French company ADEO brings together an ecosystem of companies dedicated to home improvement. In this conversation with McKinsey’s Franck Laizet, Hai Ly Nguyen, and Holger Harreis, ADEO’s chief digital and information officer Matthieu Grymonprez discusses AI’s importance, adoption challenges, and how the technology is reshaping the company’s strategy. The interview has been edited for clarity and length.
AI will widen the gap between frontrunners and laggards, leaving companies that missed the first wave of digital transformation even further behind.
Q: How is AI reshaping ADEO’s strategy and competitive advantage?
Matthieu Grymonprez: AI has been reshaping our business in phases. ‘Classic AI’ and machine learning have already delivered significant value in our core operations, optimizing supply chain forecasting, sourcing, and in-store productivity. Now, gen AI is unlocking new capabilities. It is transforming how we manage our product information at scale, for instance, by automatically generating product descriptions and analyzing documentation from thousands of sellers. This was critical in expanding our online assortment from 1.5 million to 7 million stock-keeping units (SKUs).
On the consumer side, we are using gen AI for visual inspiration. Our “Shop the Look” feature, which used to be a costly and slow process involving photoshoots, is now powered by AI that can generate inspirational room settings with our products in seconds. We are also finally cracking challenges like image recognition, so a customer can take a picture of a screw, and our app can identify it instantly.
Fundamentally, AI is an incredible enabler, but only if you have a solid digital foundation. AI will widen the gap between frontrunners and laggards, leaving companies that missed the first wave of digital transformation even further behind. The real challenge was building our product knowledge base and mastering our data model. If you have done that work, AI is a powerful layer that comes on top to boost everything.
Q: How do you track the adoption and value delivered by these AI initiatives?
Grymonprez: We have an “AI Radar” where any team can propose and test new ideas. With over 250 use cases now, we let this innovation happen organically to not block ideation. Once a use case proves its ROI, we centralize it, productionize it, and scale it across the group.
We also drive adoption by embedding AI directly into our digital products and core processes. This includes features like “next product to buy” recommendations and automatic pricing for our marketplace. At a foundational level, we have a global contract with Google, so every employee has access to Gemini and we have an internal chatbot for our stores that provides access to company data. Our main challenge now is managing this wave of innovation—we need to create the right guardrails to ensure quality and strategic alignment without stifling creativity.
Q: What is your view on the rise of agentic commerce?
Grymonprez: Fully autonomous commerce feels like a threat, not a genuine opportunity for retailers. Fully agentified commerce will remove our ability to cross-sell or upsell, turning us into a simple delivery company in a price-driven red ocean. This model is much riskier for commoditized consumables, while for our category—home improvement—touch and feel are critical.
Q: What have been the key challenges in deploying AI at scale and what are the biggest risks?
Grymonprez: The biggest challenge is not the technology; it’s the impact on our governance and people. AI automates decisions that were previously owned by our managers. For years, teams would debate which product to feature first on a webpage. Now, we let the machine decide based on performance. The same is happening with pricing and inventory. This takes the power of decision-making away from the very people whose job it was to make those calls.
While senior leaders are happy to see their objectives and key results improve, it creates a serious disruption in the ownership and governance of the company. We are moving toward a future where roles like UX (user experience), product, and business will combine into one. The reconfiguration of these roles and responsibilities is the most difficult thing to manage.
We believe we can address the risks to consumers by being trustful and by fixing problems as they arise but the bigger challenge is internal. When an AI-driven pricing model goes wrong, who is accountable? The tool cannot be. We are in a phase of inflated expectations, and we will go through a period of disillusionment. My job is to keep everyone grounded and focused on creating real value, not just chasing the hype. There is so much hype, and people see AI as a magic tool that can solve problems that have existed for years.
Q: Looking ahead, what do you see as the big unknowns in the evolution of AI?
Grymonprez: I am focused on achieving consistency over time, especially as we move from experimenting with models to embedding them in long-term applications that require stability. There is also a fundamental question on the long-term economic viability: when will the immense investment currently pouring into AI truly pay back?
