Introduction — Your competitor can now overtake you outside the Google results page

In the world of traditional search engine optimization, we used to define competitors according to relatively clear rules: run keyword research, check which websites hold the first three positions in Google results, then analyze their backlink profile. But the generative AI search environment — GEO — has fundamentally rewritten this familiar system.

Your potential customers and B2B partners now ask AI systems natural-language questions such as: “Which is the best cloud-based invoicing tool for a growing SME?” or “Which Budapest agency would you recommend for building complex AI visibility?”

AI does not return a numbered list of ten blue links. Instead, it synthesizes, argues and often names or directly recommends only one or two brands. That is why a comprehensive AI marketing and search engine optimization strategy must now pay attention not only to Google rankings, but also to brand recommendations emerging inside generative AI systems.

What is AI search competitor analysis?

The most important insight: a classic SEO competitor and an AI competitor are not the same category. While your SEO competitor tries to rank for the same keywords, your AI competitor is the company that models actively recommend for the same decision question or buyer intent.

To analyze the modern market, you need to separate the following three partially overlapping competitor groups:

🌐 3. AI Recommendation Competitors

These are the companies that ChatGPT, Gemini, Perplexity or Google AI Overviews consistently name first when a potential customer asks AI for advice or an expert recommendation. This is the group you must analyze most closely in 2026.

This is why a modern digital marketing and AI SEO strategy increasingly examines the full online entity of the brand, not merely the ranking of a few keywords.

Why is an AI recommendation more important than a simple search position?

In classic search engines, users receive a list and review the results with their own judgment, selecting among ads, maps and organic links. In generative search — GEO and AEO — AI performs the filtering and decision preparation itself.

When someone asks ChatGPT a question, the answer may start like this: “There are three Budapest providers in this field that are especially worth considering based on experience and customer analysis…”. If your company does not appear among those three named brands, the potential customer may never reach your website — your business becomes practically invisible inside the decision process.

In the new search environment, AI visibility is no longer merely a ranking problem; it is a direct recommendation and credibility problem.

Step one: map the questions your customers actually ask

AI competitor analysis cannot start from a traditional keyword list. You need to build a systematic buyer question matrix — or prompt matrix that reflects the queries customers use across different decision stages:

• Category-based prompts: “Which is the best cloud-based ERP system for SMEs?”
• Local-focus prompts: “Which recognized AI visibility agencies operate in Budapest?”
• Comparison prompts: “Compare the case studies and results of [Your Company] and [Your Competitor].”
• Problem-based prompts: “Who can help my brand become included among Perplexity’s citation sources?”
• Price-sensitive prompts: “How much does a comprehensive AI SEO and GEO project cost in 2026?”

Our recommendation: create a test list of 20–50 high-business-intent prompts that cover your industry’s most valuable sales channels.

Step two: run the same questions across multiple AI systems

It would be a serious methodological mistake to draw global conclusions from a single ChatGPT answer. Test prompts must be run systematically and repeatedly across generative systems with different architectures:

Step three: look for recurring winners

If AI mentions your competitor once, it may be statistical noise — hallucination or temporary source usage. The real threat is represented by competitors that appear consistently, across multiple platforms and across different prompt variations among the first recommendations.

Determine competitors’ AI Share of Recommendation using the following formula:

AI Share of Recommendation = Number of Brand Recommendations / Total Tested Recommendation Opportunities

If “Competitor A” appears in recommendations 35 times out of 50 tested prompts while your brand appears only 5 times, then that competitor’s online entity is your primary AI visibility benchmark — not necessarily the company you previously considered your main competitor based on traditional SEO rankings.

Step four: find out why AI trusts your competitor

The most important question in AI competitor analysis is not who the machine recommends, but why semantic trust has developed around your competitor.

You must examine the winning competitor’s full digital footprint based on the following dimensions:

• On-site factors: Do they have deep expert topic clusters? Do they use structured FAQ blocks and precise JSON-LD Schema markup? Do they have credible author pages on the site?
• Off-site factors — external proof: Which professional media and PR mentions consistently describe the competitor’s activity? Are they referenced in industry databases and lists?
• Third-party validation: If ten different trustworthy and independent external domains — not the company’s own site — say your competitor is an expert on a topic, AI will treat that as proven evidence.

In the Roth Creative approach, AI visibility is therefore not merely a content-production question: the website, the brand entity, external mentions and credible professional proof all matter together.

Step five: examine which sources AI uses

The most valuable data in building AI visibility comes from identifying the external sources and domains from which AI models extract information when composing answers. We call these nodes AI Citation Hubs.

With a detailed source map, you can see exactly where ChatGPT or Perplexity collects its data: professional media outlets, Reddit forums, expert blogs, Wikipedia databases, or directly from a competitor’s own website. Your strategic goal is to build your brand presence across these key information nodes.

Step six: compare your brand’s and the competitor’s “evidence”

During the analysis, create an Evidence Gap Analysis table that separates assumptions from real competitive disadvantage through quantifiable data:

Step seven: identify the AI visibility gap

At the end of the analysis, you receive a clear and accountable list of gaps:

• Content Gap: The competitor answers significantly more expert questions in detail and with precision on their website.
• Entity Gap: Because Schema and Wikidata signals are missing, AI does not precisely understand our brand classification.
• Authority Gap: The competitor has far more independent external mentions and professional links.
• Citation Gap: The competitor dominates the AI Citation Hubs from which AI draws answers.

What not to do: do not simply copy your competitor’s content

The worst tactic after the analysis is to ask AI to copy and rewrite your competitor’s articles. This only multiplies homogeneous mass content, which Google Helpful Content systems and AI RAG filters quickly penalize.

The goal is to understand the competitor’s information and credibility signals, then build a much stronger, more credible and more unique proof system than theirs — using your own research, unique client data, original expert commentary and video materials.

How often should AI competitor analysis be performed?

The world of AI answers is highly dynamic and variable. Model updates, competitors’ GEO settings and the appearance of new online sources continuously reshape recommendation patterns. That is why AI competitor analysis is not a one-off project, but a continuous strategic monitoring system.

Our recommended model: begin with a deep audit of 30–100 strategic prompts; monitor the most important 15–20 business prompts monthly; and perform a comprehensive analysis of source and citation nodes quarterly.

The new SEO competition is really a Share of Recommendation competition

In 2026, organic search marketing is undergoing a fundamental paradigm shift. The main question in traditional SEO was: “Where does our website rank?” In AI-driven and generative search, the new key business question is:

Are we among the top 3 brands filtered and recommended by AI?

This requires measuring and continuously optimizing Share of Voice, Share of Citation and Share of Recommendation together. The winners of the future will be the companies that occupy the largest share of the generative recommendation space.

Summary — first find out who is winning instead of you

Before investing significant budgets into unchecked AI content production or link building, you need precise, quantified answers to three fundamental questions:

• What specific prompts and questions do your potential customers ask AI?
• Whom do generative systems recommend for these decision questions instead of your company?
• Which technical, content-related and credibility proofs cause AI to choose those competitors?

Once you clearly understand these relationships, building AI visibility is no longer a vague marketing concept based on guesswork, but a precisely plannable, measurable and improvable business strategy.