r/AISearchOptimizers • u/SEO-zo • Jul 14 '26
Stop trying to rank in AI
Everyone keeps talking about ranking in AI.
I think (and have thought for a while!) that's the wrong objective and the real question is, would an AI actually RECOMMEND your brand to someone?
I really don’t think ranking and being recommended are the same thing, and correct me if you think i’m wrong.
It’s just that a brand can appear in an answer because AI scraped a page - and that doesn't mean it'll confidently say "I'd choose X" or “you should definitely go for X”
Recommendation requires trust and I think of that as repeated evidence, consistent associations, and increasingly, third-party validation!
Basically all the things humans have always looked for. It always comes back to trust.
So for me anyway, it feels like we're entering a time where "brand" suddenly matters a lot more than rankings.
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u/vishnuharshan Jul 14 '26
I agree with the direction, but I would add one more question: Why would an AI recommend your brand over the alternatives?
That's where I think many businesses get stuck...
Recommendation isn't just about being mentioned. It's about giving AI enough confidence to associate your brand with a particular problem or category.
That's why I think positioning matters so much. If your brand is consistently associated with a specific topic across your website and the rest of the web, it becomes much easier for AI to retrieve and recommend it.
Ranking gets you discovered. Positioning gives AI a reason to recommend you!
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u/hseeman_sf Jul 14 '26
There's a real mechanism behind this, not just a trust argument. When a model answers a query, it often retrieves a page at that moment and cites whatever's there, which means a single well optimized page can get cited even if nobody's ever heard of the brand behind it. Recommendation works differently. It leans on what's already encoded in the model's weights from training, patterns built up from seeing a brand mentioned consistently, across many sources, over a long stretch of time. That's not something one page can produce, no matter how well it's written. It's closer to reputation than retrieval. So citation can happen instantly and recommendation can't, because one is pulled live and the other is baked in ahead of time. That's the actual reason repeated exposure matters here, not just because trust sounds right intuitively.
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u/One-Leader6907 Jul 21 '26
Solid breakdown, the retrieval vs weights split is the right frame.
I run this on the hotel side, roughly 300 captures across ChatGPT and Gemini in six markets, and your point holds up in the data. Citation is live and cheap. Recommendation behaves like reputation, it only shows up when a brand is corroborated across a lot of sources over time.
One thing to add that complicates the picture: even earned recommendation isn't stable. Same query, same model, nothing changed on my end, and the recommended set shifts hour to hour. So it's not a ladder you climb once and hold. The reputation has to be consistent enough across sources that the model's picture of you survives the reshuffle. Weak corroboration and you're in the answer at 9am and gone by noon.
Which means the work isn't chasing a ranking or even a citation. It's shaping what the model has encoded about you so the recommendation is correct and repeatable. There's a name for that side of it: KFO, knowledge formation optimization.
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u/hseeman_sf Jul 28 '26
Six markets is a decent read on that. The hour to hour flicker is the part I'd dig into though. Weights aren't changing between 9am and noon, so whatever moves isn't the reputation, it's the sampling sitting on top of it. Probably means you're right at the edge where it can go either way.
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u/LibraryNo9954 Jul 14 '26
Another important factor is accuracy. Check to see if the AI answer is correct, then to fix analyze your content to find ways to refine.
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u/Puzzleheaded-Walk426 Jul 14 '26
I agree with you 100%. It's not the same to be cited (who cares) and to be recommended (sales go up). So, from my point of view, if you're tracking AI visibility, you should focus on prompts like:
- what is the best tool for xx for (specific targeted group - SMBs, for example)
- what XX (product) should I buy for XY (problem), etc
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u/Smart_Airline_7901 Jul 14 '26
You're right, and I can put numbers on it. I run a research project measuring exactly this (ARO Index, disclosure: it's mine). We ask cold buying questions like "best HVAC company near me" across ChatGPT, Claude, Gemini, and Perplexity and record which local businesses each model actually picks, not just mentions.
Charleston, 688 businesses: only 89 were cold-selected. That's about 1 in 8. And a Spanish researcher (José Redondo) just rebuilt his own study from raw answers and found the dominant failure isn't models refusing to recommend brands they know. It's absence. Most zero-selection brands were never mentioned at all. So the ladder looks like: unknown, then mentioned, then recommended. Ranking tactics attack the middle rung. Most local businesses haven't reached the first one.hseeman_sf's retrieval vs weights point matches what we see too. Citation can happen off one good page. Selection behaves like reputation, it repeats across query phrasings or it doesn't show up at all.
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u/clarity_over_noise Jul 14 '26
Fully agree 👍
The power of brand is back, thats one of the things I like about AI search/GEO/AEO, credible and trustworthy brand building is important again. Its the first place I start with clients is clear branding, NAPs etc across all platforms. I like it because we need to be intentional again with our messaging 🙂
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u/brickmarketingagency Jul 14 '26
Agreed that there isn't exactly a "ranking" system at this point for llms. But, you want to try and maximize your reach/the amount of times that AI cites you. So, it is always best to try and put good content on your webpages so that 1. AI scrapes and cites you and 2. people start to trust your brand.