Five Emerging Priorities

01

Incentivise Faculty to Experiment

Faculty-led experimentation is one of the strongest levers to accelerate innovation, yet current reward systems don't recognise risk-taking or pedagogical progress. Institutions must align incentives with the behaviours they want to see.

Five Emerging Priorities

02

Co-Design with Students

Leading institutions are co-creating with students. Emerging models include paid and trained student AI assistants who support faculty with syllabi, assessment design, and operational workflows.

Five Emerging Priorities

03

Address Fragmentation

The next phase of AI implementation requires stronger alignment across senior leaders, administrators, faculty, and support teams so isolated initiatives become coordinated progress.

Five Emerging Priorities

04

Build Shadow Governance

Institutional governance does not have to move slowly. Faster institutions create controlled routes for experimentation outside standard committee cycles, then use pilot evidence to inform formal decision-making.

Five Emerging Priorities

05

Build a Shared Evidence Base

59% of leaders want a best practices collection. Equally important: a failure library — anonymised documentation of what didn't work and why. Together, they turn isolated lessons into shared capability.

Higher education knows where it is heading, but most institutions struggle to move fast enough.

With 86% of students already using AI and one in two saying they do not feel ready for an AI-enabled workforce, the pressure to act is mounting faster than the sector can respond.

Drawing on the DEC Leadership Summit EMEA 2026, where 41 senior leaders from 30 institutions across 14 countries met in Paris, this report names the ten signals shaping higher education's AI agenda, examines the governance and measurement gaps slowing progress, and identifies what distinguishes the institutions making real headway from those still building foundations.