Is Your Brand Ready for an AI-First Internet?

Why the future of brand visibility depends on being recommended, referenced and trusted by artificial intelligence

The shift from search engine optimisation to AI-driven discovery is fundamentally changing how consumers find, evaluate and choose brands. For businesses, the challenge is no longer simply securing a position on the first page of Google. It is ensuring they are part of the answer when AI systems make recommendations, compare products and influence purchasing decisions.

Introduction: The internet is changing, and the rules of visibility are changing with it

For more than two decades, digital marketing has operated around a relatively familiar principle: if a business wants to be discovered, it needs to be visible in search.

Brands invested in search engine optimisation (SEO), paid search, content marketing and digital advertising to secure their position in the customer journey. Consumers entered keywords into search engines, reviewed a list of results, visited websites, compared information and eventually made a decision.

The model was well understood. The objective was to attract attention, earn a click and convert that visit into a customer.

But something fundamental is changing.

Increasingly, consumers are turning to artificial intelligence to research products, evaluate suppliers, compare services and answer complex questions. Rather than navigating multiple websites, they can ask an AI assistant to do much of the research for them.

Consider the difference.

A traditional search might involve someone typing:

Best enterprise content management platforms.

An AI-driven search might look like:

We're a multinational organisation with several regional websites, complex content approval processes and multiple languages. Which enterprise content management platforms should we consider, and what are the advantages and disadvantages of each?

The second interaction is fundamentally different.

The consumer isn't simply looking for a list of websites. They're asking for analysis, context, recommendations and a considered response.

AI systems can synthesise information from multiple sources, explain differences, identify relevant providers and, increasingly, help users take action.

This changes the competitive landscape for brands.

A company might have excellent search rankings, a sophisticated website and a substantial content marketing programme, yet still be absent from the answers generated by AI platforms.

Conversely, a competitor with a smaller digital footprint could become highly visible because its products, expertise and reputation are represented consistently across the sources AI systems reference.

The next generation of digital visibility isn't just about being found. It's about being understood, trusted and recommended.

For marketing leaders, this creates an important strategic question: is your organisation optimising for the internet that exists today, or the one customers are increasingly beginning to use?

1. The evidence: AI is changing how people discover brands

It would be easy to dismiss AI search as another technology trend that attracts significant industry attention but has limited impact on everyday consumer behaviour.

The evidence suggests otherwise.

Forrester's February 2026 Consumer Pulse research found that 34% of UK online adults had used ChatGPT to search for products they were interested in buying during the previous month. This had increased from 22% in October 2025.

Traditional discovery remains significant: Google and Amazon continue to command substantial shares of product research. However, AI assistants are becoming an increasingly important part of the consideration process.

Source: Forrester – UK Consumers Are Slowly Adopting Answer Engines For Product Discovery.

Gartner's research provides another important perspective.

Its survey of US consumers found that 51% had changed their research habits as a result of generative AI. Among those changing their behaviour, consumers were increasingly using more specific questions and conversational queries.

Importantly, 31% said AI summaries caused them to spend more time researching, while 31% considered more product options as a result.

This challenges the assumption that AI necessarily shortens the buying journey. It may actually broaden the consideration set and introduce brands that consumers would not previously have encountered.

Source: Gartner – Marketers Must Optimise for Both AI-Driven and Traditional Search.

The implications are significant.

AI is not simply replacing traditional search. It is adding another layer to the discovery process, influencing what people consider, which brands they investigate and how they evaluate their options.

The zero-click challenge

There is another development that deserves particular attention.

In traditional search, a successful result often meant generating a website visit. A user found a page, clicked through and engaged with the brand.

AI-generated answers can provide much of the information a user needs without requiring a visit to the original source.

Pew Research Center analysed browsing behaviour from 900 US adults in March 2025. It found that users encountering a Google AI summary clicked a traditional search result in 8% of visits, compared with 15% when no AI summary appeared. Just 1% of visits resulted in clicking a link within the AI summary itself.

Source: Pew Research Center – Google Users Are Less Likely to Click on Links When an AI Summary Appears.

This has important consequences for traditional digital marketing measurement.

A brand might be influencing purchasing decisions without receiving the corresponding website traffic.

Its products may be recommended, its expertise may inform an answer, and its reputation may influence a shortlist, yet conventional analytics may not fully capture that influence.

The challenge is therefore twofold:

• Maintain the ability to attract and convert visitors through traditional digital channels.

• Build visibility and influence within AI-generated answers, recommendations and discovery experiences.

The brands that understand both sides of this equation will be better positioned as the digital landscape evolves.

