The G2 AEO question has a measured answer, and it’s a small one: categories with 10% more reviews have 2% more AI citations, per the analysis G2 published on its own learn site, and reviews explain under 2% of the variance in who gets cited. So your G2 presence is part of the work of getting your brand into what assistants say, and position within your G2 category matters more than raw review count.
Whether G2 matters for you at all depends on whether G2 pages actually show up in the sources behind your answers, and that part you can check. The checking is what an AI visibility platform is for: Omnipresence records, for each buyer question you track, the searches each assistant ran before answering and every page it read.
The public evidence on G2 AEO genuinely disagrees about how much G2 matters, from G2 calling itself the most cited source to a study that found it got zero citations. The way to settle it for your category is to read the sources behind your own answers.
What is G2 AEO
G2 is the review platform an assistant may read before recommending software, and AEO is the work of getting your brand into what it says. G2 publishes AEO research in its own right, so the two meet in three places.
First, G2 pages are a source. When a buyer asks an assistant for software recommendations, the assistant runs searches and reads what comes back, and G2 category and review pages rank for product queries, so they’re in the mix. Whatever your G2 page says about you is in the running.
Second, G2 runs an AEO software category of its own, where AEO tools compete for reviews. It grew from seven products to more than 150 since launching in March 2025, growth of over 2,000%, according to G2’s announcement. A category needs at least six products, each with 10+ reviews, and 150+ reviews in total before G2 gives it a Grid Report, and the AEO category earned its first in December 2025, with nine products and Profound named Leader.
Third, G2 publishes AEO research on its Research Hub, under the name G2 AEO Insights.
How AI assistants use G2 when they recommend software
When recommending software, an assistant usually doesn’t answer from memory. It runs a series of searches, reads the pages that come back, and builds the recommendation from what it found.
We measured this behaviour directly in our Credibility Layer study, which recorded how ChatGPT recommends local businesses: 200 reasoning sessions, 5,933 searches and 1,982 citations, with every recommendation coming out of a 30-search credibility audit of third-party pages. It’s a local-business study, so don’t read its exact numbers as a B2B finding. The behaviour is the point: the model checked what third-party pages said before it put a name forward.
G2 says its pages get read a lot. In the September 2025 announcement of its partnership with Profound, G2 called itself the most cited B2B software source across large language models, fourth among the top ten cited sources on ChatGPT and ninth on Perplexity, currently.
That’s G2’s own claim, in a partnership announcement, so hold it loosely. It still tells you the stakes: if the assistant reads a G2 category or review page about you, whatever that page says about you is part of what it answers with.
Do more G2 reviews mean more AI visibility
Yes, but not much: categories with 10% more reviews have 2% more citations. That’s the headline of Kevin Indig’s analysis, published on G2’s learn site in October 2025, which used 30,000 AI citations and share-of-voice data from Profound across 500 G2 software categories.
The regression coefficient behind the headline is 0.097, and reviews explain less than 2% of the variance in citations, which is pretty low. Share of voice behaved similarly, with a coefficient of 0.113.
The backdrop is that buyers have already moved: G2’s survey of 1,000+ B2B buyers in August 2025, reported in the same analysis, found 87% say AI chatbots are changing how they research software, and half now start their buying journey in an AI chatbot instead of Google, a 71% increase in four months.
Hold the caveats. It’s G2 publishing an analysis of G2’s own effect, and the author says so plainly: the relationship is an association, it doesn’t establish cause, and it’s a cross-sectional look at one window of data. What survives is still useful.
Reviews from the last 12 months are what the analysis counted, so a profile that stops collecting goes stale inside that window. Your position within the category matters more than your raw review count: being the third-reviewed product in a category reads differently from being the thirtieth. And the analysis points to complete profiles, with descriptions of 250+ characters and pricing details filled in.
What G2 AEO Insights is
G2 AEO Insights is G2’s AEO research, published on its Research Hub, and the main piece is the State of AEO 2026 report. It’s G2 AI Custom Research commissioned by HubSpot: 153 voice-to-voice interviews with B2B marketing decision-makers in North America, run in August 2026.
The numbers marketers quote from the report: 76% of respondents say AI answer engines are becoming buyers’ first research step, and 43% say AI is already the primary first step. On the doing side, 75% say they have only partial or manual visibility into how AI engines represent their brand, only 17% use tool-based monitoring they’d trust, and 44% describe their AEO investment as gated by ROI proof. One in three report declining organic traffic, and 68% of them attribute at least part of the drop to AI engines.
It’s a survey of opinions, so it tells you what marketers believe, and the sample is 153 interviews, which is small. It isn’t a measurement of what models actually do.
The research programme sits next to the AEO software category itself, and the AEO category page on G2 shows the current field. The Profound partnership also ships an AI Visibility dashboard inside my.g2, tracking how a vendor’s G2 brand presence is cited by ChatGPT, Gemini and Perplexity.
