Your AI Strategy Can’t Belong to the AI Taskforce Only

Aug 23

By Claire L. Brady, EdD

This post is part of a series exploring Achieving the Dream’s Creating the AI-Enabled Community College framework through a leadership lens. While ATD is focused on community colleges, the report’s core questions about strategy, governance, culture, workforce readiness, professional learning, and student success apply across institution types. Throughout the series, I’ll examine what its eight action areas look like in practice and what it takes to move from scattered AI activity toward intentional, sustainable institutional change.

One of the fastest ways to get AI work moving on a college campus is to find the people who are already interested in it. You know who they are. The faculty member who has been experimenting with generative AI for two years. The instructional designer who has tried every new tool. The IT leader tracking developments almost daily. The staff member who has figured out how to save hours each week by redesigning a workflow. Put those people together, give them a charge, call them an AI task force or committee, and suddenly there is momentum.

There is nothing wrong with that. In fact, those early adopters are often invaluable. They bring energy, expertise, experimentation, and credibility to work that institutions desperately need.

But they cannot own your AI strategy.

That is one of the strongest messages in the first action area of Achieving the Dream’s Creating the AI-Enabled Community College. ATD argues that AI integration cannot be left to a “coalition of willing early adopters.” It requires an institutional strategy that connects AI to the college’s priorities, allocates resources, establishes expectations, and makes leadership accountable for the work. And this is where I think many institutions are reaching an important transition point.

Enthusiasm Is Not Strategy

The first wave of institutional AI work needed enthusiasts. People had to experiment before we fully understood what was possible. Faculty needed room to test new approaches. Staff needed opportunities to explore how AI might change their work. Institutions needed pilots, conversations, working groups, and people willing to go first.

But eventually, experimentation surfaces questions that enthusiasts cannot—and should not—answer on behalf of the institution. Which AI opportunities matter most to us? What problems are we actually trying to solve? Where are we willing to invest? What level of risk are we comfortable accepting? What outcomes matter enough to measure? Which pilots should scale? Which should stop? Where should AI remain optional, and where might it eventually become an expected part of institutional practice?

Those are leadership decisions.

This is a distinction I make throughout AI with Intention. AI leadership is not about executives becoming the institution’s foremost technical experts. Leaders do not need to know every platform, understand every model, or personally evaluate every new feature. They do need enough understanding to make informed choices about direction, resources, risk, people, and mission.

That responsibility cannot be delegated to the people who happen to know the most about AI.

When it is, institutions can end up with plenty of good work but no clear institutional direction. One group develops guidelines. Another pilots a tool. IT negotiates a license. Faculty development builds workshops. Student affairs experiments with a chatbot. Marketing develops its own practices. Everyone may be making reasonable decisions within their own area, but no one is necessarily answering the larger question: What are we trying to accomplish institutionally with AI?

That is the difference between having an AI committee and having an AI strategy.

Cabinet Has Work to Do

If AI is going to affect teaching and learning, workforce preparation, student support, institutional operations, employee roles, data practices, budgets, procurement, risk, and public trust, then it belongs in the same leadership conversations as every other significant institutional priority.

ATD calls for leaders to articulate an institutional vision for AI, align it with the college’s mission and strategic priorities, allocate resources, establish measurable outcomes, and communicate clearly about why the work matters. That means the cabinet cannot simply approve the recommendations of an AI committee and consider its strategic work complete.

Cabinet needs to make choices.

Where does AI fit among the institution’s existing priorities? If student success is the priority, which AI investments could meaningfully advance it? If employee capacity is a challenge, where could AI remove administrative burden rather than simply create another expectation? If workforce readiness matters, what changes are needed in curriculum and professional learning? If the institution is investing in enterprise tools, what resources are being committed to helping people use them well?

And perhaps most importantly: What are we not going to do?

Strategy is as much about saying no as saying yes. If every interesting AI use case becomes a pilot, every new capability becomes a priority, and every unit is encouraged to pursue its own direction, the institution has not made strategic choices. It has simply distributed experimentation.

That may have been appropriate in the earliest stages of AI adoption. It becomes increasingly difficult to sustain as AI moves into core institutional work.

Leadership Without Losing Expertise

None of this means dismantling the AI committee or moving decisions into a closed cabinet room. Quite the opposite. Institutions need the expertise of faculty, staff, students, technology professionals, academic leaders, accessibility experts, institutional researchers, legal counsel, and others who understand both the opportunities and consequences of these decisions.

The distinction is between expertise and accountability.

A strong AI committee can surface opportunities, identify risks, gather input, recommend priorities, test ideas, and help the institution understand what is changing. Durable governance structures can then ensure that expertise continues to inform decisions long after the original task force has completed its work.

But institutional leaders remain accountable for deciding where the college is going.

In my book “AI with Intention”, I return repeatedly to the importance of moving beyond reactive leadership. When every new AI development triggers another urgent meeting, policy revision, or pilot, institutions spend their energy responding to the technology rather than deciding what they want the technology to accomplish. Intentional leadership reverses that relationship. Mission and institutional priorities set the direction; AI becomes one of the tools available to advance them.

That shift also changes the questions leaders ask. Instead of “What should our AI committee be working on?” the question becomes “What institutional priority are we asking this group to help us advance?” Instead of “What AI tools should we buy?” it becomes “What problem are we trying to solve, and is AI the right response?” Instead of “Who is our AI person?” it becomes “Who owns each part of this work across the institution?” Those are much more consequential questions.

From Committee Work to Institutional Strategy

For cabinets trying to determine whether AI has truly become part of institutional strategy, I would start with five questions:

Where does AI connect to our institutional priorities? Can we identify the specific strategic goals, student outcomes, workforce needs, or operational challenges our AI investments are intended to advance?
Who owns what? Which decisions belong to cabinet, IT, academic affairs, student affairs, procurement, legal counsel, faculty governance, or a standing AI governance structure? Where is accountability clear, and where is it still fuzzy?
What are we funding? Have resources followed our stated priorities, including investment in people, professional learning, implementation, and assessment—not just technology?
What outcomes are we trying to change? Are we measuring whether AI improves learning, student experience, employee capacity, accessibility, effectiveness, or other mission outcomes, rather than simply counting licenses, users, and pilots?
What are we intentionally choosing not to do? Where have we decided that the risk is too high, the value too low, the timing wrong, or human interaction too important to replace?

If cabinet cannot answer those questions, the answer is not to give the AI committee a bigger charge. It is for institutional leadership to step more fully into the work. The early adopters got many of our institutions started, and we should be grateful they did. We still need their curiosity, expertise, and willingness to experiment. But the next phase of AI adoption requires something different: institutional choices about priorities, resources, outcomes, risk, ownership, and mission.

Your AI committee can help shape those choices. It should not have to make them for you.

A Question for Leaders

If your AI committee disappeared tomorrow, would your institution still know where it is going with AI, who owns the work, what you are investing in, and what outcomes you are trying to achieve?

If not, you may have an excellent AI committee. You just don’t have an institutional AI strategy yet.