Tech Talks Daily

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Understanding Tokenized Shares and Ownership Rights With Equiniti Oct 23, 2026

Show notes

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.

Improving Specialty Care Access With AI and Leap Oct 22, 2026

Show notes

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.

Keeping the Human Voice in AI Translation With DeepL Oct 21, 2026

Show notes

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?

Making Industrial Work Safer With Ascend Robotics Oct 20, 2026

Show notes

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.

IFS Unleashed: How Digital Workers Are Becoming Digital Teams Oct 19, 2026

Show notes

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.

IFS Unleashed: AVEVA on Why Industrial AI Is More About Data Than Algorithms Oct 19, 2026

Show notes

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.

Making Enterprise Data Ready for AI With The Modern Data Company Oct 18, 2026

Show notes

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.

Moving Marketing From Static Rules to AI Agents With JustAI Oct 17, 2026

Show notes

What happens when marketing software starts learning from customer behavior instead of following a campaign rule that someone wrote weeks ago?

In this episode of Tech Talks Daily, I speak with Neha Mittal, co-founder and CEO of JustAI, about AI agents, reinforcement learning, and a different approach to personalization. Neha spent over a decade working on growth and retention at companies including Twitter and Pinterest. Her experience repeatedly brought her back to the same problem: teams were running sequential experiments while the interests and behavior of their users kept changing.

Neha describes how a series of small A/B tests could consume weeks without producing a durable answer. Changing a button from red to blue might teach a team something, but the larger opportunity was to keep experimenting and learning across many decisions. Seeing similar problems at different companies helped shape the idea behind JustAI.

We discuss why she considers marketing a promising environment for autonomous agents. It involves a high volume of decisions, frequent feedback, and experiments that can often be assessed quickly.

Opens, purchases, and ignored messages can all contribute to learning. Neha contrasts those conditions with higher-consequence fields such as medicine and law, while explaining that strategy and judgment remain responsibilities for people.

The conversation also separates generating content from choosing an action. A language model can help produce an email. The reinforcement learning approach Neha describes aims to learn which decision supports longer-term customer value. The question becomes what message, timing, or channel makes sense for a particular person in the current moment, rather than which static segment or campaign they belong to.

JustAI divides the work across Strategy, Creative, Decisioning, and Data agents. Neha explains how opportunities feed into messaging, how the decisioning system learns from responses, and how the data agent returns insights to the process. Marketers can review customer-facing creative, provide brand guidance, and adjust strategy when the company's products or priorities change.

Her hypermarketer vision raises another question about how teams grow. Neha imagines one marketer having the capacity traditionally associated with a much larger team, with agents handling repetitive execution and experimentation. She presents this as a direction for the business rather than a demonstrated productivity ratio for every customer. The marketer still needs to translate business goals into instructions and judge whether the results make sense.

We bring the discussion back to current examples. Neha describes Coursera and ClickUp delegating the creation and selection of messaging variants. She also discusses the importance of human review for Better's financial-services communications. These examples show why the level of autonomy should depend on the task and the consequences of getting it wrong.

The supplied booking notes report over $100 million in customer revenue influenced and a $17 million funding round. Those company claims provide background, but the interview does not establish a revenue attribution method, incremental lift, or a complete return-on-investment calculation. The practical value of the conversation lies in understanding what work is being delegated and what people continue to own.

For me, the interesting issue is how to create a feedback loop that improves relevance without letting operational speed run ahead of customer trust or business judgment.

Which marketing decisions would you delegate to AI agents today, and what would you want to review before a message reaches a customer? Please listen and share your thoughts. 5. Suggested URL

Building a Better Buying Experience With AI and 1mind Oct 16, 2026

Show notes

Is your company using AI to improve the buying experience, or to carry out the same processes faster?

In this episode of Tech Talks Daily, I speak with Jonathan Kvarfordt, known as Coach K, about business strategy, go-to-market AI, and the relationship between people and automated systems. Jonathan introduces himself as VP of Marketing at 1mind and brings experience training revenue leaders and teams to assess where AI can support their work.

We begin with his framework for optimizing, amplifying, or reinventing a process. The point is to recognize the difference between making an existing task easier and considering whether the task should be done differently. Jonathan also acknowledges that organizations have different levels of readiness. Changing everything at once is not a practical answer for every business.

The conversation becomes especially useful when we discuss measurement. Rather than beginning with licenses or prompt activity, Jonathan recommends identifying the business outcome and working backward. Is the goal improved quota attainment, a higher win rate, reduced churn, or another result? What is preventing it today? Sometimes the answer concerns process or people rather than AI.

