A school or trust is ready for AI when seven things are in place: a purpose everyone can state, permission staff can find, a named owner with time, the capability to judge what AI produces, protection for people who report mistakes, foundations that hold the data, and evidence of what changed. Your readiness is your lowest score across the seven, not your average.
Three quarters of American middle school educators now use AI every week, and 24% of educators think their education system is adapting very well to it. Those two numbers sit a few lines apart in the AI Readiness in US Schools report that IBM published on 2 September 2026, from a Morning Consult survey of 1,019 school staff and 1,029 parents in July. IBM's headline was that adoption is outpacing readiness. The more useful question is what readiness is made of, because you cannot close a gap you cannot name.
What are the seven dimensions of AI readiness?
The Seven Dimensions of AI Readiness are the seven things an organization must have in place before its use of AI can be called ready rather than merely widespread: purpose, permission, ownership, capability, protection, foundations and evidence. Your readiness is your lowest score across the seven, not your average. That is my suggested way of thinking about it, built from the leadership teams I have sat with rather than from a survey, and it is the diagnostic that sits underneath a test some readers will already know.
The Readiness Test I set out earlier asks three questions an organization must answer consistently, from the top to the front line: what is AI for here, what may I do with it, and who decides when it goes wrong. That test tells you whether you are ready. It does not tell you why not. The seven dimensions do, and the ninety-day order tells you where to start. The first three dimensions are those three questions. The other four are what makes the answers hold when a model changes, a parent complains or the person who wrote the policy leaves.
The AI readiness checklist: seven questions and a score
Copy the seven rows below, ask each question this week, and write a number against it. Each dimension has exactly one question, chosen because it can be answered by asking rather than by reading a document.
| # | Dimension | The question to ask this week | Score |
|---|---|---|---|
| 1 | Purpose | Do three people at different levels give the same answer to "what is AI for here?" | |
| 2 | Permission | Could a new member of staff find out in five minutes what they may use AI for, what they may not put into it, and which tools are approved? | |
| 3 | Ownership | Who made the last AI decision, and how long did it take from the question being asked? | |
| 4 | Capability | How many people used AI on their own work this week, and would they notice a wrong answer? | |
| 5 | Protection | When did a member of staff last tell a leader, unprompted, that AI had got something wrong in their work? | |
| 6 | Foundations | Could you list within an hour which AI tools hold your data and who signed for them? | |
| 7 | Evidence | What did your last AI decision change, and how do you know? |
Score each one 0 if it is absent, 1 if it is partly in place, 2 if it is in place and known. Then ignore the total. Your readiness is the lowest number on the sheet, because a single zero is what the first difficult week will find.
Three rules keep the scoring honest. Score from the front line, not the boardroom: leaders describe the organization they intended to build and staff describe the one they work in. Score by asking, not by reading, because a policy that exists earns nothing until people can find it and repeat it. And rescore each term, since readiness that is not re-examined decays quietly as the tools change under it.
The seven sections that follow explain what weak and strong look like on each row.
1. Purpose: does everyone give the same answer to "what is AI for here?"
Purpose is in place when three people at different levels give the same answer to what AI is for, without looking anything up, asked separately so they cannot align their answers.
Weak looks like three different answers, each sincere. The head says "to reduce workload", the head of department says "to personalize learning" and the teacher says "I don't know, I use it for emails". None is wrong, and that is the problem: an organization with three purposes has none. Strong looks like one sentence everyone recognizes that names what will change, not what AI will be used for. "We are using AI so that teachers get their Sunday evenings back and feedback reaches pupils within a day" is a purpose. "We are exploring AI's potential" is a hobby, and it is the quickest dimension to fix because it is a conversation rather than a purchase.
2. Permission: could a new member of staff find out in five minutes what they may do?
Permission is in place when anyone can find out, quickly and without asking a favor, what they may use AI for, what they may not put into it and which tools are approved, from what you have published rather than by asking a colleague who knows.
Weak permission is a policy that exists and is not known. Microsoft's AI in Education Report, published 24 June 2026 from 3,345 students, educators and leaders in six countries including the UK, found 58% of education leaders saying their institution is implementing or scaling AI. Scaling without known permission means staff working out the rules themselves, which is how one department bans a tool another department requires.
