Most organizations that tell me they are AI ready are describing their software. They have licenses, a policy document, a training day on the calendar and a few enthusiastic early adopters. None of that is readiness. It is activity. And I have watched activity like that fall apart the moment a governor or board member asks a hard question, a parent complains, or a new model arrives and changes what the tools can do.
So here is the definition I work to, and the test I use to check it.
What does it mean to be AI ready?
Being AI ready means your organization can make, communicate and hold good decisions about AI, consistently, without depending on any one person. That is the whole definition. It says nothing about which tools you own, how many people use them, or whether your data is in the cloud. Those things matter, but they are inputs. Readiness is the capacity to decide well, and to keep deciding well as the technology moves.
Notice what the definition rules out. An organization with one brilliant AI lead who holds everything in their head is not ready, because when they leave, so does the readiness. An organization with a beautifully written policy that staff have never read is not ready, because a decision nobody knows about is not a decision, it is a document. And an organization where most of the workforce quietly uses AI every day, while leadership has not agreed what it is for, is not ready either. It is exposed.
I define readiness as a capacity rather than a state because AI does not stand still long enough for a state to mean anything. If readiness were a checklist, you would complete it and be unready again within a term. A capacity keeps working when the ground moves.
Why does the question matter now?
The question matters now because usage has run far ahead of leadership, and the gap between the two is where the risk lives. AI has already arrived in your organization, on people's phones, in their inboxes and in tools nobody approved. The live question is whether leadership has caught up with what its own people are already doing.
The National Education Union's State of Education survey, published on April 2, 2026 from responses by more than nine thousand teachers in English state schools (fieldwork February 5 to 16), found that 76 percent now use AI tools for their day-to-day work, up from 53 percent a year earlier. In the same survey, 49 percent said their school had no AI policy for staff or students at all. Read those two figures together. Three in four teachers are using AI, and half of them are doing it in an organization that has not yet decided what it thinks.
The United States shows the same gap from the guidance side. Gallup's survey for the Walton Family Foundation of 2,069 US public K-12 teachers (fieldwork February 9 to March 2, 2026; published May 26, 2026) found that just 18 percent had received any type of formal guidance from school administrators on the AI tasks it asked about, and about a third, 34 percent, had received no guidance at all. The tools arrived first. The decisions are still catching up.
That pattern is not unique to schools. Cisco's AI Readiness Index, published on October 14, 2025 from a survey of about eight thousand senior IT and business leaders across 30 markets, found that only about 13 percent of organizations qualified as what it calls Pacesetters, the group that consistently turns AI activity into value. Ninety-nine percent of those Pacesetters had a defined AI roadmap, against 58 percent of respondents overall. The difference between the groups was not enthusiasm for AI. It was whether the organization had decided where it was going.
So the question "are we AI ready?" is really the question "has our leadership caught up with our own workforce?" For most organizations, the honest answer is not yet.
The Readiness Test: three questions
The Readiness Test is three questions an organization 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? I use these questions because they are quick to ask and hard to fake. A leadership team can usually answer all three in the boardroom. The test is whether a teacher in a Year 8 classroom (eighth grade in the United States), or a finance officer, or a newly appointed middle leader, would give the same answers.
The test tells you whether you are ready. It does not tell you why not, or where to start, which is the job of the seven dimensions of AI readiness: the same ground broken into seven things you can score, with the rule that your readiness is your lowest score rather than your average.
What is AI for here?
The first question tests whether your organization has a purpose for AI, rather than a collection of tools. When I ask a leadership team this question, the strongest answer I hear is a single sentence that connects AI to something the organization already cares about: giving teachers time back for the parts of teaching only humans can do, or removing the administrative drag that keeps senior staff away from the work they were appointed for. The weakest answer is a list of products. If the purpose cannot be said in a sentence, it cannot be shared, and if it cannot be shared, every department will invent its own.
What may I do with it?
The second question tests whether your decisions have reached the people who act on them. Most organizations I work with have a policy. Far fewer have staff who could tell you, without looking it up, what they are allowed to put into an AI tool, what they are not, and what they must check before they use the output. Readiness here is not the existence of rules. It is the confidence of the person using AI at four o'clock on a Thursday, with nobody to ask, who knows the answer anyway.
Who decides when it goes wrong?
