What I do, in five lines
Every one of these ends with something written that a board can hold you to: which decisions the machines may make, who can stop each one, and how you would know if it went wrong. That is what you are buying. Control you can show, rather than advice you have heard.
A second opinion on your AI strategy. Send me the strategy you have built and two working days later you have an independent written view: where you are not in control, which decisions machines are already making in your name, who can stop them, and how you would find out if one went wrong. A fixed fee, agreed before you send anything. Send it.
Rules Before Tools. One day with the executive team. The rules, written in the room’s own words, before another tool is bought: what the machines may do, who can stop each one, and how the organisation would know if it went wrong. The session.
The Drift versus Design review. Across a function or the whole organisation. A written view of where you are designing the relationship between your people and these systems, and where you are drifting into one. The instrument.
Standing advisor. Monthly. The trusted second opinion as the decisions arrive, and a written brief each month on what has changed and what it means for you. A handful at a time, never more. How it works.
For boards. A briefing, a view on one decision, or a non-voting seat held over a period. Board advisory.
Advisory assignments are scoped, and most are five-figure. Retainers are monthly, and I take on no more than a handful at once.
What this is, in one sentence
The technology strategy tells you what AI can do. This work is about what the organisation should allow it to do, what its people must remain capable of doing, and who remains accountable for the result.
Those are leadership decisions and they are being made by default in most organisations, which is a different thing from being made.
I have done this job
I am not an AI engineer, and this is not an engineer’s advisory. I have run, grown, bought and advised businesses with AI in them, which is the vantage point a leader actually needs.
At Avallain, I was brought in to grow the business and to put AI to work across it, in an organisation that already had strong tools of its own. That work included the acquisition of TeacherMatic, a company of three and a half people whose AI product was selling into schools one at a time, and I became its nominated director. Before that I founded EtonX, which prepared students for the world of work in markets already moving fast on AI, and was acquired by Eton College. At Quizlet I led international growth into more than 60 countries for a business whose thesis rested on what AI could do with the data under the hood. Since then I have advised early-stage AI-first companies, none of which I can name, sat on boards in the UK among others, and spent years in partnership conversations with businesses from China to the UAE, Europe and the US.
None of that makes me the person to build your stack. All of it was spent deciding what to let the technology do, what to keep, and who carries it when it goes wrong.
Two different advisory conversations, and most organisations only have one
The technology conversation. What can we automate? Which systems should we deploy? How do we implement them? What efficiencies can we capture? These are good questions, answered well by consultancies and internal technology functions built for them, and this work does not compete with any of it.
The conversation almost nobody is having. What should we delegate? Where must judgement remain ours? Which capabilities do we have to protect? How should roles change rather than shrink? Who remains accountable when a machine took part in the decision?
The second set does not arrive on an agenda by itself. Nothing fails while it goes unasked: the work ships, the output often improves, no control is breached and no incident is logged. That is why it usually surfaces after something has already been lost, and why it is a poor fit for a risk register and a good fit for a leadership session.
Is this AI strategy? Not the kind you think you want
The tools and the pipes you can buy anywhere. This is the half that decides what they are allowed to do, and who answers for it.
So, still: no Copilot rollout, no AI roadmap, no vendor selection, no model choice, no agent deployment, no data architecture. Those are real disciplines, done well by firms built for them, and an organisation buying them from somebody who writes about human capability is buying the wrong thing.
What is left is the part that has to come first. Rules before tools: which decisions a machine may inform, recommend or execute, which stay human, who is accountable for each, and what the people must remain capable of. That gets written down before another tool is bought, and it is the work here. The argument was set out in Rules Before Tools in August 2025, a year before it became fashionable to say it.
Saying the exclusions plainly costs enquiries. They are here because an advisory relationship that starts with a misunderstanding about scope ends badly for both parties, and usually about four months in.
Five questions, and each one has a decision at the end of it
Judgement. Which decisions can AI recommend, which can it execute, and which require a human to make them? Most leadership teams have never written that list down, and writing it is usually the most useful hour of the engagement.
Capability. Where is AI removing the practice through which people become competent enough to supervise it? An organisation can lose the ability to check the work while its output is still improving.
Work design. When AI removes thirty per cent of a role, what happens to the other seventy, rather than simply banking the saving? Licences are the cheap part and do nothing on their own. Value, where it appears, comes through reorganising the task, which is a management decision rather than a procurement one.
Leadership. What must the executive team understand personally, rather than delegate to the CIO or to an AI steering committee? A board that cannot interrogate its own AI decisions has delegated more than it realises.
Accountability. When an AI system contributed to a bad decision, who can explain why the organisation made it? A human in the loop is not governance until you can say who, at what point, with what authority to stop it, and how often they actually disagree.
Five ideas that give a leadership team language for this
Each has its own page, a dated first publication, and is free to use without engaging anybody. An advisory proposition whose intellectual property cannot be inspected before the first conversation is asking for trust it has not earned.
Drift versus design. How intentional is this organisation’s adoption, actually?
Capability debt. What human competence are today’s efficiencies quietly consuming?
Human at the start. Where does human judgement need to frame the problem before the machine begins, rather than review it afterwards?
The missing rungs. What happens to the pipeline when AI takes the junior work people used to become senior on?
Synthetic seniority. Are your people producing senior-looking output without acquiring senior judgement?
Why this person
Operator. Twenty years building education technology before writing about it. Founded EtonX, the online learning venture of Eton College, starting the business in China and partnering with schools in Shanghai and across the country. Led Quizlet’s international expansion across sixty countries. Earlier, worked for the government of Abu Dhabi. That matters here more than it does on a keynote page: a leadership team is not looking for somebody who has read the papers, they want somebody who knows what a decision looks like from inside an organisation.
Researcher. SuperSkills is published by Kogan Page, which puts the intellectual foundation through somebody else’s editorial judgement rather than his own. Behind it, the research estate: 249 pages, 811 questions and 367 graded sources, each recording what it does not prove. The claims are inspectable before the first invoice, which is unusual in advisory work.
Adviser. The work runs across organisations and sectors rather than attaching to a technology, so there is nothing to sell you afterwards. Six of the seven engagements published at case studies produced further work after the session rather than at it.
Who this is wrong for
An organisation that wants its AI strategy validated. This is more useful when the answer is allowed to be uncomfortable, and a leadership team that has already decided will spend money confirming it.
An organisation looking for a technology partner, a systems integrator or an implementation team. Named above, and meant.
A team wanting predictions about 2030. The argument is about decisions being taken this quarter, and the research is explicit about how weak the forecasting evidence is, including where that cuts against this position.
