AI agents and what they mean for ecommerce in 2026
Gartner predicts that by 2028, 90 percent of B2B purchasing will be AI agent intermediated. For ecommerce and retail, the implications are significant and require attention now.
AI agents are systems that can take actions autonomously rather than just generating text responses. In the ecommerce context, an AI agent might research products, compare options, check pricing, and complete a purchase on behalf of a user with minimal human input. This is still emerging but the direction of travel is clear, and businesses that understand the implications early will be better positioned than those that wait for the changes to become unavoidable.
What agentic commerce looks like in practice
A user asks their AI assistant to find and order the best value business travel insurance for a specific trip. The agent searches across comparison sites, reads policy documents, checks review data, compares prices, and books the most suitable option - all without the user visiting a single website. The user's touchpoint with the purchasing process is the conversation with the agent, not the supplier's website or ad. This is already possible with current technology. It is not yet common. It is becoming more so.
What this means for paid search
If AI agents increasingly handle the research and decision-making phase of purchasing, traditional paid search ads displayed to human users browsing search results may become less relevant for certain purchase categories. The question for advertisers is how to maintain presence in the decision-making layer that AI agents use. This means being present in the data sources agents draw from: review platforms, price comparison APIs, authoritative directories, and structured product data that agents can parse efficiently.
The product data imperative
For ecommerce specifically, comprehensive, accurate, and machine-readable product data becomes increasingly important in an agentic world. Schema markup, accurate pricing information, clear product specifications, and structured availability data are all signals that AI agents can use to evaluate and recommend products. Retailers with high-quality, well-structured product data will be more visible to AI purchasing agents than those with thin or poorly organised product information.
The near-term priority
Do not deprioritise traditional paid search to chase agentic commerce. The traditional search model still handles the overwhelming majority of commercial queries and will continue to do so for years. But begin building the foundations - structured data, review platform presence, comprehensive product information - that will matter more as agentic behaviour increases. These investments complement existing paid search strategy rather than replacing it.
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