Approach
How I sequence AI adoption in an institution.
The enterprise AI playbook doesn't transfer cleanly to higher education. This is the four-phase maturity model I use instead — one that lets different departments move at different speeds without the whole program stalling behind the most cautious stakeholder in the room.
Assisted
AI supports individual tasks — drafting, summarizing, search — with a human reviewing every output. Low risk, fast to deploy, builds literacy.
Advisory
AI recommends decisions across a defined workflow — admissions triage, resource planning — but a person still approves the action.
Adaptive
Systems begin to act within guardrails, learning from outcomes and adjusting — student support routing, dynamic scheduling — under active oversight.
Autonomous
Agentic systems execute defined processes end to end, with governance built into the architecture rather than bolted on after.
I built this model running a structured AI discovery across fifteen departments at Shiv Nadar Institution of Eminence — mapping more than forty interconnected systems across admissions, registrar, finance, research administration, and learning management. What became clear was that treating "AI strategy" as a single institution-wide initiative was the wrong frame. Different departments landed at different phases by design: student-facing support workflows moved to Advisory quickly, because the downside of a bad recommendation was bounded and reviewable. Anything touching academic records or accreditation-relevant decisions stayed in Assisted far longer, because the cost of an ungoverned error there isn't just operational — it's regulatory and reputational.
That unevenness isn't a failure of the program. It's the discipline working as intended — and it's the same instinct that made the ServiceNow/ITSM transformations I ran in enterprise telecom actually stick: sequence the change so the governance keeps pace with the ambition, not the other way round.