What Are The Key Performance Indicators For AI

In this episode of The AI Moment, Jonathan Wagstaffe and me (Danny Denhard) tackle the most pressing question facing modern executives: How do we actually measure the success of AI?

As businesses move past the initial excitement of generative tools, the challenge is to move away from "instinctive" utility and towards rigorous, actionable KPIs that satisfy the boardroom.

The discussion centres on moving the goalposts from measuring the AI itself to measuring the impact on existing business metrics. Danny introduces a robust four-pillar framework for leaders to adopt: Velocity, Quality, Economic, and Strategic. This includes looking at "Keep-Me-Out-Of-Jail" metrics like hallucination rates and the "Human-in-the-Loop" (HITOR) rate—measuring how much human intervention is required to make AI output viable.

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Jonathan and Danny also explore the departmental nuances of these KPIs, noting that success in Sales looks very different from success in Support or Operations. Whether it is reducing proposal turnaround time or decreasing product decay rates, the message is clear: AI is a lever for business outcomes, not an outcome in itself.

Key Takeaways for This Week:

• Identify the "business results" you want before deploying the tool.

• Track "saved time" through the lens of what that time is reinvested into.

• • Establish "Trust and Reliability" metrics to manage hallucination risks.

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