# What Good AI Leadership Looks Like: Five Visible Signs

Canonical URL: https://blog.theaieducator.io/posts/what-good-ai-leadership-looks-like
Publication: Dan Fitzpatrick Insights
Author: Dan Fitzpatrick
Topic: AI Leadership and Strategy
Published: 2026-09-08T14:14:22.000Z
Modified: 2026-09-08T14:14:52.229Z

Two schools can hold the same AI policy and be led in opposite directions. Good AI leadership is observable, not documentary: here are the five things you would see, and the Decision Trail that separates leading from announcing.

## In brief

Good AI leadership is observable rather than documentary. Where AI is led well you would see five things: a short trail of dated decisions with names attached, a leader using AI on their own work in front of staff, something that has visibly stopped, a line the organization will not cross that the leader can state without opening the policy, and at least one open question the leader admits to. Dan Fitzpatrick's Decision Trail asks a leadership team for the last five decisions they made about AI, when each was made, who made it and what changed the week after. Good AI leadership leaves a trail of decisions. Weak AI leadership leaves a document.

## Key takeaways

- Good AI leadership is visible in behavior, not in the strategy document: two organizations holding identical AI policies can be led in opposite directions.
- The Decision Trail asks a leadership team for the last five decisions it made about AI, when each was made, who made it and what changed the week after; announcements have no week after.
- Five things are observable where AI is led well: dated decisions with names attached, the leader's own visible use of AI, something that has stopped, a stated line the organization will not cross, and an open question the leader says out loud.
- Only 23 percent of learners named their school or training program as a primary source of information about AI, against 48 percent who named social media (Jobs for the Future, March 18, 2026), which is a measure of leadership rather than of students.
- The six-domain AI leadership framework published by DeMatthews and colleagues in Educational Management Administration & Leadership on February 5, 2026 defines a leader's responsibilities, not their behaviors: a leader can hold all six and still be invisible on all five signs.
- Every real AI decision carries a subtraction; a time saving that nobody redirects teaches staff that the tool changed nothing except their workload.
- Keeping Children Safe in Education 2026, in force in England from September 1, 2026, turns AI into a dated safeguarding decision rather than an optional strategy topic.

Two schools can hand you the same AI policy and be led in opposite directions. Across the leadership teams I work with, the difference never shows up in the document. It shows up when you ask one question: what has this team actually decided about AI since the start of the year, and what changed the week after you decided it? One team answers in about fifteen seconds. The other describes its governance structure.

That gap matters more this term than last. In England, Keeping Children Safe in Education 2026 came into force on September 1, 2026, writing images "digitally altered or wholly generated using artificial intelligence" into the statutory definition of sharing nudes and semi-nudes, and requiring senior leaders, the designated safeguarding lead and IT to review filtering and monitoring at least annually ([EdTech Innovation Hub, July 7, 2026](https://www.edtechinnovationhub.com/news/dfe-publishes-kcsie-2026-with-mobile-phone-free-default-and-ai-safeguarding-rules)). In the United States, districts opened the school year with state model policies already in hand. Nearly everyone now has the documents. Very few can show you the decisions.

## What does good AI leadership actually look like?

Good AI leadership looks like five things a visitor could observe in a morning: a short trail of dated decisions, a leader using AI on their own work where other people can see it, something that has visibly stopped, a line the organization will not cross that the leader can state without opening the policy, and at least one question the leader admits out loud is still open.

None of the five requires a leader to be technical. None can be bought in, delegated to a working group, or copied from another school. That is why they are worth watching: they are the parts of AI leadership that only the leader can supply.

## Why an AI strategy document tells you nothing

A document records what a leadership team wanted to be true, which is a different thing from what happened. The sharpest evidence for the gap comes from the people the documents are about. In a survey of 3,020 learners run by AudienceNet for [Jobs for the Future](https://www.jff.org/newsroom/press-releases/ai-usage-in-education-is-growing-but-gaps-in-guidance-persist-new-survey-finds/), fielded November 28 to December 8, 2025 and published March 18, 2026, only 23 percent named their school or training program as a primary source of information about AI. Forty-eight percent named social media. Thirteen percent did not know what their own institution's policy allowed.

Read those numbers as a measure of leadership rather than of students. The policy exists. The signal did not arrive. When the people a policy is written about cannot say what it says, the document has been produced and the decision has not been led.

## The Decision Trail: what to ask for instead of a strategy

The Decision Trail is what I ask a leadership team to show me instead of their AI strategy: the last five decisions they have made about AI, when each was made, who made it, and what changed the week after. Good AI leadership leaves a trail of decisions. Weak AI leadership leaves a document.

