Neil C. Hughes

Tech Talks Daily

A Technology , News and Tech News podcast

#46 in Technology · 🇵🇭 #58 in Technology · 🇦🇪

About the show

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.

Newest episodes

Recovering what matters first with Fusion Risk Management

Nov 11, 2026 23 min

If your business lost several systems at once, would the recovery order reflect what your customers need most? In this episode of Tech Talks Daily, I speak with Mike Campbell, CEO of Fusion Risk Management, about operational resilience and the connections that can turn one disruption into a wider business problem. We discuss why application recovery, supplier continuity, employee knowledge and customer service need to be understood together when leaders decide what to restore first. Mike shares an example involving an unnamed retailer whose existing IT recovery plans followed a logical technical sequence. During a demonstration using those plans, he says the business identified a different priority: restoring its online presence before concentrating resources on stores that had some manual alternatives. The lesson is about the assumptions inside the plan. This was a planning example, rather than evidence of a measured recovery during an actual outage. What interested me was the distance between knowing which applications sit on a server and knowing which business activities depend on them. That connection affects how teams judge financial impact, customer consequences and the order in which work should happen. It also explains why operational resilience is a boardroom discussion, with choices about suppliers, investment and acceptable disruption sitting alongside technical decisions. We consider the role of simulation and testing across different scenarios. Mike describes how Fusion's platform can help teams compare variations and identify recurring weaknesses. Those findings depend on the information and assumptions behind the model, and the conversation doesn't establish that a simulation can guarantee what will happen in a real incident. It does offer a useful way to question whether a single rehearsal has covered enough possibilities. There is also a very practical communications example. If the usual email service becomes unavailable, teams need a way to c...

Understanding the Data Costs Behind Enterprise AI With Acceldata

Nov 10, 2026 23 min

What happens to your AI budget when one business question becomes a series of data queries across several systems? In this episode of Tech Talks Daily, I speak with Rohit Choudhary, founder and CEO of Acceldata, about the work taking place behind an AI response. An agent may need to find information, call several tools, and repeat parts of that process before it can return an answer. Each step can consume resources, and the person asking the question may have little visibility into the infrastructure activity it creates. Rohit uses the example of looking for jewelry without knowing which vault contains it. The analogy helps explain why an AI system can spend time and compute finding the right source before it does anything useful with the information. We discuss his view that repeated queries and tool calls are putting pressure on enterprise infrastructure budgets, particularly where data is spread across older systems and cloud platforms. His banking example brings the problem into focus. Information can pass from mainframes through multiple processing stages before reaching the applications people use. Copies may represent different stages of transformation, and the data that is suitable for one business purpose may be unsuitable for another. An agent needs to use the appropriate source, while the organization needs to understand the reliability and cost of that decision. The banks are not named, and the example should be understood as Rohit's account of the patterns he sees. We also discuss why data quality cannot be separated from governance. Different business teams have different requirements, but AI services can draw on information that crosses those boundaries. Rohit argues for controls that operate throughout the data pipeline, including access restrictions, quality checks, and reconciliation. The business question is whether a policy actually changes what a running system can do. One of his clearest principles is that an agent acting for a person shou...

Securing AI Generated Code Without Losing Speed With Checkmarx

Nov 9, 2026 24 min

If AI can help anyone create an application, who checks whether the result is secure enough to put into production? In this episode of Tech Talks Daily, I speak with Frank Emery, VP of Product Management at Checkmarx, about the skills and security practices needed as AI becomes part of software development. We begin with developers entering the workforce alongside coding assistants and the experience they still need to build. Frank argues that architecture, review and an understanding of what code actually does become increasingly valuable when generating that code takes less time. We discuss what happens when output grows faster than teams can review it and vulnerabilities accumulate across repeated changes. I ask who should own the risk when AI-generated code passes through several tools and teams. Frank places responsibility with the organization releasing the product, supported by developers who validate their work, team leaders who maintain that discipline and application security teams that establish standards. He also explains why a feature behaving as expected does not establish that it is secure. The conversation considers how much independence coding agents should have. Frank describes the importance of existing controls, the difference between routine changes and architectural decisions, and the need to separate code generation from security testing. We then discuss layered checks across the development process, with quick feedback during generation, further checks around pull requests and deeper testing as an application develops. The aim is to make each check appropriate to the stage rather than applying every test at the earliest possible moment. We close by considering continuous validation and the measures that can show whether a team is reducing risk. Discovery time, detection quality, remediation time and exposure all offer a more useful perspective than counting how much code has been produced. For leaders adopting AI development tools, t...

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All episodes

The newest 50 of 2,000 episodes

Recovering what matters first with Fusion Risk Management

Nov 11, 2026 23 min

Understanding the Data Costs Behind Enterprise AI With Acceldata

Nov 10, 2026 23 min

Securing AI Generated Code Without Losing Speed With Checkmarx

Nov 9, 2026 24 min

Controlling AI Agent Access and Accountability With Ekco

Nov 8, 2026 28 min

What happens when an AI agent pursues an approved task in a way nobody expected, using access that reaches further than it needs? On Tech Talks Daily, I'm joined by Jonathan Trayers, a director at Ekco, to discuss the security decisions behind agentic AI. The conversation focuses on what changes when a system can act with credentials, connect to business applications, and trigger workflows. A useful answer in a chat window and an automated action inside a company create different responsibilities. Jonathan describes working with both deterministic workflows, where rules specify what should happen, and agentic steps, where AI interprets a situation before deciding what to do. Bringing those approaches together raises practical questions about boundaries. Which actions can the system take, what information can it access, and where should a person remain involved? What interested me was his emphasis on limiting the impact of unexpected behavior. Predicting every failure is unrealistic, so he argues for reducing the area an agent can affect. The permissions attached to its identity help determine that boundary. An agent given broad access may be able to create problems across several systems, even when its original assignment sounds narrow and reasonable. We discuss separate identities for agents and their connections, restricted permissions, continuing monitoring, and the ability to revoke access. Jonathan also describes governance as a series of practical decisions about the use case, the risks the business accepts, and the people responsible for approving the deployment. Prompts can express instructions, while controls around the systems an agent reaches need their own attention. Testing is another part of that process. Jonathan recommends beginning in a controlled environment, observing the outcome, and then using supervised operation before handing over greater responsibility. His invoice example makes the point understandable. Processing an invoice and author...

