Ask most B2B organizations why they haven’t invested in conversational or AI-powered product discovery and you’ll hear a familiar response: our buyers already know what they’re looking for. They’re procurement professionals. They order the same products every quarter, search by SKU or part number, and know their catalog inside and out. Unlike consumers, they aren’t browsing for inspiration.
It’s a reasonable assumption, but it overlooks how B2B buying actually works.
During a recent webinar hosted by Coveo and Experian, Juliette Meugniot, Business Value Engineer at Coveo, described a pattern the company sees across manufacturers, distributors, and wholesalers. As she explained during the discussion, “when you look at search sessions rather than individual queries, what you usually see is that most sessions include more than one type of query.”
Buyers may begin with a precise search, but their journey rarely ends there. When Coveo looks at complete search sessions rather than individual queries, many include a mix of exact product searches, broader category searches, and informational queries. That tells us something important: even experienced buyers don’t spend every session behaving like experienced buyers.
That distinction matters because it shifts the conversation away from search and toward product discovery. Search is designed to retrieve something you already know exists. Product discovery helps buyers navigate everything that happens when they don’t find exactly what they expected, or when they need help understanding what to buy next.
Search Is Only Part of the Buying Journey
The assumption that B2B buyers don’t need help usually comes from looking at individual searches rather than complete buying journeys.
A procurement manager might search for an exact part number before asking whether the replacement model is compatible with an existing machine. A maintenance engineer might begin with a broad category search before narrowing the results to a specific specification. Another buyer may know the SKU they ordered last year only to discover that it has since been discontinued.
Viewed individually, these searches appear unrelated. Viewed together, they reveal a buyer moving between certainty and uncertainty throughout a single purchasing session.
This is where traditional search begins to show its limitations. It excels at matching keywords but struggles when products evolve, catalogs expand, or buyers need guidance rather than retrieval. Product discovery fills those gaps by connecting buyers to the products, information, and context they need to complete their task, even when the path isn’t straightforward.
The repeat buyer is perhaps the best example. Imagine a customer who has reordered the same replacement component every few months for years. When that product is replaced with a newer version, a simple keyword search may return no results or surface the discontinued item. Meanwhile, the replacement product remains buried because it has little purchase history. The buyer most likely to convert suddenly encounters unnecessary friction, not because they didn’t know what they wanted, but because the discovery experience couldn’t connect intent with the right outcome.
Conversational AI vs. Agentic AI: Why the Distinction Matters
One of the most useful distinctions Juliette made during the webinar was between conversational and agentic AI. They’re often discussed as though they’re interchangeable, but they solve different problems. “Conversational AI is really about information and knowledge,” she explained. “Agentic AI then takes action on that information.”
Conversational AI helps buyers understand information. It can compare products, answer technical questions, explain specifications, or recommend products based on a particular use case. Agentic AI goes a step further by recommending the best option, adding it to a cart, or completing part of the purchasing workflow.
While agentic commerce is generating much of today’s excitement, Juliette’s point was that most organizations aren’t there yet. The more immediate opportunity is using conversational AI to improve product discovery. Helping a buyer determine whether a replacement part is compatible with existing equipment, identify the correct substitute for a discontinued product, or compare technical options removes friction from complex purchasing decisions without requiring a fully autonomous buying experience.
That’s also where Coveo’s perspective differs from much of the current AI conversation. The goal isn’t to introduce AI for its own sake; it’s to make product discovery more relevant. Whether the interaction is conversational today or becomes agentic tomorrow, success still depends on surfacing the right information at the right moment, grounded in trusted product and enterprise data.
Better Discovery Depends on Better Data
Throughout the discussion, one theme kept resurfacing: AI is only as useful as the data behind it.
A conversational interface can’t recommend the correct replacement product if that relationship doesn’t exist in the catalog. It can’t answer compatibility questions if technical specifications aren’t structured and accessible. It can’t recommend approved substitutes if those relationships haven’t been defined.
Trust also depends on where AI gets its answers. As Juliette noted, one of Coveo’s guardrails is ensuring that conversational experiences are “grounded only in the content that is being indexed” rather than pulling information from the open internet. That keeps recommendations tied to approved product information, documentation, and enterprise knowledge, an important distinction when customers are making purchasing decisions rather than asking general questions.
The same principle extends beyond commerce. Sales teams searching for technical documentation, support agents looking for similar cases, field service engineers troubleshooting equipment, and product teams researching previous work all face the same challenge: trusted information exists somewhere, but finding it quickly is often harder than it should be. Better discovery improves productivity just as much as it improves buying experiences.
Relevant reading: The AI Agent Readiness Checklist for Ecommerce
Start With Business Problems, Not AI Projects
Perhaps the most practical advice from the webinar had very little to do with AI itself. Organizations seeing the greatest success aren’t trying to deploy AI everywhere at once. They’re identifying specific customer problems and solving those first.
One Coveo customer built an application that helps restaurants understand recipe costs, recommends seasonal ingredient substitutions, and simplifies reordering. Manufacturers are embedding conversational experiences directly into product pages so buyers can verify compatibility before ordering instead of contacting support or risking an incorrect purchase.
Each initiative began with a clear business problem. AI became the mechanism for solving it rather than the objective itself.
That same mindset applies when measuring success. Rather than focusing on AI adoption, organizations should define the business outcomes they want to improve—whether that’s conversion rate, revenue per visit, search adoption, average order value, or reduced support costs—and establish a baseline before launching a project. Without that foundation, demonstrating ROI becomes far more difficult.
Relevant reading: The B2B Search & Product Discovery Field Guide
Product Discovery Is Becoming a Competitive Advantage
The buyers who already know what they want are often an organization’s most valuable customers. They’re ready to purchase, familiar with the catalog, and likely to convert. Yet they’re also the first to encounter friction when products are replaced, documentation is outdated, or compatibility isn’t obvious.
That’s why the future of B2B commerce isn’t simply about making search more conversational or deploying autonomous AI agents. It’s about building product discovery experiences that understand buyer intent, connect people with trusted information, and adapt when purchasing journeys become more complex than a single keyword search.
As Juliette put it,“this technology is super powerful, and we’re just scratching the surface of what we can do with it.” The organizations that see the greatest return won’t necessarily be the first to deploy fully agentic commerce. They’ll be the ones that start by solving real customer problems with better product discovery, prove the value, and build from there.
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