The problem isn't AI. It's the missing framework.
For two years, it's the question that keeps coming up across discussions, reading, and conversations with executives on the subject: "Which AI use cases should we prioritize?" That's the wrong question.
Before choosing use cases, an organization needs to answer three governance questions:
- Who decides? Which AI-related decisions stay at executive level, which are delegated, and on what criteria?
- Who is accountable? If an AI system fails, bias, error, drift, who bears responsibility, and to whom?
- What gets measured? Without governance indicators (not just performance ones), the organization is flying blind.
What I see in the field
The most advanced organizations aren't the ones that deployed the most use cases. They're the ones that set a governance framework before scaling deployments, a framework that clearly separates controlled experimentation from industrialization.
What needs deciding now
Three trade-offs can no longer wait:
- The acceptable level of risk by decision category (operational, strategic, regulated).
- The board's role in AI oversight, often absent from current governance charters.
- The line between automation and human decision, which cannot be left to each team's discretion.
These three trade-offs are governance matters, not technical ones. That's exactly why they belong at the executive committee level, and why they can't be delegated to the technical function alone.
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