Introduction: Why AI Strategies Fail Before They Scale

The push for AI in higher education has moved faster than the understanding of what it takes to use it well. Universities often deploy smart tools before defining smart outcomes. The result isn’t failure; it’s friction.

Dashboards multiply, automation expands, yet the core question remains unanswered: is learning actually improving? True progress depends on design, not deployment. Responsible AI adoption means building systems that learn with the institution, not just from its data.