This is my suggested way of looking at it rather than a validated instrument. Its value is that it is quick, it can be run in a corridor, and it is close to impossible to fake. A team that is leading fills in five rows from memory and then argues about one of the dates. A team that is not leading fills the rows with things that were announced rather than decided: a working group formed, a webinar attended, a policy adopted, a supplier met. An announcement has no week after. That is how you tell the two apart in under a minute.

## The five things you would see

In an organization where AI is being led rather than described, five things are visible to anyone who spends a morning there, and each of them belongs to the leader rather than to the policy.

### 1. Decisions with dates and names attached

Strong AI leadership produces decisions small enough to date. "We will not use AI to write the first draft of a safeguarding referral, from October, and the deputy head owns it" is a decision. "We are committed to the ethical use of AI" is a value, and values have no week after. Weak leadership talks in commitments because commitments cannot be audited. If your team has made no dated AI decision this term, the honest position is that AI is being managed rather than led.

### 2. The leader's own work, done where people can see it

Watch what the leader does with AI on their own tasks, in front of others, including the parts it gets wrong. Staff read a leader's private enthusiasm as permission for nobody. They read a governor's report drafted in the open, with the two paragraphs the model got badly wrong shown on the screen, as permission for everyone. That is also the fastest available demonstration that outputs are drafts requiring judgment, which is the thing every policy asserts and almost no policy teaches.

### 3. Something that stopped

Every real AI decision has a subtraction attached to it. If AI has saved forty minutes a week on reports and nobody has said what those forty minutes are now for, the saving is a rounding error, and staff will draw the obvious conclusion: the tool changed nothing except the amount of work they are expected to absorb. Leaders who are serious name the thing that stops: the form nobody reads, the summary that is now written once instead of three times, the meeting that shortens. This is the point at which [AI adoption stops being a training problem and becomes a leadership one](https://dan-fitzpatrick-blog.replit.app/posts/why-ai-adoption-is-really-a-leadership-problem).

### 4. A sentence about what we will not use AI for, said without checking

Ask a leader what their organization will not use AI for and time the pause. A leader who has led the decision answers immediately and in their own words. A leader who has adopted a policy goes to find the policy. England's Department for Education makes this a practical requirement rather than a philosophical one: its guidance on [generative AI product safety expectations](https://www.gov.uk/government/publications/generative-ai-product-safety-expectations), published January 22, 2025 and last updated January 19, 2026, positions schools and colleges as the assessors of whether an AI product is safe for educational use. Somebody in the building has to be able to say no to a product, out loud, and be backed. I have set out how to draw that line so it survives contact with a real case in [which decisions AI should never make](https://dan-fitzpatrick-blog.replit.app/posts/which-decisions-should-ai-never-make).

### 5. A question the leader says out loud they cannot answer

The most reliable marker I know of good AI leadership is a leader who names, in front of staff, the thing they have not worked out yet: what happens to coursework, whether the assessment policy holds, how they will know if any of this improved anything. A leader who admits an open question is a leader who has been thinking about it. A leader with a confident answer to everything has usually been reading vendor material. Openly held questions also do something practical: they tell staff which problems are worth bringing upward, and they make it safe to disagree in a meeting.

## What the research says leaders are responsible for, and what it leaves out

Academic work has started to define the AI job of a school leader, which is progress, though it defines responsibilities rather than behaviors. In February 2026, David DeMatthews, Pedro Reyes, Torri D. Hart and Lebon James published [a six-domain framework for AI leadership in schools](https://journals.sagepub.com/doi/10.1177/17411432261418940) in *Educational Management Administration & Leadership*, aligned to the Professional Standards for Educational Leaders: ethical AI use, strategic planning and visioning, curriculum and instructional innovation, professional development and capacity building, data-informed decision-making, and operational efficiency. Their argument, which I agree with, is that AI is now a leadership imperative rather than a technology project.

Here is the limit worth naming. It is a conceptual framework, not a study of what distinguishes schools where AI is led well from schools where it is not, and a leader can hold all six responsibilities and still be invisible on all five of the signs above. Domains tell you what sits on your desk. The Decision Trail tells you whether anything left it. The same instinct shows up in district practice. Reporting the ILO Group's case for adaptive governance in *Government Technology* on March 30, 2026, Julia Gilban-Cohen quotes Delaware's education secretary, Cindy Marten, setting the filter as "if it doesn't make learning stronger, safer or fairer, it's just noise," and describes Charlotte-Mecklenburg Schools consulting roughly 10,000 community members before deploying anything.

