If AI Is Working, What Should Leaders Actually Be Seeing?

What leaders start to experience

As AI adoption matures across institutions, a new question begins to surface at the leadership level.

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Not “Are we doing anything with AI?”

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But “How do we know if this is actually working?”

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On the surface, there is no shortage of activity. Teams report efficiency gains. New capabilities are being tested. Stories of success circulate. Yet many leaders struggle to answer a more fundamental question: whether these efforts are producing meaningful, institution-level impact.

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That uncertainty is not accidental.

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Why progress feels harder to assess than expected

Most early AI initiatives are launched to learn, not to measure.

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They are designed to explore possibilities, reduce friction, or test assumptions in specific contexts. Success is often defined locally and informally. A process feels easier. A response is faster. A team reports that something “works better than before.”

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Those outcomes matter—but they are difficult to aggregate.

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As a result, leadership teams are often left with signals that are encouraging but incomplete. Progress is visible, but uneven. Benefits appear in pockets rather than across the institution. Some efforts feel promising, others incremental, and many resist simple explanation.

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This creates a familiar leadership condition: sensing momentum without being able to clearly articulate its value.

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Why traditional measures fall short

Institutions are accustomed to evaluating progress through established metrics. Enrollment, persistence, completion, satisfaction, cost, and efficiency are familiar reference points. Leaders know how to ask whether an initiative is “working” in those terms.

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AI does not fit neatly into those categories—at least not early on.

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Its impact is often indirect. Improvements show up as time saved, effort reduced, or decisions made with better information. Benefits may be distributed across multiple steps in a process rather than concentrated in a single outcome.

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When leaders apply traditional measurement expectations too early, AI impact can appear unclear or overstated. When they avoid measurement altogether, confidence erodes for a different reason.

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What’s missing is not rigor—it’s translation.

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What leaders are actually looking for

At this stage, most leaders are not asking for perfect metrics. They are asking for confidence—confidence that AI efforts are producing real, defensible improvements in how the institution operates and how students experience it.

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That confidence does not come from anecdotes alone.

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To build it, leaders need to set clearer expectations about the kinds of evidence teams should bring forward.

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Rather than broad claims that a process is “more efficient” or “working well,” leaders benefit from asking for answers to questions such as:

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• How much time did this actually save staff?
  
  Not impressions or estimates, but measured change. How long did the process take before? How long does it take now? What does that difference translate to in hours saved per week or per term?
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• How is the experience demonstrably easier or better for students?
  
  ‍Are more students completing the process successfully? Did completion happen earlier, with fewer reminders or interventions? Where can we see a measurable improvement in follow-through?
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• What evidence do we have that friction was reduced?
  
  ‍How many fewer steps are required now? Where are fewer handoffs, errors, or repeat interactions occurring—and how do we know?
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• What changed because this improvement occurred?
  
  ‍How are staff using the time that was saved? Are students engaging differently as a result of the change?
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• What did we learn that can be applied elsewhere?
  
  What assumptions were confirmed or challenged? Which elements of this success are context-specific, and which could inform other processes?

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These questions are not about adding bureaucracy.

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They are about setting the expectation that AI efforts should include ways to verify time savings and experience improvements—not just promise them.

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When leaders make that expectation clear, teams design work differently. Measurement becomes part of the effort, not something bolted on later.

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Why confidence erodes when answers are unclear

When leadership cannot clearly see where time was saved or how experiences improved, momentum becomes fragile.

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Some initiatives continue because they are visible or well-supported. Others stall quietly as attention shifts. Leaders may sense progress, but lack the evidence needed to confidently reinforce, redirect, or scale what’s working.

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Over time, uncertainty replaces enthusiasm—not because AI isn’t delivering value, but because that value is difficult to articulate and defend.

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The issue here is not adoption. It is interpretation.

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Institutions often move from experimentation to evaluation without first agreeing on what “impact” should reasonably look like at this stage. Leaders are left trying to judge outcomes without a shared frame for how progress unfolds.

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Why this moment matters

Eventually, institutions reach a point where they must decide what to reinforce, what to redirect, and what to scale.

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Those decisions require more than activity reports. They require shared judgment about what matters most, what success looks like now, and how today’s signals connect to tomorrow’s outcomes.

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Without that clarity, AI remains active—but its role in advancing institutional goals remains difficult to explain.

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Understanding what leaders should actually be seeing—and asking for—is often the difference between momentum that fades and progress that compounds.

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Closing reflection

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Questions about impact rarely stand alone. They are closely tied to how institutions prioritize efforts, assign responsibility, and build shared understanding at the leadership level. Seen together, these patterns reveal why AI adoption often feels harder to lead than expected—even when real progress is underway.