ETIM 10.0 contains 5,640 product classes and 16,728 features. Neither of those numbers is the one that hurts. The one that hurts is 73,085, the count of class-feature relations in the model. A feature in ETIM carries no fixed unit and no fixed value list. Both are set per class. That single design decision is why ETIM mapping projects fall apart in month two. It is the first thing we walk through on every ETIM engagement.
AI in product data has a clean dividing line, and it is not the one most articles draw. The question is not whether a task is complex. It is whether the output can be checked against something.
Product classification is not filing. It is the structure that decides whether an engineer with a specification in mind finds your part in twenty seconds. Or buys it from someone else. In a catalogue of tens of thousands of SKUs, an inconsistent structure is not untidy. It is lost revenue that never appears in any report, because nobody measures the searches that returned nothing useful.
Clean product data pays back. What nobody can honestly tell you is by how much, in your business, without measuring it first. Every vendor case study you have been sent describes a different merchant, with a different catalogue, at a different starting point.
Do you need a PIM? For most businesses that put the question to us, the answer is yes, no, or not yet. It is rarely a close call once the right evidence is on the table. The difficulty is that almost everyone you can ask has a reason to say yes. Vendors sell platforms.
Industrial product data is not marketing collateral. It is the evidence an engineer uses to decide whether your part belongs in their design. And whether the plant is still running on Monday. Bearings, valves, and safety equipment are not bought on brand preference. They are selected on performance, compliance, and fit.
Product content performance is a different question from product data quality, and most teams only measure the second. Completeness scores tell you the attributes are populated. They tell you nothing about whether the content is doing any commercial work.
Product data sourcing sets the ceiling on everything downstream. No amount of cleaning, enrichment, or platform investment recovers information that was never obtained, and most catalogues are limited by acquisition rather than by processing.
A product description backlog is a prioritisation problem before it is a writing problem. Generation capacity stopped being the constraint some time ago. What still constrains you is deciding which products deserve description, what good enough means for each, and who checks. Get those three right and a backlog of thousands clears in weeks. Get… Read More »Clearing a Product Description Backlog With AI at Scale
Shopify product content usually fails in one specific way, and it is not the way merchants expect. The descriptions are fine. The photography is fine. What is missing is anything a customer can filter on, because the details sit inside prose rather than in structured fields.
Building materials product data has stopped being an administrative task and started deciding commercial outcomes. It determines whether a merchant will list your product and how long a new range takes to reach the trade counter. It also decides whether you clear the compliance bar on a tender.
Product content enrichment is the work of turning a record that exists into a record that sells. A product name, a code, and three specifications will get an item onto a website. They will not answer the questions a buyer asks before committing, and they will not satisfy a marketplace with forty mandatory attributes.
We have seen the same five failure modes across merchant and rural retail catalogues on both sides of the world.
We have run product data and PIM projects with companies including RS Group, APS Industrial, and Maxiparts, and across all of them we see the same seven patterns. Every single time.
The supplier onboarding process should be a repeatable workflow with templates, validation rules, and exception handling, not a one-off project run from scratch every time.
The question is no longer whether AI product descriptions are good enough to use commercially. They are. The question is which of three approaches fits the catalogue, the team, and the buyer.
ACES and PIES are the two XML data standards that underpin automotive aftermarket cataloguing.
Most Akeneo vs Bluestone vs Plytix articles are written by people who have read the marketing pages, watched the demo videos, and assembled a feature comparison table from the vendor websites.
Most PIM vendor shortlists are built for retailers. The evaluation criteria assume a few thousand SKUs, consistent brand-owned product data, and a single primary ecommerce channel.
Shopify handles checkout, orders, and storefront design well. What it does not handle is complex product data at scale. Once your catalogue reaches a few thousand SKUs, or you start publishing to channels beyond your Shopify store, the standard product editor becomes a serious bottleneck.