Bringing enterprise AI inference on premises with Scality

Nov 7, 2026 19 min

What should a business consider before moving AI inference onto infrastructure it owns? In this episode of Tech Talks Daily, I speak with Paul Speciale, Chief Marketing Officer at Scality, about the decisions behind enterprise AI infrastructure. Following the launch of Scality AI Inference Factory, we discuss why organizations are assessing on-premises inference alongside cloud services, with attention to costs, model control and the location of sensitive data. Paul expects a hybrid approach, with different workloads running in the environments that suit their requirements. The conversation begins with the work a GPU repeats when an AI session needs to process familiar context. Paul explains how saving and retrieving that information can reduce recomputation, and why storage and networking deserve attention alongside compute. We discuss where the economics change as usage grows and why owning the infrastructure also means taking responsibility for utilization, performance and the operating bill. We then consider data placement across storage tiers. Paul argues that there is no single best location for every workload, with performance, cost and sovereignty requirements shaping the decision. I ask where automation should stop and human approval should begin. He describes automating health checks, forecasts and recommendations while retaining approval for consequential changes such as deleting data or changing policies. Cyber resilience is another part of the discussion. Paul explains why an immutable copy needs to be supported by tested recovery, and why sovereignty rules should be checked through attempts to perform actions the system is meant to refuse. We close with the responsibilities infrastructure teams take on as they manage inference directly, from tracking time to first token and context reuse to deciding when a model should change. For leaders assessing their next AI deployment, the conversation offers practical questions about control and the evidence...

Why AI Agents Need Better Data With Steffen Hoellinger of Confluent

Nov 6, 2026 20 min

What if many of the problems businesses blame on AI are actually problems with their data? It's a question I wanted to examine when I sat down with Steffen Hoellinger, Field CTO EMEA at Confluent, during Current 2026 in San Francisco. After a year dominated by conversations about AI agents, I'm increasingly interested in the less glamorous work companies need to get right before these technologies can deliver anything useful. Steffen works with enterprises across Europe, helping them manage real-time data and develop AI applications. Our conversation begins with the difficulties businesses face when information is scattered across departments, applications, and different locations. He explains why simply moving data between systems is no longer enough, and how processing, filtering, and preparing information as it moves can help applications make better decisions. We also discuss Confluent's Streamhouse announcement and the growing role of Apache Kafka and Flink. Steffen explains how these technologies help businesses process information closer to where it originates, rather than waiting for scheduled transfers and reports. For companies trying to put AI agents into production, he argues that having the right information available when it is needed can make an enormous difference. One of my favorite moments comes when our conversation turns to where this technology is actually being used. Steffen shares an unexpected example from the America's Cup, where Kafka helped exchange information between a racing yacht and a separate support boat. We also talk about factories and retail stores that can continue operating independently when their connection to the cloud is interrupted. From there, we examine the particular challenges facing European companies. Data sovereignty, privacy, regulation, and concerns about relying too heavily on public cloud infrastructure all influence technology decisions. Steffen explains why open standards and the ability to operate across...

Current 2026: Why Confluent's Streamhouse Could Solve AI's Real-Time Data Problem

Nov 5, 2026 22 min

What happens when an AI agent makes a business decision using information that's already out of date? It's a question I wanted to put to Stephen Deasy, CTO of Confluent, when we met at Current 2026 in San Francisco. After attending more than 20 technology conferences this year, I've heard countless promises about what AI agents will eventually achieve. But there's a growing problem behind those ambitions. Businesses are still struggling to get accurate, current information out of the systems they already own. Confluent has introduced Streamhouse, a data architecture designed to address that problem. Stephen explains why the company believes existing approaches can leave applications working with delayed or incomplete information, and how the new architecture brings streaming, processing, governance, and querying together. We also discuss Lightning Tables, one of Confluent's major announcements at the event, and what direct access to current Kafka data could mean for developers and their businesses. I was particularly interested in what this means for companies that have already invested heavily in data warehouses and lakehouses. Stephen makes the distinction between analyzing past performance and running business operations using current information. He explains why Confluent sees these approaches working together, and why existing technology investments can provide the foundation for introducing Streamhouse. We also discuss the experiences of Instacart and Citizens Bank. Stephen recalls how Instacart faced a sudden increase in demand during the pandemic, when accurate information about orders, inventory, and deliveries became especially important. He also describes how Citizens Bank improved loan fulfillment processes through real-time data streaming. Both examples help explain why the speed at which information moves between business systems can affect customers directly. The conversation also covers IBM's relationship with Confluent, open technologies includi...

Electrifying Collection Fleets and Improving Driver Safety With Republic Services

Nov 4, 2026 26 min

How do you introduce electric vehicles and AI-assisted safety technology into a collection fleet that customers depend on every day? On Tech Talks Daily, I'm joined by Brett Rogers, Vice President of Operations Technology at Republic Services, to discuss the engineering, infrastructure, and human decisions behind modern waste and recycling operations. Brett's background includes work at Toyota, General Motors, and Cruise. He describes arriving from the automotive sector and discovering how much complexity sits behind a service most people expect to happen without thinking about it. We give particular attention to electric refuse trucks. Range is part of the calculation, but the vehicle also powers equipment that lifts containers and handles material. Brett explains how Republic Services models energy use across the chassis and collection body, taking account of terrain, climate, speed, and the work required on a route. The goal is to identify where an electric vehicle can replace a conventional truck while maintaining service. Route selection can produce some surprising answers. A residential truck making frequent stops may benefit from regenerative braking, while a vehicle with fewer container lifts can spend much of its day covering distance. Brett describes why his team began with residential applications, then used operational data to inform expansion into other vehicle types. The number of lifts alone does not tell the whole story. Charging has its own planning cycle. Brett says utility discussions, design, and construction can require decisions roughly two years before trucks are needed. We discuss why depot readiness has to lead vehicle delivery, alongside the arrangements that keep service running if power is interrupted. He describes spare vehicles and equipment that can accept emergency power, while on-site storage and renewable energy form part of the options being considered. These approaches have different levels of maturity, and the conversation ke...

