Most leaders at this level talk about AI. I build it. My AI Chief of Staff, Jeeves, runs my mornings, my portfolio, and half my admin, and I wrote every line myself, starting from zero code experience.
I've spent three decades building the partner infrastructure that lets large organisations actually adopt new technology: AWS, NVIDIA, Symantec, Citrix. Not the pitch. The plumbing. The relationships, the enablement, the go-to-market motion that turns a platform into something enterprises actually use.
Right now I lead Training and Certification Partnerships for AWS across EMEA: 189 partners, 50+ countries, a team of ten. Before that, NVIDIA's enterprise channel through the accelerated computing shift. Before that, Symantec, Citrix, and a board pitch to Cable and Wireless in 1997 that trained 2,000 staff through a technology transition most of my current peers weren't yet in the workforce for.
The throughline hasn't changed in thirty years: making the human case for technology change, at scale, at the executive level. Only the technology has changed.
I had never written a line of code before this. Jeeves is a fully agentic system I built and continue to build myself: he reads my portfolio, tracks career opportunities, manages my calendar and reminders, drafts and ships his own code updates with my approval, and talks to me over WhatsApp like a member of staff would.
He isn't a chatbot wrapped around a prompt. He has memory, a scoring engine for investment signals, a paper trading desk running a real strategy, and a build loop where he drafts his own code changes for me to review and ship. He runs on infrastructure I set up and maintain: GitHub, Render, Twilio, a proper email pipeline. I lead him the way I've led every team for thirty years: give him trust, clarity of purpose, and room to do the job.
Most people building an AI agent for the first time end up with something that answers questions. Jeeves does more than that, and the best way to show it is to walk through one real thing he did, start to finish.
The problem. Jeeves needed a way to spot promising investment signals without me having to check the market all day. Not a headline scanner. Something that could weigh several kinds of evidence together and get more useful over time.
What we built. A signals engine that pulls in fresh news, checks momentum, reads insider trading data, and scores each stock on a composite scale. It runs on its own schedule, re-checking names through the trading day, and it flags anything that crosses a meaningful threshold.
Where it got interesting. A scoring engine is only as good as its weights. So we added a second layer. Every week, Jeeves reviews his own closed trades, works out which factors actually predicted a good outcome, and proposes small adjustments to how much weight each signal gets. He proposes. He never applies. I get a message asking me to approve or skip each change. Nothing adjusts itself without me saying yes.
The governance bit that actually matters. Every change to Jeeves, including changes to how he scores investments, follows the same rule. Draft it, show me the diff, wait for explicit approval, then ship. Nothing merges into anything he actually runs without me looking at it first. That is not caution for the sake of it. It is the same discipline I would expect from any team I have run for thirty years. Trust people, give them room, and keep the checkpoints that matter.
I'm building toward a senior AI leadership chapter: Chief AI Officer, VP Partnerships, Chief Partnerships Officer, or a Non-Executive Director role where practitioner credibility and ecosystem experience both matter.
The best way to reach me is LinkedIn or email. I read everything myself.
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