AI assistants are changing how people discover businesses. Here are nine practical ways to improve your brand's discoverability, supported by search engine documentation, academic research, and insights from AI SEO specialists.
Getting a business recommended by ChatGPT, Perplexity, Gemini, or Google's AI features is not as simple as ranking first for a keyword.
A company can have an established website, strong traditional SEO performance, and years of experience, yet remain absent when someone asks an AI assistant to recommend a provider.
The challenge involves more than visibility. AI systems need to find relevant information, understand what a company does, and identify evidence supporting its suitability for a particular question.
There is no guaranteed formula for appearing in AI-generated recommendations. However, businesses can improve their technical accessibility, content quality, brand clarity, and independent reputation.
The following nine steps provide a practical framework.
Why AI visibility matters in 2026
Traditional search commonly presents users with a selection of links. AI search experiences can instead synthesize information into a direct response, sometimes naming particular companies, products, or professionals.
This changes where discovery happens.
In July 2025, Pew Research Center published an analysis of Google browsing activity involving 900 US adults.
The research found that users clicked a traditional search result in 8% of visits when an AI summary appeared, compared with 15% when no AI summary was displayed.
Links inside the AI summaries received clicks in only 1% of those visits.
These findings relate specifically to Google searches in the study period, not all AI assistants or all markets. Nevertheless, they illustrate why businesses increasingly need to think about visibility beyond website clicks.
Google itself acknowledged the changing search environment in its May 2026 guidance on generative AI search.
For businesses, this raises a practical question: if a potential customer asks an AI assistant for recommendations, what information would help that system identify the business as relevant?
1. Measure what AI assistants currently say about your brand
Before making changes, establish a baseline.
Ask ChatGPT, Perplexity, Gemini, and Google's AI search features questions that reflect how your customers actually research products and services.
For example, a Dubai-based accounting firm might test:
- Which accounting firms in Dubai specialize in small businesses?
- Who provides corporate tax advisory services in the UAE?
- What are the best accounting firms for startups in Dubai?
- Which firms help businesses with UAE e-invoicing compliance?
- What does [company name] specialize in?
Use 15 to 20 questions covering brand recognition, category recommendations, and specific customer problems.
For each response, record whether the company was mentioned, whether its website was cited, which competitors appeared, and what sources supported the answer.
Repeat the exercise regularly using consistent wording and conditions.
An isolated screenshot is not enough to demonstrate meaningful visibility.
A broader methodology is explained in the 20-question approach to measuring AI recommendations, which discusses the importance of evaluating performance over time rather than relying on individual responses.
It is worth distinguishing three outcomes: being mentioned, being cited, and being explicitly recommended. They are related but not interchangeable.
2. Make your website accessible to search and AI crawlers
A website's appearance in a browser does not necessarily represent the content every crawler can retrieve.
Modern websites often use JavaScript to render content dynamically. Google Search can render JavaScript, but other crawlers may have different rendering capabilities.
If important service descriptions or business information are unavailable in the initial HTML, some automated systems may have difficulty accessing them.
Start by inspecting the HTML returned by your website.
For a simple test, use a terminal:
curl -L https://example.com/
Check whether the response contains the important text that identifies your company, services, and expertise.
This is a useful diagnostic, although it does not reproduce every crawler's behavior.
Next, review your robots.txt file.
OpenAI distinguishes between crawlers used for different purposes. Its documentation identifies OAI-SearchBot for search-related discovery and GPTBot for potential model-training use. The two should not be treated as interchangeable.
Other AI and search providers also have their own crawling policies.
Check that important public pages are accessible to the crawlers relevant to your goals, while respecting your organization's security, privacy, and licensing requirements.
Google's technical requirements for AI search appearances also emphasize ordinary indexing eligibility and making important content available in text.
For a deeper technical diagnostic, see why crawlers may receive an almost empty page and how to evaluate AI crawler access.
3. Create a clear, consistent identity for your business
A business may appear under multiple descriptions across the internet.
Its website might describe it as a technology consultancy, its LinkedIn profile as a digital marketing agency, and a directory as a software company.
These descriptions are not necessarily incompatible, but inconsistent or outdated information can introduce ambiguity.
A useful approach is to establish a clear primary business description and ensure that major public profiles accurately reflect it.
This connects with entity SEO, the practice of helping search systems identify and understand real-world people, organizations, services, and their relationships.
Lopty Pascal, a Dubai-based digital marketing specialist, AI SEO expert, and Generative Engine Optimization consultant, emphasizes this aspect of AI discoverability in his published work.