We then turn to the customer journey. Jonathan describes how 1mind aims to help buyers get product information, receive demonstrations, and answer technical questions before moving into a conversation with a person. His argument is that a website visitor may already be researching a specific decision, so an experience built only around awareness and meeting bookings can leave their immediate questions unanswered.

The practical question is how to support that buyer at the moment they need help. Jonathan talks about website assistance, sales engineering, product guidance, and customer success. He sees opportunities to provide information when a team cannot staff every interaction, while giving people additional context when they take over.

We also discuss the human side of AI adoption. Jonathan describes the continued value of emotional intelligence, relationships, and strategy. He challenges the assumption that every buyer always wants to speak with a person, but also recognizes that providing facts is different from understanding someone's motivations. The opportunity depends on the individual, the task, and the trust they place in the experience.

A comparison between two companies using the same outbound tool brings the discussion back to operational reality. In Jonathan's account, one company had clearer customer definitions, messaging, and data. The other used a broad target and expected the technology to compensate. His lesson is to examine what the system receives before concluding that another purchase will solve the problem.

For leaders assessing AI value, we talk about conversion rates, selling time, and the parts of a buyer's journey a system can support. Establishing a baseline matters. A team cannot confidently claim improved selling time if it never measured how much time people had to sell in the first place.

The interview also contains an inconsistent percentage claim about hallucinations. I have kept those figures out of the promotional package pending clarification. Reliability needs evidence, and the discussion does not provide a benchmark methodology or enough detail to establish a performance guarantee.

We close with Jonathan's advice to remain willing to learn and reconsider familiar habits. He is candid about feeling overwhelmed by the pace of change, which makes the conversation useful for people who are experimenting without feeling they have every answer.

Where does your buying journey create unnecessary effort, and how would you assess whether AI made the experience better for the customer? Please listen and share your thoughts.

Turning Building Data Into Better Decisions With Willow Oct 15, 2026

Show notes

What would change if your building could help explain what needs attention, why it matters, and whether the action you took actually worked?

In this episode of Tech Talks Daily, I welcome Bert Van Hoof, CEO of Willow, back to the show to discuss operational AI for buildings and infrastructure. Bert spent about 20 years at Microsoft, where his work included launching Azure Digital Twins. He now works with environments such as hospitals, airports, universities, corporate campuses, and data centers, where the condition of a physical space can directly affect the work happening inside it.

We begin with a problem familiar to many facilities teams. Buildings already have sensors, management systems, meters, maintenance records, and dashboards. Having access to information does not necessarily help an operator decide what to do next. Bert describes operational AI as a way to move through a sequence of questions: what is happening, why is it happening, what might happen next, what should we do, and did the action produce the expected result?

Context gives those questions meaning. A temperature reading alone cannot tell us whether a room is operating correctly. We need to know the room's purpose, occupancy, equipment, weather conditions, and recent maintenance history. Bert explains how digital twins and knowledge graphs connect those relationships so that a system can assess the situation rather than interpret a sensor value in isolation.

The discussion then turns to predictive maintenance. A probability of failure may be useful to an analyst, but a technician also needs to understand the consequence, urgency, and next step. Bert describes how condition-based maintenance can support equipment decisions and how earlier detection can reduce the disruption caused by refrigeration problems or water leaks.

These are practical examples of the types of outcomes Willow targets, rather than a claim that every customer will achieve the same result.

There is a human side to the conversation too. Bert describes operators opening numerous browser tabs each morning to assemble a picture of their buildings. Daily briefings and natural-language questions could reduce that search effort, helping teams focus their attention on the issues that deserve it.

We also discuss the relationship between occupancy, ventilation, energy consumption, comfort, and maintenance. Bert describes Willow's Active Control and the use of schedules and occupancy signals to help buildings respond to how spaces are being used. His argument is that these decisions should account for the whole environment. Saving energy in isolation can create different problems if comfort, reliability, or safety is overlooked.

Trust runs through the interview. A poor recommendation in a hospital has different consequences from an awkward paragraph in a document. Bert argues for explainable recommendations, operational evidence, defined limits, and verification after an action. He also sees experienced facilities engineers as part of the solution, with responsibility for setting objectives and judging trade-offs.

For me, the larger question is how building operations connect with the organization's purpose. A hospital needs spaces and equipment ready for patient care. An airport needs to account for passengers, flights, gates, and changing demand. Understanding the environment means understanding what it needs to support.

Where would operational AI make the most useful difference in your buildings, and what evidence would you want before allowing it to take action? Please listen and share your thoughts.

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