The competitor analysis most marketing teams run was designed for a different era of search. Keyword rankings, backlink counts, page speed scores, content gap analysis. These still matter, but they no longer tell you the full story of how your brand competes in the discovery process. AI search products now mediate a meaningful share of category research, comparison queries, and product decisions. If your competitor analysis ignores how AI products describe competitors, you are missing the channel where buyers form their initial impressions.
This piece is for the marketing leader, AEO consultant, or content team trying to understand competitive AI visibility and identify the moves that would shift it. Specific methodology, specific tools, specific outputs that translate into strategy.
What you are actually measuring
AEO competitor analysis measures three things that traditional SEO does not capture cleanly.
The first is citation share. When buyers ask AI products category-level questions, how often is each competitor cited and where in the answer? A competitor cited in the first paragraph of an AI answer captures more attention than one mentioned in passing in paragraph four. A competitor cited as a primary recommendation captures more than one cited as an alternative.
The second is description quality. How does the AI describe each competitor? The descriptions form the audience’s first impression. A competitor described as “an established platform with strong support” lands differently than one described as “a smaller alternative with mixed reviews.” The descriptions come from the AI’s synthesis of available sources, and they are surprisingly stable across queries once you have observed the pattern.
The third is source ecosystem. Which sources does the AI cite when describing each competitor? For one competitor, the AI might draw heavily from G2 reviews and a Forbes article. For another, it might draw from Reddit threads and a recent product launch press release. The source ecosystem reveals where each competitor has built earned media and citable infrastructure, which informs where your brand needs to compete.
These three dimensions together tell you the actual competitive picture in AI search. Traditional SEO analysis gives you adjacent data (the same competitors appear in both contexts) but misses the synthesis layer that AI products perform.
Pick the right competitors to analyze
The competitor selection step is where most analyses go wrong. Two failure modes are common.
The first is analyzing the wrong competitors. The marketing team’s preferred list of competitors often does not match the list the AI products treat as competitors. The AI’s clustering is built from third-party sources (review sites, comparison articles, listicles), and it often groups your brand with companies you would not consider direct competitors and excludes companies you compete with constantly. The AI’s clustering matters because it determines who appears in answers about your category.
The second is analyzing too many competitors. Comprehensive analysis across 10 to 15 competitors produces a report that takes weeks to create and is too long for anyone to act on. Strategic analysis of 3 to 5 direct competitors plus 2 adjacent players produces a tighter, more actionable report.
Run these queries first to identify the right list:
“What are the best [your category] tools” → Note which competitors appear. “[Your brand] alternatives” → Note which competitors the AI suggests. “[Your brand] vs [competitor]” → Note how the AI frames the comparisons. “What is [your category]” → Note which competitors get cited as examples.
The competitors that appear consistently across these queries are your real competitive set in AI search. Pick the 3 to 5 with the strongest presence as your primary analysis targets.
Build the query inventory
The analysis depends on running a consistent set of queries across competitors. Build the query inventory carefully.
Include four query types:
Category queries. “What is the best [category] for [use case].” “Top [category] tools in 2026.” “How do I choose a [category] solution.” These queries surface the AI’s category-level competitive ranking.
Brand queries. “Tell me about [competitor].” “Is [competitor] a good company.” “What does [competitor] do.” These queries reveal how the AI describes each competitor and which sources it uses.
Comparison queries. “[Competitor A] vs [Competitor B].” “[Your brand] vs [competitor].” “Should I use [Competitor A] or [Competitor B] for [use case].” These queries show the AI’s framing of head-to-head decisions.
Specific feature queries. “Does [competitor] integrate with [tool].” “Does [competitor] offer [feature].” “How does [competitor] handle [specific use case].” These queries test how deep the AI’s knowledge goes for each competitor.
Build 20 to 30 queries across these four types. Document them in a spreadsheet so you can rerun them consistently each quarter.
The actual analysis workflow
Run each query in five AI products: ChatGPT, Perplexity, Google AI Overview (search the query in Google with AI Overview enabled), Claude, and Gemini. Each product has different retrieval behavior, so cross-product analysis is required.
For each query in each product, document:
The full answer. Copy the response into your analysis spreadsheet or document.
Which competitors are mentioned and where. First paragraph, middle, end, not at all. The position matters because attention drops fast in AI answers.
How each competitor is described. Pull the exact phrases the AI uses. The phrasing tends to be stable across queries, so a few queries reveal the AI’s persistent framing of each competitor.
Which sources are cited. AI products vary in how transparently they cite sources. Perplexity cites explicitly. ChatGPT cites less consistently but the underlying sources can usually be inferred. Note specific URLs and source types.
The tone and confidence of the AI’s answer. Hedged answers (“[Competitor X] may be a good option for some users”) signal that the AI does not have strong information. Confident answers (“[Competitor X] is widely recognized as the leader for [use case]”) signal that the AI has high-quality consistent sources backing its description.
Run all queries in all products. The total volume is 100 to 150 queries depending on inventory size. The work takes a full day done carefully. Do not split it across days because the AI products’ answers change frequently and you want a consistent snapshot.
Reading the source ecosystem
After collecting the data, the most valuable analysis is the source ecosystem map. For each competitor, list the sources the AI cited or implied across all queries. The map reveals where each competitor has built citable infrastructure.
Common source patterns:
Strong G2 and Capterra presence. Indicates the competitor invested in review platform optimization. Their G2 profile is fully filled, they have hundreds of recent reviews, and the reviews include the kinds of detailed feedback that AI products extract. This pattern is common among B2B SaaS competitors.
