Tech Talks Daily

What happens behind the scenes when a cross-border payment uses stablecoins, and how much of that should the customer need to understand?

In this episode of Tech Talks Daily, I speak with Shah Ramezani, co-founder and CEO of Noah, about the infrastructure that connects stablecoins to business payments. The discussion goes beyond the transfer itself to the practical work required to move between currencies, choose a route, verify identities, and reconcile the result.

Shah starts with the friction he sees in correspondent banking and the opportunity for a different settlement layer. He explains how stablecoins can move quickly between participants and then connect to local payment methods at either end. His example of a payment from the United States to Argentina helps explain the choices involved, including whether the recipient wants local currency or a dollar-denominated stablecoin. It is an illustrative scenario rather than a named customer case or a universal comparison of costs and delivery times.

I ask what makes that experience work technically. Shah breaks the process into liquidity, routing, compliance, settlement, and reconciliation. The available partner, customer type, destination, and pricing can all affect how a payment is handled. Noah's role, as he describes it, is to bring those components together so partners can connect without building every part themselves.

Identity is another part of the conversation. Shah discusses know-your-customer and know-your-business checks, including arrangements in which partners rely on checks performed by Noah and receive the information they need. His account makes clear that reducing repeated document requests requires coordination among the organizations involved. It also raises questions about who is responsible for each step and how that responsibility changes across markets.

One idea I found most interesting is that users may encounter stablecoin infrastructure without choosing to hold or handle stablecoins themselves. Shah describes payments in which the technology operates behind a familiar interface. For a business considering that approach, the useful comparison is the full payment journey, including conversion, local payout, support, and each participant's obligations.

We also discuss banks' continuing role. Shah rejects the idea that they disappear and instead describes a different distribution of responsibilities across payment networks and financial institutions. The conversation gives listeners a way to consider how new settlement technology can coexist with established banking relationships.

Trust and regulation remain part of that assessment. Shah argues that stronger regulation provides reassurance, while acknowledging the difficulty of serving customers across jurisdictions with different reporting obligations, identity requirements, and fraud patterns. His confidence in the market's direction is his perspective. It should not be read as a guarantee that every stablecoin, provider, or transaction carries the same protections.

Finally, Shah discusses Noah's plans to broaden its capabilities and invest in product development and hiring. He describes an opportunity to make the payment experience simpler and less complex for the user. For me, the accompanying question is how providers make their responsibilities clear when the underlying process spans several organizations.

I would love to hear your thoughts, so please share your experiences. When you assess a new payment route, how do you weigh speed and cost against clarity about responsibility, access to funds, and help when something goes wrong?

What happens when the pressure to complete an ERP transformation leaves access governance waiting until after go-live?

In this episode of Tech Talks Daily, I speak with Chris Radkowski, GRC Expert and Advisor at Pathlock, about the business consequences of access decisions.

Chris previously spent 17 years at SAP and brings experience across access control, governance and security. We discuss why ERP risk belongs in transformation planning, with examples that connect system availability to manufacturing, incoming deliveries and the processes that keep a business operating.

The conversation considers privileged access inside business applications. An identity does not need to administer a server to hold permissions that affect sensitive information, supplier records or financial transactions.

Chris explains why the combinations of entitlements assigned to a person deserve attention, and how that assessment becomes more complicated as processes extend across several systems. AI agents and automated workflows add to the challenge when they can carry out transactions quickly or operate through credentials associated with a user.

We also discuss the limits of scheduled access reviews. Chris describes the work involved in checking whether an inappropriate permission was used, including the manual effort that can arise when teams need to investigate historical transactions. He argues for combining access information with activity and transaction data, so teams can examine an unexpected pattern and initiate a review rather than waiting for the next certification cycle.

For leaders planning an ERP or AI transformation, Chris recommends considering role design, access assignment, certification and emergency access early in the program. We discuss how changes between SAP ECC and S/4HANA can affect the permissions and transactions on which existing controls depend.

Our conversation offers practical questions about who can act, what they actually do and how the organization responds when operations are under pressure.

Which access decisions would you want resolved before your next go-live, and where are the gaps today? Please share your thoughts with me.

What happens when an organization restores its systems after an incident but employees still cannot connect to them?