## The Mistake I See Most Often

The most common mistake is a leadership team that has confused being informed about AI with leading on it. I see it in the newsletter replies most weeks, and in the room when a senior team has read widely, attended the webinars, brought in a speaker, and has still not made a decision that anyone below them could name.

Being informed is comfortable because it has no cost and no week after. Leading has both. The team that reads less and decides more will be further ahead by Christmas, because every dated decision teaches the organization something about how AI questions get settled here, and reading teaches it nothing. If you want the sharper version of this, the [ten questions every leadership team should be asking about AI](https://dan-fitzpatrick-blog.replit.app/posts/questions-every-leadership-team-should-ask-about-ai) are the ones that cannot be outsourced to a report.

## Where to start this term

Draw the four columns before your next leadership meeting and fill in the last five decisions from memory. If you cannot reach five, you have found the work, and the fix is not a strategy day. Make one dated decision this fortnight, attach a name and a subtraction to it, and tell people what changed. Then check it against the wider picture using [the seven dimensions of AI readiness](https://dan-fitzpatrick-blog.replit.app/posts/seven-dimensions-of-ai-readiness), which will show you where the next decision needs to come from.

If your leadership team is working through these questions, this is the kind of work I do through AI strategy sessions and leadership mentoring with senior teams.

## Sources and further reading

- DeMatthews, D., Reyes, P., Hart, T. D. and James, L., "[Leadership for artificial intelligence use in schools: A six-domain framework for ethical, equitable, and effective integration](https://journals.sagepub.com/doi/10.1177/17411432261418940)", *Educational Management Administration & Leadership*, February 5, 2026.
- Jobs for the Future, "[AI Usage in Education is Growing, But Gaps in Guidance Persist, New Survey Finds](https://www.jff.org/newsroom/press-releases/ai-usage-in-education-is-growing-but-gaps-in-guidance-persist-new-survey-finds/)", March 18, 2026, reporting an AudienceNet survey of 3,020 learners fielded November 28 to December 8, 2025.
- Department for Education, "[Generative AI: product safety expectations](https://www.gov.uk/government/publications/generative-ai-product-safety-expectations)", published January 22, 2025, last updated January 19, 2026.
- EdTech Innovation Hub, "[KCSIE 2026 published with new AI, mobile phone, and vetting rules for schools](https://www.edtechinnovationhub.com/news/dfe-publishes-kcsie-2026-with-mobile-phone-free-default-and-ai-safeguarding-rules)", July 7, 2026, on Keeping Children Safe in Education 2026, in force September 1, 2026.
- Gilban-Cohen, J., "[School Districts Prioritize AI Governance, Not Adoption Speed](https://www.govtech.com/education/k-12/school-districts-prioritize-ai-governance-not-adoption-speed)", *Government Technology*, March 30, 2026.

*Dan Fitzpatrick is The AI Educator: a Forbes contributor, international keynote speaker and author who works with school and organizational leaders on AI strategy, readiness and leadership. [More about Dan](https://www.theaieducator.io/about).*

## Frequently asked questions

### What does good AI leadership look like in a school?

It looks like five observable things: a short trail of dated AI decisions with named owners, a leader using AI on their own work where staff can see it, something that has visibly stopped, a line the school will not cross that the leader can state without opening the policy, and an open question the leader admits to.

### Who should own AI in a school or district?

A named senior leader should own it, with something taken off their plate to make room, and the decision rights written down. Ownership spread across a working group with no named person tends to produce documents rather than decisions, because nobody is answerable for the week after.

### Do school leaders need to understand how AI works?

Not technically. Leaders need enough understanding to judge whether an output is good enough and to ask why a tool was chosen. The observable markers of good AI leadership are decisions, visible personal use and honest open questions, none of which require technical knowledge.

### Is an AI policy enough?

No. A policy records what a leadership team wanted to be true. If staff and students cannot say what it allows, the document has been produced and the decision has not been led. Test the policy by asking three people at different levels what it means for their week.

### How do you know whether your AI strategy is working?

Look for changes that followed decisions: a task that stopped, a saving that was redirected, a rule people can recite. If the only evidence is usage figures or a completed training day, the strategy has produced activity rather than results.

### What should a leadership team do about AI first this term?

Make one dated decision within a fortnight, attach a named owner and a subtraction to it, and tell people what changed. One small decision that people can name teaches an organization more about how AI questions get settled than a strategy day does.

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Source: [What Good AI Leadership Looks Like: Five Visible Signs](https://blog.theaieducator.io/posts/what-good-ai-leadership-looks-like)
Publisher: [The AI Educator](https://theaieducator.io)