Strong permission fits on one page, names tools rather than categories, and says what is not allowed in the same plain voice as what is. It is the first of the three conditions I described in how leaders build AI confidence across a workforce: people cannot use well what they are unsure they are allowed to touch.
3. Ownership: who decided the last AI question, and how long did it take?
Ownership is in place when a named person can decide an AI question, has had something removed from their role to make room for it, and has a deputy.
Weak ownership is a committee, a keen volunteer with a full timetable, or the head of IT by default. In each case "how long did it take?" is answered in terms, and staff learn that asking is slower than doing. This is the pattern behind why AI adoption is really a leadership problem: the Sponsorship Test asks "have we removed what it replaces?", and ownership fails there more than anywhere else. It is also why naming an AI lead so rarely settles the matter, since the decision, the delivery and the expertise are three different jobs.
Strong ownership is a name, a deputy, a decision route rehearsed for the day something goes wrong, and a leadership team that backs the decision the owner makes. The owner need not be the most technical person in the building, only the one whose judgment colleagues already trust.
4. Capability: would your staff notice a wrong answer?
Capability is in place when most staff have used AI on real work recently and can tell when its output is wrong. The second half of that question matters more than the first.
This is where the readiness gap shows most plainly in the evidence. IBM found only 20% of US educators had received extensive AI training, and lack of training was the barrier they named most often. Microsoft found 53% of educators without formal training and 66% wanting it monthly or quarterly. In England, Pearson's School and College Report 2026, published 4 June 2026 from more than 11,000 teachers and tutors and 3,000 students, put the share confident in teaching about AI at 16%, up from 9% a year earlier, with 8% saying they are currently preparing students for an AI-enabled future. Use is high; the ability to judge the tool is not.
Weak capability is fluency without judgment: staff who can produce a worksheet in thirty seconds and would not spot the invented citation in it. Strong capability is dull and repeated: people using AI on their own work beside someone who has done it before, and a habit of checking before trusting. Training buys the first half. Only practice buys the second.
5. Protection: when did you last hear about an AI mistake from the person who made it?
Protection is in place when it is safe to be seen using AI and safe to say when it went wrong, and the organization extends the same openness to the people its AI use affects. The question is a date, and the date is the answer.
If the answer is "never", you do not have a workforce that makes no mistakes. You have one that has decided not to tell you. Weak protection shows from outside too. IBM found 77% of parents want a say in how AI is used in their child's classroom, and only 20% clearly understand the guidance their school has given them. An organization whose own community cannot describe its AI use is not protected from the first difficult conversation. It is simply not having it yet.
Strong protection treats the first reported mistake as information and says so publicly, and tells parents, customers or pupils what AI is used for in plain words before they ask. I count it as a dimension of readiness because an organization that cannot hear about its errors cannot correct them, and a readiness that cannot correct itself will not survive the next model release.
6. Foundations: could you list which tools hold your data and who signed for them?
Foundations are in place when the organization knows which AI tools hold its data, under what agreements, on what devices and networks, and has checked those tools against the standards it is bound by.
This is the dimension most readiness tools measure, and measure well. The K-12 Gen AI Maturity Tool from CoSN (the Consortium for School Networking) and the Council of the Great City Schools, published on 9 April 2024, rates US school districts as emerging, developing or mature across six domains: executive leadership, operational, data, technical, security, and risk and legal. Several states publish their own instrument instead; Wisconsin's Department of Public Instruction asks districts to complete an AI Readiness and Governance Self-Assessment to "establish a shared picture of current strengths and gaps". If your foundations score is low, use one of those; either is more thorough than an article can be.
But notice their shape. Five of CoSN's six domains sit inside what I am calling foundations, and purpose, permission, protection and evidence barely appear, because a rubric written for technology leaders measures what technology leaders control. Weak foundations are a real risk: a data breach or a procurement failure can end an AI program in a week. Strong foundations, on their own, are a well-secured organization that still cannot say what AI is for. Necessary. Nowhere near sufficient.
7. Evidence: what did your last AI decision change, and how do you know?
Evidence is in place when the organization can say what its use of AI has changed, for whom, against a measure it named in advance, and has a date on which it will look again.
