguing that demanding AI workloads require teams to understand how storage, networking, and compute interact. A fast network cannot compensate for storage that cannot feed it. His steamship analogy makes the point familiar: the engine needs a steady supply of fuel if it is going to maintain its speed. We talk about lessons from high performance computing, Dave's experience at Genesis Cloud, and the practical constraints of finding data center space and power. He explains why location and available capacity can influence architecture before a team even starts comparing GPUs. For business leaders, the conversation becomes especially useful when we turn to measurement. Dave describes assessing internal model services through their effect on spending and judging customer projects by their output. His hotel occupancy analogy raises a question for anyone approving an infrastructure budget: how much of the asset's capacity will actually be used? There is also a tension between engineering discipline and experimentation. Dave describes starting an internal inference service with existing servers and GPUs, then expanding it as demand grew. Proving that people wanted the service helped establish a case for a fuller investment. The lesson is to distinguish a useful experiment from evidence that a system is ready for wider demand. We close with the Stelia newsroom and its engineer-written technical content. The supplied guest notes also introduce Stelia AI OS, described by the company as a full-stack operating system for enterprise AI. The interview focuses on deployment principles rather than a detailed assessment of that platform. Where does your organization encounter the biggest obstacle when moving AI into production, and what evidence helps you decide where to invest next? Please share your thoughts.

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