Data & AI leadership · London
Governed · owned · connected
Every domain gets an owner, a definition and one source, built so your own team can run it after I've gone.
The outcome
I help executive teams turn data and AI spend into results a CFO can count: lower cost, faster decisions, less risk. Based in London, working with UK enterprises in regulated and consumer sectors.
Capability assessments improve year on year, but cost, speed of decision and risk look the same as they did before the programme started.
Every quarter brings a new AI proof of concept. Very few are running, monitored and used by the business a year later.
The executive meeting spends its first twenty minutes reconciling figures. The platform exists, but nobody trusts it enough to decide on it.
The answer usually isn't more capability. It's making the capability you've already paid for change decisions, and measuring that instead.
§ 02 — Tools
Twelve questions for executives, not engineers. You'll get a position on capability built against outcomes delivered, plus the three moves I'd make first.
Names are withheld by agreement and shared on request. Where a figure is an estimate, it says so.
Reporting depended on more than 100 disconnected sources. Teams spent days assembling figures by hand, numbers differed between functions, and platform running costs kept rising.
Security leadership had no single source of truth for assets and control coverage. Each tool reported its own version, so posture reporting was slow and inconsistent.
A cloud migration had stalled. Hidden dependencies made it unsafe to retire servers based on utilisation alone, and nobody could say with confidence what would break.
Writing investment cases took weeks and drafts often missed the funding criteria, which delayed decisions on work that mattered.
An independent read of where your data and AI spend is converting into results and where it isn't. You get a short, ranked list of moves, each tied to a number your CFO already tracks.
I lead the architecture and the team through to production: platform, pipelines, models, and the operating model that keeps them running after I've gone.
Senior data and AI leadership for the period between strategy and a permanent hire. I set direction, build the team, and hand it over cleanly.
For the right organisation, I'll lead the data and AI function from the inside, accountable for the outcomes rather than advising on them.
I trained as a control engineer. My first research measured what happens to a national power grid when large numbers of solar panels and electric vehicles connect to it, and that work was published through IEEE, IET and MDPI.
Then I spent three years on a medical-device factory floor at Baxter, where a bad number shows up as a quality failure and not as a slide. I've applied that standard to data ever since: if a measurement doesn't change a decision, it isn't worth producing.
Since 2021 I've led enterprise data and AI work for FTSE 100 and regulated-sector organisations, and built a data engineering and architecture practice from one engineer to twelve.
Close enough to the code to catch a bad architecture decision early. Senior enough to own the outcome with the board.
Why maturity frameworks produce reports instead of outcomes, and the operating-model shift that changes that.
Why AI pilots stall before production, and the delivery discipline that gets models shipped, monitored and used.
How public-sector organisations break down data silos and get strategic value from the data they already hold.
Based in London · Working across the UK
Thirty minutes, no deck. Tell me where data or AI spend isn't paying back, and I'll tell you honestly whether I'm the right person to help.
