Why AI Decisions Are Being Made Before Leaders Feel Ready

Across higher education, AI adoption is no longer hypothetical.

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Faculty are incorporating AI into courses. Staff teams are using it to streamline advising, communications, and operations. Vendors are embedding AI into existing platforms. Peer institutions are sharing examples of early wins.

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From a distance, this looks like progress.

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From inside leadership teams, it often feels more complicated.

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What many leaders are experiencing is not resistance to AI, nor uncertainty about its importance. It is something more subtle: the pressure to make decisions before there is shared confidence in what those decisions should be based on.

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The paradox leaders are navigating

Senior leaders are being asked to do two things at once.

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On one hand, they are expected to encourage experimentation. AI is evolving quickly, and institutions do not want to slow learning or signal risk aversion. On the other hand, they are also expected to provide direction—to decide what should be supported, scaled, paused, or funded.

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Those expectations collide early in AI adoption.

Understanding is still forming at the same moment decisions are required. Signals are emerging, but they are uneven. Some use cases feel promising. Others feel incremental. Many are difficult to compare. Leaders are asked to weigh requests without a shared framework for judging progress or value.

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This creates a familiar but uncomfortable leadership condition: being accountable for decisions without feeling grounded in the criteria that should guide them.

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What happens in the room

In practice, this tension shows up in very real ways.

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A cabinet discussion turns to AI. One leader references a successful pilot in admissions. Another raises questions about classroom use. A third flags compliance or risk. Each perspective is grounded in legitimate experience—but those experiences are uneven, and the assumptions behind them are rarely shared.

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The conversation doesn’t stall, but it doesn’t converge either.

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At that point, it is both reasonable and necessary to involve others. Leaders recognize that AI touches too many functions for any one person or small group to “do” the work alone. Working groups, task forces, and cross-functional teams are an appropriate response.

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The challenge is not the creation of these groups.

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It is what has—or has not—happened before they are formed.

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Often, responsibility is delegated before leadership has developed a shared understanding of what matters most, what tradeoffs are acceptable, or how progress should be judged. In that context, working groups are asked to move forward without clear direction—not because leaders are disengaged, but because shared judgment has not yet formed.

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What’s rarely acknowledged is why this sequence feels so relieving.

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Delegation, in this moment, reduces the immediate discomfort of paradox—the discomfort of being expected to lead without yet having a common frame. Progress continues. Momentum is preserved. The pressure to resolve ambiguity at the leadership level recedes.

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The quiet tradeoff

This pattern is understandable—and common.

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But when delegation happens before shared understanding is established, it quietly shifts the role leadership plays. Leaders remain supportive, but their engagement becomes reactive rather than directional. Updates replace judgment. Activity continues, but expectations remain implicit.

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Meanwhile, working groups do exactly what they can. They make good-faith decisions, often without clarity about which outcomes matter most at the institutional level. Success becomes local rather than cumulative.

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The issue is not that leaders should do the work themselves.

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It is that leadership work—establishing shared understanding and setting direction—cannot be delegated.

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Why this keeps happening

This is not a failure of courage or competence.

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Institutions are designed to build shared understanding deliberately and over time. AI compresses that timeline. Learning accelerates faster than leadership structures were built to accommodate.

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When leaders are not given space to learn together—to align on what signals matter, what tradeoffs are acceptable, and what “good” looks like in their specific context—delegation becomes the path of least resistance.

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It resolves the immediate tension, but postpones the deeper work.

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The moment institutions are reaching

Many institutions are now at a point where this pattern is becoming visible.

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AI activity is widespread. Decisions are happening. Responsibility has shifted. And yet, leadership confidence feels thinner than expected.

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Recognizing this moment matters.

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Not because it demands immediate correction, but because it signals a transition. The challenge is no longer whether AI is happening. It is whether leadership has the shared understanding necessary to guide it intentionally as it matures.

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That recognition is where more effective leadership of AI begins.

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Closing reflection

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This dynamic—where decision pressure outpaces shared understanding—is one of several leadership tensions shaping how AI adoption unfolds across institutions. Understanding it in isolation is useful. Seeing how it connects to other patterns is where clarity begins to form.