Last updated: October 2026. Data window: June 22 to July 24, 2026, with Reddit tracked weekly through early September. Next refresh lands with the next Foundation × AirOps tracking window.
By Ross Simmonds, founder and CEO of Foundation Marketing and author of Create Once, Distribute Forever. Data comes from Foundation’s research with AirOps. Data window: June 22 to July 24, 2026.
Most AI visibility advice assumes every B2B software company is playing the same game. The data says otherwise.
The sources behind an AI answer change as much by software category as they do by AI engine. That’s the clearest pattern in the 81 B2B software categories Foundation tracked with AirOps across roughly 380,000 AI answers this summer.
A marketer selling applicant tracking software and a marketer selling managed databases need two different AI visibility plans. A blended industry benchmark gives both of them the wrong one.
So I built this page as a lookup. Find your category, or the one closest to it. The row tells you where the third-party citations in your category come from, which AI surfaces lean hardest on them, and what to build first.
Key findings
- Review sites’ share of AI citations ranges from 7.64% (managed databases) to 25.07% (applicant tracking and recruiting) across the six categories in the public report, a 3.3x spread (Foundation × AirOps, 2026).
- Community sources’ share ranges from 5.50% (SEO platforms) to 13.89% (EDR/XDR security), a 2.5x spread (Foundation × AirOps, 2026).
- Product and vendor pages are the largest citation source on every surface, from 51.74% of citations on Perplexity to 71.63% on Gemini. The category effect lives in the remaining share, where reviews, communities and video trade places.
- Platform and category effects stack. In applicant tracking, review sites take 35.60% of Perplexity’s citations, 32.67% of Claude’s and 32.42% of ChatGPT’s, against 18.37% on Google AI Mode. Gemini sends 64.82% of its ATS citations to product domains.
- G2 reaches 23% to 30% of citing answers on five of six surfaces, and even on Gemini it’s the widest-reaching external domain at 13.59%. It’s the one third-party profile every category has to maintain.
- YouTube appears in 47.02% of Google AI Mode’s citing answers and 37.86% of AI Overview’s. In ChatGPT, Claude and Gemini it barely registers.
- Reddit appears in 51.56% of AI Mode’s citing answers. On ChatGPT, its citation rate fell from 21.41% in July to 10.05% in August and has held near 3% weekly since August 17. Community-led categories should point Reddit effort at Google’s surfaces first.
The six published categories, side by side
These are the six representative categories from The State of B2B AI Discovery 2026. Reviews and community figures are citation share across all six AI surfaces.
| Category | Reviews share | Community share | Lean | Surfaces that amplify the mix | Build first |
|---|---|---|---|---|---|
| Applicant tracking and recruiting | 25.07% | 8.05% | Review-led (highest reviews share of the six) | Perplexity, Claude and ChatGPT, each above 32% reviews share in this category | G2 and Capterra profiles treated as owned pages, then comparison pages against the four or five vendors buyers name |
| SEO platforms | 20.53% | 5.50% | Review-led (lowest community share of the six) | Claude and Perplexity | Comparison pages and review profiles. Community is the lowest-return channel here |
| Employee engagement and culture | 18.11% | 5.54% | Review-led | Claude and Perplexity | Review profiles, then use-case pages for each buyer (the HR lead and the people ops lead ask different questions) |
| EDR/XDR security | 17.89% | 13.89% | Contested (high on both) | Google AI Mode and AI Overview for community, Claude and Perplexity for reviews | Comparison pages first, then practitioner communities and independent testing coverage |
| CI/CD | 8.33% | 11.53% | Community-led | Google AI Mode and AI Overview | Presence in the developer communities your answers cite, plus documentation built to be quoted |
| Managed databases | 7.64% | 13.33% | Community-led (lowest reviews share of the six) | Google AI Mode and AI Overview | Technical documentation and community threads. Review profiles are the lowest-return channel here |
Applicant tracking is the only category with a published per-surface breakdown. For the other five, the surface column applies the all-category surface data (Claude and Perplexity give reviews roughly 20% of citations, Google’s surfaces cite Reddit in 39% to 52% of answers) to each category’s lean.
The pattern splits the market in two. On one side sit buyer-reviewed categories like HR and marketing software, where the person signing the contract reads G2 and Capterra. On the other sit practitioner-researched categories like developer and security tooling, where the person doing the evaluation reads Reddit threads, forums and docs.