The third question tests accountability, and it is the one leadership teams most often fail. AI will produce something wrong, biased or inappropriate at some point in every organization that uses it. That is not a reason to avoid it, any more than the certainty of a safeguarding incident is a reason to close a school. But it does mean someone has to own the response. When I put this question to a room, the tell is a pause followed by several people looking at each other. If the answer is not immediate, the organization is not ready, however good its tools. The step that turns that pause into a name is set out in who is accountable when AI gets it wrong.
What does the evidence say about readiness?
The research is consistent on one point: readiness is a leadership variable, not a technology variable. McKinsey's State of AI survey, published on August 25, 2026 from 1,719 respondents across 97 countries, found that nearly nine in ten respondents report regular use of AI in at least one business function, yet high performers, the organizations that could attribute significant value to it, account for just 6 percent of respondents, unchanged from 2025. High performers were twice as likely as others to say their senior leaders demonstrate commitment to AI initiatives, and nearly three quarters reported fundamentally redesigning workflows because of their AI use, against about a quarter of other respondents.
Cisco's index tells the same story from a different direction. Its Pacesetters were four times more likely to move pilots into production, and far more likely to have thought about the risks: 87 percent were aware of AI-specific threats, against 42 percent of respondents overall. Awareness of the downside is a marker of readiness, not a sign of hesitation.
Two limits on this evidence are worth stating. Both Cisco and McKinsey surveyed organizations broadly, and neither is a study of schools. And both rest on what leaders reported about their own organizations, which is useful but is not the same as observing them. I read the pattern as consistent with what I see every week, not as proof of it. The organizations that get value from AI are not the ones that adopted it first or bought the most. They are the ones whose leaders made decisions, said them out loud, and changed the way work happened as a result.
What I See in Practice
Across the leadership teams I work with, the organizations that turn out to be least ready are often the ones that look most advanced from the outside. They have the highest usage figures and the most visible enthusiasm, and that is precisely the problem. Usage without decisions is not maturity. It is a set of habits forming in the dark, and habits are much harder to reshape than a blank page.
The pattern I see most often runs like this. A few early adopters start using AI and get real gains. Word spreads. Leadership notices, feels behind, and responds with the two things that are easiest to buy: a license and a training day. Usage jumps. Then the questions start arriving that no license can answer. A parent asks whether their child's work is being marked by a machine. A member of staff pastes something they should not have into a tool nobody approved. A department produces something with AI that another department would never have signed off. At that point the organization discovers that it has adopted AI without ever having decided anything about it.
I recognize this pattern because I have been on both sides of it. As an assistant headteacher, and later as Director of Digital Strategy in further education, I led technology change in organizations that were expected to move fast, and I saw firsthand how easy it is to confuse rollout with readiness. What worked was not more technology. It was fewer, clearer decisions, made by the leadership team, communicated until people were tired of hearing them, and revisited on a schedule.
That is also what the strongest organizations I work with now have in common. They are not the most sophisticated users. They are the ones that can answer the three questions above from any seat in the building.
What keeps organizations from being AI ready?
The mistakes that keep organizations unready are almost never technical; they are decisions that were delegated, deferred or never made. Four come up again and again.
The first is treating a policy as a strategy. A policy tells people what they may not do. It does not tell them what the organization is trying to achieve, so the most cautious reading wins and the ambitious staff quietly go around it. The distinction is worth settling early, because a document that only prevents decisions is a policy whatever the cover says.
The second is outsourcing readiness to the most enthusiastic person in the building. Every organization has one. They are valuable, and they are also a single point of failure. If your readiness lives in one person's head, you do not have readiness. You have a risk with a name. Spreading that risk starts with separating the three jobs hidden inside the word "owns", because the decision, the delivery and the expertise rarely belong to the same person.
The third is measuring usage and calling it maturity. Dashboards showing how many staff logged in this month are seductive, and they measure the wrong thing. High usage in an organization that has not decided its purpose is a warning sign, not a success metric. Some numbers do tell you something, and the way to sort a measure of maturity from a measure of activity is to ask whether the number could have risen while the work itself stayed exactly the same.
The fourth is waiting for certainty. Leaders tell me they will decide once the technology settles or the guidance is clearer. It will not settle. Readiness is built by deciding in uncertainty and being willing to revise, which is the ordinary condition of leadership, not a special condition of AI.
What does an AI ready organization look like?
