Articles
On outcomes, technology and what actually changes an institution.
The current series is about AI in higher education, and specifically about the distance between a working technology and a better institutional result, which is where most of the difficulty lives.
Where should a university actually use AI?
The challenge is not identifying what AI can do. It is deciding where AI can materially improve an institutional outcome that matters.
Read →AI use cases are not an AI strategy
Strategy requires choices about what the institution should pursue, what capabilities it must build, and what it will deliberately defer or not pursue.
Read →Why AI transformation in higher education is primarily an institutional problem
Data, processes, systems, roles, mindset, incentives, governance and willingness to change often determine whether AI produces institutional value.
Read →From AI experiment to institutional outcome
A successful pilot demonstrates that something can work. The more important test is whether it can be implemented at scale and improve the outcome that justified it.
Read →More to come on the measurement side: how ranking components actually respond to institutional decisions, and what a technology product would have to do to move one.