AI shopping assistants like ChatGPT are reshaping product search in 2026 — but most shoppers still won’t pay in-chat

AI chat assistants are quickly becoming part of how people shop online, especially when they’re searching for and discovering products. That’s a central takeaway from the Baromètre GEO by Valiuz, an ad-sales group tied to France’s Mulliez retail ecosystem and to Intermarché, as cited by La Revue du Digital on July 8, 2026.

Behind the novelty factor, the data points to real, everyday use: shoppers are already leaning on AI to compare options, filter choices, and rephrase what they’re looking for. But the shift still stalls at the moment of purchase, where trust, payment, and responsibility remain the biggest barriers.

Valiuz’s “funnel” shows AI use drops sharply at checkout

The Baromètre GEO by Valiuz frames AI adoption as a funnel: high at the top of the shopping journey, then narrowing when it’s time to pick a seller, pay, and arrange delivery. As summarized by La Revue du Digital, the pattern reflects what’s now becoming easy to observe—AI works best as a tool for phrasing, exploring, and sorting, not as a full transactional channel.

In the discovery phase, the assistant often acts like a translator between a vague intention and usable criteria. A request like “comfortable shoes for walking around the city” can quickly turn into constraints around sole type, weight, material, width, and budget. The same dynamic shows up in electronics and appliances: users ask for recommendations, then push the assistant to clarify tradeoffs—battery life versus weight, noise level versus performance, repairability versus price. The value, the barometer suggests, comes from speed and synthesis.

But the report also points to a technical reality: assistants can aggregate information and suggest directions, yet they don’t always have access to real-time pricing, local inventory, detailed return terms, or a customer’s purchase history. E-commerce platforms typically keep that data inside closed systems. The result is that AI becomes an entry point, but shoppers often return to a retailer’s internal search, a marketplace, or a specialized comparison site to confirm details.

Friction spikes at the purchase step. A source cited within the publication ecosystem around the barometer points to usage falling to 30% at the moment of purchase—an indicator that the transaction itself is the main lock. Even when AI helps narrow the choice, entering card details, confirming an address, selecting a carrier, reviewing warranties, and knowing what proof exists in a dispute are steps where shoppers prefer a checkout controlled by an identified merchant. In France, the article notes, concerns about getting the wrong item, buying a counterfeit, or dealing with complicated returns still weigh heavily.

ChatGPT is pulling product search into conversation, not payments

In e-commerce, using an assistant like ChatGPT is showing up first as an alternative to a traditional search engine—built around longer, more contextual queries. Instead of typing “bagless vacuum,” a shopper might describe the size of their home, whether they have pets, a noise constraint, and a budget, then ask for a shortlist. For retailers, that shifts part of the SEO fight: products need to be discoverable through rich descriptions, structured reviews, and reliable information—not just keywords.

That change also reshapes comparison shopping. The comparison is no longer a chart on a third-party site; it becomes a conversational answer. For consumers, the risk is flawed summaries, outdated specs, or confusion between models. For brands, the risk is losing control of the product narrative if the underlying data is incomplete or contradictory. In that environment, the quality of spec sheets, manuals, FAQs, and help content becomes a visibility issue.

Payment is harder to hand off to AI for a simple reason: it requires clear consent and accountability. A bad recommendation can be corrected; a disputed charge can turn into a formal dispute. Shoppers want transparency about the seller, the final price, shipping costs, delivery timelines, and return policies. If an assistant can’t guarantee those elements, users move back to the merchant’s site. That’s why the promise of end-to-end purchasing inside a chat is advancing more slowly than AI-assisted search.

Retailers also have reasons to keep checkout on their own pages—compliance, anti-fraud controls, consent management, and marketing measurement. When decisions happen inside an assistant, ad attribution gets murkier: what actually influenced the purchase—a campaign, a piece of content, a review, or the assistant’s answer? Ad networks and platforms are looking for ways to improve traceability, but standards remain uneven.

