There is a query pattern that drives more high-intent B2B software traffic than almost any other: "alternatives to [some tool]," and "[tool A] vs [tool B]." A buyer who types it is not browsing. They are comparing, and they are close to a decision. For years that traffic went to Google. Now a large share of it goes to ChatGPT, Perplexity, and Gemini, and the brands named in the AI answer are the shortlist the buyer acts on.
Here is the part most B2B teams miss: this is the one query pattern, in the one category, where you can publish your way into the answer.
AI answers "alternatives" from brand sites, not review sites
We classified every source cited across CRM & Sales in the August 2026 Cited Index. The mix is unlike anything in consumer categories:
| Source type | Share of CRM citations |
|---|---|
| Brand websites | 84% |
| Editorial (tech and business press) | 6% |
| Community (Reddit, LinkedIn) | 5% |
| Review platforms (G2, Capterra) | 2% |
| Marketplaces | 0% |
| Other | 3% |
Eighty-four percent of what AI cites for CRM sits on brands' own domains. Comparison pages, product pages, pricing, documentation. Marketplaces are effectively zero, because nobody buys enterprise software off Amazon. And review platforms, the surface most B2B teams pour budget into, are about 2%.
This holds across B2B software. HR & Payroll is 90% brand-site, Conversational AI is 84%. When an AI engine answers a software question, it reads what brands publish about themselves. That is the opposite of a D2C category, where the brand's own site can be a minority source and the answer is assembled from off-site places you cannot edit. We mapped that split in full in whether AI reads your website depends on what you sell.
What that means: your comparison content is your citation engine
If 84% of the citations live on brand sites, then the alternatives query is decided by whose brand's content is the most extractable answer to it. And most SaaS companies are leaving that content unwritten or writing it badly.
The pages that get cited for "alternatives to [competitor]" are the ones that answer the question directly and in a form AI can lift:
- A real "[your product] vs [competitor]" page for each competitor that matters, with an honest, specific comparison. Not a rigged table where you win every row. AI engines read the whole page, and a page that reads as fair gets cited; a page that reads as marketing gets skipped.
- An "alternatives to [competitor]" page that names the actual alternatives, including ones that are not you, and says who each is for. Counterintuitive, but it is exactly the shape of content the engine is trying to assemble, so it becomes the source.
- Extractable product facts: pricing, integrations, who it is built for, in plain language and ideally in structured markup, so the model can pull a clean fact instead of guessing.
The brands winning these queries in AI answers are not the ones with the most G2 reviews. They are the ones whose own site already contains the comparison the buyer asked for.
The playbook, in priority order
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- Map your alternatives and versus queries. List every "[competitor] alternatives" and "[you] vs [competitor]" a real buyer types. That is your target set.
- Build the owned comparison layer. One honest page per competitor, one alternatives page per cluster. Write for extraction, not for a slogan. This is 84% of the citation opportunity and the one asset no competitor can take from you.
- Add the small off-site layer that remains. The remaining 16% is mostly tech-press roundups (6%) and community (5%), with review platforms and a long tail making up the rest. Earn a spot in the category comparison articles real publishers write, and show up credibly where your buyers discuss tools. This is the same layer we cover for ChatGPT specifically in how to get your SaaS recommended by ChatGPT.
- Keep review sites for trust, not for AI citations. They convert a buyer who is already comparing. They are 2% of what AI cites.
One honest caveat
Two things this data does not do. It measures the B2B software market as a whole, not your specific product, and it shows the citation lives on brand sites without promising the model picks your page over a rival's on any given prompt. What it does establish is that your page is even eligible, which a consumer brand's rarely is. Turning that into wins is unglamorous: pull your real alternatives and versus queries, see whose page each engine cites today, and out-write it.
That is a rare opening in this field. The highest-intent query in your category is answered from a surface you own outright, and most of your competitors have not written the page. Someone in the category will. It may as well be you.
Methodology. Citations classified across CRM & Sales in the August 2026 Cited Index (711 citations), pooled by source-domain type across all five engines. Aggregate and market-wide: no individual product is named, and this is a different measurement system from the per-brand Citation Mix in the Cited dashboard. HR & Payroll (90% brand-site) and Conversational AI (84%) cited for cross-category context. This is Citation Source Mix, the sixth of the Cited 8 metrics.
Want to see where AI cites for your category? Get a free AI Visibility Report across ChatGPT, Gemini, and Perplexity.