Strong owned-media presence. The competitor’s blog, documentation, and case studies show up frequently in citations. Indicates they invested in content depth and structured publishing. This pattern is common among technical brands and developer tools.
Strong earned-media presence. Major outlet coverage (Forbes, Inc, TechCrunch, Wired) shows up frequently. Indicates they have invested in PR and have legitimate news to pitch. This pattern is common among well-funded startups and category leaders.
Strong Reddit and forum presence. r/[topic] threads and forum discussions show up frequently. Indicates the competitor has organic community traction. This pattern can be a strength (engaged community) or a weakness (community criticism dominates).
Strong Wikipedia and Wikidata presence. The competitor has a clean Wikipedia entry and proper Wikidata entity. Indicates strategic AEO investment. This pattern correlates with consistent AI visibility.
Mixed or weak sources. The AI cites a grab bag of low-quality sources or has to hedge because no clear sources exist. Indicates the competitor has weak third-party citable infrastructure. This is an opportunity if your brand can build stronger sources in the same space.
The source ecosystem map tells you where each competitor invested, which reveals where they are vulnerable and where they are strong.
Identifying gaps and opportunities
The opportunity analysis is the synthesis output that informs strategy.
Compare your brand’s source ecosystem against each competitor’s. Where do they have citable infrastructure that you lack? Where do you have infrastructure that they lack? The asymmetries are the opportunities.
Common opportunity patterns:
Competitor has strong G2/Capterra, you do not. Investment in review platform optimization is a high-impact move. The work is concrete (encourage review velocity, fill out the profile, respond to reviews) and the AI search benefit is direct.
Competitor has weak owned content on a specific topic. If the AI’s answers about a comparison query rely on third-party sources because the competitor has no canonical comparison page, your brand can publish a fair comparison that becomes the cited source for that query.
Competitor has strong earned media on category-level positioning. Their CEO has been quoted in trade press as a category authority. Your CEO has not. Investment in earned-media positioning would shift the AI’s framing of category leadership over six to twelve months.
Competitor has mixed reviews surfacing in AI answers. The AI is hedging when describing them because the source signals are mixed. If your brand has cleaner signals, AI answers that compare you to this competitor will favor you when the comparison is run honestly.
Each opportunity has its own cost and timeline. The strategy output is prioritizing the opportunities by impact and feasibility, then sequencing the work over the next two to four quarters.
The competitor that is invisible
Sometimes the analysis reveals that a competitor is significantly stronger in actual market share but weaker in AI visibility. This is increasingly common because many established companies have not invested in AEO infrastructure even though they have substantial customer bases.
Invisible competitors create opportunity for newer brands with strong AEO discipline. The newer brand shows up in answers more often than the legacy competitor, which shifts the buyer’s mental shortlist. The buyer who does not encounter the legacy competitor in their AI research often does not include them in their evaluation.
The tactical move when an invisible competitor exists is investing harder in AEO to widen the gap. Their slow response gives you a window. Most legacy companies will eventually catch up, but the 18-month window between your investment and their response is when newer brands consolidate audience capture.
The competitor that is dominant
The opposite case is the dominant competitor: the brand that owns the AI answers across nearly every query. The dominance usually reflects years of accumulated infrastructure (Wikipedia, Wikidata, G2, owned content, earned media all working together).
Trying to displace dominance directly is rarely worth it. The path that works is competing on specific use cases or audience segments where the dominant competitor is generic and your brand can be specific. The AI describes them as a generalist solution. You position as a specialist for a specific segment. Audiences researching that specific segment find you in the answers because you are explicitly the segment-focused option.
Specialization is the answer to dominance. Document the segments where the dominant competitor is generic. Build content, reviews, and earned media around your strength in those segments. Over time, the AI’s answers for those specific queries will surface your brand more prominently than the generic dominant competitor.
Reporting the analysis
The output document should be 5 to 10 pages, not 50. The marketing team will reference it during planning, not read it linearly.
The structure that works:
Page 1: Executive summary with the three to five most important findings and recommendations.
Page 2: Competitor visibility scorecard. A table comparing each competitor across citation share, description quality, and source ecosystem strength.
Pages 3 to 7: One page per competitor with their AI presence summarized: how they are described, where they show up, what sources cite them, what their strengths and weaknesses look like.
Page 8: Opportunity matrix. The asymmetries identified, prioritized by impact and feasibility.
Pages 9 to 10: Recommended actions for the next two quarters with owners and timelines.
The report should be visually scannable. Tables, bullets in the data sections, prose in the analysis sections. The team should be able to find any specific finding within 60 seconds.
Repeat the analysis quarterly
The single biggest mistake teams make with AEO competitor analysis is running it once and treating it as a static deliverable. The AI products’ answers change. Competitors invest in their own AEO. New competitors emerge. Source ecosystems shift.
Quarterly analysis with the same query inventory shows the trends. Which competitors are gaining citation share. Which are losing it. Which sources are becoming more or less prominent. The trends are more strategically valuable than any single snapshot.
Monthly spot-checks on 10 to 20 priority queries catch large changes early. If a major competitor suddenly disappears from a query they used to dominate, that change is worth investigating immediately. Maybe they got delisted from a key source. Maybe an AI product changed its retrieval pattern. Maybe their press strategy faltered. Whatever the cause, the early signal is valuable.
The teams that compound advantage are running this discipline continuously. The teams that run a one-off analysis and put it in a folder do not develop the muscle and lose ground to teams that do.