AI competitor analysis benchmarks your brand against rivals in AI search: who gets cited, on which prompts. Here's how to run one, step by step.
Frequently asked questions
How do I compare my brand to competitors in AI search?
Build a prompt set of the questions where you compete, run it across AI engines, and record how often each brand is mentioned and cited. Comparing those results gives you share of voice: who AI names most for the prompts that matter.
Which competitors does ChatGPT recommend?
Ask ChatGPT the category and comparison questions your buyers ask and note which brands it names. Run them repeatedly, since answers vary. That set of recommended brands is your real competitive set in AI search, which may differ from your usual list.
How do I find AI visibility gaps?
Track a prompt set and look for questions where competitors are mentioned or cited and you aren't, or where AI describes you less favorably. Each gap is a specific content or authority opportunity to prioritize.
AI competitor analysis benchmarks how your brand shows up in AI answers against your rivals: who gets mentioned, who gets cited, and on which questions. Because AI search usually returns a single answer rather than a page of links, the goal isn't to outrank competitors; it's to be the brand AI names instead of them. A competitor analysis tells you exactly where you're winning and losing that race.
Key takeaways
In AI search you compete for one cited answer, not a spot on page one.
Benchmark share of voice, citations, the prompts you compete on, and sentiment.
Your real AI competitors are the brands AI names, which may surprise you.
The output is a prioritized list of gaps to close with content and authority.
Run it on a schedule; competitive position shifts as models and content change.
If a competitor is the brand ChatGPT recommends for your category, that's a deal you may never know you lost. AI competitor analysis makes that invisible competition visible and measurable. This guide explains why it matters, what to benchmark, and how to run one.
Traditional competitive SEO is about relative position: you want to rank above your rivals, but you can both appear on page one and both get clicks. AI search compresses that. For many questions there's one synthesized answer naming a few brands, and if you're not among them, you're absent, no matter how strong your page is.
That changes the stakes of benchmarking. It's no longer "are we ranking near competitor X?" but "when a buyer asks AI to recommend a tool, does it say us or them?" Being named in the answer is the whole game, and your competitors are quite literally taking your place when they're cited and you aren't. Benchmarking quantifies that head-to-head reality, your share of voice against named rivals, so you can act on it.
A useful AI competitor analysis measures four things, all derived from running the same prompts across engines for you and your rivals:
Share of voice: what portion of relevant answers names each brand. This is the top-line scoreboard.
Citations, not just mentions: which competitors get cited (linked as a source) versus merely named. A citation can send a visit, so it's a stronger signal.
Which prompts each brand wins: the specific questions where a competitor is named and you aren't. This is where the actionable detail lives.
Sentiment comparison: how AI describes each brand. Being mentioned alongside a competitor but framed as the weaker option is its own kind of loss.
One subtlety worth flagging: your AI competitive set may not match your usual one. The brands AI names for your category questions are the ones you're actually competing with in answers, sometimes including tools you didn't consider rivals, and sometimes omitting ones you assumed were. Let the data define the field.
Identify your real AI-search competitors. Ask the engines your category and comparison questions and note which brands recur. That's your benchmarking set.
Build a shared prompt set. Assemble the category, comparison ("X vs Y"), and problem-based questions where you compete, phrased the way buyers actually ask. This is the same prompt set discipline, applied competitively.
Run the prompts across engines. Query ChatGPT, Perplexity, Gemini, and Google AI Overviews and record, for each prompt, which brands are mentioned and which are cited.
Compare share of voice and sentiment. Roll the results up into share of voice per brand and note how each competitor is characterized.
Identify gaps and prioritize. Flag the prompts where competitors win and you're absent, weight them by buyer value, and turn them into a ranked to-do list.
Repeat on a cadence. Competitive position in AI search isn't static. It moves as models update and as you and your rivals publish.
A benchmark is only worth running if it changes what you build. Each gap is a specific brief: a comparison where a rival is cited, a category question where you're missing, a prompt where AI frames a competitor more favorably. The fix is the answer engine optimization playbook aimed at that gap: publish the clear, citable answer the competitor currently owns, build the authority that earns the citation, and correct any inaccuracy that's costing you.
This is where a competitor analysis connects to your whole content cluster. The gaps tell you which pages to write or strengthen, and the right tools help you find and prioritize them. For options, see our roundup of the best AI visibility tools. Elmo is an open-source tool that benchmarks your share of voice and citations against named competitors across the major AI engines, so the gaps surface automatically and you can track whether your fixes close them. For the underlying method, see how to track your brand in AI search.