2. From search rankings to AI recommendations: understanding the new visibility model

To understand the opportunity, it is important to recognise that AI-driven discovery is not a single channel.

There are several overlapping experiences through which consumers can encounter a brand.

AI-powered search

Search engines are increasingly integrating generative AI into their results.

Google AI Overviews and AI Mode, alongside Microsoft's Copilot Search, provide synthesised answers that draw on multiple sources.

Instead of presenting only a list of links, these experiences can explain a topic, compare options and provide supporting references.

For brands, appearing in these answers creates another opportunity to influence discovery.

Standalone AI assistants

Platforms such as ChatGPT, Gemini, Claude and Perplexity allow users to ask detailed questions and receive conversational responses.

These platforms can be used to research a category, compare providers, investigate products and evaluate potential suppliers.

The important distinction is that users often approach these systems with a specific problem rather than a collection of keywords.

A consumer might ask:

• Which are the most reliable family SUVs for long-distance driving?

• Which UK energy suppliers offer the best options for businesses?

• What are the best project management platforms for distributed teams?

• Which banks offer the most competitive business accounts for SMEs?

The AI assistant becomes an intermediary between the consumer and the brands competing for consideration.

AI-assisted purchasing and agentic commerce

The next development is particularly significant.

AI is increasingly moving from providing information towards helping users complete tasks.

Imagine a consumer asking an assistant to find a suitable hotel, compare prices, check availability and identify the best option based on their requirements.

Or a business buyer asking an AI assistant to shortlist software providers, compare licensing models and prepare an initial procurement recommendation.

In these situations, the brand's ability to be discovered is only the beginning.

Its products, services, pricing, availability, specifications and business information must also be accessible, accurate and understandable.

This is where AI discovery begins to converge with digital experience, commerce, product information management and customer experience management.

The opportunity extends well beyond SEO.

3. Why traditional SEO is still important – but no longer tells the whole story

There is a temptation to position Generative Engine Optimisation (GEO), Answer Engine Optimisation (AEO) and AI SEO as entirely new disciplines that make traditional SEO obsolete.

That is a mistake.

Google's own guidance on generative AI search, published in May 2026, reinforces the importance of foundational SEO practices.

Google explicitly recommends valuable, original content, accessible websites, strong technical foundations and clear information architecture. It also warns against relying on supposed GEO hacks or unnecessary technical tricks.

Source: Google Search Central – Guide to Optimising for Generative AI Features.

This is an important distinction.

AI systems still need to discover, access, interpret and evaluate information. A technically inaccessible website, poorly structured content or inconsistent business information is unlikely to provide a strong foundation for AI visibility.

However, traditional SEO metrics alone are insufficient.

A business might rank highly for a particular keyword but fail to appear when an AI assistant answers a more complex question about its product category.

Equally, a brand might appear in an AI recommendation because its expertise is referenced by third-party publications, review platforms, industry associations or other authoritative sources.

The visibility model is therefore expanding.

Traditional SEO	AI-driven visibility
Keyword rankings	Presence in relevant AI answers
Search impressions	Brand mentions and citations
Organic traffic	AI-influenced discovery
Backlinks	Wider reputation and source representation
Click-through rates	Recommendation and consideration influence
Landing-page conversions	AI-influenced enquiries and conversions
Search position	Competitive share of AI visibility

These are complementary measures, not replacements for one another.

The important change is that brands must understand not only where they rank, but whether they are represented accurately and favourably when AI systems discuss their category.

4. What determines whether AI recommends your brand?

There is no universally published formula that guarantees inclusion in ChatGPT, Gemini, Claude or Google's AI-generated answers.

Each platform uses different retrieval systems, models, sources and ranking mechanisms. Their behaviour also changes over time.

However, several important factors influence whether a brand is discoverable and represented accurately.

4.1 Brand authority and reputation

AI systems do not operate in isolation from the wider internet.

Information about a business can exist across its own website, independent publications, review platforms, industry directories, news coverage, social platforms and specialist communities.

A brand that is consistently discussed and referenced across credible sources has a stronger foundation for being recognised as an established entity.

Consider two competing software companies.

Company A has a sophisticated website containing hundreds of pages describing its products.

Company B has a smaller website but is also regularly referenced in independent industry research, analyst commentary, customer reviews, specialist publications and comparison articles.

Company B may have a stronger overall information footprint.

This doesn't guarantee an AI recommendation, but it illustrates why brand authority must extend beyond owned content.

The strategic implication: reputation management, digital PR, customer advocacy and content marketing are increasingly interconnected with AI visibility.

