While search engines remain dominant, driving around 80 percent of all search traffic, the rapid rise of AI-driven discovery highlights a fundamental change in how people find and evaluate products. Nearly a quarter of global consumers already rely on generative AI as their main starting point when shopping. As tools become more advanced and trusted across different use cases in everyday life, more consumers will adopt AI for shopping. 23 percent of consumers primarily use generative AI to discover products, while the majority still use traditional search.

Though still comparatively small, shopping-related generative AI searches grew 4,700 percent between July 2024 and July 2025, with AI supporting inspiration and product comparison — especially helpful in the fashion category where choice abounds. This is impacting brands’ traffic sources. According to SimilarWeb, ChatGPT accounted for 16 percent of Zara’s and 8 percent of H&M and Aritzia’s inbound traffic between June and August 2025. 53 percent of US consumers who used generative AI for search inQ2 2025 also used it to help them shop.

Unlike paid ads which are clearly sponsored, over 40 percent of consumers say AI responses feel more reliable, while only 15 percent consider them less reliable. Shoppers are therefore increasingly willing to make purchases from AI-based recommendations, as seen in the 84 percent growth in AI-driven revenue per visit on US retail sites between January and July 2025. 41 percent of consumers say they trust generative AI search results more than paid search ads.

Technical improvements, such as reduced hallucinations (where a model generates incorrect, misleading or nonsensical information), combined with access to consumer behavioural data are making generative AI more reliable and user-friendly. These models provide more relevant recommendations, reduce search friction and build consumer confidence as the algorithm learns and improves over time. 85 percent of US consumers that use generative AI for shopping say they have a better experience than traditional methods.

Generative engine optimisation is becoming an essential add-on to search engine optimisation

Generative engine optimisation (GEO) is fast becoming a critical counterpart to search engine optimisation (SEO) as AI’s role in product discovery continues to grow.

GEO ensures content is visible to and optimised for generative AI models. Large language models (LLMs) like ChatGPT, Claude, Perplexity and Gemini respond to consumer search queries by providing conversational, synthesised and structured answers rather than ranked lists of links. To appear in answers, website content must be easily read, contextualised and considered credible by the AI. GEO focuses on optimising content such as products listings to be just that, with early movers like Estée Lauder Companies, L’Oréal and Mejuri experimenting with GEO.

Bettered-structured content is more likely to be surfaced by AI. Making content visible and easy to pick up means showing up in more places (different channels, languages, platforms) and making sure the basics are done right — like clear product titles, descriptions and metadata tags (descriptive data labels that help AI tools understand and surface products). Reviews, blogs and partnerships are also powerful signals that AI tools use to judge trustworthiness that brands should amplify.

Brands and retailers should tackle GEO alongside SEO. Traditional search, including paid and organic, still drives most search traffic. (Bing alone accounts for 4 percent of global search volume, more than all generative AI platforms combined.) Overlooking either GEO or SEO risks leaving critical gaps in visibility.

In the years ahead, consumers could rely on autonomous AI agents to help them shop

Advances in AI are opening the door to autonomous agents that act on behalf of consumers and businesses. These agents could streamline journeys by condensing multiple steps into a single action.

While still nascent, agentic AI has the potential to fundamentally transform digital commerce, taking on the role of intermediary between brand and consumer, like retailers and marketplaces today. With AI simplifying product comparisons, brand loyalty will become even more important in purchase decisions. Brands should double down on retention tactics to build the emotional resonance that will influence consumers’ choices.

The technologies that would allow agents to transact on someone’s behalf remain in development. As they mature, agentic AI models may either integrate into existing e-commerce ecosystems or create entirely new channels of discovery and purchase, reshaping how brands both market and sell their products.

Some AI players are already testing these models. For example, OpenAI struck deals with Shopify and Etsy to let shoppers buy products from those platforms directly in ChatGPT, while Amazon’s “Buy for Me” lets consumers buy from third-party platforms within the Amazon app.