An AI ready organization is recognizable by its behavior, not its inventory. The leadership team can say in one sentence what AI is for, and staff give the same sentence back. There is a short list of approved uses and tools, and everyone knows where it lives and who to ask when it does not cover their situation. Somebody owns AI at leadership level, with a named deputy, and it appears on the leadership agenda on a rhythm rather than in a crisis. When something goes wrong, the response is calm because the route has already been agreed.
Good also looks like honest limits. The best organizations I work with are clear about what they will not use AI for, and say so publicly. That does more for trust among staff, parents and boards than any amount of enthusiasm, because it shows the leadership has looked at the downside and chosen anyway.
And good looks like a rhythm of review. AI ready organizations do not finish. They revisit the three questions every term or quarter, because the answers will have moved.
When is the Readiness Test not enough?
The Readiness Test is not enough when the question is where you are weakest rather than whether you are aligned. It measures one thing: whether the same decisions are held at every level. It is silent on data quality, security, skills and infrastructure, and an organization can pass it and still lack the capability to act on what it has decided. It also relies on honest answers from people who may not feel safe giving them, which is why the front-line question should be asked by someone they trust, not by the person who wrote the policy. Use the test to find the gap, and the seven dimensions of AI readiness to locate it. If the gap you suspect is between what people already do with AI and what the organization has given them, my Lead-Lag Map puts those two columns side by side for staff, students, families and governors. If your real question is how you compare with other organizations, what benchmarking actually tells you is the better place to start.
The Readiness Gap Sheet: copy this
The Readiness Gap Sheet is the one-page way I suggest running the test. It puts the leadership team's answers beside the answers from someone three levels down, so the gap is on the page instead of in people's heads. It is my suggested way of working, not a validated instrument. Copy the layout into a document, ask each question of each group separately, and write down the answers in their own words.
| The question | Leadership team's answer (one sentence) | Answer from someone three levels down | Match: yes, partly or no | What a gap means |
|---|---|---|---|---|
| What is AI for here? | No agreed purpose: you need a decision | |||
| What may I do with it? | The decision has not reached the front line: you need communication | |||
| Who decides when it goes wrong? | Nobody owns the response: you need accountability | |||
| Hypothetical example: What is AI for here? | "Giving teachers back the time they lose to admin." | "I think we're meant to be trying it out." | No | The leadership sentence has not been communicated |
Then read the sheet back with four rules:
- Write the answers down before anyone sees the others. If the front-line answer is edited to sound like the leadership one, you have measured the editing.
- Treat "partly" as "no." Readiness is the same decision held at every level. A close paraphrase usually hides two different purposes.
- Fix one gap, the biggest. Name the decision, the message or the owner it needs, and who will provide it by when.
- Ask again next term. A sheet filled in once describes October. A sheet filled in each term is a measure of whether readiness holds when the ground moves.
Where should you start?
Start by asking the three questions of your own leadership team, then asking them of someone three levels down, and compare the answers on the Readiness Gap Sheet. The gap between the two is your readiness gap, and it is more useful than any maturity score because it tells you exactly what to fix. Vague leadership answers mean you need decisions. Clear leadership answers that the front line cannot repeat mean you need communication. Clear answers everywhere except on who owns it when things go wrong mean you need accountability.
Then fix the biggest gap first, say the decision out loud, and put a date on the calendar to ask the questions again. That is not glamorous. It is what readiness is.
Working through this with your leadership team
If your leadership team is trying to work out how ready you really are, and what to do about the gaps, this is the kind of work I support through AI readiness reviews for schools and leadership strategy sessions, which start from the gap between the two sets of answers rather than from a tool. Leaders working through the three readiness questions can also join my weekly newsletter.
Sources and further reading
Sources last checked October 17, 2026.
- National Education Union, State of Education: AI, press release, April 2, 2026, reporting a survey of NEU teacher members in English state schools, fieldwork February 5 to 16, 2026.
- Ash, A. M. and Senseman, K., Most Teachers Receive No Formal Guidance on AI Use, Gallup, May 26, 2026, reporting the Walton Family Foundation-Gallup survey of 2,069 US public K-12 teachers, fieldwork February 9 to March 2, 2026.
- Cisco, Cisco AI Research: The Most AI-ready Companies Outpace Peers in the Race to Value, AI Readiness Index 2025, October 14, 2025.
- McKinsey and Company, The State of AI: Global Survey 2026, August 25, 2026.
Dan Fitzpatrick is the founder of The AI Educator, a Forbes contributor, a bestselling author on AI in education, and an advisor to school and system leaders on AI strategy. Read more about Dan.
