Last month, Coveo was named a Leader in the 2026 Gartner® Magic Quadrant™ for Search and Product Discovery for the third consecutive year. 

For many organizations, the Magic Quadrant is where the evaluation process starts. It helps buyers identify vendors with the vision and execution to make the shortlist.

But choosing the right platform requires a deeper look.

That’s where the companion Gartner® Critical Capabilities for Search and Product Discovery Report comes in. Rather than evaluating vendors overall, it assesses 12 search and product discovery solutions across five real-world use cases, helping organizations understand how products perform in the scenarios that matter most—from product search and discovery, B2B search, commerce content search to multisite and globalization and GenAI product discovery.

We’re pleased to see Coveo recognized across the report, including being ranked #1 in the Commerce Content Search use case (4.26/5) and being amongst the three highest ranking vendors in three additional enterprise commerce use cases (Product Search and Discovery, B2B Search, and Multisite and Globalization).

For us, those recognitions aren’t simply about rankings. We believe they reflect the direction enterprise commerce is moving: beyond traditional search and toward AI-powered discovery experiences that unify products, content, and customer context.

Commerce Has Moved Beyond Product Search

For a long time, the measure of a good commerce search experience was simple: could a customer find a product quickly? That was the job.

That’s no longer the whole job.

Today’s buyers, whether they’re consumers researching a major purchase or procurement managers sourcing industrial equipment, don’t just search for products. They research them,  read buying guides, compare specifications, look for compatibility information, and work through installation documentation. In complex B2B environments especially, a product search and a content search aren’t two different tasks. They’re one continuous journey.

The problem is that most commerce platforms were built to treat them as separate. Products live in the catalog. Content lives somewhere else. And when a buyer’s question spans both, the experience falls apart.

Generative AI is making this gap more visible, not less. An AI assistant can only synthesize what it can retrieve. When products and content exist in separate indexes, the answers it returns are incomplete — and in high-stakes purchasing decisions, incomplete answers cost sales.

This is the shift that Coveo has been building toward for years: a unified discovery experience where structured product data and unstructured enterprise content are indexed and retrieved together, ranked by relevance within a single query.

Why Use Cases Matter

In our view, one of the strengths of the Critical Capabilities methodology is that it recognizes there isn’t one definition of “best.” Instead, a Gartner Critical Capabilities document is a comparative analysis that scores competing products or services against a set of critical differentiators identified by Gartner. It shows you which products or services are a best fit in various use cases to provide you actionable advice on which products/services you should add to your vendor shortlists for further evaluation.

A global manufacturer managing millions of parts has very different requirements from a fashion retailer.

A distributor serving business buyers has different priorities from a consumer brand focused on inspiration and merchandising.

Evaluating technology through real deployment scenarios provides buyers with a much richer understanding than a single overall ranking ever could.

Why Commerce Content Search Stands Out

The Commerce Content Search use case in the Gartner Critical Capabilities report is specifically designed for organizations aiming to deliver relevant nonproduct content across storefronts to support purchasing and engagement decisions

Coveo ranked #1 in this use case with a score of 4.26 out of 5. In practice, this matters across a wide range of industries and buying scenarios.

A procurement team sourcing commercial refrigeration equipment may need installation specifications, maintenance documentation, and compliance certifications before they can issue a purchase order. A technician searching for a replacement part needs to find not just the part, but the service manual that tells them whether it’s compatible with their existing system. A buyer evaluating outdoor gear may be equally influenced by a product comparison article as by the product listing itself.

In each case, separating product results from content results doesn’t reflect how buying decisions are actually made. And as more organizations deploy conversational AI experiences, that separation becomes increasingly costly. Generative answering built on top of a unified index returns richer, more trustworthy responses — because it has access to the full picture.

Proof in Practice

Dow’s vast digital experience spans 16,000 pieces of technical content across 500 applications and 9,000 products. Despite this depth, customers initially struggled to find the right information—a challenge directly reflected in Dow’s Customer Experience Index (CXi) scores.

To bridge this gap, Dow partnered with Coveo, which immediately triggered a 70% drop in zero-result searches and boosted Dow’s Relevance Index to an impressive 87%. Building on this solid AI foundation, Dow has launched an internal pilot program leveraging Coveo’s generative answering capabilities. This next-generation initiative allows customers to instantly compare materials and receive accurate, concise responses in an easy-to-understand visual format, all backed by trusted sources and robust governance guardrails.

Built for the Complexity of Enterprise B2B

Coveo was ranked #3 in the B2B search use case, with a score of 4.17 out of 5.

