Agentic Commerce Will Run on Product Data

Two years ago, you could plan around how a buyer found a product. They searched, they got a page of results or found a page in the catalog, and they examined a few of them before deciding. Product content was optimized for that path, and it worked.

AI is now in the middle of that moment. A shopper asks an assistant for a recommendation and gets one answer instead of ten links. Procurement teams are pointing agents at supplier catalogs, and retailers are piloting storefronts where the customer on the other end is also software. Inside manufacturers, teams are launching products faster than ever. Very little of this has settled into anything you could call the standard way it works.

We think that transition is the best argument for investing in product information right now.

Why PIM is no regret work

When the future is this open, you look for the work that pays off however things land. If agents take over discovery, they will need product data that is complete, accurate and readable by a machine, or they will skip you. If the shift happens more slowly, you still have more products, more channels and more regulation than last year, with the same data problem underneath.

In both cases, the companies that do well will be the ones whose product information is already governed and connected and can be picked up by systems they haven’t built yet. Product data is simply far too important to be neglected or treated as an afterthought.

PIM has become the work that every AI-driven growth scenario depends on.

PIM stopped being an IT project this year

The clearest sign that the market has reached the same conclusion is our own calendar.

2026 saw PIM conversation move outside the realm of IT, with increased interest from teams like ecommerce or marketing, mostly driven by AI adoption. The category is expanding into parts of the business that didn’t consider product data their problem until it became one.

As businesses adopt AI for go-to-market, how product data is handled must change as well. A PIM built as a simple storage layer can’t adapt to the complex data needs of AI-driven workflows and agentic commerce.

Why we believe we were recognized

Most organizations are trying to support a modern business with product information practices designed for a different era. Eventually growth outpaces the operating model. We believe the next generation of PIM is AI-powered, connected and built for orchestration, and that is what we spent the year building. Three things we’d point to:

It connects at every layer. Inriver has shown an ability to quickly respond to market trends, such as our MCP servers that let AI agents interact directly with governed product data, so a team standing up a new agent or channel doesn’t need custom development to get what it needs.

It learns from every channel. With Signals and Projects, the platform reads performance signals from the digital shelf and from answer-engine results, identifies where content is falling short, and turns that into work inside the PIM. It orchestrates at the core. The visual workflow builder and Inspire AI let business users define how workflows and what AI is allowed to act on. Product data moves faster, stays controlled and scales with the business, with the people closest to the products steering.

The shift from storing product information to orchestrating the people, workflows, systems and AI behind it is, we believe, what this recognition reflects.

What to do now

If you’re responsible for how your products show up when AI is increasingly doing the finding and surfacing in online search, the most valuable thing you can do this quarter is make your product data ready for whatever asks for it next.

Get complimentary access to the 2026 Gartner® Magic Quadrant™ for Product Information Management Solutions and see how Gartner defines the category as AI reshapes product discovery.

Read the report

Gartner, Magic Quadrant for Product Information Management Solutions, Jason Daigler, Greg Carlucci, Nicholas Mortensen, October 12, 2026.

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