Building Trust in Production AI Agents With Caylent.

Nov 3, 2026 26 min

What would make you comfortable giving an AI agent the authority to act inside a live business process? In this episode of Tech Talks Daily, I speak with Valerie Henderson, CEO of Caylent, about what has to happen between a successful demonstration and a system a business can depend on. The conversation centers on production readiness, including access controls, spending limits, traceability, workflow design, and the people responsible for the outcome. Valerie describes why introducing agents requires changes to how work gets done. If an organization adds new technology without reconsidering the handoffs and decisions around it, the hoped-for productivity gains can remain difficult to achieve. Her advice is to begin with a bounded problem where automating a few steps could provide a clear benefit, then build on the evidence rather than starting with an ambitious network of agents. We discuss the difference between read-only workloads and agents that can alter live systems. Valerie says many customers are already comfortable with agents retrieving information, while autonomous operations such as self-healing infrastructure are concentrated among the most advanced customers she sees. Those observations reflect her experience with Caylent engagements. They do not establish a universal level of enterprise adoption. For me, one of the most useful questions is what meaningful human oversight looks like. Valerie is clear that people remain accountable for the instructions and access they give agents. She emphasizes deliberate testing, appropriate governance, and the ability to understand what a system has done. We also discuss how agents can repeat work without producing anything useful, creating a bill that needs to be considered alongside accuracy and reliability. The conversation moves into Caylent's own operating model. Valerie describes moving away from assumptions built around human time and materials, making existing technical knowledge easier to reuse, and cha...

Connecting AI Fall Detection With Care Workflows at KamiVision.

Nov 2, 2026 25 min

What makes an AI fall alert useful to the person who needs help and the team responsible for responding? In this episode of Tech Talks Daily, I speak with Mike Link, chief operating officer at KamiVision, about deploying computer vision in residential care. We discuss the connection between the sensor, the people receiving its notifications, and the decisions that follow. The conversation also looks at privacy, connectivity, false alarms, and why a system that works in a care facility needs further work before it can serve a wide range of homes. Mike describes KamiVision's path from home-security cameras to its KamiCare platform. His account of fall detection involves three stages. A device identifies a possible event, a cloud model filters notifications, and a person reviews a blurred, encrypted clip before an alert reaches the user. The human review is part of the service he describes, so it belongs in any discussion of how the system performs. I ask about the company's 99.5% accuracy claim and what buyers should understand before relying on a number like that. Mike explains the filtering process, but the interview does not establish a test population, a missed-event rate, or a complete statistical definition. His answer is a starting point for further questions about performance across the whole service, rather than proof that a device detects 99.5% of all falls by itself. One of the most useful examples concerns a resident found on the floor. Mike describes how reviewing an event can help staff understand whether someone fell or deliberately lowered themselves because they needed help with a task. That information may lead to a change in a routine or room layout. It is an example of how visual context can inform care, rather than an independently measured claim that the technology prevents injuries. We also discuss the difference between a managed care setting and a home. A residential facility can have relatively consistent room layouts and lighting. Homes...

What Proof of Reserves Can Tell You With LeveX

Nov 1, 2026 27 min

What can a crypto exchange's proof of reserves establish, and what might still be missing from the picture? On Tech Talks Daily, I'm joined by Harvey Liu, co-founder and CEO of LeveX, to discuss transparency, platform engineering, and the responsibilities that come with operating a centralized exchange. Harvey brings a computer science background, an MBA, and experience in venture capital. Our conversation looks at how becoming a founder changed the questions he asks about growth, spending, product delivery, and customer assets. We begin with the community idea behind LeveX. Harvey recalls meeting other crypto investors and wanting an online environment where people could discuss ideas alongside trading. He describes that as part of the motivation for building the company. The interview also makes clear that some social features remain in development or are planned for a later release. A community vision and a finished product roadmap are different things, and the discussion gives listeners a view of that work in progress. The central technology question is proof of reserves. Harvey explains why a snapshot should be considered in context, including its date, the assets it contains, and the liabilities it may not reveal. He discusses the limitations of relying on a platform's own token and the difficulty of understanding obligations outside the information shown. Publishing reserve information can contribute evidence, but the conversation does not establish that a snapshot alone proves a business is solvent or that customer assets are protected under every condition. We also discuss the engineering approach Harvey says LeveX uses. He describes Merkle-tree verification, user account inclusion, and separating parts of the reporting process from the live trading environment. The challenge is to provide useful information while managing privacy and system load. His explanation is a founder's account of the architecture, rather than an independent technical audit or a...

Reducing IT Firefighting With ControlUp

Oct 31, 2026 28 min

What needs to happen before an IT team can trust an AI agent to resolve a problem rather than simply report it? In this episode of Tech Talks Daily, I welcome Jed Ayres, CEO of ControlUp, and Gadi Feldman, Chief Product Officer, to discuss autonomous endpoint management and the practical work behind it. We begin with a familiar problem for enterprise IT teams, plenty of monitoring data and recurring issues that still require investigation, ticket transfers and manual intervention. Jed explains why the aim is to follow an operational problem through to an authorized action and then verify that the issue has been resolved. Gadi describes how specialist agents could bring together expertise across areas such as endpoint management, networking and storage. We discuss the connections those agents need to existing tools and why an understanding of the technology is only part of the job. Agents also need access to company procedures, priorities and knowledge, much as a new member of an IT team needs to learn how the organization works. Gadi explains how configurable human approval and other controls form part of that approach, with teams able to begin with analysis and recommendations before allowing additional actions. Jed shares a CHRISTUS Health example involving an audio driver issue across approximately 900 devices, illustrating the value of seeing a common pattern rather than treating every device as a separate support problem. We also discuss what a useful business case should measure. Alongside the time spent by IT, leaders need to consider the employee's experience of waiting for help or finding a workaround. Device performance and software usage can also inform decisions about equipment replacement and unused licenses. The conversation considers what becomes possible when repetitive work takes less of the team's time, while acknowledging the need to establish trust and define the limits of autonomy. For leaders looking for a starting point, Jed describes Cont...

