AI Competitor Analysis: Benchmarking Their Visibility

01A different question from your own visibility

Most AI visibility work answers the question “how visible are we?” It produces a percentage, the percentage moves a little each month, and nobody knows what to do on Monday.

Competitor analysis asks a better question: which specific buying questions do our competitors win, and why. That reframing changes the unit of analysis from the brand to the question, and questions are actionable in a way that aggregate scores are not.

That last row matters more than it looks. When an engine changes what it retrieves, every brand’s absolute score moves at once. Your position relative to a named competitor is far more stable, and it is the number that actually describes your business. The volatility problem in absolute measurement is covered in AI brand visibility gap analysis.

02Why AI answers are more zero-sum than search

A search results page has ten organic slots. An AI answer typically names three. That single structural difference is why competitor analysis matters more here than it did in SEO.

In search, ranking fourth still produced traffic. In an answer, being fourth usually means not being mentioned at all, and there is no equivalent of scrolling. The buyer receives a shortlist and the shortlist is the whole competitive outcome.

This has a direct consequence for how you prioritise. In SEO, a broad improvement across many keywords was a reasonable strategy. Here, the question is binary per question: you are in the named set or you are not. Winning three more questions outright is worth more than improving marginally on thirty.

03Setting up the comparison

The setup is the same measurement you would run for yourself, with one change: log every brand named, not only yours. That change costs nothing and doubles what the run tells you.

1. Pick three to five competitors from lost deals. Not a market map. The names that actually appear when you lose. Answers name few brands, so a long list produces mostly zeros.
2. Build 30 to 50 unbranded buying questions. Category, problem, comparison, and use-case shapes. Sourcing these properly is covered in finding the prompts your buyers ask.
3. Freeze the wording and pick your engines. Rewording between runs measures your editing rather than the market.
4. For each answer, record four fields. Every brand named; which brand was presented as the recommendation; the sources cited; and the run date and engine.
5. Repeat each question at least three times. Answers vary. A single run tells you almost nothing, and inconsistency is itself a finding.

One practical note on picking competitors: let the measurement correct your list. Teams start with the three names sales complains about, and after two runs the data usually contains a fourth name appearing regularly that nobody had on the list. That name is worth more attention than any of the original three, because it is a competitor winning buying questions while remaining invisible to your internal narrative. Add it and keep the set to five, dropping whichever original name the engines never mention.

The fourth field is the one teams skip and later wish they had. The cited sources are what convert a competitive loss into a task, because they name the specific page that produced the recommendation.

04Reading the result: three gap types

Not all losses are the same loss. Three distinct situations hide under “the competitor won”, and they need opposite responses.

The distinction between the first two is the one practitioners arrive at independently once they start measuring properly:

A tool that reports one blended visibility score makes the first two rows of that table indistinguishable, which means it cannot tell you whether you have a coverage problem or a positioning problem. Those require completely different work, so collapsing them into one number destroys the only decision the measurement was supposed to inform.

The third is the most urgent and the least common. If an answer consistently attaches a caveat to your name, something specific is being cited, and that is findable. It is usually a review theme, a comparison page written by a competitor, or an outdated claim that has propagated.

The first is the most common and the most tractable. Being absent is almost never mysterious once you read the citations: the answer was built from three sources, and you are not in any of them.

05Which gaps are actually winnable

The measurement will produce more gaps than you can close. Sorting them by cause rather than by size is what makes the exercise useful.

Sorting by cause also stops the most common waste in this work, which is spending a quarter on the biggest gap because it is the biggest. Gap size tells you how much you are losing. Cause tells you whether you can do anything about it. A small closeable gap beats a large structural one every time, and the closeable ones are usually the unglamorous coverage fixes: a review profile that is thin, a comparison page that does not exist, a documentation page that never got written.

The last row deserves saying plainly because AEO advice tends to skip it. Sometimes the answer is correct. If an engine recommends a competitor for a use case they genuinely serve better, no amount of content optimisation should change that outcome, and trying is how teams end up with claims that get contradicted in sales calls.

The fourth row is where most of the practical opportunity sits for smaller companies. Incumbency dominates broad category questions and matters much less on constrained ones: a specific integration, a specific team size, a specific regulatory context. Those questions have fewer competing sources and the engine has less consensus to fall back on.

06Finding the questions competitors own

Sort your results by consistency, not just by whether you lost. Consistent competitor wins are informative; inconsistent ones are noise.

The second row deserves more attention than it usually gets. A question where results flip between runs is a question the engines have not settled, and unsettled questions are the cheapest ground in the whole exercise. Nobody has to be displaced. The answer is genuinely uncertain, which means a single well-sourced page answering it directly can tip it, where the same effort spent on a settled question would move nothing.

The third row is the finding people miss because it looks like a null result. A buying question where the engines produce inconsistent, hedged answers with no dominant brand is a question nobody has answered well on the open web. That is the cheapest visibility available in the whole exercise, and it usually appears in the constrained, specific questions rather than the head terms everyone competes for.

07Your competitor set changes by engine

Run the same questions through different engines and the competitive set changes. This is not measurement error, it is the market your buyers actually see.

A practitioner ran one category question through three major assistants and recorded three different top recommendations, with each engine reasoning from a different basis: review consensus in one, feature depth in another, price and ease of use in the third. The underlying cause is that the engines retrieve from substantially different source sets, with one practitioner comparison of 100,000 prompts reporting roughly 11 percent domain overlap between two major engines.

The third row is quietly one of the most valuable outputs of the whole exercise. Because engines synthesise rather than rank by domain authority, they name smaller players earlier than a search results page does. A competitor appearing consistently in your measurement that nobody on your team has heard of is genuine early warning, and it costs nothing extra to catch since you are already logging every brand named.

The practical implication for prioritisation: pick the engine your buyers actually use before optimising for divergence. Self-reported attribution answers that better than any benchmark, and without it you are guessing which of several diverging markets to compete in. Source differences by engine are covered in which websites AI engines actually cite.

08Cadence and what to report

Run monthly, hold the question set constant, and report relative position rather than absolute scores.

Monthly is right because AI answers change on model updates and content cycles, neither of which moves weekly. Quarterly lets a competitor establish a position before you notice.

Keep the raw log, not just the summary. The value of this measurement compounds only across runs, and the questions you will want to answer in six months are ones you cannot predict now: when did this competitor first appear, which source were they cited from before they started winning, did our position move before or after the model update. A summary discards exactly the detail those questions need. A dated row per question per engine per run is unglamorous and it is the asset.

On reporting: a competitor comparison survives model updates that destroy absolute-score reporting. If an engine changes retrieval and every brand’s visibility falls, your score drops and your position may not have moved at all. Only one of those two facts describes your business, and it is the one a board can act on. This is covered further in AI search ROI and attribution.

09Frequently asked questions