Presidents, provosts, CIOs, and cabinet leaders
AI will not fix enrollment. Govern it anyway.
Every vendor on your calendar this year has an AI product that solves retention, enrollment, or advising capacity. None of them do, because those are structural problems with demographic and financial causes, and no model changes the number of eighteen-year-olds in your region. What AI does require is the unglamorous work you already know how to do: an IT audit of what is actually in use, transparency with faculty and students about where their data goes, a purchasing process that can say no, and an exit plan for every vendor. This assessment scores your campus on that work — the manageable part — and tells you where the exposure sits.
Questions you already have
These are answerable. Most of them in five minutes.
The assessment is built around exactly these. Your report answers them with your own answers and your own website in front of it.
- Which AI tools are already touching student data, and who approved them?
- Are we paying for AI features three times across different contracts?
- What do we owe faculty and students in disclosure, and are we meeting it?
- Is any of this actually moving enrollment or retention, or are we buying comfort?
- If we leave a vendor in two years, do we get our data back in a usable form?
- What does our board need to hear from us before they read about it somewhere else?
Before you start
The silver-bullet check
Five governance questions. If you cannot answer one of them today, that is not a failing — it is the shortest route to a defensible position, and the assessment will sequence the work for you.
- Has anyone audited which AI tools are actually in use on campus? Departmental purchases and free-tier accounts do not appear in IT inventory, and they are where the exposure lives.
- Can a student find out what happens to data they put into a campus AI tool? Transparency is cheaper than the incident, and FERPA obligations do not pause for a pilot.
- Does an AI purchase need approval from anyone who can say no? A process that has never rejected anything is not a process; it is a routing step for vendor invoices.
- Do faculty and students know when AI is being used on their work? Disclosure rules you set before the first complaint are policy. The ones you set after are damage control.
- If you cancelled a vendor next year, would you get your data back? Exit terms are negotiable exactly once — before you sign. After that they are whatever the contract already said.
Note which ones you answered with "probably". Those are the findings your report will open with, and they are the cheapest exposure on campus to close.
How we look at it
Which lens bites hardest here
Fletching Vane brings the same lenses to every AI decision. Each one is named, and each name comes with what it means. Here is what they ask in your situation.
- Productnomics Run the roadmap as a portfolio of investments, not a list of requests.
- Every AI dollar — licences, pilots, the vendor nobody approved — is an allocation against enrollment, retention, or risk. If the campus cannot say which account a spend sits in, it cannot say whether it worked.
- Aim. Release. Impact. Tie every release to one measurable outcome, then reinvest on the evidence.
- Pilots that end without a decision are the campus version of releasing without impact. Each recommendation names the metric a pilot has to move and who reads it.
- Human Operating Core Keep the traits AI cannot replace — integrity, initiative, ingenuity — at the center of how the organization runs.
- Disclosure norms, faculty and student judgment, and who is accountable for an AI-assisted decision are governance questions before they are technology questions.
- FIIT The four stages an organization moves through to run on decisive data instead of directional data: Fluency, Initiation, Impact, Transformation.
- Most campuses are between Fluency and Initiation on AI: people know the tools exist, nobody has surfaced where they would move the enrollment, retention, or cost numbers first. The maturity assessment maps you to a stage and names the next one.
The work this usually needs
The parts of the job this runs into
Product and business management is specific work. These are the pieces that tend to come due here, and each one has a question you can put to anyone you are thinking of hiring.
- Running a roadmap as a portfolio Deciding, once a year and on purpose, how much of the team goes to new bets, to keeping customers, and to not breaking — then scoring every request against the same inputs.
- Owning a P&L and getting back to profitable Being the person whose budget, headcount, and roadmap have to add up — and making the cuts and the bets that follow from that.
- Building the go-to-market data loop Wiring CRM, marketing automation, product analytics, and the website into one loop, so pipeline runs on data instead of on the founder.
- Hiring, developing, and restructuring a team Deciding who to hire first, what a product person actually does here, and how to keep a team shipping while the org around it changes.
- Operating through acquisition and recapitalization What a buyer, a board, or a private-equity owner will actually look at — and running the product so those numbers hold up.
- Governing with a board, a budget, and a regulator in the room Making decisions that have to be defensible to people who were not in the meeting — trustees, auditors, a cabinet — and building the reporting that lets them trust the answer.
- Rolling AI out to a real workforce Choosing the tools, writing the governance, redesigning the workflow, and handling the people side — so adoption shows up in the numbers, not just in the demo.
- Enrollment marketing strategy, from the inside Knowing how a campus actually decides — the stakeholder map, the procurement path, the difference between a data-grounded decision and a political one.
- Making an organization run on data Taking a team from directional reports to decisions made on evidence — the instrumentation, the reporting, and the habit of asking what an outcome would prove before building.
What you get
Your report will include
- A governance maturity read across audit, transparency, procurement, and exit
- Where your AI spend is likely duplicated, unowned, or unmeasured
- Compliance and disclosure gaps in plain language, with what they expose you to
- A 30 / 90 / 365-day sequence a cabinet can actually approve and staff, each step written as Aim, Release, Impact
- The questions to put to a vendor, your IT leadership, and any consultant pitching transformation
- A close that names the work your situation is asking for, and who has done it
The terms, such as they are
- Free, and it stays free — there is no paid tier behind this.
- We read the website you give us, and nothing else.
- Your answers write your report and tell Matt a real person showed up. Nothing is sold or syndicated.
- No newsletter, no drip sequence, no call on the calendar unless you ask for one.