As AI transforms both how products are discovered and how product data is managed, the role of product teams is changing. Freed from repetitive data entry tasks, they are becoming strategic orchestrators focused on discoverability, performance and commercial growth, explains Romain Fouache, CEO, Akeneo.
No product team has ever enjoyed the endless cycle of manual work when it comes to adding products to the website and other channels. Supplier spreadsheets arrive in different formats, product attributes need mapping, channel requirements vary, missing information has to be chased down, data errors are corrected repeatedly, the same product information is reformatted, enriched and redistributed across multiple channels over and over again.
This will all sound familiar, hence the advent of automated Product Information Management (PIM) and its execution friend, Product Experience Management (PXM). And yet, until now, the very product teams responsible for one of the most commercially important assets in commerce are often still having to spend their time on administration rather than optimisation.
AI has changed all that. The rise of agents is automating many of the repetitive mechanics of product information management, creating an opportunity for product teams to move beyond data entry and into a far more strategic role. Rather than acting as catalogue administrators, they are becoming commercial orchestrators responsible for discoverability, performance, compliance and growth.
This is happening because, after years of optimising products primarily for search engines, discovery is increasingly being mediated by AI. Consumers are asking conversational questions through tools such as ChatGPT, Google Gemini and AI-powered shopping assistants. Instead of searching for a particular product, they are describing an outcome, a need or a problem they want solved. This is best defined as the transition from SEO to Generative Engine Optimisation (GEO).
The challenge for brands and retailers is that AI does not evaluate products in the same way as traditional search engines. AI systems analyse structured product information, specifications, attributes, compatibility data, reviews, sustainability credentials and contextual relevance before deciding which products to recommend. If the data is incomplete, inconsistent or poorly structured, the product may never enter the consideration phase.
Recent Akeneo research illustrates how quickly this shift is happening. Ahead of Prime Day 2026, 43% of consumers reported using AI tools to research products, compare prices or find deals. More significantly, 22% said they had already purchased a product based on an AI recommendation, while a further 20% said AI had influenced their purchase consideration.
At the same time, shoppers are becoming more demanding. Nearly two thirds compare prices across retailers and only 9% trust a deal without verifying it. Product information is therefore actively shaping discoverability, trust and conversion. This is where AI is creating a second transformation, inside the organisation itself.
While consumers increasingly use AI to evaluate products, product teams can now use AI to manage the information that powers those evaluations. AI agents can automatically classify products, map supplier data, generate descriptions, identify missing attributes, enrich product records, translate content and prepare information for different channels. Tasks that once consumed countless hours can now be completed in minutes.
There is great value here in terms of efficiency but more important is the ability to redirect human expertise towards higher-value activities. As AI assumes responsibility for repetitive execution, product professionals can focus on understanding what actually drives commercial performance. For instance, which product attributes increase conversion, which sustainability claims influence buying decisions, which content reduces returns, which product characteristics improve visibility within AI-driven discovery engines and which enrichment strategies create competitive advantage? The depth of this data and insight changes the role of product teams from managing product records to managing product intelligence.
This also changes how success is measured, not just whether product data is complete but whether it is effective. Brand and retailers need visibility into which attributes improve discoverability, which information increases customer confidence and which content contributes directly to revenue growth. Product information becomes a strategic and dynamic commercial asset rather than a static operational resource.
This goes way past product management. As AI becomes embedded inside shopping journeys, the quality of product data increasingly determines whether products are surfaced, recommended and trusted. This means product teams need to connect performance signals, customer behaviour, AI discovery trends and market feedback directly back into their product records and do so continuously.
With AI handling the mechanics, people provide governance, strategy and commercial direction so this is not about replacing human judgement because creativity, intuition and customer understanding remain essential. Yes, AI can identify the patterns, but humans still determine priorities, define strategy and interpret context. What they are not is any longer spending their days fixing spreadsheets, but becoming architects of discoverability, guardians of product trust and the orchestrators of commercial performance.
