Shifting AI Strategy at Shaw University: From Top Down Automation to Bottom Up Empowerment

The Starting Point: A Top-Down Vision

Six months ago, I believed Shaw University's best AI strategy was simple: identify and automate 15-20 repeatable tasks. The appeal was clear: efficiency, control, and rapid wins. That approach fits the traditional top-down management model: leadership decides, technology enforces, teams comply.

However, as I worked with external partners and saw how AI adoption unfolds, I realized that the model is dated. It creates processes, but not ownership. It installs tools but doesn't inspire use. The risk is shiny dashboards, limited adoption, and minimal impact.

Why We're Pivoting

Technology doesn't thrive on command. It thrives on curiosity, relevance, and hands-on use. A strategy that dictates from above misses the most essential ingredient, faculty and staff discovering how AI can help their work.

This shift is about moving from control to enablement. Instead of telling people what to automate, we'll let them explore AI on their own terms, then support them in building processes that fit daily realities.

The New Playbook

1. Show, Don't Tell: Hands-On Sessions We'll hold workshops with different departments and faculty, showing them what Microsoft Copilot can do. These aren't lectures, they're practice sessions, designed to break down stigma and spark ideas.

2. 120-Day Check-In: Listen First After those sessions, we'll step back. About 120 days later, we'll return, not with directives, but with questions. What stuck? What tools felt natural? What problems are they now curious to solve with AI?

3. Empower Workflows From Within With that input, we'll help faculty and staff design internal processes using Copilot. These workflows will be theirs, not imposed from above, and we'll ensure semi-private data protections are in place.

4. Build for Scaling: Agents and MCP Server Behind the scenes, we'll set up an MCP server so that when faculty build agents or tools that work, they can be shared across offices. The infrastructure exists to make good ideas spread.

Why Bottom-Up Beats Top-Down

This pivot is about respect. Faculty and staff know their jobs best. They don't need another layer of oversight; they need lighter tools, more innovative processes, and the freedom to shape them.

By moving bottom-up:

• Adoption rises because people build what they actually need.

• Resistance falls because no one is being told how to work.

• Innovation grows because solutions emerge from the ground, not from a static list of tasks.

And importantly, the pace is sustainable. We'll measure progress at 90 and 180 days, refining along the way.

What Comes Next

We're starting now. In the months ahead, I'll share updates on what departments are trying, which workflows are sticking, and how the MCP layer is helping connect the dots.

I believe this bottom-up strategy will accelerate AI adoption at Shaw University and create a stronger, more resilient culture of innovation: one where technology supports people, not the other way around.

I'd love to hear your perspective if you're leading AI at a university. Have you tried bottom-up approaches? What worked, and what didn't?

Stay tuned for the 90-day update.