4.2 Content that answers genuine questions

Traditional SEO has often encouraged content strategies built around keywords and search volumes.

AI discovery places greater emphasis on understanding the context behind a question.

For example, a user researching enterprise CMS platforms may want to understand:

• Which platforms support complex multi-site environments?

• What are the differences between SaaS and self-hosted CMS architectures?

• Which platforms support global content governance?

• How do licensing models compare?

• What should an organisation consider when migrating from a legacy CMS?

A collection of shallow product pages is unlikely to provide the same value as comprehensive, technically accurate material that addresses these questions.

Brands should develop content that demonstrates real expertise, explains complex subjects and helps people make informed decisions.

4.3 Clear, structured and accessible information

AI systems need to interpret the information they encounter.

This makes content architecture important.

Product descriptions, service information, specifications, pricing, company details, technical documentation and customer support content should be organised in ways that are understandable to both people and machines.

For example, a retailer should not rely solely on promotional copy to describe its products.

It should provide clearly accessible information about:

• Product dimensions.

• Materials and ingredients.

• Compatibility.

• Availability.

• Pricing.

• Warranty information.

• Delivery and returns.

• Product variants.

Similarly, a financial services company should make eligibility requirements, fees, interest rates, product features and relevant limitations easy to identify.

The objective is not to write for machines at the expense of people.

It is to make important information unambiguous, accessible and useful.

4.4 Freshness and accuracy

An AI assistant recommending a product with discontinued specifications or outdated pricing creates a poor customer experience.

This makes content governance an important part of AI readiness.

Organisations need to understand how information is updated, where authoritative product data resides and how changes propagate across digital channels.

For businesses operating in multiple markets, this becomes even more complicated.

Different languages, currencies, regulations, product availability and regional variations all create opportunities for conflicting information.

A centralised approach to content and product information management can help reduce these inconsistencies.

4.5 Independent validation

A brand saying it is the market leader is very different from independent sources demonstrating that leadership.

AI systems can draw on information beyond a company's own website.

Customer reviews, industry reports, research papers, product comparisons, expert commentary and independent publications all contribute to the wider picture.

This creates an important challenge for marketing teams.

It is no longer sufficient to control the story a brand tells about itself. Organisations must also understand how their brand is represented elsewhere.

5. Real-world examples: what AI visibility looks like in practice

The principles are interesting, but the commercial opportunity becomes clearer when we examine how organisations are approaching the problem.

The following examples include published vendor case studies. Their reported results are useful illustrations, but should be treated as vendor-reported outcomes rather than independently audited proof that the same approach will work for every organisation.

Case study 1: A B2B SaaS company – moving beyond traditional search performance

The challenge: Strong organic performance wasn't translating into AI visibility.

A B2B software company featured in a case study published by Marcel Digital had an established SEO programme but needed to understand how it was performing across emerging AI search experiences.

The organisation expanded its measurement beyond traditional rankings to include:

• Google AI Overview visibility.

• Presence in AI-generated responses.

• AI referral traffic.

• AI-attributed leads.

• Marketing-qualified lead performance.

The results reported by Marcel Digital included:

• 350% growth in commercial and transactional Google AI Overview rankings.

• 144% growth in total AI Overview rankings.

• AI prompt visibility increasing from 33% to 73% across its tracked prompts.

• 16,462 visits from AI platforms.

• 290 lead form submissions from AI platforms.

• AI-driven form submissions increasing 240% year over year.

Source: Marcel Digital – Building Visibility Across Search Engines and AI for a B2B SaaS Company.

What can other organisations learn?

The most interesting aspect is the measurement framework.

Rather than treating AI visibility as an isolated marketing activity, the company connected discovery to commercial outcomes.

It recognised that the value of AI visibility is not simply the number of times a brand appears in an answer. The important question is whether that exposure contributes to meaningful business activity.

This is particularly relevant to B2B organisations with long sales cycles, complex buying committees and high-value transactions.

Case study 2: Private Label MFG – turning AI recommendations into revenue

The challenge: A retailer was almost invisible in AI-driven product recommendations.

Private Label MFG, an automotive aftermarket parts retailer, was featured in a case study by Visibility Labs.

The company was not receiving meaningful visibility from AI search and recommendation platforms.

Its GEO programme focused on improving how the brand and its products were represented within AI-driven discovery.

The case study reports:

• AI search visibility increasing from 1% to more than 20% over six months.

• AI-attributed sales increasing from 0.5% to 5% of total sales.

• A tenfold increase in the reported share of sales attributed to AI.

