Tech Talks Daily

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 adoption rather than an afterthought.
One of the more surprising examples concerns legacy modernization. Drawing on his experience with caseworkers using different workarounds, Ben discusses separating a technical platform replacement from changes to how people work. He describes AI-assisted rewriting that preserves existing behavior before a later, more gradual process redesign. The conversation presents this as an approach to consider, rather than a demonstrated deployment or a reason to preserve security defects.
We also examine AI measurement. Ben challenges the emphasis on how many roles a company can remove and suggests looking at revenue, client service, profitability, and the repetitive work people no longer have to do. His argument is that the way a program is described affects whether employees support it. Leaders still need evidence that a project works, alongside an understanding of how their success measures influence behavior.
We finish with Newton's hands-on AI work, including internal hackathons, model access, and Ben's account of investment in sovereign compute for inference costs and UK data requirements. He makes a useful distinction between giving new users a strong first experience and asking experienced users to improve efficiency. Cost control and adoption need to be considered together.
I'd welcome your thoughts. How is your organization measuring AI value, and which small improvement has persuaded people to change the way they work?

What if your ERP system came to you through a conversation, instead of asking you to learn its menus before you could get anything done?
Recording at SuiteWorld in Las Vegas, I welcomed Evan Goldberg, founder and executive vice president of NetSuite at Oracle, back to the podcast. We last spoke two years earlier about the company's origins. This time, the discussion centres on his description of AI as the UI and what that could mean for the people using business software every day.
Evan describes an ERP experience in which users explain what they want in natural language. One example is adding a dashboard metric by describing the information it should show. If the result is not quite right, the user can explain what needs to change. For me, the useful question is whether that makes software easier to fit around a person's work and how reliably it understands the request.
We discuss the knowledge behind those answers. Evan describes SuiteSkills as a way to bring together information about NetSuite and the procedures businesses use within it. He connects that work with consistent answers through NetSuite Next and through external tools using the AI connector service. His ambition is to support the models and ways of working customers choose, while making the information returned from NetSuite dependable. It is a product direction he describes, rather than an independently tested guarantee that every model will behave identically.
The conversation then moves from answers to actions. Evan points to order approvals, accruals, and agents looking for exceptions that deserve attention. He argues for beginning with a process people already want to improve and deciding which parts can run with human approval or greater autonomy. The appropriate balance will depend on the company and the work involved. A conversational interface does not remove the need to decide where authority belongs.
Personalisation is another part of adoption. Evan's view is that people are more likely to keep using an AI feature when they can explain how they want it to work. The dashboard example makes that concrete, but it also raises a wider question about how companies maintain consistent procedures while allowing people to adapt their tools.
We also discuss smaller businesses and the opportunity to access capabilities without building a large specialist team. Evan describes a startup offering and his belief that the same AI models can help companies of different sizes. The practical considerations still include eligibility, the work a team needs to accomplish, and the controls around its business information.
For partners, he sees an opportunity to make industry expertise available through reusable skills. A specialist's knowledge of a particular niche could inform how Ask Oracle helps customers, while the partner spends time developing that expertise further. It is an interesting view of how advice and software configuration may change as AI becomes part of delivery.
I also ask about software pricing. If agents complete tasks that once required someone to log in, what happens to the traditional per-user model? Evan acknowledges the question but offers a counterpoint: easier access to information and personal agents could encourage people to remain users. He does not announce a pricing change, leaving us with a business-model question that is still developing.
We finish with an unexpected customer conversation at Disneyland, which Evan uses to illustrate interest from everyday business users. It is a memorable anecdote rather than a measure of adoption, but it brings the discussion back to the people who ultimately decide whether these features earn a place in their working day.
I would love to hear your thoughts, so please share your experiences. If AI becomes the interface to your business systems, what would you want it to understand before you let it act on your behalf?

What needs to happen before AI agents can reliably complete work across the systems that run a business?
In this episode of Tech Talks Daily, I welcome back Irfan Khan, Chief Product Officer for SAP Data & Analytics, to discuss the distance between a useful AI pilot and a dependable operational workflow. Our conversation begins with the autonomous enterprise and what that means for teams working across finance, procurement, supply chain and HR. Irfan explains why agents need a shared understanding of business activity, including the state of purchase orders and inventory, before coordination can produce a useful result.
We discuss the data quality, permissions and governance questions that become harder to manage as deployments grow. Irfan also considers how leaders should measure AI performance. Generating additional content or consuming additional tokens tells us something about activity, but leaders still need to understand the effect on productivity, accuracy and the cost of completing work. He shares how SAP has approached developer experimentation and why teams need to become deliberate about which tasks benefit from AI consumption.
The conversation also covers company memory, an initiative Irfan describes as a way to bring institutional process knowledge into agent workflows. We discuss how employee trust depends on the task, why order entry requires a different level of precision from a forecast, and how open architecture can preserve choices as models and infrastructure develop. Irfan then explains SAP's approach to connecting SAP and non-SAP data through SAP Business Data Cloud and the wider SAP Business AI Platform.
For leaders assessing the next stage of their AI programs, the discussion offers practical questions about context, access, cost and the business outcomes they want to improve. You can find the resources discussed in the accompanying show notes at TechTalksNetwork.com. Where is your organization finding the biggest obstacle to operational AI, and what would help you move forward?

When AI makes writing code faster, how do you decide whether your team is building the right thing?
In this episode of Tech Talks Daily, I speak with Johnny Halife, CTO at Southworks, about what happens to software engineering when code generation becomes easier but decisions still require experience. The pressure to deliver faster is familiar. The harder question is whether teams have the review capacity, testing processes, and deployment infrastructure to turn that extra output into useful software.
Johnny describes a different way of directing AI coding agents. Engineers need to specify the outcome, define what finished work should look like, and provide checks that can show whether it meets the required standard. Constantly telling an assistant what to do next can keep a person busy without giving the system enough responsibility to complete a coherent piece of work. We discuss how to create tasks that are small enough to review and substantial enough to delegate.
We also talk about context. Giving an AI system access to institutional knowledge sounds sensible, but adding every document to every conversation creates its own problems. Johnny describes borrowing from open source contribution practices, including README files, contribution guidelines, and records of architectural decisions. His approach combines an explanation of why the system works as it does with directions for finding additional information when needed. It is a useful way to think about onboarding an agent without expecting it to absorb the entire history of a business at once.
The phrase human in the loop comes up frequently in AI discussions, so I ask what it should mean in practice. Johnny describes checks before work begins and a review of test results and other evidence at the end, supported by progress updates along the way. The person responsible needs to set expectations and assess the outcome. Being available for an alert is only one part of that responsibility.
An unnamed streaming platform project provides a practical example. Johnny explains how Southworks developed the supporting process in stages, improving documentation, tests, and the infrastructure around the developer workflow. He reports a tenfold increase across requests, changes, and delivered value, although we do not have an independently verified measure or a detailed breakdown. His broader observation is that the same improvements made the environment easier for human engineers to understand and work in.
Finally, we discuss hiring and professional development. Johnny values software and cloud knowledge, curiosity, clear writing, time management, and the judgment to choose between competing possibilities. AI can make experimentation quicker, but someone still needs to decide which experiment deserves attention and when to stop.
I would love to hear how this compares with your own experience, so please share your thoughts. Where does work now wait in your engineering team, and how are you helping people make better decisions about what to build?

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?