Making AI the ERP Interface With NetSuite

Oct 30, 2026 23 min

When would you trust your ERP system to move from answering a question to carrying out the work? Recording at SuiteWorld in Las Vegas, I spoke with Brian Chess, who leads AI product and technology for Oracle NetSuite, about what that progression looks like in practice. Brian was an early developer when the business was called NetLedger, later left to build a company, and returned with a different perspective on the needs of small and growing businesses. Our conversation focuses on how AI can help people understand their systems, complete unfamiliar tasks, and gradually delegate work without losing control of the process. One of Brian's most useful ideas is AI acting as a teacher. He describes users asking Ask Oracle who has logged into their account, or asking it to explain a script they do not fully understand. The benefit is the answer and the explanation of how to find it. Someone who previously needed help from a colleague can begin to understand a part of the system for themselves. He also shares an example involving NetSuite customer MCNC. When the payroll administrator was unavailable, Brian says a controller who had not been trained on the process used Ask Oracle for guidance. The outcome he describes is straightforward and meaningful: payroll ran on time. It is a customer account from the interview, rather than an independently assessed result, but it illustrates how useful AI assistance can be without requiring a dramatic productivity claim. Financial close provides another practical example. Brian describes starting with the spreadsheet a finance team already uses, modelling that process inside NetSuite, and introducing automation in stages. I recognise the appeal of that approach from my own time supporting month-end and year-end work in IT. Teams need to understand how the process will run, especially when a mistake can affect reporting or the system of record. We discuss how permissions apply as software gains the ability to act. Brian says Ask Or...

Turning AI Experiments Into Business Value With Newton

Oct 29, 2026 30 min

What if the biggest gain from AI comes from reducing the distance between the person who understands a problem and the person building its solution? On Tech Talks Daily, I'm joined by Ben Grinnell, Chief AI and Capabilities Officer at Newton, a strategic delivery consultancy working with private and public sector organizations. Ben's career stretches from an early web consultancy to enterprise technology delivery and today's AI tools. He describes returning from retirement after reading a draft book about vibe coding and realizing he wanted to be part of what came next. What interested me was his practical explanation of how AI can change delivery. When a consultant or business specialist can test an idea while hearing the problem directly, fewer handoffs may mean less lost context and quicker feedback. Ben sees an opportunity to build useful solutions closer to the work. He also warns against confusing a demonstration that looks finished with software that is ready to run reliably. Security, architecture, testing, and maintainability still need people who know which questions to ask. We discuss the relationship between business teams and IT. Employees have been building spreadsheets, macros, and local workarounds for years, so the challenge of technology appearing outside central oversight is familiar. AI changes the speed and scope of that activity. Ben argues for keeping technical expertise close to the business, connecting small projects to existing enterprise investments, and producing value early enough that people want to participate. Trust runs through the conversation. Ben describes a progression from private use of AI assistants to shared agents that appear within a team's existing tools. Visibility can help colleagues understand what is happening, but easier access to company information can also expose old permissions problems. A document that has sat unnoticed on a shared site can become easier to find, making sensible access controls part of adopti...

NetSuite Founder on the End of ERP and What Comes Next

Oct 28, 2026 18 min

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 like...

Giving Enterprise AI Agents the Business Context They Need With SAP

Oct 28, 2026 26 min

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...

Putting Engineering Judgment to Work With AI and Southworks

Oct 27, 2026 28 min

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 streami...

Understanding the Infrastructure Behind Stablecoin Payments With Noah

Oct 26, 2026 20 min

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...

Designing ERP Access Governance Before Go Live With Pathlock

Oct 25, 2026 34 min

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 organizati...

Losing the Endpoint Recovery Gap With IGEL

Oct 24, 2026 19 min

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 i...

Understanding Tokenized Shares and Ownership Rights With Equiniti

Oct 23, 2026 25 min

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 receiv...

Improving Specialty Care Access With AI and Leap

Oct 22, 2026 28 min

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, dependin...

Keeping the Human Voice in AI Translation With DeepL

Oct 21, 2026 27 min

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...

Making Industrial Work Safer With Ascend Robotics

Oct 20, 2026 13 min

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 robo...

IFS Unleashed: How Digital Workers Are Becoming Digital Teams

Oct 19, 2026 14 min

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 capa...

IFS Unleashed: AVEVA on Why Industrial AI Is More About Data Than Algorithms

Oct 19, 2026 12 min

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...

Making Enterprise Data Ready for AI With The Modern Data Company

Oct 18, 2026 29 min

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...

Moving Marketing From Static Rules to AI Agents With JustAI

Oct 17, 2026 22 min

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 retu...

Building a Better Buying Experience With AI and 1mind

Oct 16, 2026 30 min

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 o...

Turning Building Data Into Better Decisions With Willow

Oct 15, 2026 48 min

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...

Why Old Leadership Playbooks Fail in the AI Era With Pantheon

Oct 14, 2026 25 min

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 no...

Making Travel eSIMs Work the Moment You Land With Holafly

Oct 13, 2026 27 min

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 c...

Who Controls Your Data in a Sovereign AI World With NetApp

Oct 12, 2026 27 min

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...

Moving Enterprise AI From Pilot to Production With Stelia

Oct 11, 2026 34 min

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 th...

