Most AI strategies are written for the people who approve them, not for the people who have to live with them. That is why so many are read once, praised in a board meeting, filed in a shared drive and never opened again.
I have sat in enough strategy sessions with senior leadership teams to know how this happens. Nobody sets out to write a document no one will use. The team is thoughtful, the language is careful, the ambitions are sincere. Then a teacher or a team leader opens it on a Tuesday afternoon with a real decision in front of them, finds nothing that helps, and does what they were going to do anyway.
An AI strategy that people actually use is a different kind of document. It is shorter, more specific, and much braver about saying no. This article is about how to build one.
What an AI Strategy That People Use Actually Is
An AI strategy people use is a small set of decisions the leadership team has already made on everyone's behalf, written down so clearly that a member of staff can act on them without asking permission. It is not a vision statement, and it is not a policy. A vision says where you hope to end up. A policy says what people must not do. A strategy says what you have chosen, in what order, and why, so that a hundred smaller decisions across the organisation start pointing the same way.
That distinction matters because "use" is a precise word. A strategy is being used when it changes a decision somebody would otherwise have made differently. A head of department decides to pilot AI for feedback rather than for lesson planning, because the strategy says feedback comes first. A manager declines a vendor demo because the strategy says the organisation is consolidating tools this year, not adding them. A new starter knows which tools are approved, and what for, by the end of the first week. Nothing in that list requires anyone to reread the strategy. It has already done its work. If a strategy does not produce moments like that, it is not being used, however elegant it is.
Why Most AI Strategies Are Never Used
Most AI strategies go unused because they answer the leadership team's question rather than the staff member's question. The leadership question is "what is our position on AI?" The staff question is "what should I do differently on Monday?" A document can answer the first beautifully and the second not at all.
The evidence on this is uncomfortable. In BCG's AI at Work survey of nearly 12,000 employees, managers and leaders across more than a dozen markets, published in June 2026, only a third of frontline employees said leadership's communications about AI were clear, and only 28 per cent saw a strong connection between what leaders say about AI and what the organisation actually does. Two thirds said they receive limited or no guidance on what to do with the time AI saves them. Those are not adoption problems. They are strategy problems wearing an adoption costume.
Gallup's earlier work points the same way. In its December 2025 workplace survey, 23 per cent of US employees did not know whether their organisation had implemented AI at all, and the further people sat from decision making, the less they knew: 26 per cent of individual contributors said they did not know, compared with 7 per cent of leaders. The strategy existed. It never reached the people it was about.
There is a second reason, and it is more awkward. Many leadership teams write the strategy before they have agreed a shared position. The document then becomes a way of avoiding the argument rather than settling it. Every hard choice is softened into a phrase everyone can live with, and the result offends nobody and directs nobody. When I run strategy sessions with executive teams and school leadership teams, this is usually where the real work is: not the writing, but the agreeing.
The Five Decisions a Usable AI Strategy Makes
A usable AI strategy makes five decisions explicitly: purpose, priorities, permissions, people and proof. Everything else is supporting material. If your current document is long, find each of the five in it; if you cannot find one in a sentence, it has not been made.
In the first Insights article I defined AI readiness this way: "Being AI ready means your organisation can make, communicate and hold good decisions about AI, consistently, without depending on any one person." The strategy is where those decisions live.
1. Purpose: what AI is for here
The purpose decision states, in one or two sentences, what AI is for in this organisation and what it is not for. Not "to embrace the opportunities of AI" but something a sceptical colleague could disagree with. "AI is for giving teachers time back for the parts of teaching only they can do, and for improving the quality of feedback students receive. It is not for replacing the judgement of a qualified teacher about a child." A school could argue with that. That is the point. A purpose nobody could argue with is not a decision.
This is also the first question in the Readiness Test, which I set out in the first Insights article: "The Readiness Test is three questions an organisation must be able to answer consistently, from the top to the front line, before it can call itself AI ready: What is AI for here? What may I do with it? Who decides when it goes wrong?" If your strategy cannot answer the first of those in plain language, stop and answer it before writing anything else.
2. Priorities: where first, and where not yet
The priorities decision names the two or three areas where AI effort goes first this year, and names at least one area where it deliberately does not. Leaders find the second half of that sentence far harder than the first. Nobody enjoys telling an enthusiastic team their idea is a good one for next year. But a strategy with twelve priorities has none, and staff know it. The organisations I see making real progress have usually chosen fewer things than they wanted to, and then finished them.
A practical rule I give leadership teams: anchor the first priorities to friction that already exists. The best first uses of AI are attached to things people already find slow, repetitive or frustrating, because the pull is already there. Novelty needs pushing. Friction pulls on its own.
3. Permissions: what people may do, today
The permissions decision tells every member of staff what they may do with AI now, with which tools, and with what kinds of information, without asking anyone. This is the decision most often missing, and its absence does the most damage. Without it, two things happen at once. Cautious people do nothing, because they assume the answer is no. Confident people do whatever they like, because nobody has said otherwise. Both groups then believe they are following the strategy.
Permissions belong in the strategy, not only in the policy, because a policy is written to limit and a strategy is written to enable. The permissions section should read as an invitation with edges: here is what we want you to try, here is where the line is, here is who to ask when you are not sure.
4. People: who leads, who supports, how capability grows
The people decision names who is accountable for the strategy, who supports its use day to day, and how the organisation will build capability beyond a one-off training session. BCG's AI Radar survey of 2,360 executives, published in January 2026, found that 72 per cent of CEOs now describe themselves as the main decision maker on AI, double the previous year. That is a sign of seriousness. It is also a risk. A strategy that lives in the chief executive's head, or the headteacher's, fails the moment they are in a different room. Ownership has to be visible, and it has to be shared.
