Competitor Analysis in AI Search: A Practical Framework

Understanding how your competitors perform in AI search is no longer optional. As AI-powered search engines become a primary discovery channel for buyers and researchers, the brands that systematically monitor and benchmark their AI visibility against competitors will have a decisive strategic advantage. This article provides a practical, step-by-step framework for conducting competitor analysis across AI search platforms.

Updated October 2026 with benchmarks from Semrush's AI Visibility Index and topic-authority study, Kevin Indig's Growth Memo citation analyses, Profound's shopper and citation-decay research and the Pew and SparkToro click studies. Sources are listed at the end.

Why Competitor Analysis in AI Search Is Different

Traditional competitor analysis in SEO is well-established: compare keyword rankings, backlink profiles, domain authority scores, and content coverage. These metrics are quantifiable, widely understood, and supported by mature tooling.

AI search competitor analysis requires a fundamentally different approach because the outputs are different. Instead of a ranked list of links, AI search produces synthesized text responses that may cite multiple sources, describe brands in qualitative terms, and vary based on query phrasing, user context, and platform. The question is not "who ranks higher?" but rather "who gets mentioned, cited, and recommended more frequently, and in what context?"

The stakes are now measurable. In Profound's shopper study with Clickstream Solutions, 56 participants ran 221 product-comparison tasks in ChatGPT. Brands they chose had averaged 24 percent share of voice in ChatGPT's answers; brands they passed over had 11 percent. 92.8 percent of tasks ended without a meaningful click to the open web. Meanwhile, Pew Research measured Google users clicking a result 8 percent of the time when an AI summary appeared versus 15 percent without one, and SparkToro's analysis of Similarweb data put US zero-click searches at 68 percent in early 2026. The comparison happens inside the answer, and the answer is where your competitor is beating you or not.

This shift demands new metrics, new methodologies, and a willingness to work with less precise data than traditional SEO analytics provide.

The Framework: Five Steps to AI Search Competitor Intelligence

Step 1: Define Your Competitive Landscape

Start by identifying the competitors you need to track. In AI search, your competitive set may include three types of entities:

Product competitors are companies selling similar solutions to the same buyers. These are the brands most likely to appear alongside you in product comparison, recommendation, and evaluation queries.

Content competitors are publishers, media outlets, analysts, and influencers who produce content about your category. While they may not sell competing products, they compete for the same citation opportunities and can shape how AI engines describe your market. They are a larger share of the field than most teams expect: in Ahrefs' analysis of ChatGPT's top 1,000 cited pages, about two thirds were sources a brand cannot influence directly, led by Wikipedia (29.7 percent) and brand homepages (23.8 percent).

Emerging disruptors are newer companies that may not appear in traditional competitive analyses but are gaining traction in AI search citations. AI engines sometimes surface newer players that produce exceptional content, even if their overall market share is small. The room exists: Semrush and Growth Memo's study of 1,094 categories in ChatGPT found 53.7 percent of categories unsettled, with no brand appearing in even three of five prompts.

Identify five to eight competitors across these categories. You can always expand later, but starting with a focused set allows for deeper analysis.

Step 2: Build a Strategic Query Matrix

Your competitor analysis is only as good as the queries you analyze. Build a matrix of queries organized along two dimensions: topic and intent.

Topic categories should cover the full scope of your market. The Semrush study defines a topic as a cluster of five prompt types, and we recommend the same minimum:

• Category definition queries: "What is [your product category]?"
• Best-of queries: "Best [product category] for [use case]"
• Comparison queries: "[Your brand] vs [Competitor]" and "[Competitor A] vs [Competitor B]"
• Alternatives queries: "Alternatives to [Competitor]"
• Problem-solution queries: "How to solve [problem your product addresses]"
• Buying queries: "Is [category tool] worth it?", "[category] pricing"
• Feature-specific queries: "Best [product category] with [specific feature]"

Intent types capture the different stages of the buyer journey:

• Awareness: Broad, educational queries about the category or problem space
• Consideration: Comparative queries evaluating specific solutions
• Decision: Specific queries about pricing, implementation, or getting started

Two findings should shape the matrix. First, engines rarely search for what the user typed: Profound observed over 10,000 prompts that engines "fan out" to related sub-queries, and the sub-queries differ by engine. Ahrefs attributes the collapse of top-ten overlap in AI Overviews (76 percent in July 2025 to 38 percent in March 2026) to the same mechanism. Second, answers vary by who is asking. Profound's September 2026 persona study found responses differ by income, age, gender and occupation, so if your buyers are a distinct audience, phrase some prompts the way they would.

A well-constructed matrix of 30 to 50 queries across these dimensions provides comprehensive competitive intelligence.

