AI Search: Is Your Content Strategy Accidentally Recommending Your Competitors?

This post was sponsored by FirstPromoter. The opinions expressed in this article are the sponsor’s own.

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 cheap and easy to scale, and for a long time it helped shape what buyers saw.

In AI search, comparison listicles backfire. Google’s AI Overview quotes the listicle as a 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 listicle earns the citation but loses the recommendation to a competitor.

In research published in June 2026, she analyzed 100 B2B “best Content Strategy software” queries in Google’s AI Overviews and checked the same queries three times between April and June.

The Results

Across the 80 queries that produced an AI Overview, self-ranked listicles were cited 323 times. In 224 of those cases, Google named a brand’s own page, then recommended a rival ranked inside it.

In other words, when a brand’s own listicle was cited, that brand was left out of the recommendation 69% of the time.

What’s the Difference Between Being Cited & Being Recommended in AI Search?

AI search produces two separate outcomes, and only one of them drives sales.

A citation means the engine named a page as one of the sources behind its answer.

A recommendation means the answer told the reader which product to choose.

The recommendation is what buyers act on.

A citation is easy to mistake for progress, because the brand still 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 answers, Ray found.

The brands that win recommendations are the established names the web already covers.

Recommended brands had far more referring domains, and far more mentions across AI Overviews and ChatGPT, than brands that were cited and passed over.

On-page changes can’t fix this. The gap isn’t on the page; the citation-recommendation gap lies within 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. Because citations and recommendations carry different intent, the goal is to separate two figures that usually get combined:

• How often your brand is cited (informational intent).
• How often it is recommended (transactional intent).

Step 1: Build Your Query List

Start with the questions a buyer would type, such as “best project management software,” “Notion alternatives,” or “best Content Strategy software.”

Step 2: Record Citations & Recommendations Separately

Run each one in Google and record two things: the pages Google cites as sources, and the products it recommends in the answer.

Step 3: Repeat Each Query

Run each query more than once, since AI answers shift from session to session.

Step 4: Score Your Share of Voice

Then score the share of recommendations won, rather than the number of citations earned.

Step 5: Extend the Audit Beyond Google

The pattern is documented for Google’s AI Overviews, so begin there. Run the same queries through ChatGPT and Perplexity to map which publishers those engines surface for your category.

Ray’s research shows what the exercise produces. For “best LMS for selling courses,” Google cited Oasis LMS repeatedly, in the body of the answer and in the sidebar. Oasis ranks itself number one in that article. Google recommended Kajabi, Thinkific, LearnWorlds, and Teachable instead, each of them named inside the Oasis piece.

Ray found the same split across categories, from CRM to help desk to SEO software.

Finding 2: Do AI Recommendations Come From Coverage You Don’t Publish? Yes.

Ray’s data shows where AI recommendations originate. Google leans heavily on third-party and user sites, with Reddit, Forbes, and YouTube among the most-cited domains. Content independent from the brand earns a recommendation: reviews, comparisons, and walkthroughs published by someone other than the vendor.

How Do You Get More Independent Brand Mentions That Win AI Recommendations?

You need to increase the number of web pages about your product on domains you don’t control, such as more:

• Reviews.
• Comparisons.
• Walkthroughs.

Each of these should be published by third parties. Not one placement at a time, but as ongoing output.

How Do You Do This Quickly?

Give creators a financial reason to publish. When a creator earns money each time their coverage converts a customer, they keep writing reviews, updating comparisons, and publishing walkthroughs, without you commissioning each piece.

You can start with a handful of creators and a revenue-share agreement. What that produces is coverage. What it does not produce, on its own, is consistency.

How Do You Keep A Consistent Flow Of Mentions?

Run an always-on channel: an affiliate program. Paying creators piece by piece gets you a review here and a comparison there. Mention velocity stays flat because every new URL requires new outreach. Consistent output takes structure: recruiting good partners, tracking what each one produces, rewarding the ones who perform, and paying them on time. An affiliate program is that structure.

Affiliates are third parties who earn a commission when a customer they refer makes a purchase. They include niche site owners, YouTube reviewers, newsletter writers, and media publishers. To earn it, they write reviews, record walkthroughs, and publish side-by-side comparisons on their own sites and channels. That content is what Google draws from when answering a “best Content Strategy software” query.

Proof of Concept: The Brands That Dominate AI Answers Already Run Programs At This Scale.

Run any “best Content Strategy software” query and the same names recur. Behind them sit networks of third-party sites reviewing and comparing those products, earning a commission on the customers they refer. Their referring domain counts keep climbing because the program funds new coverage continuously.

Programs built for editorial output win; programs built for referral volume don’t. A program aimed at raw referral volume tends to attract coupon and deal sites, which drive clicks but rarely publish the editorial content AI Overviews cite. A program aimed at AI recommendations recruits partners who write and review for a living, and favors partners with real audiences over partners who only distribute discount codes.

Telling a strong partner from a weak one takes judgment. The signals worth checking are long-term organic search performance, credible mentions on sites the partner doesn’t control, and a presence across more than one platform. Partners who rank well in AI Overviews usually have that track record already.

“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 Overview today doesn’t mean much on its own, because we’re seeing AI-generated sites get cited for a few weeks and then disappear once Google catches up to them. So check the organic history behind it first, look at the fluctuations, scroll through the content. And do that for every partner type, not just websites. A YouTube channel or an influencer can end up in an AI answer too, and they need the same check.” – Tautvydas Vasiliauskas

A referring domain earned this quarter doesn’t keep earning on its own. The brands that hold AI recommendations are the ones whose third-party coverage keeps growing, and that output depends on partners staying active.

Partners stay active when the program is run well. Each task involved is simple. Done by hand, together they consume the hours the program was supposed to save.

Time is not the only thing at stake. AI systems draw recommendations from the pool of referring domains that mention a brand. Low-quality affiliates and self-referrals pollute that pool., and when they do, the citations a program earned stop counting in the brand’s favor.

Keeping the pool clean requires detecting fraud, vetting partners, and blocking self-referrals, continuously, not as a one-time cleanup.

This is the operational work FirstPromoter handles. It tracks each partner’s performance and ties it to revenue, so you can see which partners produce sales, and which produce the coverage AI Overviews cite. It keeps partners motivated with contests, performance tiers that pay higher commissions, one-time placement fees, and target bonuses. Payouts run on a scale from do-it-yourself to fully managed, and setup requires little to no developer resource.

The software won’t choose partners or brief them; that judgment stays with you. It handles the operations, so the coverage keeps compounding without constant hands-on work.

Stop Building Content That Benefits Your Competitors. Start Building Connections That Reinforce Your Brand.

The self-ranked listicle had a good run, and that run is ending. In AI search, Google gives the recommendation to the brands the wider web already trusts, and it builds that trust out of independent content.

An affiliate program is one of the most direct ways to produce content that gets you brand recommendations, and you pay for it based on results rather than adding headcount. It’s worth considering whether you’re starting a program or already run one. FirstPromoter offers a free trial to test the approach.

Image Credits

Featured Image: Image by FirstPromoter. Used with permission.

In-Post Images: Images by FirstPromoter. Used with permission.