AI can only work with what you give it
In June 2025 we predicted product data would become the interface. A year on, it has.
In June 2025, we wrote that product data was becoming the interface between your brand and every AI agent that might recommend you.
That the agentic web was taking shape, and that brands with inconsistent, shallow product data would be invisible to it. That the work sitting inside your PIM and ERP was about to matter in a way it never had before.
That was a prediction about where things were going. It is not a prediction any more. That argument has not aged. It has arrived.
What has changed in twelve months?
A year ago, the agentic web was infrastructure being built by Microsoft and a handful of early adopters. Today it is a product decision Shopify has made at the platform level.
Shopify Catalog, which launched earlier this year, automatically standardises and enriches product data so AI models can reason about it. Shopify claims data it syndicates drives twice the conversion in AI shopping interactions. The unit that matters in agentic commerce, they have decided, is the product. Not the brand, not the campaign, but the product, and the data behind it.
MCP, which we flagged as the plumbing between AI interfaces and your actual systems, is no longer theoretical. Brands are rolling it out now. The journey from a messy reporting setup to a data layer with a natural-language tool on top, where any team member can query and analyse for themselves, is a project operators are completing in months rather than years.
The early adopters are no longer Tripadvisor and O’Reilly. They are brands in the same category and size range as yours.
The problem nobody expected AI to solve first
The assumption was that the agentic web would expose product data problems externally. That AI agents shopping on a customer’s behalf would simply skip brands whose data was too inconsistent to reason about. That the pressure would come from outside.
The more immediate pressure has turned out to be internal.
Brands pointing AI tools at their own operations are discovering exactly how inconsistent their product data is, faster and more visibly than ever before.
One product tagged as boots, another as ankle boots and another as footwear. Sizes in one format in one category, a different format in another. Colour buried in a title rather than sitting as a clean attribute.
We’ve seen it on audit after audit this year. One product tagged as boots, another as ankle boots, another as footwear - same product, three vocabularies. Sizes in UK numeric in one category, S/M/L in another. Colour sitting inside a title string instead of living as its own attribute, so nothing downstream can filter, compare or reason on it.
None of this is new. What’s changed is that the moment you point an AI tool at it, every inconsistency surfaces in front of a decision someone needs to make now - not buried in a data quality report nobody reads, but in the actual output the business is trying to act on.
These were always problems. AI has made them impossible to ignore because it surfaces every contradiction at speed, in front of a decision that needs making now.
The mess was always there. The tools have just made it visible.
The bit nobody is selling a tool for
Shopify Catalog enriches what you feed it. It does not fix what you feed it with. MCP exposes your data layer to the systems querying it. It does not make that data layer coherent if it was not already.
We’d say roughly a third of the problem that tooling solves is the visible part. The rest is whether the brand can describe its own products the same way everywhere they live, and whether the operating model can keep them that way as the business changes.
That’s an ownership question. Who is accountable for product data quality across the business? Which system wins when three systems disagree? These are the questions that determine whether the tooling pays off, and they have no vendor category.
What to actually do about it now
Get your product data consistent across every system that touches it before you point an AI tool at any of them.
That means naming a source of truth for every product attribute and writing down which system wins when systems disagree. It means deciding who owns product data quality across the business, not who manages the PIM. And it means doing that work before the AI surfaces the inconsistency in a context where it costs you.
The brands that will perform well in AI-driven commerce are not the ones with the boldest agentic strategies. They are the ones that had something coherent to syndicate when the tools arrived.
A year ago, that was a prediction. Now it is just what is happening.
This is exactly the kind of gap our product ops work is built to find - where your data breaks down, which system should be the source of truth when they disagree, and what it’d take to get there.
If you want to talk through where your own catalogue would trip up an AI agent today, grab time with Luke - worth having that conversation before the tooling forces it.