From search engine to buying agent: how agentic commerce increases the importance of product data

For years, e-commerce revolved around visibility in search engines, marketplaces and advertisements. A new playing field is now emerging. AI agents compare products, analyse specifications, interpret reviews and make recommendations based on context and user preferences. This development, also known as agentic commerce, is transforming not only the customer journey, but also the way products are discovered and selected. 

Staying visible in AI-driven search experiences

This also changes how organisations remain visible across digital channels. While traditional SEO focused on rankings in search engines, visibility is increasingly shifting towards AI-generated answers and recommendations. This development is often referred to as Generative Engine Optimisation (GEO). 

Product data plays a bigger role than ever in this shift. AI systems do not only look at branding or advertisements. They primarily rely on data: product specifications, availability, compatibility, context and the reliability of information. As a result, organisations with incomplete, inconsistent or difficult-to-access product data risk becoming less visible in AI-driven purchasing processes. 

The question is therefore gradually shifting from: “How do I get found by consumers?” to: “How do I get understood and recommended by AI?”

How AI evaluates products

In agentic commerce, AI systems support users in searching for, comparing and selecting products. Instead of visiting dozens of websites themselves, consumers give instructions to an AI assistant, for example: “Find the best noise-cancelling headphones under €300 for hybrid working.” AI then analyses product information, reviews, pricing and user preferences to provide a recommendation.

Although this development is still evolving rapidly, the first applications are already visible. Major technology companies are investing heavily in new AI-driven search and shopping experiences, and consumers are becoming increasingly familiar with AI-powered recommendations. Gartner expects that by 2030, around 20% of digital commerce transactions will take place via AI platforms or AI agents.

The impact lies not only in the technology itself, but mainly in the way products are evaluated. While consumers can often cope with incomplete information or inconsistent specifications, AI systems place much greater emphasis on context, relevance and the reliability of information. This places different demands on product information than many organisations are used to.

Why PIM is becoming increasingly strategic

In a commerce environment where AI plays a larger role, Product Information Management (PIM) is becoming increasingly important for many organisations. Not only as an operational system, but as the foundation for scalable and consistent product information.

Organisations not only need to centrally manage product data, but also structure, enrich and consistently distribute it across all digital channels in a way that both people and systems can understand.

A modern PIM system supports this by making product information available from one central source for webshops, marketplaces, AI platforms and other digital touchpoints. In practice, this helps organisations maintain more consistent product information across channels, enrich content at scale and gain greater control over product data. In an environment where AI plays an increasing role in product selection and recommendations, this consistency becomes ever more important.

Product data is shifting from a supporting process to a strategic foundation

Agentic commerce marks a broader shift within digital commerce. It is no longer just the webshop that matters, but increasingly the quality of the information systems use to select, compare and recommend products. This is transforming not only search and e-commerce, but also the role of product data within organisations.

In an AI-driven commerce world, visibility is becoming increasingly dependent on whether systems can understand, trust and use product information. Organisations that invest in high-quality product information, machine-readable data structures and consistent content are building a stronger position for the future.

Good product data is therefore evolving from a supporting process into a strategic component of digital commerce.