Every major AI vendor is promising community colleges the same things: students identified before they disengage, advising scaled without adding headcount, institutional intelligence delivered automatically to every person who needs it. The technology to deliver on those promises exists. For most institutions, the data environment to support it does not – yet.

This session makes the case that AI readiness is a data problem before it is a technology problem. Drawing on a five-level hierarchy of institutional data maturity and a six-dimension readiness framework, participants will examine what AI-ready data actually requires, why community college data environments are structurally vulnerable to AI failure, and what the sequence of preparation looks like before any AI initiative can deliver on its promise.

After attending this session, participants will be able to:

• Identify which of five common community college AI applications their institution’s current data environment can reliably support, using the six-dimension AI readiness framework introduced and applied during the session.
• Locate their institution’s position on the five-level data readiness hierarchy and name at least two specific gaps that must be addressed before the next level of AI capability can be deployed reliably.
• Distinguish between AI initiatives likely to succeed and those likely to fail based on the state of the underlying data environment – applying this diagnostic before a contract is signed.
• Articulate the four-step implementation sequence – assess, unify, govern, deploy – and identify where in that sequence their institution currently stands.