In this episode of Tech Talks Daily, I speak with Sterling Wilson, Global Field CTO for Business Continuity and Disaster Recovery at IGEL, about the endpoint recovery gap. Sterling explains why a laptop that is encrypted, unstable or being preserved for investigation can prevent work from resuming even when applications and infrastructure are available. We discuss why continuity planning needs to account for the device people use to reach those services.

The conversation examines IGEL Emergency Mode, an administrator-led capability for supported IGEL Dual Boot-enabled endpoints. Sterling describes moving those devices into IGEL OS so employees can reach resources IT has approved while security teams continue investigating the Windows environment. He emphasizes that this is an alternative access path, rather than Windows repair, threat detection or a replacement for the wider incident response process.

I ask how organizations should prioritize employees and services during a disruption. Sterling recommends making those decisions around business functions and dependencies rather than seniority, with the priorities established before a crisis. We also discuss who has authority to initiate a transition, whether people can still sign in and reach their applications, and how a team returns devices to its chosen operating state afterward.

Sterling draws a distinction between a recovery time objective and the time it actually takes people to resume work. That brings the discussion back to testing, including scenarios where management systems, connectivity or other dependencies may be unavailable. For leaders assessing business resilience, the conversation offers a way to look beyond infrastructure status and examine whether the organization can deliver its services again. When did your team last test recovery from an employee's perspective, and what did it reveal? Please share your thoughts with me.

What does an investor actually own when a share becomes a token, and who keeps the official record accurate?

In this episode of Tech Talks Daily, I speak with Dan Kramer, Chief Executive Officer of Equiniti, about the infrastructure behind tokenized securities.

While much of the discussion focuses on trading venues and settlement, Dan brings the conversation back to the record of ownership and the responsibilities of a transfer agent, or registrar in the UK. He explains why issuers want a consistent register rather than separate versions of ownership across different systems.

We discuss the difference between shares issued by a company and tokens created by a third party to represent an interest in an underlying asset. Dan explains why voting rights, information rights and exposure to another organization deserve attention when assessing a tokenized product.

We also consider how existing market infrastructure connects to this work and why the technology itself cannot answer every question about rights and responsibilities.

The conversation then turns to AI in shareholder services. I ask Dan about accountability when software assists with proxy voting, and we discuss the importance of being able to understand how decisions were reached.

He also shares Equiniti's approach to employee training and its ambition to make interactions with its systems available through natural language by the end of 2027. This is a future target, rather than a description of capabilities already available across the business.

Dan also discusses the planned Bullish acquisition and how the companies intend to bring together issuer relationships and blockchain infrastructure.

We close with a practical example of a customer portal moving from a Monday discussion to a Thursday delivery, illustrating his view of how development is changing.

For leaders assessing tokenization, the conversation offers useful questions about ownership, controls and the experience shareholders should receive. Which questions would your organization need answered before considering tokenized shares?

Please share your thoughts with me.

What good is a new treatment if patients still struggle to get the approvals, appointments, and support needed to receive it?

In this episode of Tech Talks Daily, I speak with Amir Sharif, president and co-founder of Leap, about the operational infrastructure behind specialty care. While AI in drug discovery attracts attention, the journey from a prescription to treatment often depends on disconnected organizations, incomplete information, and manual coordination. Amir describes how Leap combines drug sourcing, distribution, care coordination, and employer benefit arrangements to address some of those gaps.

His interest in patient experience grew through work helping build healthcare businesses including Care/of and Tend. Those experiences led him to question why everyday expectations around scheduling, pricing, and customer support can be so difficult to meet in healthcare. Specialty therapies bring another level of complexity because they can require clinical administration, recurring appointments, insurance authorization, and expensive medication procurement.

We look at what happens after a physician writes a prescription. Approval may depend on the patient's health plan, supporting medical information, an administering provider, and a permitted site of care. Coordinating those pieces can fall to the patient, even while they are dealing with an illness. Amir explains why access depends on the administrative process as well as the treatment itself.

