For years, product data often sat quietly in the background of ecommerce operations.
As long as product titles, descriptions, images, and attributes were broadly accurate, many businesses viewed product information management primarily as an operational requirement rather than a strategic priority.
The focus tended to sit elsewhere on acquisition, customer experience, performance marketing, and platform development.
The rise of AI is changing that very quickly.
As ecommerce increasingly shifts towards intent-driven discovery, recommendation engines, conversational interfaces, and AI-assisted purchasing journeys, the quality of product data is becoming far more commercially important than many businesses perhaps previously realised.
That was one of the key themes explored during a recent episode of The FODcast, where Tim Roedel sat down with Romain Fouache, CEO of Akeneo, to discuss how AI is reshaping digital commerce and why clean product data is becoming critical to future ecommerce success.
Why AI changes the importance of product data
One of the clearest themes throughout the conversation was that AI fundamentally changes how products are discovered.
Historically, ecommerce largely relied on structured search behaviour. Customers typed keywords into search bars, filtered categories, and navigated websites using relatively predictable browsing patterns. Product data still mattered, but often within fairly rigid ecommerce structures.
AI introduces a much more fluid and intent-led experience.
As Romain discussed, customers increasingly expect systems to understand what they mean rather than simply what they type. That creates a very different challenge for retailers because AI systems require significantly richer, more accurate, and more contextual product information in order to interpret customer intent properly.
The risk for businesses is that products with incomplete, inconsistent, or poorly structured data become harder for AI systems to surface effectively. In an AI-driven environment, weak product data may not simply create friction. It may reduce discoverability altogether.
Why clean data is becoming commercially important
Another important point raised during the discussion was that product data quality increasingly has direct commercial implications.
For many businesses, product information management has historically been treated as a technical or operational discipline sitting behind the scenes. Increasingly though, data quality directly influences customer experience, conversion, search visibility, and revenue performance.
As AI-driven recommendation and discovery systems become more prominent, product data effectively becomes the language through which ecommerce systems understand products themselves.
That means businesses need far greater consistency and depth across product attributes, descriptions, taxonomy, imagery, and contextual information. The companies investing properly in their data foundations are far more likely to create stronger discovery experiences and more relevant customer journeys.
This reflects a much wider shift happening across digital commerce, where operational data increasingly influences front-end customer experience directly.
The changing nature of ecommerce discovery
One of the more interesting themes explored during the conversation was how customer journeys themselves are beginning to evolve.
Traditional ecommerce websites are not disappearing, but they are increasingly being supplemented by AI-driven experiences that sit earlier within the discovery process. Customers may begin product research through AI assistants, conversational tools, or recommendation systems long before reaching a retailer’s website directly.
That changes the role product data plays within ecommerce ecosystems.
As Romain explained, customers do not always know exactly what product they want at the beginning of a journey. AI increasingly acts as the bridge between broad customer intent and specific product recommendations. For that to work effectively, retailers need product information capable of supporting far more nuanced interpretation and contextual understanding.
In many ways, ecommerce is moving away from purely keyword-driven experiences towards much more intent-driven interactions.
Why feedback loops and optimisation matter
Another important point raised during the conversation was the importance of continuous improvement.
AI systems become more effective when businesses understand how customers interact with products, where discovery succeeds or fails, and how user behaviour evolves over time. That means product data management cannot remain static.
As customer expectations and AI capabilities continue to evolve, retailers increasingly need feedback loops that allow them to refine, improve, and optimise product information continuously. Businesses that treat product data as a living commercial asset rather than a fixed operational requirement are likely to adapt more successfully over time.
This also reflects a broader change happening across ecommerce strategy more generally, where iteration, optimisation, and adaptability are becoming increasingly important.
The capability challenge behind AI-driven commerce
One of the wider themes that continues to emerge across digital commerce is the growing overlap between data, customer experience, ecommerce operations, and AI strategy.
Product data now sits at the centre of many of those conversations simultaneously. That means businesses increasingly need teams capable of understanding not just ecommerce platforms themselves, but also taxonomy, customer intent, AI-driven discovery, and data governance more broadly.
From our perspective, this is another example of how ecommerce capability is evolving beyond traditional channel management alone. The businesses best positioned for AI-driven commerce are often those building stronger operational foundations underneath the customer experience itself.
Final thoughts
What came through clearly in this conversation is that product data is no longer simply an operational necessity sitting quietly behind ecommerce platforms.
As Romain highlighted, clean and structured product data increasingly shapes discoverability, customer experience, and commercial performance in an AI-driven retail environment. The businesses investing in those foundations now are likely to be far better positioned as AI continues to reshape how customers discover and purchase products online.
For retailers, that means treating product data less like back-office administration and more like a strategic commercial asset that directly influences future growth.
A big thank you to Romain for sharing his insight and perspective on this topic. If AI-driven commerce, product information management, or ecommerce data strategy is something your business is currently exploring, the full episode is well worth a listen.
