Retail search and discovery is entering a new phase. Instead of relying primarily on keywords, filters and navigation, shoppers are increasingly turning to AI assistants and agents to help them find, compare and evaluate products. For retailers, this shift has direct implications for how discovery systems, data and governance need to be designed.

In a newly published research report, titled The Future of Search and Discovery: A strategic playbook to understand agentic commerce, published by RetailEconomics, they describe this transition as ‘agentic retail search and discovery.’ It points towards a future where intelligent systems interpret consumer intent, scan content across the web, filter and rank options, and increasingly complete parts of the purchase process on the shopper’s behalf. As these agents become more capable, they also become a new channel that retailers must optimize for.

To understand what this means in practice, their research combines analysis of technological change with new evidence on consumer behavior. They surveyed 6,000 consumers across the UK, US and France to identify where agentic discovery is already influencing shopping journeys, where trust remains limited, and which parts of retail are most exposed to change.

From keywords to agents: How product discovery is evolving

Product search and discovery has changed significantly over the past 15 years. Keyword-driven search gave way to mobile browsing, personalised feeds and recommendation systems. Each stage reduced friction and compressed the path from intent to transaction.

The current shift is more structural. AI-driven discovery is changing where journeys begin and how products are surfaced, researched and selected, which stretches the limits of traditional SEO and onsite optimization. Visibility increasingly depends on whether product data can be parsed, verified and ranked by AI systems.

For example, many AI bots struggle with JavaScript-heavy implementations. If product content is obscured, poorly structured or inconsistently rendered, agents may miss it or misinterpret it. Structured product data, consistent attributes, clear provenance and reliable access policies become critical inputs to discovery performance. 

A systems framework for agentic discovery

To help retailers respond, the report introduces a systems framework that connects consumer behaviour to operational priorities.

On the consumer side, it tracks the journey from information clarity, through discovery experience, to decision and loyalty. On the retailer side, it translates these stages into three operational layers:

• Visibility: how product data is structured, governed and made accessible
• Control: how AI agents are managed, monitored and integrated into journeys
• Trust: how security, reassurance and transparency are maintained

Together, these layers provide a practical way for retailers to align discovery performance, operational control and customer lifetime value in an AI-led environment.

Inside the mind of the AI-assisted shopper

AI is already influencing how consumers explore and evaluate products. Nearly four in ten consumers (38%) have used third-party assistants such as ChatGPT or Copilot for product ideas, suggestions or comparisons, while around one in five (21%) consumers say they have used tools that make decisions or purchases automatically. 

This influence is strongest at the start of the shopping journey, particularly among younger shoppers. Among 18–24 year olds, AI assistants and social discovery channels now match or exceed traditional search in influence, reflecting a shift towards more conversational, assisted and content-driven pathways into shopping journeys. 

Which industries will AI reshape first?

AI adoption will not be uniform. It will accelerate first where it reduces complexity and effort, and move more slowly where trust, emotion and personal judgement dominate.

• Electronics and appliances are likely to be impacted first. These purchases typically involve technical considerations, rapid product cycles and meaningful price differences, making them well-suited for AI support. 
• Travel and leisure also reflects growing acceptance of AI tools that can synthesise preferences, budgets and availability into structured itineraries.
• Clothing and footwear emerges as another category with rising exposure. Large online assortments, frequent browsing and highly individual preferences create fertile ground for AI-led personalisation. 
• Jewelery, beauty, and homewares, on the other hand, tell a different story. Many of these purchases are highly emotional, tactile, and identity-driven, limiting the willingness to delegate.

Different shopping missions also produce very different levels of openness to AI assistance. Missions characterised by uncertainty, comparison, and cognitive load (such as considered or technical purchases) sit highest on trust and willingness, emotionally driven missions remain more resistant. Urgent replacements remain a cautious use case, while routine replenishment is the least AI-delegated mission.

The AI discovery personas

Not all shoppers are on the same journey with AI. The research identifies four distinct cohorts who use AI assistants for search and discovery: AI-first optimisers, Assisted explorers, Guarded adopters and Human loyalists. 

Together, they show where agentic commerce is already taking hold, where reassurance is still needed, and how quickly retailers can expect behaviour to shift across their customer base.

From insight to action: What you will find in the full report

This blog post only scratches the surface of what’s changing in retail search. The full report goes into much more detail, including deeper analysis of the forces reshaping discovery, the emerging competitive landscape, and the practical trade-offs retailers now face. 

It explores questions such as where agentic disruption will hit hardest, how browser and platform dynamics are evolving, when allowing AI agent access creates advantage versus risk, and how trust in different tools varies across markets and missions. It also introduces new ways to think about performance and measurement in an AI-mediated discovery environment.

In short, the report is designed as both a strategic lens and a practical playbook, helping leaders understand what’s happening and where to act first. It outlines three readiness workstreams, best practices for managing AI agent access, and a practical checklist to help retailers prioritize what to do now versus what to monitor.

Download the full report to explore the data, frameworks and strategic playbook in detail.