Reducing Hospitality Friction Without Losing Human Service With Aven Hospitality

Oct 10, 2026 27 min

Can a hotel modernize its technology without turning hospitality into another automated transaction? In this episode of Tech Talks Daily, I speak with Mark Hollyhead, Chief Transformation Officer at Aven Hospitality, about the systems behind thousands of hotels and the operational decisions that affect both employees and guests. Mark brings experience from British Airways, Heathrow Airport, Expedia, and travel management before joining the company carved out from Sabre with support from TPG. His argument begins with a distinction that many technology programs miss. Guests rarely remember the property management system, API, or data pipeline behind a stay. They remember whether somebody knew their name, solved a problem, explained when the room would be ready, or prevented them from repeating information after a long journey. Technology matters because it can remove the administrative work surrounding those moments. Mark wants frontline employees to have time to be hospitable, especially when a situation requires empathy or judgment. That means looking beyond the promise of convenience and asking whether a system gives staff better information, reduces repeated work, and leaves them free to focus on the person in front of them. Mark shares an example from a hotel he visits regularly in Dallas. Different employees recognized a few preferences from previous stays, including his connection with Seattle, a request for two bottles of water, and a preference for a corner room. None of those details required the hotel to know everything about him. They gave the employee enough context to make the interaction feel personal. The example also illustrates the boundary between thoughtful personalization and an experience that feels intrusive. Mark's test is whether the customer would be surprised by what the company knows. If the guest begins wondering how the information was obtained, trust has already weakened. Hotels hold identity, payment, preference, and location data,...

Building Trust in Autonomous IT With LogicMonitor

Oct 9, 2026 34 min

When should an AI system be allowed to fix an IT problem without waiting for a human to approve the action? In this episode of Tech Talks Daily, I speak with Matt Tuson, General Manager for Europe, the Middle East, and Africa at LogicMonitor, about the practical path from reactive IT operations toward autonomous IT. The promise is attractive: fewer repetitive tasks, faster incident resolution, less disruption, and systems that can correct familiar problems before users feel the impact. The difficult part is deciding when the available data, controls, and evidence are strong enough to trust an automated action. Matt argues that observability is taking on a different responsibility. It once helped engineers answer what happened and why. In an autonomous environment, it must also give AI enough connected context to recommend or execute what should happen next. That means understanding how infrastructure, networks, cloud services, applications, and other operational signals relate to one another. A fast decision based on an incomplete view can resolve the wrong symptom, create duplicate incidents, or make the original problem worse. Trust therefore begins with the information supplied to the system. Many IT environments contain separate monitoring and management tools introduced by different teams to solve specific problems. Those specialist products may continue to do useful work, but their signals often remain disconnected. Matt describes a business combining nine organizations, each bringing its own tools and operational practices. The challenge was not simply the number of products. It was the inability to connect their outputs into a reliable view of what was happening across the combined environment. This matters because AI can process poor information as quickly as good information. Matt warns that adding autonomous remediation to incomplete, duplicated, outdated, or poorly contextualized data risks creating automated confusion. False positives multiply, root...

Moving AI From Pilot to Production With Broadridge

Oct 8, 2026 38 min

What has to change before an AI pilot can become part of a regulated financial workflow? In this episode, I speak with Tom Carey, President of Broadridge's Global Technology and Operations Business, about the work required to move AI beyond personal productivity tools and prototypes. Tom brings an unusual perspective to the conversation. He studied AI in the 1990s, long before current computing power and language models made commercial deployment practical, and now works where financial market infrastructure, technology and operations meet. Tom describes four levels of AI productivity. The first helps an individual complete a task. The second turns successful personal patterns into tools that other employees can use. The third places agents inside production workflows while a person verifies the result. The fourth allows agents to operate without constant human review, but only within a controlled environment with monitoring, policies and evidence that the system is behaving as expected. One of the strongest lessons from our conversation is that AI can force an organization to examine a process it may have accepted for years. Tom says Broadridge removed 30 percent of the email traffic entering one operational group after finding duplicates, messages that required no response and work that should not have existed. His own estimate is that around half the benefit of an AI program may come from simplifying the current process before advanced models are applied. That is an important counterweight to the assumption that every inefficiency needs a language model. We also discuss the economics of enterprise AI. Tom compares current token-cost surprises with the early cloud projects that moved existing workloads without redesigning them for a different computing model. Broadridge baselines costs, tests use cases through its operations business and monitors token consumption. He shares one example in which a team's default configuration automatically moved to a newer and...

Measuring Workplace AI by Employee Outcomes With DXC Technology

Oct 7, 2026 34 min

What if the most useful measure of workplace technology was not the number of tickets closed, but the amount of productive time returned to employees? In this episode of Tech Talks Daily, I speak with Kelly Candler, Global Offering Lead for Workplace and Business Process Services at DXC Technology, about moving digital workplace services away from activity metrics and toward employee and business outcomes. Kelly has worked on both sides of the managed services relationship. Before joining DXC, she was a customer of the company and several of its competitors, giving her a practical view of what large enterprises expect from workplace technology and where support models continue to disappoint. Kelly begins with a simple distinction. Traditional IT reporting often focuses on ticket volumes, response times, and calls answered. Employees care about whether they can do their jobs, how much time they lose, and how quickly they can return to productive work. When the outcome becomes the starting point, support is judged by its impact on the employee rather than the amount of activity generated behind the scenes. That matters because many companies already have crowded workplace technology estates. Years of investment have produced overlapping tools, legacy applications, automation, device platforms, and service channels. Adding another AI product can increase cost and confusion if it replaces nothing and connects to little. Kelly argues that organizations should understand the technology they already own, identify the employee outcomes they want, and use orchestration to connect those investments rather than discarding them automatically. DXC positions its Workplace Services offering as a people-centered, AI-native service enabled by DXC OASIS, its orchestration platform for Human+ agentic AI workflows. In the interview, Kelly describes Human+ as a partnership in which AI handles repetitive work while people retain creativity, judgment, and accountability. The intention...