Source: Visibility Labs – Private Label MFG Case Study.

What can other organisations learn?

This example illustrates why product information is central to AI discovery.

A customer searching for specialist automotive components may have very specific requirements around vehicle compatibility, performance, materials and intended use.

If that information is incomplete or difficult to interpret, the brand risks being excluded from the recommendation process.

For retailers, product data quality, product descriptions, structured specifications, reviews and availability information should all be considered part of the AI visibility strategy.

Case study 3: Global hospitality – competing for recommendations, not just rankings

The challenge: A hospitality brand was being out-cited by larger industry competitors.

A February 2026 case study published by Search Agency describes a global serviced residence and extended-stay hospitality business.

Despite having 162 properties, the brand was losing visibility to larger competitors across ChatGPT, Gemini, Google AI Mode and Perplexity.

Its GEO programme focused on improving the brand's representation across AI discovery experiences.

The agency reported:

• AI search audience growing from 7.4 million to 34.6 million in one quarter.

• AI mentions increasing by 97%.

• 77% favourable sentiment.

• 162 properties optimised.

Source: Search Agency – AI Search Case Study.

What can other organisations learn?

Hospitality is an excellent example of why AI discovery requires accurate, consistent information across many locations.

A customer might ask:

Which serviced apartments in London are best for a family staying for three weeks, with access to public transport and kitchen facilities?

A successful recommendation requires more than brand awareness.

It requires location-specific information, amenities, pricing, availability, reviews and relevant descriptions.

The opportunity is to make the entire property portfolio understandable and discoverable, rather than relying on a generic corporate brand page.

Case study 4: A global activewear retailer – understanding the competitive AI landscape

A February 2026 case study from G10X describes work with a global activewear retailer seeking to improve its representation across AI search and recommendation experiences.

The programme examined how customers discovered products through Google AI summaries, ChatGPT and Perplexity, and how the brand compared with competitors.

The focus was on understanding visibility within AI-generated answers, rather than relying solely on traditional search rankings.

Source: G10X – Improving Brand Visibility in AI-Generated Answers.

What can other organisations learn?

The lesson is that AI visibility needs to be assessed at category and product level.

A brand might be well known generally but invisible when customers ask specific questions about individual product categories.

For example, a sportswear company may be recognised as a major brand but fail to appear when a customer asks for the best running shoes for long-distance training or the best technical clothing for cold-weather running.

The distinction between brand awareness and category-level recommendation is increasingly important.

6. Practical use cases: how different industries should approach AI visibility

The opportunity is not limited to consumer brands.

Different industries have different discovery patterns, customer questions and information requirements.

The following examples illustrate how organisations can translate AI visibility into practical business priorities.

Financial services and banking

The customer question:

Which banks in the UAE offer the best business banking facilities for a growing SME that needs international payments, multi-currency accounts and competitive fees?

An AI assistant might compare financial institutions based on product features, eligibility, fees, customer reviews and publicly available information.

The opportunity for banks:

• Make product features and eligibility requirements easy to discover.

• Publish accurate fee schedules and product comparisons.

• Provide comprehensive information about business accounts, loans, cards and international payments.

• Ensure regional product information is consistent.

• Build independent credibility through research, customer experience and financial publications.

The commercial impact:

Greater visibility during the research stage can help a bank enter consideration earlier, before a prospective customer has already narrowed their shortlist.

Retail and ecommerce

The customer question:

I'm looking for a washing machine for a family of five. It needs to be energy efficient, quiet, have a large drum and cost less than £700. Which models should I consider?

An AI assistant can potentially compare specifications, customer reviews, prices and availability.

The opportunity for retailers:

• Improve product information quality.

• Maintain accurate specifications and availability.

• Make product variants and compatibility information clear.

• Ensure product reviews and customer feedback are accessible.

• Provide useful buying guides and genuine product comparisons.

The commercial impact:

AI recommendations can influence which products make it onto a customer's shortlist, potentially changing the competitive dynamics of ecommerce.

Travel and hospitality

The customer question:

Find me a family-friendly hotel in Copenhagen with connecting rooms, a swimming pool and easy access to the city centre.

This is not simply a search for hotels. It is a request for a recommendation based on multiple requirements.

The opportunity for hospitality businesses:

• Publish detailed property and room information.

• Maintain accurate amenities and location data.

• Make family facilities and accessibility information clear.

• Ensure booking and availability information is consistent.

• Encourage genuine, detailed customer reviews.

The commercial impact:

Hotels and travel providers that make their inventory and experience information accessible have a better opportunity to be considered when AI systems help users narrow their choices.

