TBM 431: The Denominator That Matters

John Cutler argues that organizations measuring AI success often fixate on the wrong denominator, counting outputs or usage rather than the outcomes that actually matter. The piece is framed around a simple model for evaluating AI impact in an organization. The stakes are high because teams can appear busy and productive with AI while delivering little real value. Getting the denominator right determines whether AI investment is judged honestly. Since the source text provided is only a single sentence, this metadata reflects the title and framing rather than detailed content.

01Key takeaways

• Judge AI success against a clearly defined denominator rather than raw activity or usage counts.
• Be wary of metrics that make AI adoption look productive without showing real outcome improvement.
• Use a simple mental model to align teams on what AI success means before measuring it.

02Key sections

Framing the question

The essay opens by offering a simple model for thinking about what success with AI looks like inside an organization.

03From the post

“Here’s a simple model for thinking about AI success in organizations.”

Summary and takeaways written by PM Atlas; quotes are short excerpts. © the original author.