LLMs Don't Know Your Marketing Is Working — And That's a Bigger Problem Than You Think

There's a question most marketing teams aren't asking yet, and it may be the most consequential one of 2026.

What does an AI assistant say about your brand when a buyer asks?

Not what Google says. Not what your website says. What ChatGPT, Perplexity, Gemini, or Claude says when a prospective customer types: "What are the best solutions for [your category]?" or "Tell me about [your company]."

For a growing proportion of B2B buyers, that AI-generated answer is now the first touchpoint with your brand. It shapes the shortlist before any human interaction takes place. And most marketing functions have no visibility into it, no strategy for it, and no measurement framework that captures it.

AI assistants are now a primary research channel for B2B buyers. When a brand is omitted, mischaracterised, or displaced in AI-generated answers, that represents a measurable gap in marketing reach — one that most marketing measurement frameworks don't currently capture. Managing AI visibility is becoming a core CMO responsibility, alongside SEO, paid media, and brand.

That's the argument this piece makes. Here's the evidence and the framework for doing something about it.

The shift that's already happened

AI-powered search tools — Google AI Overviews, Perplexity, ChatGPT search, and others — have moved from novelty to default for a significant and growing segment of buyers, particularly in B2B. Search behaviour has changed faster than most marketing teams have responded.

When a buyer uses an AI assistant to research a category, they receive a synthesised answer rather than a list of links. That answer cites some brands and ignores others. The brands cited earn disproportionate consideration — research suggests AI-cited brands convert at significantly higher rates than those appearing only in traditional results. Many buyers never scroll further.

If your brand isn't in that answer, you've been excluded from the consideration set before your marketing had a chance to work. Your campaigns ran, your content was published, your SEO was optimised — and none of it reached the buyer at the moment that mattered. The funnel was bypassed entirely.

In an audit I ran across five B2B brands in my network in early 2026, three had significant mischaracterisation issues — AI tools were describing their products inaccurately or for the wrong audience — and two didn't appear at all in AI-generated answers for their primary category. None of them knew.

Why AI visibility is harder to fix than it looks

The instinct is to treat AI visibility as an SEO problem — optimise the right content, build the right links, and the AI tools will follow. That instinct is partially right but mostly incomplete.

LLMs don't work like search engines. They don't crawl and rank in real time. They were trained on data with a cutoff, they weight sources differently than Google does, and they synthesise rather than list. What they say about your brand is a function of what they learned during training — which means the most recent campaign you ran probably didn't move the needle, and the content shaping their perception of you was written months or years ago.

There are three specific ways AI tools misrepresent brands, each with real commercial consequences.

Omission

Your brand simply doesn't appear in answers about your category. You exist in the training data but not prominently enough to surface in generated responses. For buyers who treat the AI answer as the complete picture, you don't exist.

Mischaracterisation

The AI describes your brand inaccurately — wrong positioning, wrong use case, wrong customer profile. One company discovered that a major AI tool was consistently describing their enterprise product as suitable for small businesses, not because the AI was hostile, but because the training data that mentioned their brand most prominently was early-stage content from their startup phase. The product had evolved. The AI's understanding of it hadn't.

Displacement

A competitor is being recommended in your place, in contexts where you should be the obvious answer. This is the most commercially damaging scenario and the hardest to detect without actively auditing AI outputs.

The brand equity angle

I covered this briefly in my piece on ISO 10668 and brand valuation in 2026, and it warrants more space here.

ISO 10668 — the international standard for monetary brand valuation — requires a behavioural analysis as one of its three core components. Behavioural analysis is about understanding how stakeholders perceive and engage with a brand. Traditionally this meant surveys and focus groups. In 2026, it should also mean auditing how AI tools represent the brand — because for a growing segment of buyers, the AI's representation has become the perception.

When an AI assistant consistently mischaracterises or omits a brand from category discussions, the consequences extend beyond a single lost deal. It affects how the brand is valued, how it appears in M&A due diligence, and how much of the marketing investment is actually reaching the market. Brand equity is partly determined by how consistently a brand shows up in the right conversations. AI is now a gatekeeper to those conversations.

The ISO 10668 revision — the first update to the standard since 2010 — is currently underway. Whether the new edition will explicitly address AI-mediated brand discovery remains to be seen. Practitioners shouldn't wait.

A practical framework for managing AI visibility

Existing marketing channels still matter. SEO, paid media, content, and brand building remain important. The gap is a new capability that most marketing functions haven't yet built: systematic monitoring and management of how AI tools represent the brand.

Step 1: Audit your current AI visibility

Spend an hour asking AI tools about your brand and your category. Use ChatGPT, Perplexity, Gemini, and Claude. Ask "what are the best solutions for [your category]", "tell me about [your company]", "how does [your company] compare to [competitor]", and "who typically uses [your company]". Record what each tool says. Note omissions, inaccuracies, and competitor displacement. This is your AI visibility baseline — and most marketing teams have never done it.

