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Provost

Evaluate and develop deans

Enhances◐ 1–3 years

What You Do Today

Assess dean performance, provide coaching and development, manage dean transitions, and hire new deans. The quality of your deans determines the quality of the entire academic enterprise.

AI That Applies

AI aggregates college-level performance data for dean reviews, benchmarks college outcomes against peers, and identifies leadership development resources based on specific growth areas.

Technologies

How It Works

The system ingests specific growth areas as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Dean evaluation becomes more comprehensive with better comparative data across colleges.

What Stays

Developing academic leaders who can manage the impossible tensions of the dean role — and making the call when a dean isn't working — requires human judgment and coaching.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for evaluate and develop deans, understand your current state.

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Map your current process: Document how evaluate and develop deans works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: Developing academic leaders who can manage the impossible tensions of the dean role — and making the call when a dean isn't working — requires human judgment and coaching. These are the boundaries AI won't cross.
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Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support performance management platforms tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long evaluate and develop deans takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your department chair or principal

“What data do we already have that could improve how we handle evaluate and develop deans?”

They influence which ed-tech tools get approved and funded

your instructional technologist

“Who on our team has the deepest experience with evaluate and develop deans, and what tools are they already using?”

They support the tech stack and can show you capabilities you don't know exist

your school counselor

“If we brought in AI tools for evaluate and develop deans, what would we measure before and after to know it actually helped?”

They see the student impact side of AI-adaptive tools

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.

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