Francesca Roche sits down with Simon Langevin, VP of Product at Coveo, to examine the trust challenge emerging as AI takes a larger role in product discovery and purchasing decisions.
AI shopping assistants promise faster answers and more personalized recommendations, but a confident response can quickly become a customer experience problem when the information behind it is incomplete, outdated, or contradictory. Product origin, availability, pricing, compatibility, and entitlement data all carry consequences when surfaced incorrectly.
For consumer brands, a flawed answer can damage the credibility that differentiates them from larger marketplaces.
“What makes you different than these big retailers is really your brand,” Langevin says.
“People trust that you know what you’re selling.”
The risk becomes more acute in B2B purchasing, where buyers may work with complex catalogs, negotiated price lists, and specific purchasing permissions.
An AI assistant that recommends an unavailable product or displays a default price instead of a customer’s agreed rate can create costly returns and difficult follow-ups.
Langevin says the issue extends beyond the behavior of the large language model itself. “It’s both,” he says, referring to hallucinations and poor data governance.
AI models may produce more refined answers through multi-step reasoning, but their outputs remain dependent on the information they can access. If a system draws on inaccurate or stale data, even a well-grounded answer can be wrong.
That raises a practical question for CX and commerce leaders: where should reliability begin?