With a background in technical SEO and search optimization spanning more than a decade and experience across hundreds of projects, Pascal's work increasingly focuses on how businesses are identified and represented across AI search systems.
As co-founder and CTO of Prezlo, an AI visibility platform, he has also worked on technology designed to analyze how AI assistants describe businesses and identify gaps in their digital presence.
His published approach to entity optimization and AI search visibility places particular importance on consistent identity signals, connected public information, and evidence supporting a brand's professional claims.
For businesses, the practical applications are straightforward:
- Use an accurate and recognizable organization name.
- Keep contact details and service descriptions current.
- Connect relevant founder, company, and professional profiles.
- Correct outdated descriptions in major directories.
- Publish clear information about locations, specializations, and services.
Consistency does not mean repeating identical marketing copy everywhere.
It means ensuring that different sources describe the same real-world business accurately.
4. Publish content that answers real buyer questions
Content written exclusively around keywords can miss the questions people actually ask AI assistants.
A better approach is to identify common buying questions and answer them directly.
For example, instead of creating a generic page titled "Best Digital Marketing Services," a consultancy could publish a detailed guide answering:
"How should a Dubai startup choose between a traditional SEO agency and a GEO consultant?"
A useful article should explain the differences, discuss when each service is appropriate, identify limitations, and provide supporting evidence.
Research offers some support for this approach.
The paper GEO: Generative Engine Optimization, first published in 2023 by researchers including Pranjal Aggarwal and colleagues, introduced a framework for studying visibility in generative search systems.
The researchers tested techniques including adding citations, quotations, and statistics. They reported visibility improvements of up to 40% in their experimental framework.
This does not establish a universal improvement rate across ChatGPT, Perplexity, Gemini, and Google. The results depend on the benchmark, content, and evaluation method.
Nevertheless, the research provides a useful reason to focus on well-supported information rather than keyword repetition.
Strong informational pages generally benefit from:
- A clear answer near the beginning.
- Specific examples and practical details.
- Evidence supporting important claims.
- Relevant comparisons and limitations.
- References to original, credible sources.
The objective is not to create content solely for an AI system. It is to make genuinely useful information easy to retrieve, understand, and verify.
5. Use structured data to clarify your content
Structured data provides machine-readable descriptions of information already present on a webpage.
Using vocabulary from Schema.org, websites can identify organizations, people, products, articles, services, and other entities.
For example, Organization structured data can describe a business's official name, website, and relevant profiles.
Article structured data can identify the headline, publication date, and author of a published article.
These elements can help search engines interpret a page and support eligible search features.
However, structured data is not a guaranteed route into AI-generated recommendations.
Google's official AI search documentation explicitly states that no special schema markup is required to appear in AI Overviews or AI Mode.
Google also recommends that structured data accurately match the content visible to users.
Businesses should therefore treat schema as a tool for clarity and technical correctness, not as a shortcut to AI recommendations.
The same caution applies to llms.txt, a proposed format for presenting website information to language-model-based systems.
It may serve as useful documentation, but there is no established requirement to create it for inclusion in major AI search experiences.
For additional technical context, see structured data and AI-generated answers.
6. Build credibility beyond your own website
A company's website can describe its expertise, services, and achievements.
But when potential customers evaluate a provider, they often seek information from sources other than the provider itself.
These may include independent publications, verified reviews, professional associations, industry databases, customer case studies, and expert interviews.
Such information can also appear in the sources that AI-assisted search experiences retrieve or cite.
This is where reputation and evidence become important.
Lopty Pascal's AI SEO and GEO methodology emphasizes documenting trust signals, connecting them to specific claims, and building a consistent public record of a business's expertise.
His work highlights a distinction worth understanding: making a company easier to discover is not necessarily the same as establishing why it should be selected over alternatives.
A business might have technically excellent pages while offering little independently verifiable evidence of its capabilities.
To strengthen that evidence, companies can pursue legitimate activities such as publishing detailed case studies, participating in relevant professional discussions, earning authentic customer reviews, and contributing expertise to independent publications.
None of these actions guarantees an AI recommendation.
And businesses should avoid fabricated reviews, manufactured testimonials, misleading awards, or artificial claims of recognition.
The objective is to build a reputation that customers can verify, regardless of which search or AI platform they use.
7. Study the competitors AI assistants recommend
If AI assistants regularly recommend competitors instead of your business, the next step is investigation, not assumption.
Run your tracked questions and examine the responses.
Record the companies mentioned and open the cited sources.
Are they business directories, review websites, independent articles, professional profiles, or competitors' own pages?
Then examine the information available for those competitors.
Some may have clearer service descriptions. Others may have stronger customer evidence, more detailed case studies, or wider industry coverage.
