AI Strategy for Higher Education

Build AI capacity without losing the human experience.

Practical, human-centered strategy for colleges and universities ready to move beyond experimentation and make thoughtful decisions about AI, work, and student experience.

The Real Opportunity

AI won’t fix a broken system.

AI can increase speed, capacity, personalization, and access. It can also automate confusion, scale inequity, and make a fragmented experience move faster.

The most important question is not simply which tools an institution should adopt. It is whether the people, workflows, policies, data, and decisions surrounding those tools are ready.

My approach starts with the work and the people experiencing it—then determines where AI can create meaningful value.

What Institutions Are Facing

The pressure to act is real. So is the risk of acting without a plan.

01

Uncoordinated experimentation

Teams are testing tools independently without shared priorities, standards, language, or institutional learning.

02

Uneven staff readiness

Confidence and capability vary widely, leaving some employees overwhelmed and others working far ahead of policy.

03

Disconnected systems

AI is introduced into workflows already shaped by fragmented platforms, unclear ownership, and difficult handoffs.

04

Competing expectations

Students, staff, faculty, leaders, and regulators may have very different expectations about where and how AI should be used.

How I Can Help

Move from scattered activity to institutional capacity.

Engagements are tailored to your institution’s goals, maturity, existing systems, and available internal capacity.

AI Readiness & Strategy

Establish a shared institutional understanding of current activity, priorities, risks, capabilities, and next steps.

  • AI-readiness assessment
  • Use-case and opportunity identification
  • Strategic priorities and implementation roadmap

Workflow & Process Redesign

Examine the work before automating it, identifying where AI can reduce friction and where human judgment must remain.

  • Workflow and handoff mapping
  • Automation-opportunity analysis
  • Human-in-the-loop process design

AI & Student Experience

Design AI-enabled experiences that improve access, clarity, responsiveness, and personalization for students.

  • Student-facing use-case design
  • Advising and support applications
  • Experience, trust, and equity review

Workshops & Capacity Building

Give leaders and teams practical frameworks for evaluating, using, and discussing AI with greater confidence.

  • Executive and cabinet workshops
  • Team training and facilitated planning
  • Custom use-case labs

Guiding Principles

Human-centered by design.

01

Start with purpose

Define the problem and desired outcome before selecting a tool.

02

Improve before automating

Avoid making a confusing or inequitable process move faster.

03

Preserve judgment

Keep people accountable for consequential decisions.

04

Design for trust

Make AI use visible, understandable, and aligned with expectations.

05

Build capacity

Help teams learn, adapt, and make better decisions over time.

Engagement Options

Support matched to your stage of adoption.

Possible Outcomes

Leave with direction—not just inspiration.

Institutional AI-readiness findings
Prioritized AI use-case portfolio
Workflow and handoff maps
Implementation roadmap
Leadership decision framework
Staff-development recommendations
Responsible-use principles
Student-experience opportunities

Let’s build an AI strategy grounded in your institution.

Start with where you are, what your people need, and the work you are trying to improve. The technology comes after that.

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