B2B technology and enterprise software

The customer question:

We need an enterprise CMS that supports global multi-site management, personalisation, content governance and integration with our existing MarTech stack. Which platforms should we shortlist?

This is a particularly interesting use case because enterprise technology decisions involve substantial research and multiple stakeholders.

The opportunity for technology providers:

• Publish detailed product and architecture documentation.

• Explain integrations and technical compatibility.

• Provide transparent product comparisons.

• Publish real customer outcomes.

• Make licensing and deployment models understandable.

• Address security, governance, scalability and implementation questions.

The commercial impact:

A technology vendor that is accurately represented in AI-generated comparisons has an opportunity to influence a shortlist before a prospect even visits its website.

Healthcare and pharmaceuticals

The customer question:

What treatment options are available for a particular condition, and what are the differences between them?

This is an area where accuracy, governance and source credibility are particularly important.

The opportunity:

• Publish clinically reviewed and appropriately sourced information.

• Maintain accurate treatment and product information.

• Ensure regulatory and market-specific information is consistent.

• Make the authorship and review process transparent.

• Correct outdated or misleading third-party information where possible.

The commercial impact:

Trust and accurate representation are essential. In regulated sectors, AI visibility must never take priority over clinical accuracy, regulatory compliance or patient safety.

7. GEO: moving from theory to an actionable strategy

So what should organisations actually do?

Generative Engine Optimisation is often presented as a collection of content optimisation techniques. In reality, a successful programme requires a much broader approach.

It should bring together SEO, content strategy, technical architecture, brand reputation, digital PR, data management and performance measurement.

I would organise this around six strategic pillars.

Pillar 1: Establish your AI visibility baseline

Before attempting to improve AI visibility, organisations need to understand their current position.

This means testing how AI systems represent the brand today.

Develop a structured set of prompts based on real customer research behaviour.

For example, an enterprise software company could test:

Category discovery

• What are the leading enterprise CMS platforms?

• Which digital experience platforms are best suited to multinational organisations?

Product comparison

• Compare the leading enterprise CMS platforms for SaaS deployment.

• What are the differences between Optimizely, Sitecore and Contentful?

Problem-led discovery

• How should a global organisation manage content across multiple markets?

• What platforms support enterprise content governance and personalisation?

Commercial intent

• Which enterprise CMS platforms offer the best value for a large organisation?

• What should a company consider when migrating from a legacy CMS?

These prompts should be tested across multiple platforms, including ChatGPT, Gemini, Claude, Perplexity and relevant Google AI experiences.

Record:

• Whether the brand appears.

• Whether it is recommended or merely mentioned.

• Whether the information is accurate.

• Whether the response includes a citation.

• Which competitors appear.

• Which sources are being referenced.

• Whether the brand is described positively, negatively or neutrally.

• Whether the response changes across different prompts and engines.

The result is a baseline that reveals where the brand is visible, where competitors are winning and where opportunities exist.

Important: AI responses are variable. A single prompt test is not a reliable measure of visibility. Use a repeatable prompt set, consistent methodology and multiple observations over time.

Pillar 2: Build a content strategy around customer intent

The next step is to understand what customers actually ask AI systems.

This requires moving beyond keyword research towards intent-based content planning.

Think about the questions customers ask at different stages of their journey.

Journey stage	Example customer question	Content opportunity
Awareness	What is an enterprise CMS?	Educational guides
Exploration	What should I look for in a CMS?	Buying guides
Evaluation	Which CMS platforms are best for global businesses?	Comparisons
Validation	How have other businesses implemented a CMS?	Case studies
Decision	How much does an enterprise CMS cost?	Pricing and implementation information
Adoption	How do I migrate to a new CMS?	Technical guides

This approach helps organisations build a connected content ecosystem rather than publishing disconnected articles.

It also creates an opportunity to use first-party customer data, sales conversations, support enquiries and search analytics to identify the questions that matter most.

Pillar 3: Strengthen your digital authority

AI visibility should not be treated as an exclusively owned-media activity.

Brands need to consider how they are represented across the wider digital ecosystem.

This includes:

• Industry publications.

• Analyst reports.

• Independent reviews.

• Customer testimonials.

• Expert interviews.

• Professional communities.

• Relevant forums and discussion platforms.

• Industry associations.

• Research and educational resources.

A digital PR programme should therefore consider not only traditional referral traffic and backlinks, but also the quality and accuracy of the wider information ecosystem surrounding the brand.

This does not mean manufacturing mentions or paying for artificial endorsements.

