Most conversations about AI in higher education focus on the wrong problem.
Some institutions are preoccupied with preventing student misuse or controlling how the technology is handled in classrooms. But the far more pressing challenge is not whether AI is adopted or regulated—it’s whether campus systems are coherent enough to make AI genuinely useful.
Without that foundation, even the most promising tools create more friction than value.
AI has arrived during a time of rising expectations, shrinking staffing capacity and mounting pressure to demonstrate the relevancy of higher education itself. The result is a wave of new tools entering environments that were already stretched thin.
When those tools collide with fragmented processes, unclear decision pathways, or siloed evidence systems, leaders quickly discover a hard truth:
AI doesn’t simplify complexity—it amplifies it
That’s why coherence, not technology, should be the first priority.
Coherence is the degree to which an institution’s people, processes and evidence point in the same direction and work toward shared goals. When coherence is strong, new tools integrate smoothly, information flows cleanly and teams can focus on meaningful work.
When coherence is weak, even the most promising technologies add friction.
This challenge isn’t new. Higher education has long struggled with overlapping committees, inconsistent documentation, parallel processes and constrained resources.
But the accelerated pace of AI-driven change has transformed what used to be a manageable inconvenience into a structural barrier. The hopeful news: coherence can be designed—and it can be designed now, before the next wave of AI adoption widens the gap.
How to create coherence
Below are five ways leaders can reduce friction and prepare their institutions for responsible, effective AI integration.
1. Reconnect mission, outcomes and metrics: Many institutions articulate their mission clearly but alignment breaks down as goals diffuse across departments. AI tools magnify this gap by producing outputs that may not reflect institutional priorities.
Leaders can strengthen coherence by revisiting core questions: What are we trying to accomplish? How do we measure progress? Who is responsible for monitoring it?
When these answers are explicit, AI can be aligned intentionally rather than adopted reactively or blindly.
2. Streamline decision-making pathways: AI initiatives often stall because no one is sure who has decision-making authority. Complex governance structures slow adoption, especially in shared-governance environments.
Mapping and simplifying approval pathways for academic, operationaland data-related changes reduces ambiguity and accelerates innovation. Clarity is infrastructure.
3. Standardize documentation and evidence expectations: AI performs best when institutions maintain well-organized, consistently structured information. Yet many campuses rely on bespoke templates, individual preferences or siloed systems that undermine shared understanding.
Standardizing documentation formats, naming conventions and evidence pathways reduces cognitive load and builds the stable foundation AI tools require.
4. Design cross-functional workflows with fewer handoffs: Accreditation, assessment, institutional research, IT and academic affairs often operate in parallel—and AI projects frequently span all five. Handoffs between these groups are where delays, misunderstandings and friction accumulate.
Designing streamlined, cross-functional workflows with shared responsibility strengthens coherence and turns AI into a support system rather than a complication.
5. Treat AI as infrastructure, not an enhancement: The most significant shift is conceptual. AI isn’t a feature to bolt on—it’s a long-term institutional capability. T
reating it as infrastructure encourages investment in training, governance and system alignment rather than rushing into tool adoption. The institutions that benefit most from AI will be those that prepare their systems, not just their software.
Long-term resilience
Higher education is entering a period where expectations for efficiency, clarity and continuous improvement will only intensify. AI can absolutely help institutions meet those expectations. but only if the underlying structures are stable and coherent.
The institutions that design for coherence now won’t just adopt AI more effectively. They’ll reduce operational friction, strengthen readiness and position themselves for long-term resilience.
Because in the age of AI, coherence isn’t a luxury. It’s the foundation everything else depends on.