Step 2: Diagnose the content and authority gap

The difference between what AI tools say and what you want them to say reveals where authority is missing. What sources are the AI tools drawing on? What content is most prominently associated with your brand in the training data? Where are competitors more prominently cited, and why? The answers usually point to a combination of content volume, citation frequency, and source credibility.

Step 3: Build for citation, not just ranking

The content most likely to be cited by AI tools is content that other credible sources reference — long-form, specific, well-sourced writing on topics you want to own. Thought leadership that gets picked up by industry publications. Data and research that other writers cite. This is a different discipline from keyword-optimised SEO content, though the two are increasingly complementary. The question to ask of every piece of content is not "will this rank?" but "will other credible sources reference this?"

Step 4: Track AI visibility as a metric

The tooling is still developing, but measurement matters here as much as anywhere else in marketing. Platforms like Supermetrics are increasingly helping marketing teams understand their AI search visibility alongside traditional channel performance — connecting the data infrastructure that makes AI visibility trackable rather than anecdotal. Visibility without measurement is guesswork, and guesswork is hard to defend at board level.

Step 5: Connect AI visibility to commercial outcomes

AI visibility should connect to pipeline and revenue. If buyers are increasingly starting their research in AI tools, brands that appear in AI answers should see higher inbound intent, shorter sales cycles, and stronger brand recognition at first contact. Measuring that correlation is how you build the financial case for AI visibility investment — and how you make it a line item that survives budget scrutiny.

The CMO accountability question

There's a harder version of this problem worth naming directly, because it will land on CMO agendas in the next twelve months.

If a growing share of your target buyers are using AI tools to research and shortlist, and your brand is being omitted or misrepresented in those tools, then a portion of your marketing budget is effectively invisible to its intended audience. The impression was never served. The brand was never encountered.

That's an accountability gap that finance-trained CMOs need to get ahead of. The CFO will eventually ask: if AI search is where buyers are starting their research, and we're absent from those answers, what is this marketing spend doing?

The CMOs who answer that question well are already auditing their AI visibility, building the content and authority signals that earn AI citation, and connecting AI presence to commercial outcomes in their measurement frameworks.

Where to start

If you take one thing from this piece, make it the audit. Spend 20 minutes today asking AI tools about your brand and your category. What you find — or don't find — is your starting point.

For a deeper look at how AI is affecting brand equity and valuation specifically, my piece on ISO 10668 in 2026 covers the brand strength and financial valuation dimensions in more detail.

And for the data infrastructure that makes AI visibility measurable and manageable, Supermetrics' 2026 Marketing Data Report covers how leading marketing teams are building the measurement foundation that connects AI adoption to commercial outcomes.

Andrea Linehan is CMO at Supermetrics, a B2B marketing intelligence platform used by 200,000+ marketers globally. She is a four-time CMO across MarTech and FinTech, and writes about AI, marketing strategy, and the finance discipline that connects them.

Frequently asked questions

What is AI search visibility for brands?

AI search visibility refers to how a brand appears — or fails to appear — in answers generated by AI assistants such as ChatGPT, Perplexity, Gemini, and Google AI Overviews. As buyers increasingly use these tools to research products and build shortlists, a brand's presence in AI-generated answers directly affects its reach and consideration rates.

How do LLMs decide which brands to recommend?

Large language models recommend brands based on patterns learned during training — which means they favour brands with high volumes of credible, well-cited content across reputable sources. Unlike search engines, LLMs don't crawl the web in real time, so recent campaigns and content updates may not be reflected in their outputs for months.

Why are brands omitted from AI-generated answers?

Brands are typically omitted from AI-generated answers because they lack sufficient training data signal in the relevant category — meaning they haven't been prominently cited by credible third-party sources, their content is thin or recent, or their positioning in the training data doesn't match the way buyers are searching. Omission is the most common AI visibility problem and the least visible to marketing teams.

How can CMOs measure their brand's AI search visibility?

The starting point is a manual audit — systematically querying AI tools with category and brand-specific questions and recording the outputs. More systematic measurement involves tracking AI citations over time using emerging tools and platforms, and connecting AI visibility signals to pipeline and revenue metrics to establish commercial impact.

How does AI search visibility affect brand equity?

AI visibility is increasingly a component of brand equity because it determines how a brand is represented in the research and consideration phase of the buyer journey. Under ISO 10668 — the international standard for monetary brand valuation — behavioural analysis is a core component of any valuation. How a brand appears in AI-generated answers is now part of how buyers perceive and engage with it, which means it belongs in that analysis.