Step 3: Collect and Analyze AI Responses

Run each query across the major AI search platforms: ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Gemini and any other platforms relevant to your audience. Treat each one as its own market. Kevin Indig's Consensus Gap analysis of 3.7 million citations found that 91.07 percent of cited URLs appeared in only one of ChatGPT, Perplexity and Google AI Overviews, and 2.37 percent in all three. Even Google's own surfaces diverge: a summary of Ahrefs' 2026 AEO report has AI Mode and AI Overviews reaching the same conclusion 86 percent of the time while sharing only 13.7 percent of their citations. The engines also differ in how much they cite at all: Semrush's 2026 AI Visibility Index puts ChatGPT at about 15 sources per response and Gemini at about 3.

For each response, document:

Mention presence. Is the competitor mentioned by name in the response? A brand that is mentioned even without a citation link still gains visibility. The Semrush study found that only 21 percent of the most-cited domains in a category were also the most-mentioned brand, so track both.

Citation presence. Is the competitor's content directly cited with a link? Linked citations are the strongest form of AI search visibility because they provide both brand exposure and a potential traffic source.

Position and prominence. Where in the response does the competitor appear? Being mentioned first or most extensively in a response carries more weight than a brief mention at the end. Indig reports that close to 75 percent of consumers pick the first result in an AI shortlist, but will choose a trusted brand that appears anywhere on it.

Sentiment and framing. How is the competitor described? Is the language positive ("industry-leading," "comprehensive"), neutral ("one of several options"), or negative ("limited in scope," "expensive compared to alternatives")? The qualitative framing shapes user perception even more than mere mention. Expect a lot of it: Profound's Parrot Problem study found roughly 47 percent of response content was unsolicited editorial material such as comparisons, rankings and caveats.

Context and association. What attributes, features, or qualities are associated with the competitor? If a competitor is consistently described as "best for enterprise" or "most affordable option," these associations reveal how AI engines have categorized them.

Step 4: Build Your Competitive Scorecard

Transform your raw data into a structured competitive scorecard that enables comparison and trend tracking.

Citation frequency score. For each competitor, calculate the percentage of your query set where they receive at least one citation across AI platforms. This is the most fundamental metric: how often does each competitor appear?

Mention frequency and recommendation share. Separately, calculate the share of answers in which each brand is named, and the share in which it is recommended first. The Semrush study's thresholds are a useful benchmark: a brand named in at least four of five prompts in a topic with a five-point lead "owns" it; named in three, it is an emerging leader.

Platform distribution. Break down citation frequency by platform. A competitor might dominate ChatGPT citations but be absent from Perplexity, or vice versa. Indig proposes three metrics that make this explicit: presence (share of prompts where a brand appears in any engine), portability (share of citations that appear across all engines) and concentration (share of citations coming from one engine). A blended "AI visibility score" hides exactly the differences you need.

Sentiment score. Classify each mention as positive, neutral, or negative, and calculate an aggregate sentiment score. A competitor with high citation frequency but negative sentiment is in a different strategic position than one with moderate frequency and uniformly positive sentiment.

Category leadership indicators. For key queries like "best [category]" or "leading [category] platform," track which brand appears first or most prominently. These high-value queries are the AI search equivalent of page-one rankings for head terms.

Content source analysis. When competitors are cited, note which specific pages are being referenced. This reveals their most authoritative content and highlights the content assets you need to match or exceed.

Step 5: Identify Strategic Opportunities

With your scorecard complete, look for actionable patterns.

Underserved queries. Queries where no competitor has strong citation presence represent first-mover opportunities. Creating authoritative content for these queries can establish your brand before competitors respond. In the Semrush data, high-demand categories were the least owned (11.3 percent), so the biggest opportunities are not in niches.

Narrow leads. Where a competitor leads, check the margin. Categories whose leader flipped month to month had a median lead of 1.3 points; where the leader held, 2.9 points. A lead under two points is contestable within a quarter.

Weak competitor positions. Queries where a competitor is mentioned but with negative sentiment or weak framing are opportunities to position your brand as the superior alternative.

Content gaps. If a competitor earns citations from a specific type of content, such as benchmark reports or technical guides, that you lack, that gap directly informs your content strategy. Profound's citation half-life study shows which types last: comparison, review, guide and product pages hold their citation share 1.4 to 1.6 times as often as news and announcements.

Platform-specific opportunities. If competitors are strong on ChatGPT but weak on Perplexity, targeting Perplexity-friendly content could help you establish dominance on a platform where your competitors have not yet invested. Semrush's most-cited domains study tells you where to earn mentions: Reddit and Wikipedia for ChatGPT, LinkedIn and YouTube for Google AI Mode, Reddit and LinkedIn for Perplexity.

Messaging differentiation. If all competitors are described with similar attributes, there is an opportunity to differentiate through unique positioning that stands out in AI responses.

Maintaining Competitive Intelligence Over Time

A one-time competitor analysis provides a valuable snapshot, but the real power comes from ongoing monitoring. AI responses change as models are updated, new content is published, and competitive dynamics shift. They also change on their own: our paper Don't Measure Once shows that the same prompt returns different sources and brands from run to run and day to day, so a single measurement is a sample, not a result. Profound's half-life study found the median cited page loses half its citation share within 11 days of its peak, and Semrush documented Reddit's share of ChatGPT responses falling from about 60 percent to about 10 percent in six weeks.

