The rise of Large Language Models (LLMs) like ChatGPT and Google Gemini is reshaping how consumers move through the buyer journey.
Once dominated by Google searches, the discovery and decision-making stages are increasingly powered by AI-driven, conversational interfaces that change not only how users find information, but how they expect to engage with it.
From Search to Conversation
Traditionally, product discovery began with keyword-based search on platforms like Google. Now, many consumers are starting their journey by asking detailed, natural-language questions in chat interfaces powered by LLMs. These tools allow users to bypass multiple pages of search results and receive instant, contextualised responses, often complete with comparisons, recommendations, and direct links.
According to a 2024 Evercore survey, ChatGPT’s share of primary search use among respondents rose from 1% in June to 8% by September 2024. During the same period, Google’s dominance dropped from 80% to 74%. Meanwhile, Gartner predicts that by 2026, traditional search usage could decline by 25% as consumers turn to AI chatbots and virtual assistants for answers.
A New Kind of Product Research
Consumers are not just casually experimenting with these tools, they’re actively using them to inform purchasing decisions. A survey by Ignite Visibility found that 62% of respondents had used ChatGPT or Google Gemini for researching products or services; and according to Adobe data via The Verge, AI-driven search referrals to U.S. retail sites spiked by 1,300% during the 2024 holiday season, with a 1,950% increase on Cyber Monday alone.
This is part of a broader trend: consumers want faster, more personalised information, and LLMs deliver. By understanding context, user preferences, and intent, LLMs generate personalised recommendations, product summaries, and pros-and-cons breakdowns. It’s no surprise that 91% of consumers value personalised suggestions when shopping, and over half feel comfortable using AI tools for product discovery (My Total Retail).
Not All AI is the Same
It’s worth noting that “AI” is often used as a catch-all term. While LLMs are revolutionising search and discovery through natural language, other types of AI are driving change in different parts of the customer journey. For example:
- Predictive AI helps brands anticipate future buying behaviour and optimise inventory.
- Computer Vision powers virtual try-on tools and image-based search in fashion and beauty.
- Recommendation engines analyse behavioural data to surface high-conversion products.
Distinguishing between these technologies helps marketers and businesses understand where to focus their optimisation efforts.
What This Means for Brands
As the buyer journey becomes increasingly influenced by LLMs, brands must ensure their content is not only SEO-optimised but also AI-optimised.
Structured, detailed product descriptions, accessible FAQs, and authoritative content across an array of platforms, are more important than ever. Retailers should consider how their data is being accessed, understood, and surfaced by these models, and where their brand appears in AI-generated answers.
The age of AI-assisted shopping is here, and it’s not a passing trend. Businesses that adapt early to these changes in consumer behaviour will be best placed to maintain visibility and relevance in an increasingly conversational and AI-driven online landscape.
