An AI recommendation share score is a summary of a more detailed picture. Understanding what that picture contains — and what it does not — is what makes the score usable rather than just a number to react to.
A recommendation share score expresses how often your business appears in AI responses across the set of prompts tested. A score of 40 percent means your business appeared in roughly 40 out of 100 relevant prompt results — or, put differently, that it was absent from 60 percent of the AI responses to queries it should plausibly have been recommended for.
The score is a compression of the pattern. It tells you the overall result. It does not tell you which prompts you appeared for, which you missed, which competitors took your place, or why the AI system made those selections. All of that is in the underlying data — and interpreting the score usefully means looking at that data, not just the summary number.
There is no universal benchmark for a "good" recommendation share score because the competitive context varies significantly by category, market, and the specific prompt set tested. A score should be interpreted primarily in relation to two reference points: your own score over time, and your competitors' scores for the same prompt set.
A business with a 35 percent score in a market where the next competitor scores 20 percent is in a strong position. A business with a 35 percent score in a market where the leading competitor scores 65 percent has a significant gap to close. The absolute number matters less than the relative standing — and the trend. A score rising from 30 to 45 over three months reflects a real change in AI signal strength, regardless of whether the endpoint is "good" by some external standard.
The most useful layer of interpretation is the per-prompt view. Looking at which specific prompts you appeared for and which you did not reveals the structure of your gaps in a way that the aggregate score conceals.
Prompts where you appear across multiple AI systems and across repeated runs represent your signal strengths. These are the intent types where your business is well-understood, well-evidenced, and genuinely being recommended. They are also the reference point for understanding what "working" looks like — so you can replicate those conditions for the categories where you are weak.
Prompts where you never appear — particularly for intent types that match your actual use cases — are your highest-priority gaps. These are not ambiguous misses; they are cases where the AI system is actively choosing competitors over you for queries you should be winning. Understanding what those competitors have that you do not is the diagnostic question.
Prompts where you appear sometimes but not others — across different AI systems, or across repeated runs of the same system — suggest a signal that is present but not strong enough to be reliable. These are often easier to improve than consistent absences, because the underlying category and intent match already exists. The gap is in signal strength, not signal type.
Recommendation share tells you whether you are being recommended — not why, and not what specifically is driving the outcome. A score decline could reflect a competitor improving, AI system changes, a weakened review profile, or a GBP change that shifted how the AI classifies your business. The score flags the outcome; the diagnosis requires looking at the underlying signals.
The score also does not tell you the conversion value of AI appearances. Some queries are higher-intent than others. Appearing for "best restaurant for a special occasion" is likely more valuable than appearing for "places to eat near me" — even if both contribute equally to your recommendation share percentage. As you mature your approach to AI visibility, weighting your prompt set by intent value gives you a sharper picture of business impact, not just coverage.
The competitor view of scan results is often the most clarifying. When a competitor consistently appears for a prompt you are missing, the question is not "what is wrong with my business?" — it is "what signal does that competitor have that I do not?" That framing is actionable. It points toward something specific to fix or build, not a general quality deficit.
Competitors who appear across a broader range of prompt types than you do are typically signaling stronger intent coverage — more secondary categories, more use-case-specific review content, or richer web content covering their range of offerings. Competitors who appear more consistently than you for prompts you both appear in are signaling stronger authority — higher review volume, stronger external mention patterns, or better-structured listing data. For more on the factors AI systems are weighing in this comparison, see Proximity vs Relevance vs Authority in AI.
The score is a summary. The prompt-level breakdown is the diagnostic. Read the summary to understand the trend; read the breakdown to understand what to fix.
Local Visibility
AI recommendations change by location, intent, and competition. Most businesses do not know when — or why — they are being excluded.
Run a Local Scan to see how your business actually appears across AI systems.
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