Establish a regular cadence for your competitive analysis:

• Daily: Automated runs of your full prompt set on every engine. Profound's analysis of its own sampling concludes that once a day is sufficient to estimate share of voice reliably; less often is not.
• Weekly: Review your highest-priority queries across platforms to detect sudden changes
• Monthly: Update your competitive scorecard and compare month over month. In the Semrush data, clear owners kept first place in 90.4 percent of monthly comparisons; in unsettled categories, the top brand changed in 1,950 of 5,470.
• Quarterly: Conduct a deep-dive analysis, revisiting your competitive set, query matrix, and strategic priorities

Track changes over time to identify trends. Is a particular competitor gaining or losing citation share? Are your own optimization efforts translating into improved visibility? Are new competitors emerging that you need to monitor?

Tools and Automation

Manual competitor analysis is feasible for small-scale assessments but becomes impractical as your query set and competitive set grow, and daily repeated runs across five engines are not a manual job at all. GEO platforms like Aurora Intelligence automate the process of running queries across AI platforms, extracting mention and citation data, and tracking changes over time.

Whether you use automated tools or manual methods, the framework remains the same: define competitors, build queries, collect responses, score performance, and identify opportunities. The methodology is what matters. The tooling simply determines how efficiently and frequently you can execute it.

Turning Intelligence into Action

Competitor analysis is only valuable if it drives action. For each strategic opportunity you identify, define a specific initiative:

• If you identify a content gap, add the topic to your content calendar with a clear brief
• If you find a weak competitor position, create content that positions your brand as the stronger alternative on that specific dimension
• If you discover an underserved query, prioritize creating authoritative content before competitors recognize the opportunity

Review your competitive scorecard in your regular marketing planning sessions and use it to inform content priorities, messaging strategy, and resource allocation. For the diagnostic side, see Why your competitor ranks higher in AI search; for turning the data into strategy, see How to use competitive intelligence from AI search. Over time, systematic competitive intelligence in AI search becomes a core capability that drives compounding advantages in visibility, brand perception, and market share.

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Sources

1. Kevin Indig, Eric Van Buskirk and Jasman Singh, Profound with Clickstream Solutions, "The shortlist is the new shelf", July 14, 2026. https://www.tryprofound.com/blog/the-shortlist-is-the-new-shelf
2. Athena Chapekis and Anna Lieb, Pew Research Center, "Google users are less likely to click on links when an AI summary appears in the results", July 22, 2025. https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/
3. SparkToro and Similarweb, 2026 zero-click study (68.01 percent of US Google searches, January to April 2026), June 2026, as summarised by Digital Applied. https://www.digitalapplied.com/blog/sparktoro-zero-click-study-2026-68-percent-seo-analysis
4. Margarita Loktionova with Kevin Indig, Semrush, "AI visibility is a topic-level game: A study of 50,000 brands in ChatGPT", July 20, 2026. https://www.semrush.com/blog/chatgpt-topic-authority-study/
5. Kevin Indig, Growth Memo, "The Consensus Gap", May 11, 2026. https://www.growth-memo.com/p/the-consensus-gap
6. Kevin Indig, Growth Memo, "AI Halftime Report: H1 2026", July 27, 2026. https://www.growth-memo.com/p/ai-halftime-report-h1-2026
7. Louise Linehan, Ahrefs, "Update: 38% of AI Overview citations pull from the top 10", March 2, 2026. https://ahrefs.com/blog/ai-overview-citations-top-10/
8. Catalyst, summary of Ahrefs' 2026 AEO report, June 2026. https://www.gotcatalyst.com/resources/articles/ahrefs-2026-aeo-report-ai-citations
9. Semrush, "Semrush Releases Expanded 2026 AI Visibility Index, Analyzing 126 Million AI Search Prompts", June 26, 2026. https://www.semrush.com/news/463141-semrush-releases-expanded-2026-ai-visibility-index-analyzing-126-million-ai-search-prompts/
10. Luke Harsel, Semrush, "The Most-Cited Domains in AI: A 3-Month Study", November 10, 2025. https://www.semrush.com/blog/most-cited-domains-ai/
11. Profound Research, "What AI engines actually search for" (April 30, 2026), "Do Answer Engines customize responses to different personas?" (September 9, 2026) and "Is once a day enough?" (July 8, 2026). https://www.tryprofound.com/blog/research
12. Jasman Singh, Matthew Huo and Ryan Van, Profound, "The Parrot Problem", June 25, 2026. https://www.tryprofound.com/blog/the-parrot-problem
13. Davis McCain, Profound, "The half-life of an AI citation is 11 days", September 30, 2026. https://www.tryprofound.com/blog/the-half-life-of-an-ai-citation-is-11-days
14. Julius Schulte, Malte Bleeker and Philipp Kaufmann, "Don't Measure Once: Measuring Visibility in AI Search (GEO)", arXiv:2604.07585, April 2026. https://arxiv.org/abs/2604.07585