Before "agentic AI" was a phrase. Before the EU AI Act was drafted. Before most organisations had an AI policy. I have been building AI systems, confronting their governance implications, and developing frameworks to manage their risks — for longer than most people have been talking about it.
It was early 2018. I was building an observability tool at Arup — powered by what we were still calling Machine Learning. Joi was a simple Azure app that would log into our top business systems, perform a set of transactions, and log out again. She would flag if systems were performing well, degraded, or experiencing an outage.
Joi needed her own identity in the HR system and Active Directory. Her own paid licence for each system. Her own userID and password. She was, literally, the first artificial employee of the company — a new generation of synthetic beings who didn't get paid, didn't need to sleep, and was happy to work 24/7.
The question someone eventually asked — half-joking, nobody laughing — was: "How long will it be before Joi takes my job?" That question has never left me. Everything I have built and advised on in AI since then has been shaped by it.
Most organisations have an AI ambition problem, not a technology problem. The models exist. The tools are available. What is missing is sustained executive-level leadership — someone accountable for strategy, governance, and delivery outcomes at the leadership table.
As fractional CAIO I embed at the leadership level — owning the AI strategy and roadmap, designing and governing the AI trust framework, and building the board's confidence that AI is being adopted responsibly. The MASTER-AI™ framework is the methodology behind every engagement.
The EU AI Act comes into force in August 2026. ISO 42001 is already the international standard for AI management systems. Most organisations are significantly behind on both — and the gap between where they are and where they need to be is wider than they realise.
As independent reviewer I assess an organisation's AI governance posture against the relevant frameworks — identifying the gaps, quantifying the exposure, and producing a board-ready findings report with a prioritised action plan. I also advise on the standards themselves, working at the level of framework design rather than just compliance.
Strategy without delivery is noise. I have built and deployed AI platforms at genuine enterprise scale — not proof of concepts, not pilots, but production systems used by tens of thousands of people generating documented commercial value.
As delivery lead I run AI transformation programmes from strategy through to production — technology selection, partner management, data governance, change management, and the responsible AI framework that ensures what gets built can be trusted. The six years between Joi and WPP Open represent the longest and most deliberate preparation for this work that I know of.
Whether you need an AI governance audit before August 2026, a fractional CAIO to own your AI agenda, or a delivery partner for an agentic AI platform — the Technology MOT gives both of us a clear picture of your AI readiness first. Fixed price. Fixed scope. No ongoing commitment. The MOT pays for itself.
“As an experienced and astute leader he was able to look at challenges from multiple perspectives, and communicate effective solutions — ensuring that I understood the ‘Why’ as much as the ‘What’.”
— Steven Charlton, Strategic Transformation Leader, WPP