AI-Driven Discovery Shifts From Search to Conversation, Changing How Businesses Reach Buyers

Discovery is becoming conversational, TechRadar said. For years, keywords shaped digital discovery: customers typed a short query into a search engine and received a list of links. LLMs allow people to solve needs in more detailed and natural ways. Instead of searching for best running shoes, a consumer may ask what products are needed for marathon training, whether certain shoes suit a specific foot type, how they compare with alternatives and where to buy them. Users are increasingly comfortable letting AI narrow the field before deciding where to go next.

Consumers are already using AI tools for practical research and purchase-related decisions. Captify found that informational prompts accounted for 39% and transactional prompts for 37% of LLM usage, more than three-quarters combined. AI is not acting as a neutral directory: 70% of LLM responses position a single brand as the primary recommendation, according to the report. Consumers are increasingly handed one curated recommendation rather than a page of results to compare themselves.

The influence extends beyond discovery to where research and purchase intent develop. Organizations have historically relied on search queries, clicks and browsing behavior to understand users. Conversational AI adds another signal in the form of questions people ask even before they know what to search for. A shopper comparing skincare ingredients, a traveler planning a family itinerary or a business buyer evaluating software vendors all reveal more context through conversations than through simple keyword queries.

Gartner found that consumers are using AI to research and compare products, but only 11% said they would be willing to let AI make purchase decisions on their behalf, according to TechRadar. The final transaction may still happen on a retailer's website, app or in a physical store, but research and decision-making are beginning much earlier inside an AI conversation. AI-influenced journeys often reach their highest conversion point after five to six prompts, with 75-85% of those journeys converting within two weeks. Consumers rely on AI to narrow choices, test assumptions and build confidence before moving to the open web.

That has implications for how organizations understand demand and speak to consumers. Less traffic may arrive directly on a website, but users who land there after an AI conversation are likely to arrive with a clearer understanding of the product and a higher likelihood to purchase. Traditional search data tends to be brief and transactional, while AI conversations are longer, more exploratory, more emotive and reveal more about what people are trying to understand before they buy. Looking at both the questions consumers ask and the responses they receive offers a richer picture of how purchase decisions take shape.

Different models also produce different pictures of the consumer. Different LLMs draw on different sources and weight information and signals in different ways. A company that appears frequently in one model may barely register in another, and brand recommendations can vary by as much as 27 percentage points across ChatGPT, Gemini, Claude and Perplexity, TechRadar reported. Much of what is marketed as AI consumer insight is not based on real consumer behavior, the report said. It often takes top search terms, feeds them into a model and treats the output as a proxy for what consumers think or want. This AEO or GEO-style focus understands the model but not the consumer. A more useful approach starts with real consumer behavior: what people actually ask, how they phrase it and what they are trying to work out, all observed directly and not inferred.

Editor's Summary TechRadar reports that AI-driven LLMs are shifting product discovery from keyword search to conversation, with consumers researching and forming purchase intent before they reach the open web. The change may reduce direct website traffic but bring clearer, higher-intent visitors, while different models produce inconsistent brand recommendations. The report points to studying real consumer conversations rather than treating model outputs as a proxy for consumer behavior.