AI Ranking Factors

Posted on Nov 3 • Originally published at kevinleary.net

Two studies published recently are worth your attention because they were actually measured rather than asserted. The first is a peer-reviewed paper presented at SIGIR 2026, “What Gets Cited: Competitive GEO in AI Answer Engines,” from a research team at Sprinklr. The second is a large observational study from The Digital Bloom covering 105,000 ChatGPT prompts. They approach the problem from opposite ends, and together they form the clearest picture we have.

Here is what they found, in plain terms.

Almost every article about AI visibility treats it as a single challenge. It isn’t. There are two distinct stages, and failing at either one makes you invisible:

The Digital Bloom study measures the first. The SIGIR paper measures the second. Keeping them separate is the single most useful idea in either study, because the fixes are completely different. If you are never retrieved, writing better content will not help you. If you are retrieved but never cited, more backlinks will not help you.

The Digital Bloom analysis looked at 145 industries, 1,595 buyer personas, and 29,562 domains, then correlated who got recommended against thirteen external signals. The strongest correlations:

The headline: traditional search visibility is the strongest known predictor of AI recommendation. Ranking well in Google and having real backlink authority remain the best-measured inputs to being recommended by an AI. Anyone telling you SEO is dead is contradicted by the only large dataset we have.

First, the industry variation is enormous. Wikidata presence explains 49.9% of the variance for furniture retailers and 42.9% for ERP software, while Reddit posts explain 27.9% for enterprise AI platforms and 25.3% for live entertainment. The signal that matters most in your category may be almost irrelevant in another. This is the strongest argument I have seen for researching your own market rather than following a generic checklist, which is the same point I make about knowing the prompts your buyers actually use.

Second, and be honest about this one: all thirteen signals combined explain well under 20% of the outcome. The authors put 80 to 85% of AI recommendation behavior down to model internals, meaning training data, fine-tuning and reinforcement learning that nobody outside the labs can observe or influence. Anyone selling you a complete formula for AI rankings is selling you the small visible slice and not mentioning the rest.

This is where the SIGIR paper is genuinely valuable, because it is a controlled experiment rather than a correlation study.

The method is clever. The researchers took 100 real product review articles, stripped out every brand and publisher name so familiarity could not skew the results, then created pairs of pages that were identical except for exactly one characteristic. They showed each pair to six different language models (GPT-5 variants, Claude, Gemini and Kimi), swapped the order to control for position bias, and recorded which one got cited first. In total: 252,000 trials across 18 content factors.

One incidental finding is worth pausing on. In 86.4% of answers, the model cited exactly one source. Not three, not five. One. AI answers are closer to winner-take-all than a search results page, which changes how you should think about “visibility” entirely.

Four factors were significant across all six models with very large effects. Fail any one of these and your citation odds collapse regardless of how good the rest of your page is:

Once the gatekeepers are satisfied, these provided meaningful additional advantage:

Notice how much of this is simply publishing complete, specific, verifiable information. Price, specs, evidence, comparisons. That is not a trick, it is a thorough product page, and it is the same conclusion I reached from client work before this research existed.

Seven of the eighteen factors showed no consistent effect, and this is the part that should change some behavior.

Formatting had no measurable impact. Structured sections versus dense paragraphs, and organized versus scattered information, made no reliable difference to which source got cited. The researchers concluded that language models parse content regardless of visual organization.

I want to be careful and precise about this, because it partially cuts against advice I have given. Formatting still matters for retrieval, for extraction, for accessibility, and for the humans who read your pages. What this study shows is narrower and still important: when two pages containing the same information compete for a citation, prettier structure does not win it. If you have been reformatting existing content into bullet lists and headings and calling that AI optimization, this is evidence that the effort is better spent adding information that is not there yet.

Also showing no consistent effect: promotional tone, stronger value proposition claims, and social proof such as ratings and review counts. Marketing language does not move the needle. Facts do.

Sensitivity to content quality varied a lot. Kimi-K2 responded to 83% of the tested factors, the GPT family sat in the middle, Claude responded to 50% and Gemini to just 33%. Gemini and Claude also tended toward near-binary decisions, either strongly preferring a source or not distinguishing at all.

All six agreed on the four gatekeepers. So the gatekeepers are safe to treat as universal, while the finer differentiators matter more in some assistants than others.

The measurable part of AI visibility comes down to being findable through conventional search, and then being the most complete, specific, current and factual answer in the retrieved set. Price, specifications, evidence, comparisons, recency. No formatting trick, no file you can upload, no schema hack substitutes for publishing better information than your competitors.

That is unglamorous, which is probably why so much of the advice in this space is about anything else. It is also the part you can actually control, and the research now supports it. If you want help working out which of the two stages is holding your brand back, that diagnosis is the starting point of most of my AEO engagements and the broader work I do as an AI consultant.