“Your AI Change Is Actually a People Change” by Julia Dhar, Kristy Ellmer, Philip Jameson, David Martin, Vladimir Lukic, and Vikram Sivakumar offers a sharp reminder for governance professionals: AI initiatives succeed or fail mainly through people, not technology. For internal audit, audit committees, CFOs, compliance officers, and supervisory bodies, the central message is highly relevant because AI adoption now depends on leadership alignment, employee behavior, and the quality of oversight as much as on the tools themselves.
A key insight in the piece is the warning against false alignment. Senior leaders may seem united on AI, yet still disagree on why the initiative exists, how success should be measured, and where resources should go. That matters for governance because unclear intent often leads to fragmented investment, weak prioritization, and inconsistent accountability. Boards and audit committees should therefore press management to define the few workflows where AI truly creates value, the time horizon for results, and the executive ownership behind each decision.
Another important theme is the difference between involvement and real agency. The authors show that middle managers need meaningful influence over how AI reshapes their work, especially because their roles are often the most disrupted. This is highly relevant for internal audit, since process change without ownership often produces resistance, workarounds, and weak adoption. A sound control environment should allow managers and operational teams to shape AI enabled workflows while still working within clear policy, risk, and control boundaries.
The article also makes a strong case that take up must be earned rather than assumed. Employees may lack the skills to use AI well, may not know whether AI use is culturally or formally allowed, and may fear that the technology will erode their expertise or professional identity. For compliance and governance leaders, these are not abstract concerns. They can affect policy adherence, create shadow use of tools, and weaken the reliability of outcomes. Training, visible leadership behavior, and clear expectations should therefore be embedded into the organization’s formal governance and performance management structures.
Especially useful for oversight functions is the authors’ focus on feedback, rituals, and momentum. Instead of relying on instinct, leaders should measure employee sentiment and confidence regularly, use structured review cycles to assess progress, and celebrate small but concrete wins. That approach is directly applicable to internal audit and supervisory review, because it encourages disciplined monitoring of AI programs through actual business outcomes rather than vanity metrics. It also reinforces the idea that AI transformation is not a launch event but an ongoing governance process that must be watched, tested, and adjusted over time.
For corporate governance professionals, the lasting lesson is that AI should be managed as a people and culture change with clear ownership, disciplined oversight, and sustained accountability. The full article “Your AI Change Is Actually a People Change” by Julia Dhar, Kristy Ellmer, Philip Jameson, David Martin, Vladimir Lukic, and Vikram Sivakumar is available here.