Security is the interesting one. It’s high on both dials, which means a security vendor is getting compared in public on two fronts at once.
Find your quadrant
The public report breaks out six categories. The other 75 rows sit in the AirOps workspace behind the September report, and I’m not putting numbers on this page until every row is reconciled to the same window. They’re coming in the next refresh.
Until then, you can place your own category with two numbers: your reviews share and your community share. Run 50 to 100 of your buyers’ real prompts across the six surfaces, classify each cited domain as product, review, community, social, educational or media, and calculate the share for each bucket.
I set the lines at 15% for reviews and 10% for community because they split the six published categories cleanly. When the full table lands I’ll swap in the true medians.
1. Review-led categories should treat G2 like a product page
Reviews at 15% or more, community under 10%. Your review profiles are doing more citation work than most of your blog. Start with G2, Capterra and TrustRadius: review velocity, recency and profile completeness. Then build comparison pages against the vendors your buyers name. ATS, SEO platforms and employee engagement all live here.
2. Community-led categories need a community budget
Community at 10% or more, reviews under 15%. Find the subreddits and forums that keep showing up in your category’s answers and earn presence in them, across many threads instead of one. Then publish documentation and technical pages written to be quoted. CI/CD and managed databases live here.
3. Contested categories should build comparison pages first
Both above the line. You’re being compared on review sites and in communities at the same time, and comparison pages are the format AI cites most. In our data, 59 of the top 100 most-cited URLs were comparison pages and another 25 were listicles. EDR/XDR security lives here.
4. Vendor-led categories win on their own site
Both below the line. Your category’s answers lean on vendor pages, so product, solution, use-case and alternatives pages carry most of the weight. None of the six published categories landed here, and I expect a handful of the 75 will.
5. Any category whose buyers use Google gets a video channel
This one sits on top of the other four. If your buyers search Google, AI Mode and AI Overview will pull YouTube into the answer whether your category is review-led or community-led. Ship comparison, alternatives and best-of videos titled the way buyers actually ask.
Applicant tracking shows how the two dials stack
Drill into one category and the platform split shows up again inside it. Here’s ATS, citation share by surface.
| Surface | Products | Reviews | Communities | Social |
|---|---|---|---|---|
| ChatGPT | 43.35% | 32.42% | 11.58% | 0.20% |
| Gemini | 64.82% | 19.30% | 9.05% | 0.15% |
| Perplexity | 41.86% | 35.60% | 7.35% | 1.87% |
| Google AI Mode | 56.17% | 18.37% | 8.38% | 6.08% |
| Google AI Overview | 56.13% | 19.79% | 8.28% | 5.73% |
| Claude | 51.85% | 32.67% | 5.27% | 0.24% |
A review-heavy category gets even more review-heavy on the assistants that already love review sites. On Perplexity, more than a third of ATS citations come from review platforms. On Google’s surfaces that number drops under 20%, and social shows up instead.
Same category. Same week. Two different recipes depending on where the buyer asks.
How to use your row
1. A 20% reviews share means your review profile is a product page
Give it the same care you give your pricing page. If your community share is 10% or more, you need a community budget, which is a different line item from social media.
2. Read the surfaces against your buyers
Ask your customers which assistant they actually use. If they’re on Claude, review platforms are close to the only third-party door in, because Claude cited Reddit and YouTube zero times in this dataset. If they’re on Google, video and community both count.
3. Build the asset before the next blog post
In every category we tracked, product, comparison and use-case pages did more citation work than editorial. The blog is the wrapper. Your row tells you what the asset is.
4. Don’t copy the category next to you
Picture a managed-database company reading a “reviews drive AI visibility” study built on HR tech data. They pour budget into review campaigns, a channel worth 7.64% of citations in their category, while the community threads worth 13.33% go untouched. The playbook was real. It was also someone else’s.
5. Re-measure every month
Your row will move. I’ve been calling Reddit a B2B channel for close to a decade, and this summer I watched ChatGPT drop Reddit from about 1 in 5 cited answers to about 1 in 30 in three weeks, while Google’s surfaces kept citing Reddit in about half their answers. In the same month, a single r/BusinessIntelligence thread lost 210 citations in 30 days after Hex acquired Hashboard and the answers moved to fresher pages.
That’s the trade-off with any lookup table, this one included. It’s a map of the terrain on the day it was drawn. At Foundation we run this exact exercise for clients before we write a single page, and we rerun it, because the cost of a stale row is budget pointed at the wrong channel for a quarter.