The capability half of this decision is where most strategies become vague, with a line about "ongoing professional development" and nothing more. Be specific: who gets what, in what order, and what they should be able to do afterwards. And treat managers and middle leaders as the priority group, not an afterthought. Gallup's July 2026 engagement research found that employees with active manager support for using AI were engaged at 48 per cent, against 30 per cent without it. Managers are the channel through which a strategy reaches people, or fails to.
5. Proof: how you will know it is working
The proof decision states what the leadership team will look at, and when, to judge whether the strategy is working, and what would cause them to change course. Usage numbers are the easiest thing to count and the least useful. Licences activated and prompts sent tell you people are touching the tools, not whether feedback improved, time was given back, or anyone's judgement got better. Choose two or three measures that connect to the purpose in decision one, review them on a fixed rhythm, and write the date of the first review into the strategy itself.
What the Evidence Says About Strategy and Use
The research is consistent on one point: clarity about the plan changes how people behave, even before it changes the tools they have. BCG's June 2026 report found that strategic clarity improves AI's impact even in organisations with limited access to AI tools. Gallup's July 2026 research, drawing on more than 43,000 US employees, found that 43 per cent of employees in organisations with a clear plan for integrating AI were engaged, against 28 per cent in organisations without one. A fifteen point gap is the difference between a workforce that leans in and one that waits to be told.
For school leaders in England the direction of travel is also becoming clearer. The Department for Education's AI in education roadmap sets out six digital standards that schools are expected to meet by 2030, records the international AI in Education Summit the department hosted in January 2026, and commits to developing education benchmarks for AI tools through 2026. None of that writes your strategy for you. All of it raises the bar for what "we are still thinking about it" will look like to governors, trustees and inspectors. Across the government and system-level bodies I have advised, the worry is rarely whether schools should have a strategy. It is how to make one land across hundreds of them, which is a translation problem. The next section is about exactly that.
The Mistake I See Most Often
The mistake I see most often is a leadership team treating the strategy document as the finish line, when it is the starting gun. The document is signed off, the announcement goes out, and everyone quietly assumes the work is done. A strategy is used when people have heard it explained by someone they trust, in language that connects to their own work, more than once.
When I work with leadership teams on this, I normally push for a translation step before publication: take the five decisions and rewrite them, briefly, for each main group in the organisation. What does this mean for a classroom teacher? For a finance team? For a head of year? For a new starter? If a decision cannot be translated for a group, it usually means it was never really a decision, only an aspiration in a suit.
The second most common mistake is close behind. Leaders write the strategy as if the sceptics were the problem. In my experience the sceptics are the most useful readers the strategy will ever have, because they will find every sentence that means nothing. Give the draft to your most doubtful senior colleague before you give it to the board. If they cannot find a decision they disagree with, the document is not finished.
The Monday Test
The Monday Test is a single question to ask of any AI strategy before it is signed off: could a member of staff read it and know what to do differently on Monday morning? Run it with a real person in mind, not a category: a specific colleague, in a specific role, opening the document with a specific decision in front of them. Then read it as they would.
Most drafts fail the test in the same places. The purpose is there but the permissions are not. The priorities are listed but nothing has been declined. The ownership is implied but no name appears. Each failure is easy to fix once it is visible, which is the whole value of the test. It turns "is this a good strategy?", which invites debate, into "would Sarah know what to do?", which invites an answer.
What Good Looks Like
A good AI strategy is short enough to be remembered, specific enough to be disagreed with, and owned visibly enough to survive a change of leader. In the strongest organisations I have worked with, you can ask three people at three levels what AI is for here and get three versions of the same answer. Not the same words. The same decision.
There are other observable signs. Middle leaders can explain the priorities without looking anything up. Staff know which tools are approved and what for, and the approved list is short. Somebody has said no to a good idea recently, and explained why in terms of the strategy. The leadership team has a date in the diary to review the proof measures, and the last review changed something. New starters hear about AI in their first week from their line manager, not from a document.
None of these signs mention the document itself. That is deliberate. A strategy that is working shows up in behaviour, not in a file.
How to Build It: A Practical Sequence
Build the strategy in this order: agree the position, make the five decisions, translate them, publish the shortest possible version, and put the first review date in the diary before anyone announces anything. The sequence matters more than the format.
Start with the leadership team's shared position, and do not skip this because it feels slow. Half a day in a room, with the honest disagreements on the table, saves months of a strategy nobody believes in. This is the part of the process I am most often asked to lead, and it is the part that leadership teams most often try to do by email.
Then make the five decisions in writing, each in a sentence or two, and run the Monday Test on every one. Translate for each group. Cut everything that is not a decision or the reason for one; background reading can live in an appendix nobody has to open. Publish, explain it in person through managers and middle leaders, and hold the first review when you said you would.
Finally, expect to revise it. Curiosity over fear, evolution over revolution: a strategy for a technology that changes every quarter should be a living document with a version number, not a monument.
The Next Step
If your leadership team has a strategy that reads well but is not changing what anyone does, or is trying to write one and keeps stalling at the hard decisions, this is the kind of work I support through AI strategy sessions and advisory work with senior teams.
Dan Fitzpatrick is the founder of The AI Educator and works with leadership teams, boards and government bodies on AI strategy and readiness. More about Dan.