Weak evidence is usage. Licenses activated, prompts run, staff trained: these count activity, and IBM's figures show why they mislead. Three quarters of middle school educators using AI weekly and a quarter believing the system is adapting very well can both be true, because usage was never a measure of readiness. Telling the two apart has a test of its own: ask whether the number could have risen while the work itself stayed exactly the same. Weak evidence also has no date, which is how pilots become habits, and I set out the Exit Question for that: if this works, what will change, who has already agreed to it, and on what date will you decide?
Strong evidence is small and specific. Feedback turnaround in days. Hours returned to a named group, and where those hours went. Pick two measures, name them before you start, and put the review in the diary.
Why is adoption outpacing readiness?
Adoption is outpacing readiness because leaders invest in the two dimensions that can be bought, foundations and capability, and leave the five that can only be decided. That is my reading of the evidence rather than a finding within it, though every survey above is consistent with it. Training and infrastructure appear on procurement lists. Purpose, permission, ownership, protection and evidence appear nowhere, because there is nothing to buy; there is only a leadership team that has to agree, write it down and be seen to hold it.
The result is an organization with good WiFi, a signed data agreement and a well-attended training day, in which nobody can say what AI is for, the owner has no time, mistakes go unreported and success has no measure. It will report high adoption, and it will be the one the newspaper writes about when something goes wrong.
What I See in Practice
Across the schools, trusts and districts I work with, in England and internationally, readiness almost always fails in dimension 1 or 3, and the leadership team almost always believes it has failed in dimension 4 or 6. They ask for training or a tools review. When I ask three people separately what AI is for here and get three answers, the conversation changes.
I taught in classrooms and then led as an assistant head before any of this arrived, which is why dimension 5 is on the list at all. A member of staff deciding whether to tell a leader that something went wrong is making a judgment about that leader, not about the technology, and it is made long before the mistake happens.
The readiest schools I have worked with have often had less technology than their neighbors, because a head had answered the purpose question in one sentence, given one person the job with time attached, and told parents what the school would and would not do before anyone asked.
The pattern worth noticing is the direction of the fix. Foundations and capability are bought from outside and improve slowly. Purpose, permission, ownership and evidence are decided inside and can improve in a month. Protection sits between: leaders decide it, and time earns it.
What should you do with your lowest score?
Fix the lowest dimension before you touch the others. A hypothetical leadership team scoring 2 on foundations, 2 on capability, 1 on permission, 0 on purpose and 0 on evidence is not "mostly ready". It is not ready, and its next action is a one-hour conversation about purpose, not another training day. If your lowest score sits in capability, permission or protection and you cannot tell which group is furthest behind, map where use runs ahead of what the school has given each group before you commission anything.
Then put the seven words on one slide, with the seven questions underneath, and take it to your board or governors. It is the fastest way I know to move that conversation from "are we using AI?" to "are we ready to be?"
Working through this with your leadership team
If the checklist has shown you a gap you cannot close alone, this is the kind of work I support through AI readiness reviews for schools, which turn readiness questions into a set of priorities and a roadmap that starts from the lowest score rather than the latest tool. I write each week about closing readiness gaps in the order that works, in the newsletter.
Sources and further reading
- IBM, New IBM Study Finds AI Adoption Is Outpacing K-12 Readiness, press release on the AI Readiness in US Schools report, Morning Consult survey of 1,019 K-12 education professionals and 1,029 parents fielded July 2026; IBM Newsroom, 2 September 2026.
- Microsoft, Microsoft's New AI in Education Report highlights widespread adoption and increasing demand for support, 3,345 respondents across the United States, United Kingdom, Australia, Brazil, Japan and Saudi Arabia; Microsoft Source, 24 June 2026.
- Pearson, The Pearson School and College Report 2026, more than 11,000 teachers and tutors and 3,000 students; Pearson UK, with the figures quoted here from Pearson's release of 4 June 2026.
- CoSN and the Council of the Great City Schools, CoSN/CGCS K-12 Gen AI Maturity Tool, 9 April 2024.
- Wisconsin Department of Public Instruction, AI readiness guidance for districts and libraries, which asks districts to complete an AI Readiness and Governance Self-Assessment.
Sources last checked October 6, 2026.
Dan Fitzpatrick is the founder of The AI Educator, a Forbes contributor and bestselling author on AI in education, and works with schools, trusts and organizations on AI strategy and readiness. About Dan.