Testing Cyber Resilience Before an Incident With SolarWinds

Oct 6, 2026 23 min

What does cyber resilience look like when leaders must make a costly decision before anyone fully understands the incident? In this episode of Tech Talks Daily, I speak with Justin Henkel, CISO at SolarWinds, about moving the security conversation beyond prevention and preparing the wider business to respond, recover, and communicate under pressure. Justin brings 22 years of Air Force experience across intelligence, cyber operations, and special operations, alongside leadership roles in the public and private sectors. His intelligence background shapes a simple but demanding approach to uncertainty. Technical teams may need time to establish exactly what happened, but business decisions cannot always wait for a complete answer. Leaders need to understand what is known, what remains uncertain, which customers could be affected, and what action limits the potential damage while the investigation continues. Justin illustrates that tension through an incident involving a database that returned larger responses than expected, including information connected with other customers. The team did not initially know the cause or full extent. His recommendation was to take the affected product offline while the company investigated. Sales and marketing colleagues were understandably concerned about revenue and reputation, but Justin framed the decision in commercial terms. A temporary loss measured in millions was preferable to allowing one problem to become hundreds of problems with consequences measured in billions. The incident was ultimately connected with the timing of a database update and a system that failed open rather than failing safely. The example shows why the CISO's job has expanded beyond explaining technical controls. Security leaders must help executives compare immediate commercial pain with the possible cost of leaving an uncertain situation untouched.

Stopping Shadow AI Without Slowing Employees With Liminal

Oct 5, 2026 28 min

Can an organization control employee AI use without making the approved tools so restrictive that people simply work around them? In this episode of Tech Talks Daily, I speak with Steven Walchek, Co-Founder and CEO of Liminal, about shadow AI, enterprise governance, data privacy, and the growing tension between employee productivity and corporate security. Steven's career includes leadership roles at FIS and AWS, along with involvement in three successful exits. His experience has given him a close view of how major technology adoption cycles begin inside companies, often before leadership has developed the policies, budgets, and controls needed to manage them. The story behind Liminal began with an intensive period of customer discovery. Steven and his co-founder spoke with 100 prospective customers in 90 days. Across large and small businesses, they repeatedly heard concerns about what would happen to company data after employees submitted it to generative AI providers. That concern has grown as AI tools have spread through the workplace. Steven describes two common responses from CIOs. Some permit employees to use almost any AI product, despite limited visibility into licensing terms, data retention, model training, or regulatory exposure. Others attempt to prohibit AI use completely and assume a written policy will stop employees from accessing these services. Neither response accounts for how people behave when they believe a tool can help them work faster. An employee may use a personal account, take a photograph of a screen, or transfer information onto another device. The company has technically established a policy, but its security team may now have even less visibility into what is happening. Steven compares this with the early adoption of cloud computing. Developers and business teams could purchase services with a credit card, while finance leaders later discovered rapidly growing AWS bills. Cloud adoption created shadow IT because people had acce...

Making Better Marketing Decisions With Braze AI

Oct 4, 2026 20 min

How can marketers make better decisions for individual customers without spending their working lives designing, running, and maintaining separate tests? Recorded at Braze Forge 2026 at the Fontainebleau in Las Vegas, this episode of Tech Talks Daily features my conversation with George Khachatryan, Head of AI Decisioning at Braze. George describes leading product management for AI Decisioning Studio and shares the story behind OfferFit, the company he cofounded before it became part of Braze. We begin with the practical limits of segmentation and A/B testing. George explains that smaller customer segments can make it harder to collect enough evidence for a useful result, while each new creative option introduces further work. His explanation of reinforcement learning offers a different approach. The marketer defines the objective and the options available to the system, which then experiments, observes the results, and adjusts its decisions. We discuss the distinction between Decisioning Studio Pro and the newly announced Decisioning Studio Go. George describes Pro as offering flexibility around success metrics and custom data, with data science support required during implementation. Go is designed as a self-service option using data generated within Braze. At the time of recording, he says Go supports email and optimizes click activity with machine clicks filtered out. Marketers choose the journey, creative options, subject lines, calls to action, available timings, frequencies, and guardrails. The limits are as useful as the possibilities. George recommends a baseline of at least a few thousand clicks per month for a journey using Go, so the model has enough information to learn. He also acknowledges that maximizing clicks will not always maximize conversions. We discuss why those objectives need to be assessed separately, rather than treating improved interaction metrics as proof of additional sales. George explains how the system can learn from similariti...

Forge26: AI Marketing With Human Creativity With Braze

Oct 3, 2026 17 min

How can marketers use AI to improve the customer experience without filling every channel with increasingly similar content? Recorded at Braze Forge 2026 in Las Vegas, this episode of Tech Talks Daily features my conversation with Christy Poulos, VP of Product Marketing at Braze. We discuss the practical choices behind AI marketing, from deciding what a campaign should achieve to keeping creative work, compliance, and customer relationships under human direction. Christy describes Forge as an opportunity for marketers to talk about their craft and the problems they face every day. Technology forms part of that conversation, but her starting point is the work itself. Teams are being asked to adopt AI while also demonstrating a useful return. Her advice is to connect the tools they choose with specific goals for the brand, the team, and the wider business. Producing additional content offers little reassurance if nobody has agreed what success looks like. We discuss Agentic Standards and the less glamorous work of checking campaigns against rules. Drawing on her experience marketing a regulated product, Christy explains why automating repetitive checks could give teams greater confidence in their output. The marketer still sets the boundaries. Her argument is that campaigns should operate within those boundaries whether a human or an agent prepares them. The interview presents the intended value of these capabilities, rather than independent evidence that they remove compliance risk. Another part of the conversation concerns where marketers actually want to work. Operator Connect introduces ways to connect AI assistants and other working environments with Braze. Christy discusses campaign briefs created in Claude and the growing interest in collaboration through Slack. She also makes a clear case for retaining the traditional product interface, where teams can review a campaign in context, check standards, and launch it. Some businesses are interested in these con...

