Imagine asking ChatGPT to recommend a new skincare brand, find an independent fashion label or suggest a boutique hotel for a weekend away. Within seconds, you receive a shortlist, complete with comparisons, reviews and reasons why each option might suit you. No scrolling through Instagram, no opening ten different websites and, potentially, no exposure to the advertising campaigns brands have spent months developing.
For consumers, this is convenient. For brands, it introduces a new competitor for something they have spent decades trying to control: the relationship between discovery and desire.
Artificial intelligence is changing how people find, evaluate and eventually buy products. And while much of the conversation has focused on how brands can use AI to improve their marketing, a more important question is emerging: what happens when brands no longer control the platforms through which customers discover them?
The recommendation is becoming the new shop window
The shift is already measurable. McKinsey's October 2026 research, based on approximately 21,000 consumers across 11 markets, found that 53% had used AI for at least one shopping task in the previous three months. Among consumers using AI, nearly a third said AI chat or search tools had been the biggest influence on their most recent purchase. They were also 3.2 times more likely to rank AI search as more influential than traditional search.
This is no longer just about asking a chatbot for a product recommendation. Consumers are using AI to compare brands, assess alternatives, narrow down options and increasingly complete purchases. The research found that 14% of AI users globally had already completed a purchase directly through an AI tool in the previous three months.
The implications for consumer brands are significant. A customer looking for a moisturiser, a pair of trainers or a hotel is no longer necessarily navigating a brand's carefully constructed digital experience. They may be receiving an answer assembled from multiple sources before ever encountering the brand itself.
Being a good brand is no longer enough
Traditionally, brands have invested heavily in controlling their own narrative. Campaigns, websites, social media, influencer partnerships and editorial coverage all help build a particular identity and influence how consumers perceive a product.
AI discovery introduces another layer. These systems do not simply reproduce a brand's marketing message. They synthesise information from product pages, reviews, editorial coverage, online discussions and other sources to produce an answer.
The Business of Fashion and McKinsey explored this in their State of Fashion 2026 report, identifying generative engine optimisation, or GEO, as an increasingly important counterpart to traditional search engine optimisation. The report highlights the importance of structured product information, clear descriptions, metadata, reviews and credible third-party references.
One particularly interesting finding from McKinsey's October 2025 analysis is that a brand's own website can account for only 5% to 10% of the sources referenced in some AI-generated answers. In other words, a brand's visibility increasingly depends on what other people and platforms say about it, not just what the brand publishes itself.
This creates a difficult challenge. A beautifully designed website and an impressive Instagram presence may communicate a brand's identity to a human visitor, but they do not automatically make its products easy for AI systems to understand or recommend.
The independent brand problem
This is where the shift becomes particularly interesting for smaller consumer brands.
A global beauty company can afford dedicated digital teams, extensive product databases, international press coverage and substantial investment in search visibility. An independent skincare label or emerging fashion brand is unlikely to have the same resources.
The issue is not necessarily that AI will favour larger companies in every recommendation. In fact, AI could help consumers discover smaller, more specialised brands they might never have found through traditional advertising.
But there is a structural imbalance. Brands with extensive product information, consistent reviews, established online coverage and a strong digital footprint have more material through which AI systems can understand and evaluate them.
An independent brand might have an exceptional product and a loyal customer base, but limited information available beyond its own channels. If an AI tool cannot establish what makes that product relevant, or find enough credible information to recommend it, the brand risks being overlooked.
And unlike traditional advertising, where a business can pay for visibility, the factors influencing AI recommendations are not entirely transparent or consistently controlled by brands.
When discovery happens without a click
There is another commercial concern: being recommended does not necessarily mean being visited.
Pew Research Center analysed the browsing behaviour of 900 US adults in March 2025. It found that when Google displayed an AI-generated summary, users clicked on a traditional search result in 8% of visits, compared with 15% when no summary appeared. Links cited within the AI summaries received clicks in just 1% of visits.
Although this research examined Google search rather than AI shopping platforms specifically, it highlights an important distinction between visibility and traffic. A brand's information can contribute to an answer without the customer ever reaching its website.
For businesses that rely on direct website visits, this raises questions about how they measure marketing performance. If AI becomes an increasingly important discovery channel, traditional measures such as search rankings, website traffic and advertising clicks may tell only part of the story.
The risk of becoming a product rather than a brand
Perhaps the biggest challenge is less technical and more fundamental.
AI is particularly useful when products can be compared through clear criteria: price, ingredients, specifications, availability, delivery and customer reviews. It makes choosing between alternatives easier.
But consumer brands are not built on product specifications alone. A beauty brand sells more than its formulation. A fashion label offers more than its garments. A hotel competes on more than its facilities and room rates. Identity, cultural relevance, design, service and emotional connection all influence why people choose one brand over another.
If the discovery process becomes increasingly automated, brands will need to make themselves understandable to algorithms without losing the qualities that make people want them in the first place.
McKinsey's research makes a similar point: brands need to balance machine-readable product information with distinctive storytelling and human connection.
AI may become another important gateway between consumers and brands, but it cannot replace the work of creating something worth being discovered.
The real competition is no longer just for a customer's attention. It is for a place in the answer they receive before they even know which brands to look for.
And in that environment, having a great product is only the beginning.