B2B commerce search is a fundamentally different problem from consumer search. Business buyers often know exactly what they need — and search using part numbers, model codes, or technical specifications rather than descriptive language. They expect the platform to understand that a search for “M8 x 1.25 hex bolt grade 10.9” is a precise technical query, not a keyword match exercise. They also expect contract pricing, entitlement-aware catalog access, and compatibility logic to operate silently in the background, shaping results without requiring manual filtering.

For manufacturers and distributors managing millions of SKUs across multiple brands, customer segments, and regional catalogs, that complexity is the baseline. 

We believe those capabilities become even more important as organizations introduce AI-powered discovery experiences. AI doesn’t simplify enterprise complexity — it has to work within it. The quality of an AI-generated answer depends on the quality, structure, and relevance of the information it can retrieve.

Proof in Practice

When its existing search failed to meet customer expectations, global distributor ADI selected Coveo for its deep B2B expertise — specifically its ability to handle complex queries, partial part-number searches, and customer-specific pricing. Managing a large catalog of over 500,000 SKUs across 200+ locations and 100,000+ customers was a major challenge, but the upgrade delivered immediate, measurable impact. Following implementation, ADI saw its null search rate drop by 91%, revenue tied to onsite search increase by 28%, and overall conversion rates climb by 16%.

Relevant Reading: The B2B Search & Product Discovery Field Guide

Scaling Across Markets Without Sacrificing Relevance

Coveo was ranked #2 in the multisite and globalization use case, with a score of 4.13 out of 5.

For global enterprises, delivering relevant search experiences becomes exponentially more complex as they expand into new markets. Supporting multiple languages is only the starting point. Organizations also need to account for market-specific product assortments, regional pricing, localized content, merchandising strategies, regulatory requirements, and customer expectations, all while maintaining a consistent brand experience.

What’s relevant to a buyer in Germany may not be relevant to a buyer in Japan or Brazil, not just because they speak a different language, but because they see different products, buy under different commercial conditions, and have different expectations. The challenge is delivering those localized experiences without creating unnecessary operational complexity every time a new market, brand, or region is added.

As digital commerce continues to globalize, the ability to scale search and product discovery across multiple markets efficiently is becoming a competitive advantage in its own right. We believe that’s why Gartner evaluates multisite and globalization as a dedicated use case, and why it’s increasingly important for enterprise organizations building for long-term growth.

Relevant reading: Multi-Market Support

Where We’re Investing Next

Coveo’s recent investments in AI and agentic capabilities includes Conversational Product Discovery, an MCP server, and our native Shopify application — alongside capabilities like Relevance Generative Answering, Agentic Merchandising Copilot, Intent-Aware Product Ranking, Listing Page Optimizer, and automatic relevance tuning.

These investments reflect a conviction we’ve held for a while: that AI changes the interface for commerce discovery without changing the underlying requirement for relevance. Shoppers and buyers are increasingly asking full questions rather than entering keywords. They expect answers, not just results. But the quality of those answers still depends entirely on the quality of what the retrieval layer can surface.

Conversational product discovery is a good example of how we’re thinking about this. Rather than building a generic chatbot layered on top of search, we’ve built a conversational experience that understands product relationships, can handle multi-turn refinement, and draws on the same unified index that powers all of our search experiences. The conversation is grounded in the catalog — which means the answers are grounded in reality.

The MCP server investment reflects a related belief: that as AI agents become more prevalent in commerce — browsing, comparing, and purchasing on behalf of users — the platforms that expose live, structured, trustworthy product data to those agents will have a significant advantage. Off-site discovery is becoming a real channel, and it rewards the vendors who’ve invested in data quality and interoperability.

Coveo’s Note on What These Rankings Mean

The Gartner Critical Capabilities methodology is designed to help organizations evaluate products against the use cases most relevant to their own business. Every organization has different priorities, and the right weighting of capabilities depends on the deployment scenario you’re actually planning for.

With that in mind, we’d encourage anyone evaluating search and product discovery platforms to work through the use case scoring in detail, adjusting the capability weightings to reflect your own context.

What we can say is that the recognition we’re most proud of, particularly the #1 ranking in commerce content search, we feel reflects capabilities we’ve invested in deliberately, because we believe they represent where enterprise commerce is heading. The boundary between product discovery and content discovery is dissolving. AI is accelerating that process. And the platforms best positioned to deliver on what comes next are the ones that never treated those as two separate problems.

Gartner, Critical Capabilities for Search and Product Discovery, Aditya Vasudevan, Sandy Shen, Mike Lowndes, Noam Dorros, 23 June 2026. 

Gartner, Magic Quadrant for Search and Product Discovery, Aditya Vasudevan, Sandy Shen, Mike Lowndes, Noam Dorros, 22 June 2026

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