This article was sponsored by FirstPromoter. The opinions expressed in this article are those of the sponsor.
For years, software companies have published pages that rank the best tools in a category and place their own product at the top. The tactic was inexpensive and easy to implement, and for a long time it helped shape what buyers saw.
In AI search, comparison lists backfire. Google’s AI preview cites the listicle as the source, then recommends a competitor from your own cited list.
Your content only gets the citation. Meanwhile, your competitor gets the recommendation and the click. Your competitor gets the sale.
What makes your cited content recommend competitors?
Lily Ray quantified how often a brand’s own listing gets the citation but loses the endorsement to a competitor.
In research published in June 2026she analyzed 100 B2B “best software (in category)” queries in Google’s AI previews and checked the same queries three times between April and June.
The results
Of the 80 queries that produced an AI overview, auto-ranked lists were cited 323 times. In 224 of these cases, Google named a brand’s own page and then recommended a competitor ranked within it.
In other words, when a brand’s own list was cited, that brand was excluded from the recommendation 69% of the time.
What is the difference between being cited and being recommended in AI research?
AI search produces two distinct results, and only one of them generates sales.
A citation means that the engine has named a page as one of the sources behind its answer.
A recommendation means that the answer tells the reader which product to choose.
The recommendation is what buyers act on.
It’s easy to mistake a quote for progress because the mark always appears on the screen.
What an engine cites depends on the content of the page. What it recommends depends on what the rest of the web says about a brand: how many independent sites mention it, link to it, and review it.
Your goal should be to increase recommendations.
Why does self-promotional content backfire in AI search?
Google now treats self-ranked pages differently in its AI responses, Ray discovered.
The brands that gain endorsements are the established names that the web already covers.
Recommended brands had significantly more referring domains and significantly more mentions in AI and ChatGPT previews than cited and ignored brands.
Edits on the page cannot resolve this issue. The space is not on the page; The gap between citations and recommendations is how often the rest of the web covers the brand.
How to measure whether AI Search recommends your brand
You can run this check for any category without special tools. Since citations and recommendations have different intentions, the goal is to separate two numbers that are usually combined:
- How often your brand is cited (informative intent).
- How often this is recommended (transactional intent).
Step 1: Create your query list
Start with questions that a buyer would type, such as “best project management software,” “Notion alternatives,” or “best (in category) software.”
Step 2: Save Quotes and Recommendations Separately
Run each one in Google and record two things: the pages Google cites as sources and the products it recommends in the response.
Step 3: Repeat Each Query
Run each query multiple times, as the AI’s responses change from session to session.
Step 4: Write down your share of voice
Then note the share of endorsements earned, rather than the number of citations earned.
Step 5: Expand the audit beyond Google
The model is documented for Google AI Previews, so start there. Run the same queries through ChatGPT and Perplexity to map which publishers these engines surface for your category.
Ray’s research shows what exercise does. For “best LMS for selling courses,” Google cited Oasis LMS several times, in the response body and in the sidebar. Oasis ranks number one in this article. Google instead recommended Kajabi, Thinkific, LearnWorlds, and Teachable, each of which is named in the Oasis article.
Ray saw the same breakdown across categories, from CRM to help desk to SEO software.
Finding 2: Are AI recommendations coming from coverage you don’t publish? Yes.
Ray’s data shows where AI recommendations come from. Google relies heavily on third-party and user sites, with Reddit, Forbes and YouTube among the most cited domains. Brand-agnostic content Gets a Recommendation: Reviews, comparisons, and walkthroughs posted by someone other than the provider.
How to get more independent brand mentions that get AI recommendations?
You must increase the number of web pages about your product on areas that you do not control, such as:
- Comments.
- Comparisons.
- Walkthroughs.
Each of these elements must be published by third parties. Not one investment at a time, but as a continuous result.
How to do this quickly?
Give creators a financial reason to publish. When a creator makes money every time their cover converts a customer, they continue to write reviews, update comparisons, and post walkthroughs, without you ordering each item.
You can start with a handful of creators and a revenue sharing agreement. What this produces is a blanket. What it doesn’t produce on its own is consistency.
How to maintain a consistent flow of mentions?
Manage an always-on channel: a subsidiary program. Paying creators piece by piece allows you to get a review here and a comparison there. Mention speed remains stable because each new URL requires new reach. To obtain a consistent result, you need structure: recruit good partners, monitor what each one produces, reward those who perform and pay them on time. An affiliate program is this structure.
Affiliates are third parties who earn a commission when a customer they recommend makes a purchase. They include niche site owners, YouTube reviewers, newsletter writers, and media publishers. To earn it, they write reviews, record walkthroughs, and post side-by-side comparisons on their own sites and channels. This is the content that Google draws inspiration from when responding to a “best (class) software” query.
Proof of concept: Brands dominating responses to AI are already running programs at this scale.
Run any “best software (category)” query and the same names come up. Behind them are networks of third-party sites reviewing and comparing these products, earning commission from the customers they recommend. Their number of referring domains continues to increase as the program continually funds new coverage.
Programs designed for editorial production win; programs designed for referral volume do not. A program targeting raw referral volume tends to attract coupon and deal sites, which generate clicks but rarely publish the editorial content cited by AI Overviews. An AI referral program recruits partners who write and review for a living, and prioritizes partners with a real audience over partners who only distribute discount codes.
Telling a strong partner a weak partner requires judgment. Signals worth checking are long-term organic search performance, credible mentions on sites the partner doesn’t control, and a presence on multiple platforms. Partners who rank well in AI overviews typically already have this history.
“Affiliates are one of the biggest sources of AI citations right now, and yet most brands don’t even think about it. A citation in an AI preview today doesn’t mean much on its own, because we see AI-generated sites get cited for a few weeks, then disappear once Google catches up. So first check the organic history behind it, look at the fluctuations, scroll through the content. And do this for every partner type, not just websites. A YouTube channel or influencer can end up in an AI response too, and they need the same check – Tautvydas Vasiliauskas.
A referring domain won this quarter does not continue to win on its own. Brands with AI endorsements are those whose third-party coverage continues to grow, and this outcome depends on their partners remaining active.
Partners remain active when the program is well managed. Every task involved is simple. Made by hand, they together consume the hours that the program was supposed to save.
Time is not the only issue. AI systems draw recommendations from the pool of referring domains that mention a brand. Low-quality affiliates and self-referrals pollute this pool, and when they do, the citations obtained by a program stop counting in favor of the brand.
Keeping the pool clean requires detecting fraud, vetting partners, and blocking self-referrals, constantly, not as a one-time cleanup.
This is operational work FirstPromoter handles. It tracks each partner’s performance and ties it to revenue, so you can see which partners are generating sales and which are producing the coverage cited by AI Overviews. It keeps partners motivated through competitions, performance tiers that pay higher commissions, one-time placement fees, and target bonuses. Payments run on a scale from DIY to fully managed, and setup requires little to no developer resources.
The software will not choose partners or inform them; this judgment stays with you. It manages operations, so coverage continues to accumulate without constant hands-on work.
Stop creating content that benefits your competitors. Start building connections that strengthen your brand.
The self-ranked listicle has had a good run, and that run is coming to an end. In AI search, Google recommends brands that the wider web already trusts, and builds that trust from independent content.
An affiliate program is one of the most direct ways to produce content that gets you brand endorsements, and you pay for it based on results rather than headcount addition. It’s worth considering whether you’re starting a program or already running one. FirstPromoter offers a free trial to test the approach.
Image credits
Featured image: Image from FirstPromoter. Used with permission.
In-Post Images: Images from FirstPromoter. Used with permission.
