The state of product data in MRO & Industrial (2026)

An MRO catalog is a moving target: hundreds of thousands of SKUs, sourced from thousands of manufacturers, each shipping specs in its own format on its own schedule. Most distributors have been patching that problem with spreadsheets and tribal knowledge for twenty years. In 2026, three things are converging to make that patchwork untenable: buyers who no longer tolerate it, AI systems that can't parse it, and a channel structure that's shifting faster than the data underneath it.

What's actually broken

Walk into any MRO distributor's product data operation and the pattern repeats: manufacturer content arrives as PDFs, cut sheets, and half-populated flat files, and someone on staff is manually retyping specs into the PIM or ERP because there's no other way to get a bearing's bore diameter or a fastener's thread pitch into a structured field. Industrial Supply's product data management overview puts the scale problem plainly: as SKU counts expand and manufacturer relationships multiply, internal teams get buried under inconsistent data and constant updates, forcing the same cleanup work over and over instead of once.

The result shows up on the product page. A raw manufacturer feed for a common industrial item might hand a distributor this:

Raw feed description: Ball Bearing, Sealed, 25mm Bore

What an enriched attribute table looks like:

One version tells a buyer this is "a bearing." The other tells a maintenance engineer whether it fits the pillow block already bolted to their conveyor, at 2 a.m., without a call to the counter.

That gap is why so many distributors still run a phone-and-counter business behind a thin website. Search on the site can't filter by a spec that was never captured, so the buyer either calls or leaves. Neither outcome shows up as a "data quality problem" in a quarterly review, but both are exactly that.

What it costs

The costs are boring and compounding, which is why they get ignored:

• Returns. Incomplete or inconsistent product data is a well-documented driver of B2B returns — the wrong thread pitch, the wrong voltage rating, the missing compatibility note — and every one of those returns costs more to process than the margin on the part itself.
• Lost search, on-site and off. Distributors managing tens or hundreds of thousands of SKUs across dozens of categories routinely lose buyers to competitors with better product discovery simply because a filter or spec was never populated in the first place.
• Thin PDPs that undersell the SKU. A product page with a title, one photo, and a price is a page an engineer can't spec against. It doesn't just lose the sale on that page — it teaches the buyer to stop trusting the site's data at all, which pushes them back to a phone call or a competitor's catalog.
• Manual rework that never ends. Every supplier onboarding, every catalog refresh, every new manufacturer line means someone re-keying specs by hand — a process that typically runs in the 30-45 minute per SKU range when done manually, which is a real number when a distributor is onboarding thousands of new parts a year.

None of this is new. What's new is that the cost of ignoring it just went up.

Why 2025-2026 changes the math

Three things are compounding at once.

AI answer engines are now where B2B research starts. Forrester's 2026 Buyers' Journey Survey of roughly 18,000 global business buyers found that 94% used AI somewhere in their most recent purchase process, up from 89% just a year earlier, with 54% using AI tools specifically for product research and 55% using them to compare vendors. When a buyer asks an assistant "find a sealed 25mm bearing rated for 12,000 RPM with pillow block compatibility," the answer engine pulls from whichever source has that spec in a clean, structured format. A PDF cut sheet or a bare product title doesn't get cited. A complete attribute table does.

Digital-native buyers expect a self-service spec search, not a counter conversation. The generational handoff on the buying side is real: procurement is increasingly done by people who grew up filtering products online before they ever picked up a phone, and they treat a distributor's search bar the way they'd treat any e-commerce site. Distributors that can't support that expectation are ceding ground to newer, tech-forward competitors who compete on speed and transparency rather than relationship history.

Channel leaders are moving first, and moving on data. Grainger's own 2026 commentary is instructive here — Digital Commerce 360 reported that CEO D.G. Macpherson pointed to years spent building "core product and customer information assets" as the foundation now enabling its AI-driven sales and search tools, with e-procurement and EDI already accounting for roughly 40% of Grainger's order origination. When the largest player in the category is explicit that clean product data is the prerequisite for its AI push, smaller and mid-size distributors don't get to treat their own data as an afterthought — the baseline buyers expect has moved.

Put together: the buyer has changed, the channel has changed, and the interface they're both using to find a part has changed. The one thing that hasn't caught up, at most distributors, is the data underneath the catalog.

Where this leaves distributors and manufacturers

None of this requires ripping out an ERP or a PIM system. It requires treating product data as a discipline that gets maintained continuously, gap-filled from real supplier documentation, and scored for completeness the way inventory is scored for accuracy — because a spec table an AI engine can parse and a buyer can trust is now table stakes, not a nice-to-have.

This is precisely the layer Anglera works on. Your PIM or ERP still stores the data; Anglera continuously scores, gap-fills, and enriches it from the source documents you already have, so a bearing entry reads like the table above instead of a five-word title — without a rip-and-replace project or a multi-year systems integration.