It means giving credible third parties something genuinely valuable to reference: original research, expert insight, useful data, strong customer outcomes and distinctive perspectives.

Pillar 4: Make your content and data AI-ready

Technical accessibility remains essential.

Organisations should review:

Crawling and access

• Can relevant search and AI retrieval crawlers access public content?

• Are robots.txt rules unintentionally blocking important resources?

• Are CDN and security controls allowing legitimate crawler traffic?

• Are important pages accessible without unnecessary authentication?

Content structure

• Is information organised logically?

• Are headings descriptive?

• Are key answers easy to identify?

• Are tables and product specifications clearly presented?

• Is important information available in crawlable HTML?

Structured data

• Is appropriate schema markup implemented?

• Are product, organisation, article and local business details accurate?

• Does structured data match the visible content?

Content governance

• Are old product details removed or updated?

• Are regional variations managed correctly?

• Are prices and specifications current?

• Is there a clear process for reviewing authoritative content?

There is an important distinction here: structured data and technical improvements can improve clarity and eligibility for discovery, but they do not guarantee AI citations or recommendations.

For organisations using complex digital platforms, this is where CMS, DAM, PIM and digital experience architecture become strategically important.

Pillar 5: Establish an AI visibility measurement framework

Traditional SEO measurement is no longer sufficient on its own.

Organisations need to introduce a new layer of performance measurement.

I would recommend six core metrics.

1. AI brand visibility

How frequently does the brand appear in relevant AI-generated responses?

Measure this against a defined prompt set, category and competitor group.

2. AI share of voice

What proportion of relevant brand mentions or recommendations belongs to your organisation compared with competitors?

Define the denominator carefully. A share of mentions is not necessarily the same as a share of citations or recommendations.

3. Citation frequency

How often are your website pages referenced as sources in AI-generated answers?

Where possible, distinguish first-party citations from third-party references.

4. Recommendation quality

Is the brand being recommended positively and accurately?

A mention is not automatically valuable. Context matters.

5. AI referral traffic and engagement

How much traffic comes from identifiable AI platforms, and how does that traffic behave?

Look beyond visits to include engagement, enquiries and conversion.

6. AI-influenced commercial outcomes

Can AI discovery be connected to leads, sales opportunities or revenue?

Attribution will remain imperfect, particularly where AI influences a customer before they arrive through another channel. However, even partial visibility is better than treating the channel as unmeasurable.

An important development here is Microsoft's AI Performance reporting within Bing Webmaster Tools.

Introduced in February 2026, it provides visibility into how website content is cited in AI-generated answers across supported Microsoft AI experiences.

In June 2026, Microsoft added further insights, including topics, intents and citation share. In October 2026, it introduced a Topics Portfolio beta to help publishers understand the broader themes in which their content appears.

These developments are significant because they demonstrate that AI visibility measurement is moving beyond experimental prompt testing towards more structured reporting.

Sources:

• Bing Webmaster Tools – AI Performance

• Bing – AI Visibility Insights

• Bing – Topics Portfolio Beta

Pillar 6: Connect AI visibility to the wider digital experience

This is arguably the most important pillar.

Being recommended by AI is only valuable if the experience that follows meets customer expectations.

Imagine an AI assistant recommending a retailer's product.

The customer clicks through, only to discover that the product is unavailable, the price is different or the specifications don't match the AI-generated description.

The experience immediately breaks down.

The same problem exists in B2B.

A software platform may be recommended by an AI assistant, but the prospective customer then discovers that the technical documentation is incomplete, the product comparison is outdated or the website cannot explain the implementation model.

AI visibility cannot be separated from the quality of the underlying digital experience.

Organisations need to connect:

• Content management.

• Product information management.

• Digital asset management.

• Customer data.

• Personalisation.

• Search.

• Commerce.

• Customer experience analytics.

• Marketing automation.

The objective is to establish a consistent, accurate and connected information ecosystem that supports discovery and conversion.

8. The technology question: does your digital architecture support an AI-first world?

The emergence of AI-driven discovery creates a new challenge for enterprise technology leaders.

Many organisations have accumulated complex digital estates over the last decade.

Content may be distributed across multiple CMS platforms, regional websites, product databases, DAM systems, customer portals and legacy applications.

In many cases, the information is inconsistent, duplicated or difficult to maintain.

This was already a problem for traditional digital experiences.

AI makes it more consequential.

When an organisation has multiple versions of the same product description, conflicting pricing information or inconsistent service details, AI systems may encounter conflicting information across different sources.

The challenge is therefore not simply publishing more content.

