We ran an audit for a mid-market CRM platform last fall. The company held strong Google rankings for their core keywords and had invested heavily in content marketing for three years. When we tested 40 purchase-intent prompts across ChatGPT, Perplexity, and Gemini, their brand appeared in six. Their two primary competitors appeared in twenty-eight and twenty-two respectively. The CRM platform was winning traditional search. They were invisible in the channel growing 527% year over year.

That gap matters more than most teams realize. AI platforms typically recommend three to four brands per response. If competitors hold those slots and you don't, you're not just missing visibility. You're being replaced in the buyer's consideration set before they reach your website.

The winner-take-all dynamic

Traditional search distributes attention across ten organic results, ads, and featured snippets. AI concentrates it. When ChatGPT answers "What's the best project management tool for remote teams?", it names a handful of brands. Everyone else doesn't exist for that query.

Onely's research found that competitive positions in AI recommendations calcify over time. More visibility leads to more mentions, which strengthens entity recognition, which increases future visibility. The brands capturing those three to four slots are building advantages that compound. A competitor who appears consistently accumulates third-party mentions, user discussions, and derivative content that reinforces their position. The cost of catching up grows with every month you delay.

This is fundamentally different from SEO, where algorithm updates can reshuffle rankings overnight. AI brand perception is built into training data and reinforced by retrieval patterns. Displacing a competitor requires changing the underlying information ecosystem, not optimizing a page.

What competitor intelligence actually reveals

Most teams approach competitor analysis in AI the wrong way. They check whether their brand appears, note a competitor shows up, and stop there. The valuable intelligence sits in the details of how and why competitors are being recommended.

Ahrefs' Brand Radar framework breaks competitive analysis into four dimensions: mentions, citations, impressions (mentions weighted by search demand), and AI share of voice (percentage of total impressions versus competitors). Share of voice matters most because it reveals your proportional visibility within your category.

But quantitative share of voice only tells part of the story. You need to analyze what AI says when it recommends a competitor. Is the competitor positioned as the "industry leader," the "most affordable option," or the "best for enterprise"? Those qualifiers reveal which positioning territory the competitor owns. If a competitor consistently appears with "best for enterprise teams" while you're described as "suitable for small businesses," that's a positioning displacement no amount of content volume will fix.

Frase's AI search tracking framework emphasizes that competitive tracking must always happen in context. A 40% citation rate means nothing without knowing whether that's up from 15% or down from 70%. Trajectory matters as much as the snapshot.

Where to look for competitive gaps

The most actionable competitor intelligence comes from identifying specific prompts where you're absent but should be present. Build your prompt library around three categories.

First, category prompts: "best [your category] for [use case]." These are the highest-value queries because they represent buyers actively evaluating options. If a competitor appears here and you don't, that's a priority gap. Second, comparison prompts: "[your brand] vs [competitor]" or "compare [category] tools." AI's response to direct comparison queries reveals how models differentiate between you and competitors, and whether the differentiation is accurate. Third, problem-solution prompts: "how to solve [specific problem your product addresses]." These queries may not trigger brand recommendations at all, representing an opportunity to be the first brand AI associates with that problem.

For each category, track which competitors appear, what sources AI cites when recommending them, and what language frames the recommendation. The source analysis is particularly revealing. If a competitor consistently appears because AI draws from a specific industry report, a set of G2 reviews, or a Wikipedia section, you've identified the exact signals you need to generate.

Turning intelligence into action

Competitor visibility data becomes strategic when you map gaps to addressable causes. If a competitor dominates "best [category] for enterprise" prompts, examine what's feeding that: enterprise case studies, reviews from enterprise customers on G2 or Gartner, mentions in enterprise publications, or structured data signaling enterprise capability.

Then build a targeted plan to generate equivalent or stronger signals. This isn't about copying a competitor's strategy. It's about understanding which inputs AI uses to form its recommendations and ensuring your brand has comparable signals for the positioning you want to own.

The brands executing this well treat competitive analysis as a monthly discipline. They maintain a prompt library, track share of voice, document competitor positioning language, and identify source attribution patterns. When they spot a gap, they know whether the fix is a content play, a PR play, a review generation play, or a structural change.

The zero-click economy means over 70% of searches end without a click. Users get their answer from AI. The competition isn't for page-one rankings. It's for the three to four recommendation slots AI offers in every answer. Knowing who occupies those slots, why, and what it takes to displace them is the foundation of competitive strategy.