Find your row. Build the one asset it tells you to build. Then go measure your own corner of the market… because nobody else’s benchmark is going to do it for you.
Where the data comes from
| Item | Detail |
|---|---|
| Data window | June 22 to July 24, 2026, with Reddit tracked weekly through early September |
| Answers analyzed | 379,810 |
| Citation events | Roughly 3 million |
| AI surfaces | ChatGPT, Claude, Gemini, Perplexity, Google AI Overview and Google AI Mode |
| Categories | 81 B2B software categories, six published in detail |
| Prompt types | Category discovery, head-to-head comparisons, “is it worth it” evaluations and use-case fit |
| Claude completion rate | 93.75% of scheduled runs |
| Tracking partner | AirOps (Foundation has a commercial relationship with AirOps) |
Foundation and AirOps tracked commercial-intent buyer prompts for 81 B2B software categories across ChatGPT, Claude, Gemini, Perplexity, Google AI Overview and Google AI Mode from June 22 to July 24, 2026. The prompts covered category discovery, head-to-head comparisons, “is it worth it” evaluations and use-case fit.
AirOps ran the tracking, logging and classification behind 379,810 answers and roughly three million citation events. Foundation designed the prompt library, ran the analysis and wrote every conclusion. A citation is one domain appearing as a source in one answer. It measures visibility inside AI answers, not traffic or buying influence.
Claude completed 93.75% of scheduled runs. Google AI Overview doesn’t trigger on every query. Rates and shares are calculated within each surface, so neither gap tilts the cross-surface comparisons. Foundation has a commercial relationship with AirOps, so read the tool recommendations with that in mind.
The full methodology and the six published categories are in The State of B2B AI Discovery 2026. The full report with the reach matrix is at content.foundationinc.co.
FAQ
Does my software category change which AI engine I should optimize for?
Yes. The share of citations from reviews, communities and video changes by category as much as by engine, and the two effects stack. A review-heavy category gets more review-led answers on Perplexity, Claude and ChatGPT. A community-heavy category gets more Reddit-led answers on Google AI Mode and AI Overview.
Which B2B software categories are most influenced by G2 in AI answers?
Applicant tracking and recruiting has the highest review-site share of the published categories at 25.07%, followed by SEO platforms at 20.53%. G2 itself reaches 23% to 30% of citing answers on five of six surfaces, so no category can ignore it. The question is whether it’s your top priority or your baseline.
Which categories should prioritize Reddit?
The community-led ones: EDR/XDR security (13.89% community share), managed databases (13.33%) and CI/CD (11.53%). Point that effort at Google’s AI surfaces first, where Reddit shows up in 39% to 52% of citing answers. On ChatGPT, Reddit’s citation rate has sat near 3% weekly since mid-August.
Why doesn’t Claude cite Reddit or YouTube?
In this dataset, Claude cited Reddit, YouTube and LinkedIn zero times and leaned on G2, Capterra, Gartner and PeerSpot instead. Reddit sued Anthropic in 2025 over data use. The absence is measurable. The reason isn’t, so I note the lawsuit as context and leave it there.
What is the difference between reach and share?
Reach is the share of sourced answers that cited a domain at least once. Share is a domain’s portion of all citations. A domain can have wide reach and a small share if it only appears once per answer.
Why does this page show only six categories?
The six categories are the representative set published in The State of B2B AI Discovery 2026. The other 75 sit in the AirOps workspace. I’ll add them once every row reconciles to the same data window.
Does ChatGPT’s August change to Reddit affect this table?
The category shares here come from June 22 to July 24, before the change. I track Reddit weekly, which is why the ChatGPT figures run through early September. The full breakdown is in ChatGPT Cut Its Reddit Citations in Half.
How often will this page update?
The next refresh lands with the next Foundation and AirOps tracking window. The last updated date at the top will change when it does.
Can I cite this data?
Yes. Use the citation line below and link back to this page.
Related reading
- ChatGPT Cut Its Reddit Citations in Half. Here’s What Our Data Shows.
- G2 Is the Quiet Kingmaker of AI Search
- Google’s AI Listens to the Crowd. Claude and Perplexity Listen to the Reviewers.
- AI Visibility Statistics 2027
- The GEO Playbook
- The Reddit B2B Marketing Blueprint
Cite this page: Ross Simmonds, “Which AI Engine Should Your Category Care About? What 81 B2B Software Categories Reveal,” rosssimmonds.com, October 2026.