This will be familiar to you; the team responsible for getting products to market is made up of merchandisers who decide what to sell and how to position it, eCommerce teams make products discoverable and purchasable, marketing creates the story around them, while data teams maintain the systems underneath. However, AI is already blurring the boundaries between them.
As AI becomes embedded in search, discovery, personalisation and purchasing, product information is no longer simply content that businesses publish for consumers, but data that machines critically need to interpret, evaluate and act upon. If only retailers and brands had control of those machines but really they will belong to the customer.
So now, everyone has to ask, who owns product truth, who is responsible for visibility when discovery happens through an AI interface and who ensures AI systems actually understand the products they are recommending? The answers will spawn a new type of product team but first some current realities need to be faced.
Product information has traditionally been distributed across multiple functions and systems. Specifications might originate with suppliers, descriptions with eCommerce teams, imagery with marketing and pricing somewhere else entirely. And this all happens across marketplaces, social commerce, retail media and international markets.
AI is not immediately the answer because an AI shopping assistant cannot compensate reliably for contradictory, incomplete or poorly governed information. If one system describes a product differently from another, or essential attributes are missing, the problem is no longer simply an untidy product page; AI may misunderstand whether the product is relevant at all.
In the new team, there will have to be clear accountability for the quality, consistency and governance of the product record that sits beneath every customer experience. The product information? team of the future will need to include people who understand merchandising and storytelling alongside people who understand data architecture, taxonomy, governance and machine-readable information.
The next challenge for the new team is visibility. This is no longer simply about appearing in the right search results, marketplace listings, category pages or advertising placements – channels traditionally optimised primarily for platforms, but ultimately designed to reach human consumers. AI is a new audience making decisions on behalf of humans, translating their intent into product characteristics and then identifying products that genuinely satisfy them.
Simple keyword optimisation won’t work here because a product can only compete if the underlying information gives the AI enough context to understand what it is, what it does, who it is suitable for and how it differs from alternatives. Talking of those blurred boundaries, visibility is now a shared responsibility between merchandising, eCommerce and data teams. The merchandiser still needs their commercial judgement but that increasingly needs to be expressed through structured product information that machines can interpret.
This approach also changes the role of data specialists. Generative and agentic AI will bring them much closer to everyday product operations, while still focusing on forecasting, pricing, segmentation and recommendation engines, so they can determine which attributes matter, how products are classified, how models interpret product information and where automated enrichment can safely replace manual processes.
There will also be a new member of the team: the AI agents that will increasingly perform parts of the product team’s work. They can already assist with classification, enrichment, translation, attribute extraction, content creation and optimization, as well as quality checking, so as these capabilities mature, businesses will delegate larger workflows to them.
This frees humans from manually completing thousands of repetitive product records, so they can establish rules, supervise exceptions and concentrate on higher-value decisions about assortment, differentiation and customer experience.
There will also be new people, responsible for supervising AI-generated product information, designing enrichment workflows, defining governance policies and measuring how products are effectively understood by external AI systems.
Ultimately, those traditional boundaries between product, marketing, eCommerce and technology will become artificial when all four are contributing to the same machine-readable customer experience. Together, the product information? team will be built entirely around the customer who expresses conversationally what they want so that intelligent systems determine which products deserve their consideration.
Retailers and brands with ambition to build this new team need to start with a fundamental understanding of definitions – product information effectively becomes infrastructure, visibility becomes an AI challenge as much as a marketing challenge, and merchandising becomes more and more about teaching machines how to understand products.