Drug pricing provides another part of the picture. Under a buy and bill arrangement, a provider purchases a drug, administers it, and bills for it. Amir says the margin added to the acquisition cost can range from 40 percent to 400 percent. These are his reported figures rather than a finding independently established in this episode. He describes Leap's model as passing its drug acquisition cost to the employer or plan sponsor and coordinating administration through home infusion or partner clinical sites, depending on patient preference and clinical requirements.

The technology discussion becomes particularly practical when Amir describes the data behind identifying eligible patients. Leap receives feeds containing diagnosis codes, claims, and prior authorization information. Its data engine normalizes those feeds so the team can act on signals that a patient is receiving, or may soon be prescribed, a therapy within the program's scope. This is a care coordination process. Amir repeatedly says Leap does not replace the prescriber or take control of clinical treatment decisions.

He also describes an average patient encounter involving over 20 coordination touchpoints. Many external parties still communicate by email or fax rather than through software interfaces. Leap uses agentic workflows to support appointment scheduling, shipment tracking, and medical order processing. According to Amir, those workflows remain supervised by care teams, and patients speak with a person when they contact the service.

For business leaders assessing AI returns, his account of model costs is especially useful. Some workflows the team considered two quarters earlier would have cost tens or hundreds of dollars per run in token consumption, he says. As models become cheaper and more capable, different operational uses become viable. The interview does not provide a measured before and after cost comparison, so the lesson is about evaluating changing economics rather than declaring a proven return for every workflow.

Leap reports a patient Net Promoter Score of 92, with surveys sent after every encounter. Amir describes this as evidence of patient satisfaction, alongside feedback gathered by care teams. Satisfaction is useful, but it should not be confused with proof of improved clinical outcomes or faster treatment across every patient. Employers can request an impact analysis comparing historical claims with the proposed cost of Leap's service.

Where would you begin when assessing specialty care, the drug price, the time patients lose, or the coordination needed to keep treatment on track? Listen to the conversation and share your thoughts with me.

What happens to a good idea when the person who understands the problem best cannot express it confidently in the language used by the rest of the team?

In this episode of Tech Talks Daily, I speak with Gonçalo Gaiolas, Chief Product Officer at DeepL, about real time voice translation and the work required to make a multilingual conversation feel natural. Our discussion moves from the business cost of language barriers to the technical choices behind speaker identity, tone, speed, and the handling of sensitive meeting data.

Gonçalo begins with a cost that is difficult to place on a spreadsheet. An employee may understand a local market, a product, or a customer need but struggle to contribute in the company's shared language. The organization can miss that knowledge even when the person is present in the meeting. Customers also tell DeepL that language barriers affect the time needed to make decisions and understand nuance. These are experiences he describes, rather than independently measured results presented in the interview.

Live translation introduces a demanding set of constraints. People pause, interrupt, change direction, and build meaning at different speeds across languages. A system needs enough context to produce an accurate translation while responding quickly enough to remain useful. Waiting can interrupt the flow, but responding too early can lose the meaning of the sentence. Tone creates another challenge because urgency, hesitation, and a question can carry information that the translated words alone do not capture.

We discuss DeepL's approach to retaining the characteristics of individual speakers. Gonçalo describes separating speakers and carrying elements of voice, rhythm, and emotion into the translated audio. He also explains that some original audio remains audible at a lower level. His aim is to help listeners follow who is speaking while understanding the translated conversation. He says users have options around retaining vocal identity, acknowledging that people may feel differently about using that capability.

The enterprise adoption discussion is equally practical. Gonçalo describes a desktop app intended to provide a consistent experience across Zoom, Microsoft Teams, and Google Meet. A global workforce may use one platform internally and encounter several others when speaking with customers and partners. For him, working across those environments is part of the product's usefulness, alongside the quality of the translation itself.

Accuracy remains a recurring theme. Gonçalo describes testing with real users and evaluating whether the result works in the customer's context. He gives the example of an engineering department whose acronyms and specialist terms can confuse a model. DeepL's customization capabilities, he says, let customers supply terminology and guidance about how language should be handled. The example offers a useful reminder that a fluent sentence can still misinterpret the way a particular team speaks.

We also discuss procurement and governance. Meeting audio can contain sensitive business information, so buyers need to understand technical architecture, policies, and how their data is processed. Gonçalo says DeepL does not use customer data to train its own models. The interview does not supply the contractual scope, retention periods, or detailed controls behind that statement. Those remain questions for buyers to check against the service and agreement they intend to use.

