2. How will you track key performance indicators (KPIs) for executive performance management around AI?
Many organizations begin tracking AI adoption by monitoring tool usage, licenses or basic engagement metrics. While these data points can be useful signals, the more strategic questions are whether leaders are giving AI the attention it warrants and using it to change how the business operates.
This shifts the conversation from tracking activity to managing performance. CFOs should work closely with CEOs, boards and compensation committees to determine whether existing executive KPIs remain fit for purpose in an AI-enabled organization.
If AI is expected to drive competitive advantage, leaders may need incentives tied to accelerating AI-driven value, such as measurable improvements in cost structures, growth, decision quality or speed. Experience has shown that behavior changes when performance expectations and compensation change.
The CFO’s role is not simply to monitor KPIs, but to help define what can be measured, how progress should be quantified and how results should be reported. Without clear accountability, AI adoption risks becoming fragmented or deferred.
3. How will you assess the quality and accuracy of AI’s outputs?
As AI becomes embedded in core processes, the control environment must evolve. Boards expect CFOs to understand how AI changes risk profiles, whether existing controls remain effective and what new controls may be necessary.
Since AI outputs are not always consistently reliable, that introduces new questions: How will AI-generated information be validated? Where is human judgment required? How will errors be detected before they create downstream impacts?
CFOs play a critical role in redesigning controls to reflect AI-enabled operating models, not only within finance but across the enterprise. This includes ensuring that controls are proportionate to risk and that testing aligns with where AI has the greatest potential impact.
Talent is also a central part of this equation. Finance organizations need people with the experience, knowledge and judgment to challenge AI outputs rather than accept them at face value. Establishing quality control around AI is as much a workforce issue as a technology one.
4. What are the financial impacts of AI-related risks?
AI introduces an evolving risk profile that is rarely limited to a one-time cost. Inaccurate outputs, inappropriate use or over-reliance on AI can lead to financial losses, regulatory exposures and reputational damage. A professional services organization that recently suffered financial and reputational harm for including inaccurate AI-generated information in a report underscores why financial oversight of AI risk is rapidly becoming a board-level concern.
Boards expect CFOs to understand these exposures and quantify their potential financial impact. This requires close collaboration with both business and functional leaders, particularly in audit, risk, cyber and technology. Questions that CFOs should ask include: Where and how is AI being used across the enterprise? What would happen if this work goes wrong? How effectively are we identifying and preventing potential failures?
Equally important is to recognize how AI influences the possible impact of external risks. Cyberattacks are an obvious threat, but CFOs should also consider how AI plays into regulatory, reputational and even competitive risk.
AI governance is moving rapidly from a technical discussion to a core leadership responsibility. For CFOs, this represents an expanding mandate. By quantifying AI’s financial impact, imposing discipline on capital allocation, reshaping performance metrics and strengthening control environments, CFOs can help boards guide their organizations beyond AI experimentation toward sustainable value creation.
