Consulting
AI advisory for teams who need it to actually work.
Most AI advice comes from people who've never had to make it work inside a real organisation, with real constraints, real risk committees, and real budget owners. I've spent fifteen years inside manufacturing, FMCG, telematics, and regulated financial services, most recently leading technology through an AI adoption programme in wealth management.
I help leadership teams cut through the noise: where AI is genuinely worth deploying, where it isn't yet, and how to get something running without creating a governance headache six months later. That splits into two pieces of work, and I do both.
Two ways to work together
AI Strategy & Operating Model
Where you actually stand, and what has to change to move.
Most organisations don't have an AI problem. They have an operating model that was never built to hold one. I start with an honest read of where you actually sit today across data readiness, governance, and delivery capability, benchmarked against your own constraints, not a vendor's maturity curve. Then we redesign the parts that decide whether an AI idea turns into anything real: who owns a use case, how a pilot gets funded past the demo, what governance actually needs to sign off, and who is accountable once it's running in production.
- –AI maturity assessment across data, governance, and delivery capability
- –Operating model redesign: ownership, funding, and governance for AI, not another framework diagram
- –A sequenced roadmap that says what to fix first and what can wait
By Gartner's latest estimate, more than 40% of agentic AI projects won't survive past 2027, and the reasons on their list are rarely about the model: cost overruns, unclear value, and governance that was never designed for it. That's an operating model failure wearing a technology costume.
AI Implementation
The build, not the deck.
Once the strategy is settled, most consultancies leave. This is the part I do myself: agentic workflows, RAG pipelines, and integrations into the tools your team already uses. Working prototypes built against your real data and your real constraints, not a demo that only survives in the sales room.
- –Hands-on build: agentic systems and RAG pipelines built against your own data and constraints
- –Standards your engineers can actually maintain, so it survives past handover
- –A defined handover point. The goal is a capable team, not a permanent retainer
The KPI I hold myself to is capability transfer, not dependency. A good build is judged by whether your team can still run it once I'm gone, not by how it looked in the room.
If you already know which one you need, that's a five-minute conversation. If you don't, that's usually the first thing we work out.
Get in touch