The Odyssiant Product AI Visibility Benchmark
A monthly benchmark tracking how brands and products appear across AI-generated answers, citations and buyer journey stages.
AI-led discovery is changing how buyers research products, services and suppliers. The Odyssiant Product AI Visibility Benchmark tracks whether brands and products are mentioned, recommended, ignored or supported by credible sources across AI-generated answers.
Benchmark snapshot
In the May benchmark, almost two thirds of product-level AI answers did not mention the product being tested. The overall visibility score improved month on month, but absence remained the dominant commercial risk. The issue is not simply that AI systems describe companies poorly. More often, they answer commercially relevant buyer questions without mentioning the product at all.
Methodology note
The May 2026 benchmark is based on 3,903 product-level AI answers from the wider Odyssiant AI Search Tracker dataset. The full May tracker analysed 4,181 AI-generated answers across eight engines, including OpenAI GPT-4o, Google Gemini, Anthropic Claude and Perplexity Sonar. Product audits accounted for 93% of the full dataset, making product-level visibility the dominant lens for this month's benchmark.
March, April and May 2026 form Odyssiant's initial benchmark period. During this period, Odyssiant refined its scoring and verdict methodology, particularly around awareness-stage answers. For that reason, these early benchmark findings should be read as an emerging baseline rather than a like-for-like performance trend. May 2026 provides the strongest view so far of how products are represented under the current scoring approach.
Verdict distribution
| Verdict | May count | May share |
|---|---|---|
| Not Mentioned | 2,366 | 61% |
| One of Several | 773 | 20% |
| Primary Recommendation | 658 | 17% |
| No Answer | 91 | 2% |
| Mentioned Only | 15 | 0% |
The visibility challenge remains weighted towards absence. In 61% of product-level answers, the audited product was not mentioned at all. Where the product did appear, it was more likely to be included as one option among several than positioned as the primary recommendation. That matters because AI-led research often shapes the buyer's shortlist before the buyer reaches a website, speaks to sales or searches for a named provider.
What the benchmark shows
1. Most product-level AI answers still do not mention the audited product
In May 2026, 2,366 out of 3,903 product-level answers were classified as Not Mentioned. That means 61% of product answers did not include the product being tested. The commercial issue is not negative sentiment — it is absence. A product cannot be evaluated, compared or selected if it is not present in the answer.
2. Being included is not the same as being recommended
When products did appear, they were more often one option among several than the primary recommendation: 20% placed the product as One of Several, 17% as the Primary Recommendation, and 2% returned No Answer. Presence alone is not selection.
3. Product visibility matters more than brand visibility
The May tracker was overwhelmingly product-led, with product audits accounting for 93% of analysed answers. A company may be visible as a brand but absent for the product, service line or practice area that actually needs to generate demand.
4. Early-stage visibility shapes the shortlist
The wider May tracker was weighted towards early and middle-stage research — Frame & Clarify (32%) and Explore Landscape (26%) made up 58% of tracked prompts. If a product is absent at that stage, it may never reach the shortlist.
5. AI visibility depends on a wider evidence layer
The full May tracker extracted 25,905 citations from 7,583 unique domains, and citation coverage rose to 87% of answers. AI answers draw on vendor pages, reviews, communities, encyclopaedic sources, documentation, government and regulatory references, analyst content and publishers — so visibility cannot be improved through website content alone.
Where AI answers get their evidence
Across the May tracker, AI-generated answers drew on a broad evidence layer, with 25,905 citations extracted from 7,583 unique domains. Vendor and owned-source material still made up the largest share, but the wider evidence layer — documentation, regulatory, social, analyst, encyclopaedic, publisher and community sources — was highly visible.
The implication for marketers is clear: AI visibility cannot be improved through website content alone. Brands need a wider evidence strategy across content, PR, proof, listings, reviews, communities and credible third-party sources.
What this means for marketers
1. You cannot optimise only for your website
AI systems can draw from third-party sources, reviews, communities, publications and specialist references.
2. You need to measure products, not just the brand
Buyers ask about problems, products, services and comparisons. Brand-level monitoring can miss the real commercial gap.
3. Awareness-stage visibility is the danger zone
If AI does not mention you when buyers are still framing the problem, you may never reach the shortlist.
4. Retesting matters
AI answers move. Citations move. Competitors move. Visibility needs to be tracked over time.