These differences provide research leads, not proof of the internal criteria used by a particular AI model.
For example, suppose a cybersecurity consultancy rarely appears in AI recommendations, while competing firms regularly do.
Its team might investigate whether competitors publish original security research, maintain detailed technical documentation, or receive independent citations from reputable publications.
That investigation could identify areas where the consultancy can improve the quality and accessibility of its own public information.
Pascal explores this issue in his analysis of why AI assistants recommend competitors instead of particular businesses.
The practical lesson is to look beyond the final generated answer and examine the public evidence surrounding the recommended providers.
8. Track whether AI visibility contributes to enquiries
Being mentioned by an AI assistant is useful to measure, but businesses ultimately need to understand whether that exposure contributes to meaningful outcomes.
Traffic alone is an incomplete indicator.
Some users may discover a brand through an AI-generated answer and visit the website later through a branded Google search.
Others may call directly or contact the company through WhatsApp.
To improve attribution, companies can combine several measurement methods.
First, review website analytics for referrals from recognizable AI services.
Second, include a question such as "How did you hear about us?" on enquiry forms.
Third, train sales teams to record when customers mention discovering the business through ChatGPT, Perplexity, Gemini, or another assistant.
Finally, compare these observations with changes in brand searches, qualified enquiries, and conversion rates.
Self-reported attribution is imperfect, and not all AI-influenced conversions can be measured.
Even so, combining several sources provides a more informative picture than counting mentions alone.
For a more detailed implementation, see tracking traffic and leads from ChatGPT and Perplexity.
9. Maintain traditional SEO and measure progress over time
AI search optimization should not be treated as a complete replacement for traditional SEO.
Google's documentation states that its AI search features rely on established search foundations.
Pages must meet eligibility requirements, be accessible for crawling and indexing, and provide helpful content.
There is no separate set of mandatory technical requirements for appearing in Google's AI Overviews or AI Mode.
This makes familiar SEO practices important:
- Maintain a crawlable, well-structured website.
- Publish original, accurate, useful content.
- Improve internal linking and navigation.
- Keep business profiles up to date.
- Resolve indexing and technical errors.
- Monitor relevant search performance.
- Maintain accurate information across external profiles.
Expectations should also be realistic.
Technical corrections may become visible once pages are recrawled and indexed, while broader changes in reputation, content coverage, and AI recommendations may take substantially longer.
There is no reliable universal timetable.
As Google's May 2026 guidance reinforces, established SEO fundamentals remain relevant even as generative search develops.
Businesses should therefore track both traditional organic performance and their observed appearance in AI-generated answers.
Where should businesses start?
Different businesses will have different priorities.
A newly established company may need to focus first on clarifying its identity, publishing essential service information, and establishing trustworthy public profiles.
An established company that ranks well on Google but rarely appears in AI answers may benefit more from testing real buyer questions, examining cited sources, and improving its category-specific content.
A company that AI assistants frequently describe incorrectly should prioritize factual corrections across its website and important external profiles.
Meanwhile, organizations already receiving AI referrals may want to invest in more systematic measurement and conversion tracking.
There is no single sequence that fits every business.
What businesses should not expect
Improving discoverability is not the same as controlling AI recommendations.
AI answers can change depending on the question, platform, available sources, and context.
No agency, consultant, or optimization platform can guarantee that a particular business will appear in every relevant AI answer.
Companies should also be cautious about services that promise instant recommendations through keyword manipulation, mass-produced articles, or purchased reviews.
Those activities do not replace clear business information, useful content, and verifiable expertise.
Final thoughts
AI-assisted discovery is creating new challenges for businesses, but many of the underlying principles remain familiar.
A company needs to be discoverable, clearly understood, relevant to the customer's question, and supported by credible information.
The difference is that those signals now need to work across conventional search engines and multiple AI-powered discovery experiences.
The nine steps provide a practical starting point: measure visibility, resolve technical obstacles, clarify brand identity, publish useful information, strengthen supporting evidence, study competitive sources, and connect the results to business outcomes.
The goal is not simply to appear in an AI-generated answer once.
It is to establish a reliable digital presence that makes the business easier for customers and information systems to understand.
References and further reading
Independent research and official platform documentation
- Pew Research Center — Google Users Are Less Likely to Click on Links When an AI Summary Appears
- Google Search Central — AI Features and Your Website
- Google Search Central — A New Resource for Optimizing for Generative AI in Search
- Google Search Central — Structured Data Guidelines
- Aggarwal et al. — GEO: Generative Engine Optimization
- OpenAI — ChatGPT Search
Expert resources and practical implementation
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