Examples range from conversations between parents and teachers to contact centers, large events, and use alongside human interpreters. Gonçalo describes customer demand that extends beyond the scenarios the team originally expected. I also ask about translating podcasts, which leads to a discussion of possible uses outside live meetings. We keep that possibility separate from a confirmed podcast production service or a promise about this show's availability in other languages.

I'd welcome your thoughts. Where could voice translation help someone contribute knowledge your organization is currently missing, and what would you need to test before relying on it?

What if the most useful industrial robots are the ones helping experienced people continue working in safer conditions?

Recording from the show floor at IFS Unleashed 2026 in Orlando, I spoke with David Askey, CEO of Ascend Robotics, about construction and maintenance machines working in spaces that are difficult or dangerous for people to reach.

We discussed the gap between a robotics demonstration and equipment that can perform reliably at a customer site, where conditions rarely match the original plans.

David shares a ship painting example in which he reports work completed three to four times faster than with typical human crews, alongside approximately twenty five percent material savings. He also describes more consistent coating thickness and explains why people continue to monitor and direct the work.

A separate example involves cleaning and coating a factory ceiling during a data center conversion, where live electrical infrastructure added to the difficulty of the job.

One of the most interesting parts of our conversation concerns the people operating these machines. David describes hiring experienced workers from painting crews as robot operators and working with unions as the equipment enters the market. For workers affected by the physical demands of construction, operating a robot can provide a way to keep contributing their knowledge from a different working environment. We also discuss how robots adapt to buildings as they actually exist and how operators help them decide what to paint and what to avoid.

David explains how AI tools have accelerated his team's software development and why connecting the data collected by robots to enterprise systems is a separate challenge. He describes IFS as a bridge between work happening in the field and the information management teams need.

For anyone considering robotics, the conversation offers practical questions about reliability, safety, integration and the outcomes worth measuring. Where could robots support your workforce, and what would you need to see before putting them to work? Please share your thoughts with me.

What happens when individual AI agents begin working together as digital teams, and how much more can businesses achieve when automation moves beyond a single task?

Recording live at IFS Unleashed 2026 in Orlando, Neil C. Hughes reconnects with Somya Kapoor, CEO and Co-founder of IFS Loops, to discuss how digital workers have developed since their previous conversation in New York.

Last year, the focus was on individual digital workers performing specific tasks. Now, IFS is bringing several workers together to support wider business processes, with early customer deployments providing evidence of what these systems can achieve.

Somya explains why the initial approach centered on low-risk, high-impact workflows rather than attempting to automate complex processes all at once. She shares how some customers who began with a single digital worker have expanded to two or three, creating opportunities to automate more work across procurement, asset management, and field service.

The conversation also examines customer examples from IFS Unleashed. Somya discusses KLM's reported efficiency improvements, the potential annual savings associated with invoice processing at Eight Group, and how Kodiak Gas is using a digital worker to make spare-parts identification and purchasing significantly faster.

These examples provide an interesting perspective on enterprise AI at a time when many organizations are still struggling to move beyond pilot projects. Rather than beginning with large transformation programs, Somya describes an approach built around focused deployments, measurable results, and the opportunity to expand when the initial benefits become clear.

But the technology is only part of the challenge. Somya also explains why change management can become a greater obstacle than building the agents themselves, particularly when employees and business leaders are unfamiliar with allowing digital workers to perform operational tasks.

As AI agents become increasingly capable of working together, will organizations find greater success by starting small and building confidence through results? And could digital teams provide a more practical route from AI experimentation to everyday business value?

Listen to the conversation and share your thoughts on where digital workers could make the greatest difference in your organization.

What if the biggest obstacle to industrial AI isn't the technology itself, but the data businesses are trying to use with it? And how much could manufacturers and industrial operators achieve if they could connect information from across the entire life of their assets?

Recording live at IFS Unleashed 2026 in Orlando, Neil C. Hughes speaks with Shawn Gregerson, VP APM & MES Business Development at AVEVA, about how industrial companies are using AI, operational data, and predictive maintenance to improve reliability and make more informed business decisions.