In practice, the most common 2026 path looks like a back-and-forth: the assistant helps with preparation, then the shopper opens multiple tabs to verify, compare, and buy. For e-commerce players, that creates a two-part challenge—capture intent early, then reassure customers at the critical moment. Conversational tools are becoming an influence channel, but the purchase funnel itself is still, for now, run by retailer sites and apps.

Valiuz and major French retailers focus on attribution and product data

Valiuz—linked to retail ecosystems associated with the Mulliez group and with Intermarché—highlights the advertising stakes behind the rise of AI assistants. If product discovery shifts away from classic search engines, the core question becomes how to measure exposure, recommendation, and conversion when a consumer arrives already “pre-educated” by a conversation. For an ad-sales group, the challenge isn’t only selling placements; it’s proving contribution.

Retailers have an advantage: they hold browsing and cart data, availability, pricing, promotions, and detailed logistics constraints. But those inputs have to be made usable—structured, documented, and kept up to date. Without that work, an assistant can recommend an out-of-stock item or miss a local promotion. Projects around product knowledge and aligning reference data—sizes, compatibility, variants—become operational work, not just technical cleanup.

Advertising is also being reshaped around recommendation. An assistant can name a brand as the best option without the shopper ever seeing a traditional display ad. That’s pushing ad sellers and brands toward formats where sponsorship is identifiable, traceable, and compliant. In the European Union, the article notes, ad transparency and fair information rules require avoiding confusion between advice and promotion—nudging the market toward hybrid models where AI suggests, but the retailer clearly labels sponsored results on its own interfaces.

Another layer is review and satisfaction data. Assistants lean heavily on reputation signals—ratings, returns, frequently asked questions. Retailers want to make those signals more reliable by detecting fraudulent reviews, better categorizing return reasons, and surfacing simple indicators like durability, true-to-size fit, delivery performance, and after-sales service.

In the near term, the goal for Valiuz and partner retailers is to stay present in the discovery phase without losing the direct relationship. That means technology partnerships, data connectors, and conversational experiences embedded into retailer sites—keeping control over inventory, pricing, and compliance. Inside e-commerce organizations, the article describes the work as cross-functional: marketing, data, legal, customer service, and logistics.

Global AI e-commerce market hits $11.21 billion in 2026

The spread of AI assistants in online shopping is part of a broader economic shift. A compilation of industry statistics cited in the article puts the global AI-in-e-commerce market at about $11.21 billion in 2026, up from $8.65 billion in 2025. While estimates vary by firm and methodology, the direction points to accelerating investment in recommendation tools, customer service, anti-fraud systems, price optimization, content production, and campaign automation.

For consumers, the change shows up in practical ways: faster support chats, more contextual answers, natural-language order tracking, and suggestions for complementary products. But that convenience also raises expectations. When a dialogue feels confident, an inconsistent answer, vague timeline, or broken promise stands out more. Customer-service teams are learning to constrain AI with explicit limits and escalation procedures.

For brands, AI is becoming a competitiveness issue on the product page. Photos, copy, spec tables, and Q&A responses all need to align. Companies with large catalogs are working to industrialize quality—spotting inconsistencies, standardizing units, and enriching semantics—both to serve customers and to be interpreted correctly by conversational assistants.

Trust remains the main boundary. An assistant can help prevent a bad purchase, but it can also hallucinate a feature. Retailers are emphasizing proof—links to internal sources, clear warranties, straightforward return policies, real-time availability. Consumers, meanwhile, are learning to verify. Personal data also remains sensitive, especially when an assistant would need purchase history, sizing, preferences, or location to be truly useful.

Over the coming months, the article argues, AI use should keep spreading in early stages—ideas, comparison, decision-making—while moving more slowly into the transaction itself. The winners will be the players that connect AI to reliable data on price, inventory, delivery, and after-sales service, while making responsibility clear. The pace at which shoppers will fully delegate purchases remains uncertain, but AI-assisted product search is already becoming a new habit.