Project
AI Strategy & Governance
The half of the job that doesn't fit in a repo: governance that makes real decisions, guidance people actually follow, and enough allies that adoption doesn't depend on one person.
The work
Half of what I do produces code. The other half produces decisions, and the second half is harder. As CSU’s AI Strategist I’m the person in the room when a data governance committee has to rule on AI access to chat histories, when legal counsel and a vendor’s documentation disagree about where data actually goes, and when a dean wants to know whether a tool is safe before ten thousand students touch it.
None of that ships as software. All of it determines whether the software matters.
Governance that actually decides things
Most governance committees are performative. They discuss vague topics, produce no decisions, and everyone leaves with the same questions they arrived with. The fix I’ve landed on is treating governance like product work: show up with a specific problem, a concrete proposal, and a genuine decision to be made. Committees are surprisingly good at deciding when you give them something decidable.
That approach has turned our data governance process from a place where AI questions went to stall into the mechanism that unblocks them: real rulings on agent data access, AI evaluation built into software procurement, and clear ownership of the questions that used to float.
Rules people can follow
“Can I use this AI tool with this data?” sounds like one question. It’s three: what does the law require, where does the data physically go, and what do the privacy settings actually guarantee. I wrote up a full worked example in the Microsoft Foundry Claude case, where the comfortable assumption (“it’s Microsoft, it’s covered”) turned out to be wrong in ways that mattered.
The output of that work isn’t a memo, it’s a system: AI tools mapped against the data classification levels the university already uses, a vetting checklist so the analysis is repeatable, and a data steward as the approval gate instead of individual guesswork. Boring on purpose. Boring is what catches problems before they become incidents.
Adoption that scales past one person
One strategist cannot personally drive AI adoption across a research university, and pretending otherwise is how these roles fail. So a good chunk of my time goes into people: a growing network of AI-curious champions across colleges and divisions who advocate and teach where I can’t be, leadership briefings that build fluency instead of dependence, and a standing policy of meeting people where they are. Help the willing. Don’t evangelize the resistant. Some people will never adopt, and that’s fine.
Why you might care
If you’re the “AI person” at your institution, the technology is the easy half, and it’s the half everyone wants to talk about. The durable work is institutional: getting committees to decide, turning legal ambiguity into usable rules, and building enough distributed capability that progress survives you leaving the room. That’s the part I’d compare notes on first.