Building Governed AI Across Distributed Data With Starburst

Oct 2, 2026 23 min

Can an enterprise move quickly with AI when the information it needs remains spread across business units, acquired companies, private data centers, multiple clouds, and systems governed by different privacy rules? In this episode , I speak with Justin Borgman, co-founder and CEO of Starburst, about the data architecture decisions now affecting how quickly businesses can turn AI investment into useful results. The conversation begins with a reality many established companies will recognize. Their technology estate reflects years of applications, acquisitions, regulatory demands, regional choices, and earlier infrastructure programs. Justin argues that this history becomes a constraint when AI teams need governed access to information quickly. A company created within the last year may design its data environment around AI from the beginning. A multinational enterprise rarely has that freedom. It must work with valuable data held across different locations while respecting security, privacy, and sovereignty requirements. The traditional response has been to centralize everything. Justin believes the single source of truth is often an impossible target rather than a finished destination. Drawing on his earlier experience at Teradata, he says even customers using a leading database continued to retain information elsewhere. New applications, company acquisitions, regulations, and changing business demands kept creating additional systems. His preferred approach is to accept that distributed data will remain part of enterprise life and build an architecture that can work across it. A federated data platform can query information where it lives while giving users a common point of access. This can reduce the time spent moving data before an analyst, executive, application, or AI agent can use it. Justin illustrates the problem through a large American bank with many lines of business and inherited data silos. Senior leaders could ask commercially important questions...

Measuring AI by Business Results Instead of Adoption With Domino Data Lab

Oct 1, 2026 29 min

How should a business measure AI success when employee adoption tells leaders very little about revenue, savings, risk, or better decisions? In this episode of Tech Talks Daily, I speak with Thomas Robinson, better known as T-Rob, who recently moved from Chief Operating Officer to CEO of Domino Data Lab. After ten years inside the company, he has seen enterprise AI move through several phases, from specialist data science projects to generative AI tools available across the workforce. T-Rob argues that businesses have become too focused on the technology itself. Generative AI has attracted attention because almost anyone can use it, but an individual productivity tool is very different from an AI system making decisions about mortgages, clinical trials, financial markets, or defense operations. As the potential value of a decision rises, so does the financial, regulatory, and operational risk. That is why T-Rob believes governance should be built alongside AI development rather than added after a system has been completed. He compares the process with constructing a building. Engineers do not wait until the work is finished before checking whether it has been designed and assembled correctly. Reviews happen throughout construction. Domino applies the same principle to AI through policy controls, production monitoring, tracing, and continued human oversight. We also discuss why companies should avoid beginning with a fashionable tool and searching for somewhere to use it. T-Rob recommends starting with the company's primary business measures and working backward. A pharmaceutical business may examine the number of promising therapies entering its pipeline, revenue, and risk. The appropriate AI system can then be designed around those outcomes. That system may combine large language models with computer vision, statistical models, rules, and company data. T-Rob believes the assumption that every business problem requires the latest frontier model can waste money...

Braze at Forge 2026: Where Should Human Judgment End and AI Decisioning Begin?

Sep 30, 2026 24 min

What happens when artificial intelligence moves beyond helping marketers create content and begins making decisions on their behalf? Recorded at Forge 2026 in Las Vegas, I speak with Astha Malik, Chief Business Officer at Braze, about how AI is changing customer engagement and what marketers should retain control over as more operational work is handed to software. Astha explains why the long-standing promise of genuine one-to-one personalization has been so difficult to deliver and why she believes AI can finally help brands move beyond broad segments toward individual decisioning. We discuss Decisioning Studio Go, where AI can optimize content, timing, and frequency for different customers, while marketers continue to define the objectives and brand boundaries within which the system operates. But greater automation creates new questions. If AI can generate more campaigns and messages, does marketing simply become noisier? Astha talks openly about the danger of "AI slop" and why using the same models and tools can make brands increasingly forgettable. We also discuss Agentic Standards and the idea of AI checking the work of other AI systems before campaigns reach customers. Astha argues that organizations need controls around agents in much the same way they already have quality processes around human teams. Our conversation also moves beyond marketing into the changing enterprise software interface. Operator Connect allows Braze capabilities to be accessed through environments such as ChatGPT, Claude, and Microsoft Copilot, raising questions about whether employees will increasingly interact with business systems through AI assistants rather than traditional applications. Finally, we examine how organizations can prove AI is creating measurable value, why some businesses remain trapped in experimentation, and Astha's advice for leaders overwhelmed by the pace of change. Her recommendation is simple: start experimenting rather than waiting for certainty. A...

Turning Disposable Research Into Continuous Insight With Cint

Sep 29, 2026 21 min

What if every market research project could continue contributing to business decisions after its original question had been answered? In this episode of Tech Talks Daily, I'm joined by Phil Ahad, Managing Director of Data at Cint, to discuss why he believes companies should move away from disposable research. For decades, the familiar model has been straightforward. A business asks a question, commissions a study, receives the answer and begins again when the next question appears. Phil argues that this process wastes useful information and repeatedly asks people for details that may already be available. His alternative is an always-on human data engine that allows new studies to build on previous research. Existing responses can be combined with first-party information, third-party sources, transactional records and behavioral signals. Phil says this can help organizations answer new questions faster while reducing the burden placed on respondents. That burden matters because survey fatigue is often misunderstood. Phil does not believe people have stopped wanting to share opinions. The problem is the experience. Customers are repeatedly asked long batteries of familiar questions, often after everyday transactions, because the structure of data collection has changed remarkably little since paper surveys. If researchers already know much of the background, they can ask fewer questions and focus on the reasons behind a person's decision. We also examine synthetic data, a term Phil openly dislikes, and the growing use of AI personas or digital twins. At one end of the spectrum, a model might add 200 modeled responses to an 800-person study so researchers can work with a sample of 1,000. Phil says this extends an existing data set rather than creating genuinely new insight. At the other end, a company may create a digital representation of a person from survey responses, purchasing patterns, mobile activity and other signals, then ask that representation new ques...

