Measuring AI Recommendation Share: From Concept to a Five-Platform Benchmark

AI systems are becoming part of the buying process.

People increasingly ask them which CRM to choose, which project management platform fits their team, or which AI coding assistant deserves a place in their workflow.

But there is a measurement problem: being mentioned by AI is not the same as being recommended by AI.

A product can appear in an answer without being endorsed. It can be included in a comparison list, mentioned as an alternative, or explicitly recommended as the best fit for a particular need.

These are different outcomes. Treating them as identical creates misleading visibility metrics.

This is why I introduced AI Recommendation Share (ARS).

1. From AI Visibility to AI Recommendation

The AI Recommendation Economy requires more than measuring whether a brand appears in an AI-generated response.

It requires measuring how frequently that brand receives a meaningful recommendation when potential buyers ask relevant questions.

I distinguish four related concepts:

• APS — AI Platform Share: The share of activity or presence associated with an AI platform.
• ACS — AI Citation Share: How frequently a brand or source is cited by AI systems.
• ARS — AI Recommendation Share: How frequently a brand receives a qualified recommendation across eligible buyer-intent questions.
• AIRS — AI Referral Share: The share of measurable traffic or referrals arriving from AI platforms, tracked through analytics.

These metrics describe different stages of AI-mediated discovery. They should not be treated as interchangeable.

2. Defining ARS

For brand b, ARS is calculated as:

[
ARS_b = \frac{\sum_{i=1}^{n} w_iY_{ib}}{\sum_{i=1}^{n}w_i}\times100
]

Where:

• (n) is the number of eligible buyer-intent question opportunities.
• (w_i) is the weight assigned to question (i).
• (Y_{ib}=1) when brand (b) receives a qualified recommendation in response (i), and zero otherwise.

For this pilot, all questions have equal weight.

An important distinction: ARS measures recommendation incidence, not exclusive market share. One response may recommend several brands. Therefore, the ARS percentages of different brands can add up to more than 100%.

A brand mention alone does not qualify.

A recommendation must communicate a positive endorsement or identify the brand as a suitable choice for the user's stated requirements. Neutral references and negative mentions are excluded.

This distinction is central to the metric: ARS measures recommendation, not mere visibility.

3. The Five-Platform Pilot

To move beyond conceptual discussion, I conducted an exploratory benchmark using 30 standardized buyer-intent questions across three software categories:

The same questions were submitted to five AI platforms:

1. Perplexity
2. DeepSeek
3. Gemini
4. Claude Sonnet 5.5
5. GPT-6

This produced 150 responses: 30 from each platform.

The questions were designed around practical purchasing situations: small-team budgets, ease of adoption, automation requirements, integrations, collaboration, privacy, coding workflows, and organizational rollout.

The purpose was not to ask which product was universally best. It was to observe which brands AI systems recommended under comparable buyer-intent conditions.

4. What Counts as a Recommendation?

A consistent classification system is necessary.

For each response, brand appearances must be classified according to their role:

• Qualified recommendation: A brand is explicitly endorsed or presented as a suitable choice.
• Alternative recommendation: A brand is positively recommended as an alternative for a specific need.
• Neutral mention: A brand is mentioned without a meaningful endorsement.
• Negative or rejected mention: A brand is discussed but not recommended.

Only the first two categories contribute to recommendation incidence.

I also separate Recommendation Incidence from Final Choice Incidence.

Recommendation Incidence counts all qualifying recommendations in a response. Final Choice Incidence counts only the primary or winning recommendation.

Combining these two measures would hide an important part of AI recommendation behavior: a model can recommend several suitable products without selecting one definitive winner.

5. Early Observations and Research Boundaries

The pilot reveals an important methodological challenge: AI systems do not always answer in the same format.

Some select one primary product. Others provide several alternatives, explaining which one fits different budgets, team sizes, or workflows.

This means a benchmark based only on the first product named—or only on the final winner—would systematically discard relevant recommendations.

The pilot also includes an important transparency consideration. Claude disclosed that it was developed by Anthropic and asked that its assessment of Claude Code be interpreted in that context. This disclosure is recorded as metadata. It is not, by itself, evidence of bias.

At this stage, the dataset contains 150 responses, but it remains an exploratory, single-run benchmark. The results should not be interpreted as stable platform behavior or a statistically representative estimate of the entire AI recommendation market.

A unified coding audit is required before publishing definitive cross-platform brand rankings. Model versions, timestamps, language differences, and repeated-run stability must also be documented wherever available.

6. Why This Matters for Brands

Traditional SEO asks:

Can customers find my website?

AI-oriented visibility asks:

Can AI systems retrieve and cite information about my brand?

ARS asks a different question:

When a potential customer asks an AI system what to choose, how often does my brand become one of its qualified recommendations?

That question has direct strategic implications.

A company may have strong search visibility but receive few AI recommendations. Another may have a smaller digital footprint yet appear repeatedly in answers to specific buyer-intent questions.

Understanding that difference could become increasingly important for product positioning, content strategy, digital reputation, and competitive intelligence.

However, ARS should not be confused with sales, conversion, or market share. A recommendation is an observable output of an AI system—not proof that a customer followed it.

7. The Next Step: From Measurement to Optimization

This pilot is the beginning, not the conclusion.

The next phase should focus on:

1. Completing a unified audit of all 150 responses.
2. Publishing category-level and platform-level ARS comparisons.
3. Separating recommendation incidence from final-choice incidence.
4. Repeating questions over time to measure recommendation stability.
5. Examining which sources, product attributes, and evidence patterns accompany qualified recommendations.

The longer-term objective is to understand not merely which brands AI recommends, but why certain brands repeatedly become recommendations while others remain invisible or receive only neutral mentions.

That is the foundation of a measurable AI Recommendation Economy.

Author: Seyed Alireza Alhosseini Almodarresieh

Website: https://almodarresieh.github.io/