AI is changing how brands get discovered – but not how they grow

What Byron Sharp and Mark Ritson reveal about the future of brand growth in an AI-first world.

Every few years, marketing declares itself dead.

Digital was supposed to replace brand building; social media was going to make mass marketing obsolete. Performance marketing promised perfect attribution that would destroy campaigns as we know them, and personalization would eliminate the need for broad reach. Yet, in spite of all of these, marketing still hasn’t been handed over to the machines.

Now AI has become the latest alleged cause for funerary preparations. As consumers increasingly turn to ChatGPT, Gemini, Claude, and Perplexity for advice, many marketers are asking a familiar question: Is marketing really dead now?

A recent – and very interesting – podcast conversation between marketing thinkers Byron Sharp and Mark Ritson had a simple answer to this: Not only is marketing not dead, but its rules may not even need to change that much. In fact, AI may reinforce many of the classical principles that marketers have spent decades trying to relearn.

Mental availability matters more, not less

One of the central arguments from legendary marketing academic Byron Sharp has always been that brands grow because they become mentally available. When customers enter a buying situation, the brands that come to mind first enjoy a disproportionate advantage.

The rise of AI doesn't eliminate this principle; rather, it creates another layer.

The challenge today is that brands must become mentally available to both humans and machines.When someone asks an LLM, "What's the best business credit card?" or "Which running shoe should I buy?" the model doesn't invent an answer. It synthesizes information from everything it has learned about brands, products, reviews, expert opinions, and content across the web.  Unlike in the SEO era of marketing, an LLM recommendation is driven by many of the same signals that shape human memory, from familiarity, distinctiveness, and consistency to category association and repeated exposure.

In other words, AI has become another consumer. And it’s one that reads everything.

The 95:5 rule becomes even more important

In the podcast, Byron Sharp frequently reminds marketers that only a small percentage of category buyers are actively in-market at any given moment.

The majority are future buyers, not current buyers. They may not become customers for months or even years. And this insight has major implications for AI and how marketers should approach it.

Large language models aren't just answering questions from today's buyers. They're continuously learning from the content that brands publish long before purchase decisions occur.

This means that educational content that appears irrelevant to immediate revenue may become the very information that AI systems rely upon months later when someone enters the market.

In short, the value of brand-building content extends beyond influencing future consumers; it also influences future AI recommendations.

AI rewards consistency, not cleverness

One of the themes throughout Sharp and Ritson’s discussion is a skepticism toward marketing fashions that promise shortcuts: hyper-personalization, micro-segmentation, and the like.

Their criticism isn't that these ideas never work, but rather that marketers often mistake novelty for evidence.  AI presents a similar temptation, with many organizations now searching for tricks to "optimize for ChatGPT." But LLMs have been around for long enough that we can say with certainty that AI systems don't reward gimmicks for very long. Instead, they reward clarity.

Brands that consistently reinforce the same associations across thousands of pages create a stronger, more coherent knowledge footprint. That coherence helps both humans remember brands, and at the same time helps AI models understand them.

Distinctive brand assets have a new role

For years, distinctive assets – colors, logos, taglines, characters, visual identity – were viewed primarily as memory structures for consumers. They’re what Mark Ritson calls the “Brand Code,” and they were seen as wholly separate from the assets whose primary use was search engine visibility.

Now, AI expands the scope of a Brand Code to encompass what we at Knotch call a “Content Code”. Every article, product page, FAQ, executive interview, research report, and support document – no matter how deeply buried within the architecture of a site they may have been in a pre-AI era – contributes to how machines understand a brand. The stronger and more consistent those signals become, the easier it is for AI systems to confidently associate a brand with specific problems, categories, and expertise.

Visitors to LLMs are often asking detailed, highly specific questions, and then engaging in extensive follow-ups in response to the initial answers. And unlike in traditional search, that LLM is often actively comparing competitive brands to one another based on the information on those brands that’s available to their knowledge base. SEO tactics could elevate your brand above others; now, details and features are being analyzed alongside one another. Your distinctiveness matters more than ever.

Why measurement must evolve

This is where marketing needs new capabilities.

Traditional SEO tells us whether content ranks; website analytics tells us whether visitors engage and convert; brand tracking measures awareness and perception.

But none of those provide insight into a new scenario: When AI answers the customer's question, is your brand part of the conversation? Whether they’re in internal team meetings or presenting to the C-suite, marketers need to have specific responses to inquiries like these:

• Which buying situations and customer profiles is AI associating with their brand?
• Which competitors might AI be recommending instead?
• What content on their brand’s properties – and their competitors’ properties – is shaping those recommendations.
• Is their brand’s distinctive positioning – their Brand Code, and now Content Code – actually reflected in AI-generated responses?
• How is their AI visibility relating to brand health and commercial outcomes?

This isn't a replacement for existing measurement, but an extension of it.

The next evolution of discoverability

For years, discoverability meant winning search rankings; today, it increasingly means becoming the source AI systems choose to synthesize.  That raises the standard for marketing, but it doesn’t change the fundamentals.

Brands still need broad reach, memorable assets, consistent positioning, and evidence-based content. The difference is that every piece of content now serves two audiences simultaneously:

The human making the purchase, and the AI helping shape it.

The real opportunity

The biggest takeaway from Sharp and Ritson’s conversation is that AI isn't rewriting marketing science. Marketing is not, in fact, dead.

Rather, AI is validating the tried-and-true principles that have always been the backbone of our discipline.

And that’s why successful brands are unlikely to be those chasing the newest optimization tactic. Rather, they will be consistently building mental availability, reinforcing their distinctive Brand Codes, educating the market, and creating coherent knowledge ecosystems that both humans and machines can easily understand.

In other words, while the future of marketing may feel radically different in today’s AI-first world, the tactics that succeed there will probably look remarkably familiar.

‍

Published on August 18, 2026