Competitive Analysis
In AI visibility, competitive analysis means finding out who AI assistants name when someone asks the questions you want to own, and why. You run the same prompts across ChatGPT, Gemini, Perplexity, and the others, log which brands come up, in what order, and with what sources behind them, then work out what those brands have that you don't. It replaces guessing with a concrete list of gaps you can close.
Why competitive analysis matters for AI visibility
A generative answer usually surfaces a handful of names, not a page of ten links you can scroll past. If a competitor is one of those names and you aren't, you are effectively invisible to that user, no matter how good your own content is in isolation. Measuring your own mentions tells you where you stand; it does not tell you who is standing in front of you or why the model reaches for them first. Competitive analysis fills that gap. It shows which providers the model treats as trustworthy references, what those providers publish, and which pages get cited as the source. That is also worth doing because citation and ranking behave differently across systems: Ahrefs found that only 6 to 8 percent of URLs ChatGPT cites also appear in Google's top 10 for the same query, and roughly 80 percent of ChatGPT's cited URLs don't rank in Google's top 100 at all. A competitor who is invisible in Google search can still be the one an AI assistant recommends, so you have to check both.
How a competitive analysis works
Start by defining the actual questions your customers ask, for example "which tax advisor handles freelancers in Cologne." Put that prompt to several AI assistants, several times each, and record which names appear, in what order, and in what wording. Next, identify which competitors show up repeatedly rather than once by chance. For each one, check which of their pages the assistant is drawing from or citing, and compare how those pages are structured against your own: how specific the facts are, how current the content looks, and how easy it would be for a model to lift a clean answer out of it. You end up with a plain list: where competitors are named and you aren't, which topics they cover that you don't, and which concrete facts make their pages easier to cite than yours.
Common mistakes in competitive analysis
The most common mistake is only checking the obvious market leaders. AI answers often favor smaller providers with well-structured, fact-dense pages over big brands with generic marketing copy. Second, a single query proves nothing: AI answers vary between runs and between models, so you need multiple prompts across multiple assistants before you trust the pattern. Third, people count mentions without checking tone; being named as a warning or a comparison point is not the same as being recommended. Fourth, treating one snapshot as the whole picture hides whether your gap is closing or widening, since you only see that by measuring again over time. And fifth, don't assume tactics like adding an llms.txt file or extra schema markup will close a citation gap on their own — Google has stated no special markup or AI-specific file is required for AI Overviews or AI Mode, and an Ahrefs analysis of roughly 137,000 sites publishing llms.txt found about 97 percent saw no measurable referral traffic tied to it. Treat competitive analysis as a recurring check, not a one-off report.
Relevance to AI recommendations and GEO
Competitive analysis is the foundation Generative Engine Optimization builds on, since you can't fix a gap you haven't located. Once you know which competitors the model prefers and what their pages have in common, you can act on it directly. Often the difference comes down to clear, verifiable facts stated plainly, consistent details repeated across many independent sources, and pages a crawler can parse without friction. That last point matters more than markup: Ahrefs data across roughly 75,000 brands found that how often a brand gets mentioned across the web correlates with AI citation rate at about 0.664, roughly three times stronger than the correlation with backlinks at about 0.218. So a competitive analysis that only looks at your competitors' on-page SEO is missing the bigger lever, which is how often independent sources talk about them. The analysis is also what gives your visibility score and share of voice actual meaning, since those numbers only tell you something once you have a competitor baseline to compare them against.
Example
A small online driving school in Hamburg can't figure out why sign-ups from AI recommendations are close to zero. It asks ChatGPT, Gemini, and Perplexity the same question five times each: best online driving schools in Germany. Three competitors show up almost every time; its own brand never does. Looking at what those competitors publish, the pattern is obvious: each has a structured FAQ page listing prices, the licensing process, and pass rates, while the driving school's own site has a single page of promotional copy and no specifics. It rewrites that page around the same concrete facts, and within a few weeks starts appearing in AI answers for the first time.
Common questions
How often should I run a competitive analysis for AI visibility?
Monthly at minimum, ideally as an ongoing check. Model versions change, competitors publish new content, and AI answers shift with both. A single analysis goes stale fast; repeated checks are what let you see a real trend instead of one moment's answer.
How does AI competitive analysis differ from classic SEO competitor analysis?
SEO competitor analysis compares rankings, keywords, and backlinks in a search results page. AI competitive analysis looks at who the assistant names in a generated answer and which source it cites for that claim. The overlap between the two is smaller than most people expect, since ranking well in Google doesn't guarantee an AI assistant will cite or recommend you, and vice versa.