It is establishing a reliable information architecture.

The role of the CMS

A modern content management system should make it easier to create, manage and distribute accurate content across multiple digital experiences.

This includes structured content, reusable components, governance workflows, multilingual publishing and integrations with other enterprise systems.

The CMS increasingly becomes part of the foundation supporting AI-driven experiences.

The role of DAM and PIM

Digital asset management and product information management are equally important.

AI-generated recommendations may rely on product descriptions, specifications, imagery, documentation and other structured information.

These systems help ensure that assets and product information remain accurate, consistent and reusable across channels.

The role of APIs and composable architecture

Organisations also need to consider how information is exposed to different digital experiences.

API-driven architecture can make it easier to distribute consistent content and data across websites, applications, commerce environments and AI-powered interfaces.

The opportunity is to move away from disconnected content silos towards a more connected information ecosystem.

The role of AI governance

Finally, organisations must establish controls over how AI systems access and use business information.

This includes:

• Data permissions.

• Content approval.

• Privacy.

• Security.

• Regulatory compliance.

• Information provenance.

• Accuracy and accountability.

The objective is not to expose everything to AI.

It is to make the right information available through the right channels, with the appropriate controls.

9. Five mistakes brands should avoid when approaching GEO

As interest in AI visibility grows, so does the temptation to chase shortcuts.

There are several approaches that organisations should be cautious about.

Mistake 1: Treating GEO as a replacement for SEO

Traditional search remains an important discovery channel.

Google itself continues to emphasise foundational SEO practices as the basis for visibility in its generative AI experiences.

A business that abandons SEO to chase AI visibility risks damaging its existing digital performance.

The better approach is an integrated strategy.

Mistake 2: Publishing huge volumes of AI-generated content

There is a misconception that the more content a brand publishes, the more likely it is to appear in AI-generated answers.

This can lead to websites filled with repetitive articles that offer little original insight.

Google explicitly advises against mass-producing low-value content designed primarily to manipulate search visibility.

The priority should be depth, originality, expertise and accuracy.

Mistake 3: Chasing AI mentions without understanding context

A brand being mentioned is not necessarily a positive outcome.

It might be included in a list of poor-performing products, outdated comparisons or negative reviews.

Measurement needs to distinguish between positive recommendations, neutral mentions, negative references and direct citations.

Mistake 4: Assuming that one prompt test proves success

AI-generated responses can change between sessions, platforms and even repeated queries.

A brand appearing in one ChatGPT response does not establish sustained visibility.

Organisations need consistent testing methodologies and sufficient observations to identify meaningful trends.

Mistake 5: Optimising for AI at the expense of the customer

Ultimately, the purpose of content is to help people.

A website that is technically optimised but difficult to navigate, unhelpful or untrustworthy is unlikely to create meaningful commercial value.

The best AI visibility strategy should also produce a better experience for human visitors.

10. The next frontier: from being recommended to being selected by AI agents

The next stage of this evolution could be even more significant.

Today, many AI search experiences help people research and compare products.

Tomorrow, AI agents will increasingly be capable of carrying out tasks on their behalf.

Consider a B2B procurement example.

A purchasing manager asks an AI agent:

Identify three suppliers that can provide enterprise-grade cloud hosting, meet our security requirements, operate in the UK and EU, and provide a service-level agreement of at least 99.99%. Compare their pricing and prepare a recommendation.

The agent may need to:

1. Understand the requirements.

2. Identify suitable suppliers.

3. Retrieve technical documentation.

4. Validate security and compliance credentials.

5. Compare service levels.

6. Review pricing.

7. Produce a shortlist.

8. Potentially initiate contact or procurement workflows.

The brands that can supply accurate, accessible and verifiable information have an advantage in this emerging environment.

The same principle applies to consumer commerce.

An AI agent might be asked to find a suitable product, check availability, compare prices and complete a purchase.

This means brands need to think beyond being visible in an answer.

They need to consider whether their products, services and digital infrastructure can participate in AI-mediated transactions.

This brings together several disciplines:

• AI search visibility.

• Structured product and service data.

• Digital commerce.

• APIs and integrations.

• Identity and permissions.

• Transaction security.

• Customer experience.

The distinction is important.

GEO helps brands become part of the conversation. Agent-ready digital experiences help them become part of the transaction.

11. A practical 90-day roadmap for becoming AI-visible

For organisations that want to move from discussion to implementation, a phased approach is sensible.

Days 1–30: Understand your current position

Objective: Establish the baseline.

Activities:

• Identify the most commercially important customer questions.

