Organizations are starting to use AI to interpret performance data and recommend management actions. But does giving AI more data actually improve the decision?

In this session, I present findings from a controlled study using three management situations and 1,200 AI responses. We tested how recommendations changed when AI received KPI data, additional metrics, short management rationale, or combinations of these.

The results were not always intuitive. Additional data often helped, but in one experiment it created a competing signal and reduced the quality of the recommendation. A short rationale consistently helped AI identify the intended management priority.

Participants will learn which management-learning mechanics become important when AI is added to performance analysis: preserving rationale, recording interventions and trade-offs, connecting related signals, and retaining the context needed to interpret why performance changed.

Target audience: business leaders, AI and transformation leaders, performance-management teams, analytics professionals, quality and operations leaders, PMOs, and managers exploring AI-assisted decision support.

Preferred duration: 30–45 minutes, including Q&A.

Suitable for business and management audiences; no technical or data-science background is required.