Q&A: Romain Fouache, CEO of Product Experience (PX) company Akeneo
For years, brands and retailers have built their digital commerce strategies around attracting consumers to websites through search rankings, advertising, and compelling digital experiences once a consumer arrives.
AI is changing that model because buying decisions are increasingly happening before a customer even visits your website, a shift already reflected in the more than 30% decline in organic traffic from search engines.
Subscribe to TRBQ. Brands and retailers have focused on winning the search race through SEO and digital marketing whereas you argue that AI is fundamentally changing product discovery. What has changed, and why should retailers rethink the way they approach digital commerce?
Consumers are asking AI assistants for recommendations rather than simply browsing category pages, while AI agents will increasingly compare products and, in some cases, complete purchases on consumers’ behalf.
This means brands and retailers are now competing not only to rank in search results, but also to have their products understood and recommended by AI. They need to think beyond SEO and focus on making their product data complete, structured, trusted, and machine-readable. After all, AI can only recommend what it can confidently understand.
Consumers are asking AI assistants for recommendations rather than simply browsing category pages, and AI agents will increasingly compare products and, in some cases, complete purchases on consumers’ behalf.
This means brands and retailers are now competing not only to rank in search results, but to have their products understood and recommended by AI. They need to think beyond SEO and focus on making their product data complete, structured, trusted and machine-readable. After all, AI can only recommend what it can confidently understand.
Q. You talk about an agentic gap between AI ambition and data readiness. What are the biggest mistakes you see brands and retailers making as they rush to adopt AI and how can they avoid them?
There is no good AI without good data. Title, picture, and price are the main trifecta for a human discovery or purchase decision. Agents, however, scan tens of thousands of products and can look at thousands of attributes to find the best matches for their users’ intent.
If product information is inconsistent or incomplete, AI will amplify those problems. We call this the Agentic Gap: organisations have ambitious plans for AI, but the underlying data is not ready to support their business ambitions.
Closing that gap starts with the data foundation and the governance around it. Businesses need a single source of truth, clear ownership of product data and consistent processes before moving to more sophisticated AI use cases. With that foundation in place, they can use AI to centralize, enrich, activate and optimize product information with far greater confidence.
Q. Akeneo claims product data is at the centre of commerce rather than back-office admin. Why should product data be considered such a strategic asset and what does that mean for brands and retailers competing in an AI-driven marketplace?
Product data has traditionally been viewed as a purely operational marketing add-on: a nice way to publish more accurate product information, but mostly unrelated to commercial strategy. That is not true.
Product data is now at the heart of discoverability, customer confidence, conversion, pricing decisions, merchandising and increasingly whether AI recommends a product at all. In an AI-driven marketplace, product data becomes a commercial asset because it determines visibility as much as it determines accuracy.
Rich, contextual product information helps customers make better decisions, but it also gives AI the confidence to understand and recommend those products. That is why product data has to be considered as the foundation connecting marketing, commerce, pricing and operations rather than simply supporting them.
Q. You recently acquired PricingHUB, bringing pricing and product information together within the same platform. Why do you believe these two disciplines can no longer operate independently and what benefits does that create for brands and retailers?
There are two key factors that drive a purchasing decision: what you are buying, for how much. Pricing and product information, despite being the intertwined foundations of the purchasing decision, have often been managed in separate systems and by different teams. In today’s fast-moving markets, that separation increasingly limits the ability of businesses to make properly informed commercial decisions.
AI-driven commerce requires organisations to make pricing decisions in the context of product attributes, customer demand, competitive positioning, assortment strategy and profitability.
Connecting pricing strategy and product information strategy gives businesses the required context to maximize their commercial performance. Ultimately, it enables organisations to optimise not just individual price, but the competitiveness and profitability of their entire product portfolios.
Q. Many retail teams still spend huge amounts of time cleaning supplier data, correcting product attributes and managing catalogue updates. How do you see AI changing the role of product teams over the next few years?
The biggest change is that as product information becomes the driver of business performance, product teams will be on the hook for revenue generation. In addition to that, the rise of AI increases the amount of work on their shoulders exponentially: more products, more attributes, more velocity, less room for error.
The current tools at their disposal to manage product information are just not relevant. What is needed now is a new class of software, where an AI agentic workforce actually performs the heavy lifting under the guidance and direction of humans. The role of the product team becomes increasingly strategic: not simply maintaining a catalogue, but ensuring products are discoverable, trusted and commercially successful across every channel, including AI-powered experiences.
Q. Looking ahead three to five years, what will distinguish the brands and retailers that thrive in the era of agentic commerce from those that struggle and what should businesses be prioritising today in preparation?
Simply deploying more AI is not the answer. The winners will be the businesses that build disciplined organizational and operating models around trusted product data, strong governance and continuous improvement.
Agentic commerce rewards readiness. AI agents need structured, reliable contextual information before they can confidently compare, recommend or purchase products.
Businesses should therefore focus on creating a single source of truth, improving data quality, connecting product information with pricing and commercial intelligence, and establishing the governance needed to keep that information accurate as markets evolve.
As AI agents take a greater role in discovery and purchasing decisions, the businesses investing in their product data foundation today will have a significant competitive advantage tomorrow.
