Turning AI Experiments Into Business Value With Newton

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?