AI share of voice translates the share-of-voice concept from media measurement to the AI engines. For a defined set of category-level prompts (‘what are the leading firms in X,’ ‘who are the major players in Y’) the metric measures how often each brand appears in responses, weighted by prominence within each response. The comparison is to a named peer set running the same prompts on the same engines. The metric is useful as part of a broader picture – it shows whether the brand is being included in the AI engines’ default category answers – but it is incomplete on its own. Two brands can have identical share of voice and very different reputation outcomes if one is described favorably and the other is mentioned as a cautionary example. AIQ™ reports share of voice alongside sentiment, theme analysis, and source attribution so the metric is interpretable in context.

An AI narrative audit produces a structured read of where the brand stands across the engines and what to do about it. The sections are consistent: full responses across the eight major engines for a defined prompt set; source attribution showing which sources each engine is citing for the prompts that matter; theme analysis identifying the recurring framings the engines apply; sentiment classification per engine and aggregated; peer comparison against a named set running the same prompts on the same engines; accuracy gaps where the engines are stating something incorrect; risk areas where the engines are weighting a problematic source heavily; and a prioritized intervention list mapping each finding to a specific action at the source layer. The deliverable is built to be acted on. A CCO reading it should know which three or four source-layer interventions will produce the most movement and on what timeline.

Peer comparison done casually is misleading. Different prompts produce different answers; different engines weight sources differently; different time windows catch different versions of the picture. The methodology that produces useful comparison data is strict: same prompt set across all brands, same engines, same time window for the analysis, same analytical lens applied to each. With that controlled, the comparison data is genuinely diagnostic. Theme distribution shows where each brand is winning and losing narrative framing. Sentiment differences show where one brand’s coverage skews more favorably than another’s. Source attribution shows which sources each brand depends on most. Prominence shows which brands the engines name first when asked about the category. AIQ™ is set up to run peer comparisons within its standard configuration so this discipline is built in rather than reconstructed each time.

An AI reputation dashboard is not a marketing dashboard with AI metrics bolted on; it is a different category of tool because the underlying data is different. The dimensions that belong on the dashboard: sentiment by engine and aggregated, source quality scored by how authoritative the engines’ citations are, theme distribution showing which framings the engines are applying, peer comparison against the relevant brand set, share of voice at the category level, and trend lines across all of the above showing how the picture is moving. The dashboard has to be powered by data that polls all eight engines daily with consistent prompts, which is what AIQ™ was built to provide. Building a dashboard without that underlying data infrastructure – manual screenshots, one-off audits, partial-coverage tools – produces something that looks like a dashboard but cannot actually support decision-making across the engines.

ROI on AI reputation work, like ROI on any reputation program, is measured against the goals defined at the start of the engagement. The leading indicators come from AIQ™ directly: sentiment improving across engines, accuracy gaps closing as source-level work lands, source quality improving as the engines start citing higher-authority content, prominence rising on category-level peer-comparison prompts. The lagging indicators are business outcomes that AI mediates: recruiting funnel performance (especially for senior roles where candidates research employers via AI), deal pipeline conversion in markets where investors and counterparties do AI-based diligence, IR meeting requests and quality, customer acquisition cost in categories where buyers ask AI for recommendations. The connection between AI metrics and business metrics is empirically observable in well-monitored programs over six to twelve months. Beyond that, the program is producing protection rather than improvement, which is harder to value but no less real.

Change tracking requires methodological consistency. The same prompts, run against the same engines, on a fixed cadence, with the full response stored verbatim each time, is what makes change detection possible. AIQ™ is built this way: daily polling, identical prompts across runs, full response storage with diff capability, theme tagging that persists across runs, sentiment scoring on the same scale. From that foundation, the analytical layers become possible: text-level diffs that show exactly what changed in an engine’s response between two dates, theme trajectory analysis that shows which framings are gaining or losing weight, source attribution shifts that show which sources are entering or leaving the engine’s citation set, sentiment trend lines per engine and aggregated. Without the methodological consistency, change detection is impressionistic at best.