Share of Model

What is Share of Model?

Share of model is a measure of how often a brand appears, is cited, or is recommended by large language models and AI assistants when users ask category-relevant questions, relative to competitors appearing for the same queries. It is positioned as the AI-search-era counterpart to share of voice: where share of voice measured a brand’s visibility across traditional media and search, share of model measures visibility inside AI-generated answers specifically.

Where is Share of Model used?

Share of model is used by B2B marketing and brand teams tracking AI search visibility as a category-level metric, typically reported alongside traditional SEO share of voice and brand tracking metrics.

Why is Share of Model Important?

• Invisibility risk: as buyers increasingly ask AI assistants for vendor recommendations and category overviews, a brand absent from those answers is effectively invisible at that research stage, regardless of its traditional search ranking.
• Comparable metric: it gives marketing and brand teams a comparable, trackable metric for AI visibility rather than relying on anecdotal spot-checks of AI outputs.
• New competitive dimension: it surfaces a new competitive dimension, since a brand can rank well in traditional search while having low share of model, indicating a gap in AI-specific content structure.

How does Share of Model Work and Where is it Used?

Share of model is typically measured by running a defined set of category and competitive queries against multiple AI assistants on a recurring basis, and recording which brands are mentioned, cited, or recommended in the responses. The brand’s mention frequency relative to competitors across the query set produces the share of model figure for that period.

Key Takeaways/Elements:

• AI-answer visibility: it measures AI-answer visibility, not traditional search ranking or web traffic.
• Comparative by design: it is comparative, tracked against competitor mention frequency across the same query set.
• Independent of SEO rank: a strong traditional SEO position does not guarantee a strong share of model.

Real-World Example:

A B2B marketing team at a mid-market software company ran a fixed set of 20 category and competitor queries against three major AI assistants each month. After six months of tracking, they found their share of model had grown steadily even though their traditional organic search rankings for the same queries had barely moved, prompting a shift in content investment toward AI-citation-focused formats.

Use Cases:

• Category visibility tracking: running a fixed query set monthly to monitor how often a brand appears in AI-generated category overviews.
• Competitive benchmarking: comparing citation frequency against named competitors across the same query set to identify visibility gaps.
• Budget justification: using trended share of model data to justify continued or increased investment in AI-search-focused content production.

Machintel Perspective

Across 4,000+ campaigns annually, what we see at Machintel is that clients with strong traditional SEO rankings are frequently surprised by how absent they are from AI-generated category answers. Share of model is becoming a standing line item in our client reporting because a pipeline-accountable program has to measure visibility where buyers are actually researching, not only where they used to.