Where the studies disagree
| Study | What it measured | Where G2 landed |
|---|---|---|
| G2’s review analysis (Kevin Indig, learn.g2.com, October 2025) | 30,000 AI citations and share-of-voice data from Profound, 500 G2 categories | Categories with 10% more reviews have 2% more citations; reviews explain under 2% of the variance |
| G2 and Profound partnership announcement (September 2025) | Aggregate citation volume across LLMs, how often G2 shows up in software recommendations | Most cited B2B software source, 4th among the top ten cited sources on ChatGPT, 9th on Perplexity |
| DerivateX snapshot (June 2026) | 40 software categories, one buyer-intent query each to ChatGPT asking for software recommendations, 188 cited sources across 105 domains | G2 and Capterra: zero citations. Vendors’ own content 51%, small often anonymous sites 23%, analyst firms, review platforms and business press combined 16% |
So does ChatGPT actually cite G2 in software recommendations? Sometimes it does and sometimes it doesn’t, and both sides of that have a number.
The two G2 figures measure aggregate citation volume, how often G2 pages show up across everything assistants read. DerivateX, in a study published June 2026, measured who actually got cited in one snapshot, and in that snapshot G2 got zero citations.
All three can be true at once, and that’s the actual lesson. A platform can be among the most cited sources overall and still miss your category, because what gets cited depends on the query.
DerivateX carries its own caveats and states them: one platform, one query per category, a single snapshot, and a commercial interest, since it sells LLM SEO services. G2’s analysis is G2 analysing G2’s effect, and says so too.
Vendors’ own content drew 51% of the citations in that snapshot and small, often anonymous sites drew 23%. So if G2 is missing from your answers, your own pages are the first place to look, and you control them anyway.
The aggregate answer doesn’t settle your question. Your category’s answer sits in the sources behind your answers, and you can look.
How to optimise your G2 presence for AI search
Here’s how to optimise your G2 profile for AI search, in the order that pays: check whether G2 is in your answers, then decide, and only then spend on review generation.
- Check whether G2 shows up at all. Ask a handful of real buyer questions on each assistant, more than once each, because a single answer is a small sample. Cover the three shapes of question: category (“best answer engine optimization tools”), comparison (“X vs Y”), and pain (“how do I get recommended by ChatGPT”). Open the sources behind each answer and look for G2 pages. If G2 appears in none of them, review generation isn’t your next move, and your effort belongs on whichever pages did show up.
- Keep reviews flowing for recency, because the analysis weights reviews from the last 12 months, and a profile that stops collecting goes stale in the same window. The reliable way is asking at the moment of success, when a customer has just had a result, rather than in a bulk email blast.
- Complete the profile. The analysis points to descriptions of 250+ characters and pricing details filled in.
- Work your position in the category, because relative position is what the measured relationship tracks. Third place reads differently from thirtieth.
- Never buy or fake reviews. It’s against G2’s rules, buyers discount it, and a model quoting a review page is quoting what the reviews actually say.
Let’s say you sell an AEO tool and you have 12 reviews. You ask the three shapes of question on all four assistants, twice each, and G2’s category page shows up in the category and comparison answers, but your listing isn’t on it, because you sit deep in the category on 12 reviews. Now you know G2 matters for your category, and the work is position: recency, a complete profile, more reviews.
What goes wrong in practice: the budget goes to review generation first, which is the smallest factor in the measured picture. The honest order is measure, then work.
Measuring this in practice
Checking if G2 appears in the sources behind AI answers starts as a manual job: ask a few real buyer questions on each assistant, click through the sources, and note whether any G2 page appears. Past a handful, that gets slow, and it only shows the answers you happened to ask.
Omnipresence records, for each buyer question you track on ChatGPT, Claude, Gemini and Perplexity, the searches each assistant ran before answering and every page it read, including the pages it read and didn’t cite. So you can see whether G2 shows up in your category, whether the assistant recommended you or only named you, and whether the page that needs work is your own or a G2 page.
Two limits are worth knowing: it tracks those four assistants, and it doesn’t audit the G2 profile itself. Ratings and review counts live on G2.
FAQ
Does a higher G2 star rating improve AI recommendations?
The published analysis measured review volume, and a star rating effect isn’t measured anywhere public. What’s measured is more reviews, slightly more citations. Don’t read a rating effect into that.
Is G2 worth investing in for AI search?
The measured effect is small but consistent: 10% more reviews, 2% more citations, and reviews explain under 2% of the variance. So check whether G2 pages appear in the sources behind your answers first, and invest based on what you find.
What does the G2 and Profound partnership do?
It puts Profound’s AEO data inside my.g2 dashboards: how a vendor’s G2 brand presence is cited by ChatGPT, Gemini and Perplexity, competitor benchmarks and the top prompts buyers use. It was announced in September 2025.
The aggregate numbers won’t decide it for you. A day of checking the sources behind your own answers will.