What Wake Forest actually announced
On July 22 Wake Forest University launched an initiative it calls AI for Human Flourishing and named a leader to run it. William Fleeson, the Hultquist Family Professor of Psychology, became Associate Provost for AI Initiatives effective July 1. His brief is broad: coordinate the university-wide program, lead implementation of campus AI priorities, support faculty and staff leaders across schools and divisions, and guide development of Wake Forest's long-term academic strategy for artificial intelligence. This is a coordination role with real authority, not an advisory committee seat.
Interim Provost Nell Jessup Newton said the effort is bringing together faculty expertise from across the university to explore how AI can strengthen teaching, learning, and research. The initiative spans classroom instruction, research workflows, and administrative operations, and it sits on five core commitments, including putting AI in service of humanity and keeping human judgment central to the Wake Forest experience. For an enterprise reader, the shape is familiar even if the setting is a campus.
A provost for AI is a chief AI officer by another name
Strip away the academic titles and this is an organization appointing a senior executive to own AI end to end. Enterprises have spent the past two years debating whether to create a chief AI officer or distribute the mandate across the CIO, CISO, and heads of data. Wake Forest picked the centralized answer and put a respected internal figure in the seat. That choice signals the institution views AI as a cross-cutting capability that needs one accountable owner rather than a feature each department bolts on independently.
The lesson worth borrowing is the emphasis on internal credibility. Fleeson is a longtime faculty member, not an outside hire parachuted in with a mandate and no relationships. In corporate settings, AI leaders who already command trust across functions tend to move adoption faster than newcomers, because the hard part is rarely the technology. It is persuading skeptical teams to change how they work. Naming a trusted insider is a governance decision as much as a personnel one.
Structure beats enthusiasm in year two of adoption
The initiative runs through three pilots that give the strategy a spine. The AI Teaching Studio, led by Fleeson, examines how AI affects teaching and learning with an emphasis on critical thinking. An AI Compass, led by Ana Iltis, explores ethical and societal implications. An AI Impact Clinic, led by Shannon McKeen, Paul Pauca, and Errin Fulp, develops practical AI solutions through applied learning. Each has a named owner and a defined scope, which is how you keep an enterprise-wide program from dissolving into slideware.
We see the same maturity curve in corporate AI programs. The first year is a scramble of ungoverned experiments, and the second is a reckoning where leaders try to consolidate what works, retire what does not, and set guardrails. Wake Forest is entering that consolidation phase with explicit structures for teaching, ethics, and applied delivery. Technology leaders can copy the pattern directly: pair every AI workstream with an accountable lead, a clear remit, and a mechanism to feed lessons back into policy.
The human-flourishing frame is a trust strategy
Naming the program AI for Human Flourishing and anchoring it to human judgment is more than branding. It is an attempt to answer the anxiety that stalls adoption inside any large institution. Faculty worry that AI erodes rigor and displaces their expertise, the same fear employees voice in enterprises weighing automation. By stating up front that AI serves people and that human judgment stays central, Wake Forest is trying to convert reluctant participants into willing ones. Trust, not licenses, is the scarce resource.
This matters for return on investment in ways spreadsheets miss. Tools nobody trusts sit unused, and shadow adoption creates the exact governance gaps leaders fear. A values-forward framing, paired with real controls, gives cautious staff permission to engage in the open. For heads of L&D and technology, the takeaway is that the messaging around an AI rollout is part of the control system. How you introduce the capability shapes whether people route around it or lean into it.
Why a university move belongs on the enterprise radar
Universities are where much of your future workforce forms its habits, and Wake Forest is explicitly trying to graduate people who use AI with judgment rather than dependence. If this model spreads, employers can expect entrants who are comfortable with AI tools and also trained to question their outputs. That is a better starting point than either blanket avoidance or uncritical reliance, and it lowers the remedial AI-literacy burden that currently falls on corporate onboarding.
The broader signal is institutional. When a university builds a provost-level AI office with named pilots and stated values, it validates the centralized, governance-first approach that many enterprises are still debating. The reader deciding how to structure their own AI leadership now has another data point favoring a single accountable owner over a diffuse committee. The specifics will differ across a campus and a company, yet the underlying choice about who owns AI, and on what terms, is identical.
What to do with this on your roadmap
If you have not yet decided who owns AI in your organization, this is a nudge to settle it. A named leader with cross-functional authority, a short list of governed pilots, and a stated set of commitments will move faster than a dozen teams improvising in parallel. The commitments do not need to be lofty. They need to be specific enough that staff can predict how AI will and will not be used, which is what turns policy into behavior.
Pair that with attention to people. Choose an AI leader your teams already trust, frame adoption around strengthening human judgment, and give every workstream an owner accountable for outcomes and guardrails. Communicate the guardrails as clearly as the capabilities, so staff can predict how AI will and will not show up in their work. That predictability is what pulls shadow usage into the open, where it can be measured and improved. Wake Forest is running a legible experiment in exactly this. The value for technology leaders lies in the reminder that AI at scale is an organizational design problem first and a tooling problem second.
