Search has always been one of the strongest indicators of purchase intent. Industry research consistently shows that shoppers who use site search are more likely to convert and often generate higher average order values than shoppers who browse alone. Yet many e-commerce search experiences still rely heavily on exact keyword matching, creating friction when shoppers search using different terminology, natural language, or incomplete product information.

This challenge is common across e-commerce. Shoppers describe products in their own words, while catalogs are built around structured product data and merchandising terminology. The result is often irrelevant search results, zero-result searches, and missed conversion opportunities.

As e-commerce experiences continue to evolve, shoppers increasingly expect search experiences that understand intent and help them quickly discover the products they are looking for.

To address these challenges, Adobe Commerce continues to invest in search innovation and AI-powered product discovery capabilities designed to improve relevance, transparency, and merchant control.

Adobe Commerce search innovation in 2026.

Search is one of the most important touchpoints in the e-commerce journey, directly influencing product discovery, shopper engagement, and conversion rates.

As part of ongoing investment in Adobe Commerce search and merchandising innovation, we are introducing a series of improvements throughout 2026 designed to help merchants deliver more relevant, intelligent, and transparent shopping experiences.

These investments include:

• Semantic search, which brings AI-powered intent understanding to product discovery.
• Exact match prioritization (private beta), which ensures that products that precisely match shopper intent appear more prominently in search results.
• Intelligent ranking configuration (public beta), which provides merchants with greater control over how search results are ranked and presented.
• Merchandising transparency and insights help merchants understand why products appear in specific positions and what factors influence ranking decisions.
• Additional AI-driven discovery and merchandising capabilities planned for future releases.

Together, these investments represent Adobe's commitment to helping merchants improve product discovery, reduce friction in the shopping journey, and create more engaging e-commerce experiences.

Improving search relevance through intent understanding.

Semantic search is a new capability within Live Search that improves product discovery by understanding the meaning behind shopper queries, not just the exact keywords used.

Traditional keyword search performs best when shopper queries closely match product catalog terminology. Semantic search complements keyword search by helping connect shopper intent with relevant products, even when the words used do not exactly match catalog content.

By understanding relationships between concepts, product descriptions, and shopper queries, semantic search helps:

• Improve product discovery.
• Reduce zero-result searches.
• Surface more relevant products.
• Help shoppers find products faster.
• Improve engagement and increase conversion opportunities.

For example, a shopper searching for a "hiking jacket for cold weather" may still find relevant products even if the catalog describes them as insulated outdoor shells, weather-resistant trekking jackets, or winter mountain jackets. Rather than relying solely on exact keyword matches, semantic search helps connect the shopper's intent with products that satisfy the need.

Semantic search represents an important step toward more intelligent product discovery experiences within Adobe Commerce. By combining AI-driven relevance with existing search, merchandising, and filtering capabilities, retailers can deliver more helpful, engaging, and effective shopping experiences.

Early Adobe Commerce beta customers have reported meaningful improvements in search quality and reductions in zero-result searches after enabling semantic search. A leading furniture retailer reduced zero-result searches by 57% within the first month of testing semantic search. A fashion retailer reduced zero-result searches by approximately 30% within the first ten days of deployment.

By helping shoppers discover products more effectively, retailers can create smoother shopping journeys, increase engagement, and reduce the likelihood of shoppers leaving the site after unsuccessful searches.

How semantic search works.

Semantic search uses machine learning and semantic similarity models to better understand the relationship between shopper queries and product catalog content.

Instead of relying exclusively on exact keyword matches, the system evaluates the meaning of both the search query and product information to identify relevant results.

This allows Adobe Commerce to return useful product matches even when shoppers use different terminology, broader concepts, or natural language descriptions.

The capability works alongside existing Live Search functionality, preserving merchandising controls, filters, facets, sorting options, and business rules while improving relevance and product discovery.

Interested in learning more? Explore the Adobe Commerce semantic search documentation for feature details, enablement guidance, best practices, and recommendations for optimizing product discovery experiences.

New search capabilities availability.

Semantic search is generally available for Adobe Commerce starting June 8, 2026.

Exact match prioritization is currently available as a private beta and can be enabled for customers interested in evaluating the capability ahead of broader availability. To request participation in the beta program, email commerce-storefront-services@adobe.com. The Adobe team will review the request and provide next steps, eligibility requirements, and onboarding guidance.

Intelligent ranking configuration is available today as a public beta, allowing all Live Search customers to benefit from enhanced control over search relevance and ranking behavior.

Looking ahead.

This represents the first step in a broader roadmap of AI-powered search and merchandising innovations planned for Adobe Commerce. Future investments will focus on continuously improving search relevance, merchandising transparency, merchant control, and product discovery experiences across the storefront.

Our goal is simple: to help shoppers find the products they want faster while giving merchants the visibility and control needed to continuously optimize the shopping experience.

The Adobe Commerce Product Team builds AI-powered commerce capabilities that help merchants deliver intelligent, high-performing shopping experiences.

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