What KPIs should a brand be tracking for AI-era reputation health?

Five KPIs give a brand a complete read on its AI-era reputation health: AI sentiment, AI source quality, AI peer comparison, AI accuracy, and AI narrative drift. The critical rule is that each one has to be tracked per engine rather than averaged, because the same query can return materially different answers across ChatGPT, Gemini, Copilot, Perplexity, Claude, Grok, Google AI Overviews, and Google AI Mode, an average across models hides where the real problem sits.

The five KPIs, and what each one captures

AI sentiment (per model)

The tone of each engine’s responses about the entity, positive, neutral, or negative. Because the models diverge, one engine can be warm while another is hostile on the same topic.

AI source quality

Which sources each model is drawing on. AI engines assemble answers from underlying source content and weight that content by credibility, so a narrative built on weak or hostile sources is fragile regardless of its current tone.

AI peer comparison

How the entity is positioned against its competitors inside the engines’ answers. Reputation is relative, so the comparison matters as much as the standalone read.

AI accuracy

Whether the engines are stating correct facts about the entity. Fluent, confident misinformation is its own distinct risk, separate from tone.

AI narrative drift

How the framing changes over time. Model updates and shifting source pools can move the narrative even when nothing about the entity itself has changed.

Why per-engine, not an average

Averaging the KPIs across engines masks divergence: a single fix does not propagate uniformly across the models, so a problem confined to one engine can be invisible in a blended score. Tracking each KPI per model, the approach Five Blocks uses in AIQ™, is what surfaces where a narrative is actually breaking down.