Shawn explains why industrial AI is increasingly becoming a data challenge rather than an algorithmic one. Although AI models and analytical techniques have developed significantly, many organizations still struggle with disconnected information spread across engineering, operations, maintenance, and asset management systems.

The conversation examines how AVEVA is bringing these different sources together through its industrial knowledge graph technology, creating a more reliable foundation for AI applications that need to understand the relationships between assets, processes, and operational conditions.

They also discuss the growing financial pressures facing industrial organizations. Shawn highlights the increasing cost of downtime and the challenge of maintaining assets that are operating beyond their original design life.

With AVEVA and IFS working together on Continuous Asset Decision Intelligence, or CADi, the discussion turns to connecting asset planning, design, operations, maintenance, and optimization so that decisions can be informed by real operating conditions.

Shawn also shares where industrial AI is already delivering results, including predictive maintenance systems that monitor equipment health, identify early warning signs, and help maintenance teams determine what action to take before failures occur. Other applications include predictive quality, manufacturing throughput, and the use of generative AI to make industrial information easier to access.

Looking beyond individual applications, they consider how connected industrial data could support agentic workflows and enable businesses to automate more of the tasks that employees currently perform manually.

As industrial organizations face pressure to improve efficiency while extending the life of existing equipment, could connecting their operational data deliver greater value than investing in increasingly sophisticated AI models alone?

Listen to the conversation and share your thoughts on how industrial AI is changing maintenance, asset management, and operational decision-making.

Could your enterprise data support a reliable report and still leave an AI agent unsure what the numbers mean?

In this episode of Tech Talks Daily, I speak with Saurabh Gupta, CEO of The Modern Data Company, about the difference between being analytics ready and AI ready.

Saurabh's background includes data strategy and governance work at Thoughtworks, Washington DC government, and international organizations. Our conversation focuses on the information around data that machines need when they begin consuming it directly.

A familiar reporting process can depend on human knowledge that never becomes part of the underlying dataset. Saurabh describes a system-of-record report exported to Excel, adjusted, and then used for compliance reporting. The spreadsheet and the original source now tell different stories. If an AI system encounters multiple copies, it needs a way to understand which version is appropriate and why.

We discuss how context includes quality, lineage, transformations, meaning, usage, and permissions. Those details often live across separate tools and teams. Bringing them together is part of the challenge of making data useful to agents, rather than simply making it available.

The distinction becomes clearer through a furniture company's date fields. Saurabh describes 14 fields that were tagged similarly but carried different business meanings. A warehouse, an ecommerce team, and a delivery team can use a label in different ways. An answer based on the wrong interpretation may look convincing while failing to address the question.

Governance also needs to reach the point of consumption. Saurabh explains why one person may be allowed to see a name, address, and date of birth while another needs the same fields masked. His approach is to connect those permissions with how the data is used, including when a machine becomes the consumer.

We then discuss DataOS, which The Modern Data Company describes as a platform for data products. Saurabh argues for starting with the outcome a user wants, then working backward to the necessary sources and controls. He explains how the company brings data from different locations into that process, while acknowledging that moving or centralizing data can still be appropriate where latency requires it.

The interview also addresses measurement. Saurabh clarifies the company's 90% claim as faster use-case delivery or time saved. It is not a 90% increase in adoption. He describes tracking data interactions to identify stale processing and report schedules that do not reflect actual use. The company also reports potential reductions in data ownership costs, but the interview does not provide enough detail to treat those figures as a universal return.

For CIOs and CDOs, his practical recommendation is to begin with a business problem and implement incrementally. A clearer view of which data people consume can help teams decide what to process, what to retire, and which investments serve a useful outcome.

We also discuss how AI may change the division of work between consumers, analysts, and engineers. Saurabh's forecast is a strong one. The broader issue is how organizations preserve business interpretation and accountability as additional reporting tasks move into automated systems.

The resources supplied after the interview include the company website, the Modern Data 101 practitioner community, and The Modern Data Report 2026. They offer destinations for listeners who want to continue learning about the team's approach.

What would make your data genuinely ready for AI, and where do definitions, permissions, or inconsistent copies still create uncertainty? Please listen and share your thoughts.