Lessons from NACUBO 2026 on aligning people, policy, and technology before scaling AI in higher ed
Artificial intelligence is rapidly reshaping how higher education operates, requiring leaders to align strategies with governance, communication, and employee readiness. There is a risk that AI-driven change accelerates faster than people can absorb it, and that came through clearly during our NACUBO 2026 conference sessions with Sandy Mooney, Interim President and Chief Financial Officer at Houston Christian University (HCU). Across several days and two different sessions, we discussed topics like sponsor readiness, project management, governance, and leadership alignment, with an emphasis on the workforce experience and how people understand, absorb, and sustain change as technology modernization and artificial intelligence reshape how colleges and universities operate. The primary takeaway for those in attendance was that institutions and leaders cannot prepare their workforce for AI without first aligning people to the “why,” the rules of engagement, and the governance needed to guide investments and actions.
Realizing the Operational Value of AI
Recent survey data shows that for finance and business officers in particular, AI is much more than a question of technology, with key concerns around operational value, financial impact, compliance risk and more. During our discussions, NACUBO participants raised questions about how to measure return on investment (ROI), how to think about scaling AI, and how to avoid fragmented experimentation across departments. Many CFOs indicated their institutions had already started an enterprise AI tool licensing journey, while very few had any AI governance in place. That imbalance reflects the pressure institutions feel to adapt quickly, but also reinforces why governance, sponsorship, project management, and change management must occur together.
Multiple CFOs asked us for advice on where AI can create operational value, which is the right question to be asking. Unfortunately, use cases and concrete, validated strategies that can convincingly answer this question are lacking. Our advice is that institutions start with a strong understanding of functional unit business processes and needs, to help define pain points, manual processes, and potential quick-win and high-value uses cases. From this step, we recommend thinking of scale first in relation to the core technological systems that collectively form the operational skeleton of the enterprise, things like the ERP, SIS, and CRM.
This strategy also avoids the siloed, “each unit has its own tools or licenses” problem which has plagued higher education technology for decades. Likewise, by anchoring discovery in the functional units and how they work currently, institutional leaders can better understand and articulate “must haves” and non-negotiables when entering discussions with potential vendors.
Managing Change to Maximize Impact
It remains critical that leaders understand and embrace core change management principles, because AI workforce transformation is not just about preparing systems for the future. As President Mooney noted, communication challenges and misalignment are often at the core of stalled, broken, or difficult project implementations. Communication should address both the business case for AI and the human impact. Employees need to understand what is being saved, gained, changed, protected, and expected of them. Rigorous planning and a focus on sustaining momentum, by celebrating even small victories, are crucial to building trust. When coupled with a clear plan, and strong communication strategies, even significant AI-driven change can be adopted and implemented effectively.