To compete in the age of agentic commerce, some players may build their own shopping agents

There are different approaches to how companies can engage with AI shopping agents — from optimising for existing ecosystems to building their own.

OPTIMISE FOR AGENT INTERACTION:

In the era of agentic commerce, optimising for engagement from shopping agents will be a high priority for nearly every brand and retailer. Common standards will likely emerge to let agents interact directly with brand and retailer ecosystems, but players must act now to control their visibility:

• Expand content reach across channels and languages while increasing volume.
• Structure content for AI readability using proper layout, data formatting and markup.
• Adopt common protocols that let agents easily pull information from and carry out actions on a brand’s website while establishing links to the CRM to track customer data and enable loyalty features.
• Actively monitor and encourage third-party content since reviews, blogs and affiliate posts make up around 80 percent of the sources that AI uses.

BUILD AN AI AGENT:

For some players, developing a proprietary shopping agent may align with strategic goals, enabling seamless onsite experiences — from tailored recommendations to checkout.

The case for proprietary agents is generally stronger for retailers than brands. Retailers — whether specialists with authority or those with large inventories — can use agents to reinforce their aggregator role and offer specialised abilities like asking style questions to better personalise their recommendations. Most brands, by contrast, can focus on optimising products to be surfaced by third-party agents. For luxury brands, which differentiate through in-store experiences, a proprietary agent may be relatively less important.

While AI costs are declining, agents remain expensive to run as of now. Complex workflows can use hundreds of thousands of tokens versus only a few hundred for a simple chatbot, according to the Wall Street Journal.

Agentic AI will shift the underlying economics of digital commerce

The growth of agentic commerce is set to accelerate in the second half of the decade as LLM-powered shopping becomes mainstream. Its value could grow to be as much as $3-5 trillion by 2030, assuming moderate adoption by both consumers and businesses.

$3-5T: potential value of agentic commerce in 2030 if adoption is similar to mobile commerce in the 2010s, with higher levels of internet connectivity globally.

As shoppers shift from apps and websites to AI agents, fashion players risk losing ownership of the consumer relationship. Going forward, brands may also need to pay for premium integration and placement in agent recommendations. However, the precise monetisation model of agentic commerce as well as the distribution of the value captured between AI platforms and brands and retailers remains undefined.

A brand’s value proposition will be more critical than ever as AI agents compare brands across dimensions like delivery time, price points, assortment range and colour variability. Excelling across these factors will provide a more distinct competitive advantage when comparisons are algorithmic and instantaneous.

11-18%: agentic commerce’s share of the total projected business-to-consumer retail market in 2030

How should executives respond to these shifts?

Assess discoverability by auditing content and digital infrastructure

Structure product content and data sharing feeds so AI agents can interpret and display the product catalogue. Whether or not proprietary agentic tools are appropriate, securing visibility at the point of consumer search is essential. At the same time, SEO remains a core driver of traffic, making a balanced approach across both GEO and SEO key.

Set your agentic AI strategy now

Set an agentic AI strategy ahead of mass adoption — whether by optimising for third-party agents or developing proprietary ones in cases where it is advantageous, such as for multi-brand retailers. Building an in-house agent will likely require leveraging an LLM-based platform, supported by dedicated engineering and continuous iteration to tailor the experience to target customers.

Dedicate marketing budget to support an AI visibility strategy

As discovery shifts from search to AI assistants, brands need to treat AI ecosystems as a new marketing channel with new rules of discovery, requiring a deliberate strategy and supporting budget.

Continue strengthening the brand’s storytelling and value proposition

Balance machine-readiness with storytelling, striking imagery and a distinctive digital identity. People — not just algorithms — remain central to the brand experience, with stores, frontline staff and human interactions continuing to inspire customers and drive conversions. Strengthen the brand’s value proposition, as differentiators like delivery speed, price and product quality will help AI surface the brand and make it stand out to humans alike.

This article first appeared in The State of Fashion 2026, an in-depth report on the global fashion industry, co-published by BoF and McKinsey & Company.