Security Posture at Machine Speed With Barracuda

Sep 28, 2026 23 min

What happens when attackers can discover and exploit a weakness faster than your organization can patch it? Recorded at Barracuda TechSummit 2026 in Alpbach, Austria, this conversation features Arve Kjoelen, CISO at Barracuda. Arve is responsible for protecting Barracuda's systems, environment, and code, which makes him the person answering the familiar question of who checks the checker. Our conversation begins with the collapse in response time. Security teams once had hours or days to investigate suspicious activity. Arve explains why they may now have minutes or seconds, while vulnerabilities can move from disclosure to exploitation within days. Traditional weekly scans and handoffs to patching teams struggle when attackers operate at machine speed. Arve offers a useful framework for understanding security posture through threats, exposures, assets, and controls. Technology changes constantly, but these categories give leaders a way to assess risk without chasing every new term. He also explains why reducing attack surface can begin with basic questions. Does a system need to be accessible from the internet? Does a web server also need remote management exposed? Does a midsize business benefit from spreading its workloads across every major cloud provider? We examine the difficult balance surrounding AI adoption. Blocking every new tool can prevent employees from benefiting from useful technology, but allowing unrestricted adoption creates new exposure. Arve argues for deliberate choices and guidance that reduce risk without stopping progress. The conversation also addresses AI-guided remediation. Barracuda uses AI internally to identify vulnerabilities, but Arve is cautious about fully automated fixes. An AI system may identify a problem and suggest a solution, while a human remains responsible for judging whether the proposed action could damage a production environment. Faster decisions are valuable only when organizations understand the consequences. A...

Giving Enterprise AI the Context It Needs With Orange Logic

Sep 27, 2026 21 min

Why do increasingly capable AI models struggle to produce reliable answers inside large organizations? In this episode of Tech Talks Daily, I speak with Misti Vogt, SVP of Engagement at Orange Logic. Her career spans military intelligence, data science, and enterprise content technology, and she also teaches in the DAM and AI program at Rutgers University. That combination gives her an unusually practical perspective on how machines interpret information and why business meaning cannot be assumed. Misti argues that enterprise AI reliability depends on the context surrounding company data. A model may be technically impressive, but it needs to understand relationships, rules, metadata, rights, and intent. Without that layer, it reasons over information originally organized for people rather than machines. The result may sound convincing while remaining disconnected from the way the business defines accuracy, trust, and permitted use. We discuss three forms of context. Static context reflects accumulated knowledge. Transactional context develops through projects and outside information. Semantic context helps systems interpret meaning and relationships across large collections of information. Misti compares this with human conversation. When an answer misses the point, we add information until the other person understands what we mean. Digital asset management sits at the center of this discussion because DAM platforms already organize master data, metadata, transactional data, governance, relationships, and usage rights. Misti believes those structures can give AI applications a stronger business foundation. She also argues that content should become self-aware, carrying information about when it was created, how it was produced, its intended audience, where it has appeared, and how it has performed. Natural language search provides a useful example of why this matters. An employee might ask for creative assets suited to a campaign and welcome a broad set of sug...

Building the Five Foundations of AI Value With Mobile Mentor

Sep 26, 2026 34 min

What happens when employees begin using AI before their organization has prepared the data, training, controls and measurement required to support them? In this episode of Tech Talks Daily, returning guest Denis O'Shea, CEO of Mobile Mentor, joins me to discuss the 2026 Endpoint Ecosystem Study. The research surveyed 2,500 workers across the United States, United Kingdom, New Zealand and Australia to understand how employees experience their devices, applications, sign-in processes, support systems and workplace AI. The findings show a gap between access and useful adoption. According to the study figures discussed in our conversation, only 29 percent of employees say AI provides regular or indispensable value in their work, while 48 percent report receiving no AI training or do not know whether training exists. Denis says the differences become sharper by sector. Finance has made greater progress with company-wide and role-specific training, while half of the healthcare and government employees surveyed reported receiving no AI training. The generational picture is equally complicated. Denis says Gen Z workers are adopting AI faster than other age groups, but they are also the group most likely to work around company policies when approved tools create friction. If employees cannot complete a task through the sanctioned route, some will use personal accounts and upload company information to public models. The same workers may also need greater support during onboarding, challenging the assumption that digital familiarity automatically means workplace technology fluency. Denis also shares Mobile Mentor's own mistakes. The company deployed Microsoft Copilot to roughly two-thirds of its workforce, ran competitions and encouraged experimentation. When the board asked whether the investment was working, Denis realized he had no dependable answer. The team had not defined use cases, assigned licenses according to the work being done or established a reliable way t...

Why AI Favors Whoever Automates Most With Barracuda

Sep 25, 2026 25 min

Can security teams defend an organization when attackers are using AI to research targets, personalize messages, identify weaknesses, and launch campaigns at a scale no human team can match? I returned to Alpbach, Austria, for Barracuda TechSummit 26 and caught up with Neal Bradbury one year after our conversation about being secure today and ready tomorrow. A lot has happened since then. Agentic AI has become a boardroom subject, employee AI use has spread across businesses, and attackers have gained access to tools that lower the cost and expertise required to launch sophisticated campaigns. Neal explains why Barracuda has continued with the unified platform strategy introduced at last year's event. In his view, AI creates additional exposure across identities, applications, email, and data, but it does not make every existing security control obsolete. The immediate requirement is to connect information across these areas and accelerate how quickly security teams can interpret and act upon it. We discuss Barracuda ONE, its Barracuda IQ intelligence engine, the Bailey assistant, Integrated Email Protection, and the recently announced Barracuda AI Data Security offering. Neal also explains why the acquisition of Evo Security adds identity protection at a time when businesses must secure human users, service accounts, and AI agents. One customer example shows why connected telemetry matters. According to Neal, Barracuda's team investigated an attempted wire fraud worth almost a quarter of a million dollars. No single product could see the complete attack. Information from email, network activity, and identity systems had to be combined before the team could understand what was happening. The conversation also examines shadow AI. Employees are already placing workplace information into chatbots and using tools outside approved systems. Neal argues that attempting to ban every tool will send that behavior further out of view. Organizations first need to understan...

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Podcast details

Created by: Neil C. Hughes
Status: Active
Latest episode: Nov 11, 2026
Episodes: 2,000
Release period: Daily
Avg. episode length: 27 min
Explicit: No
Language: English

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