What reporting should stakeholders receive about AI reputation?

Stakeholder reporting on AI reputation has to be built around decisions, not just observations. A decision-grade report carries six components and puts the recommendations at the front, supported by the data beneath them. The report that fails this test is purely descriptive: it tells the audience what is happening without telling them what to do.

The six components of a decision-grade report

1. Dashboard summary, the headline metrics (sentiment, share of voice, accuracy concerns) at a level a CEO or board can absorb at a glance.
2. Peer comparison, how the entity is represented against the named competitor set, since AI engines absorb competitor content into their synthesized summaries and answer comparative prompts by drawing on third-party sources.
3. Theme trends, which framings are gaining or losing weight in the AI narrative, reflecting how engines synthesize recurring themes across many sources rather than any single mention.
4. Source-quality assessment, what the engines are citing and how authoritative it is, because engines weight sources by credibility and assemble answers from that underlying source pool.
5. Accuracy concerns, where an engine is stating something incorrect, including the confident, fluent fabrications (nonexistent lawsuits, fictitious executives, unshipped features) that AI engines deliver in the same tone as true statements.
6. Prioritized interventions, the actions the data is recommending, with timing and ownership attached.

Descriptive vs. decision-grade

The distinction is what separates a report stakeholders act on from one they merely read. A descriptive report lays out sentiment, themes, and citations and stops there, leaving the audience to work out the implications. A decision-grade report leads with the prioritized interventions and uses the same underlying data, the dashboard, the peer set, the theme trajectory, the source quality, and the accuracy flags, as the evidence supporting each recommendation. Putting the recommendations first, with the supporting analysis behind them, is what makes the reporting useful rather than informational.