EY’s analysis suggests AI’s biggest problem for many organizations is not adoption speed but the “tempo gap”—the mismatch between machine-generated outputs and human comprehension and decision-making time. The firm describes how AI systems can rebook travelers, auto-fill sensitive forms, or move financial interactions forward before users fully process trade-offs. EY’s point is that workflows may technically function while still producing user hesitation and uncertainty. It frames the issue as a design and process challenge rather than a model quality failure, arguing that enterprises often bolt AI onto processes without rethinking how humans will validate, interpret, and act on outputs. For higher education leaders rolling out AI for advising, admissions workflows, or learning analytics, the implication is clear: implementation needs human-in-the-loop controls, timing considerations, and risk controls tied to student outcomes—not just deployment.