• Build a structured prompt library.

• Test brand visibility across relevant AI platforms.

• Benchmark against five to ten competitors.

• Review the sources cited by AI systems.

• Audit website accessibility and crawler configuration.

• Identify outdated, conflicting or incomplete content.

Deliverables:

• AI visibility baseline.

• Competitor benchmark.

• Technical readiness assessment.

• Priority content gap analysis.

Days 31–60: Improve your information footprint

Objective: Address the most significant gaps.

Activities:

• Prioritise high-value customer questions.

• Update key product and service information.

• Improve technical and informational content.

• Publish original research and expert-led material.

• Strengthen relevant third-party coverage.

• Improve content structure and internal linking.

• Establish content ownership and review processes.

Deliverables:

• Priority content optimisation programme.

• Improved product and service information.

• Digital authority plan.

• Updated content governance framework.

Days 61–90: Measure, refine and scale

Objective: Establish an ongoing AI visibility programme.

Activities:

• Repeat the original prompt tests.

• Measure changes in brand visibility and competitor presence.

• Review citation frequency and source quality.

• Analyse identifiable AI referral traffic.

• Connect AI discovery with lead and conversion data.

• Identify the next set of content opportunities.

• Establish a recurring reporting process.

Deliverables:

• AI visibility performance dashboard.

• Competitive share-of-voice assessment.

• AI referral and conversion analysis.

• Ongoing GEO improvement roadmap.

The key is to treat AI visibility as an ongoing business capability rather than a one-off optimisation project.

12. The questions every CMO and digital leader should be asking

AI-driven discovery is moving quickly, but organisations do not need to wait for the market to settle before taking action.

There are several questions that should already be part of executive digital strategy discussions.

Brand and marketing

• Does our brand appear when customers ask AI systems for recommendations in our category?

• Are competitors being recommended ahead of us?

• Are AI platforms describing our products and services accurately?

• Do we understand how our brand is represented across independent sources?

Content and digital experience

• Does our content answer the questions customers are actually asking?

• Is our information structured and accessible?

• Can we maintain consistent product and service information across markets?

• Are we publishing genuinely differentiated expertise?

Technology and data

• Can AI retrieval systems access our public information?

• Is our digital architecture built around reliable, reusable information?

• Do our CMS, DAM and PIM platforms work together effectively?

• Can we safely expose relevant information to AI-powered experiences?

Measurement and performance

• Do we have an AI visibility baseline?

• Can we compare our visibility with competitors?

• Are we tracking citations, mentions and recommendation quality?

• Can we connect AI discovery to commercial performance?

These are not simply SEO questions.

They are questions about brand strategy, technology architecture, customer experience and the future of digital commerce.

Conclusion: The next generation of digital leaders will compete for relevance, not just rankings

The internet is entering another period of fundamental change.

For years, brands competed to appear at the top of search results. Now they must also compete to become part of the answers generated by AI systems.

The implications extend across marketing, content, technology, commerce and customer experience.

Traditional SEO remains important. Websites remain important. Content remains important.

But the way these elements contribute to customer discovery is changing.

Brands must develop a deeper understanding of how AI systems discover information, which sources they trust, how they interpret products and services, and what influences their recommendations.

They must also recognise that AI visibility is not something that can be achieved through a handful of technical adjustments or a larger content production budget.

It requires a connected approach to brand authority, content quality, data accuracy, digital architecture and performance measurement.

The organisations that start building these capabilities now will be better positioned to influence the next generation of customer journeys.

And perhaps the most important question for every marketing leader is this:

When your next customer asks an AI assistant which brand they should choose, will your organisation be part of the answer – or will your competitors be making the recommendation instead?

The future of digital visibility is no longer just about being found.

It is about being the brand that AI understands, trusts and recommends.

Further reading and research

The following sources underpin the research and recommendations in this article:

1. Forrester – UK Consumers Are Slowly Adopting Answer Engines For Product Discovery (April 2026)

2. Gartner – Optimising for AI-Driven and Traditional Search (January 2026)

3. Pew Research Center – Google AI Summaries and Click Behaviour (July 2025)

4. Google Search Central – Guide to Optimising for Generative AI Features (2026)

5. Google Search Central – AI Features and Your Website

6. Microsoft Bing – AI Performance in Webmaster Tools (February 2026)

7. Microsoft Bing – AI Visibility Insights (June 2026)

8. Microsoft Bing – Topics Portfolio Beta (October 2026)

9. OpenAI – Searching the Web with ChatGPT

10. Marcel Digital – B2B SaaS AI Visibility Case Study