The customer, a major retailer, is a global wellness brand that is growing at a pace that is almost unprecedented: a new store opens around the world about every working day. This scale offers opportunities, but it also involves enormous complexity. In supply chain, merchandising, workforce planning, customer experience and operations. That's why they decided to take a bold step: not seeing AI as an efficient add-on, but as a foundation for the next growth phase. They didn't want to build AI projects, they wanted to build an AI-native organization. Not improving what is already there, but designing what it takes to win in the next three years.

That is why the central question was one question that gave everything direction:
What is our AI-end game? And which organization do we need to be in three years to structurally win for customers and employees? Strategy, brand, people and scalability were the starting point of this discussion with the leadership team, not technology.

‍

The challenge wasn't in technology. It is changing rapidly, but it is available. The real complexity lay in making explicit strategic choices. Where does structural value shift in retail? What role will Agentic AI play in the store of the future? Which processes deserve a complete redesign instead of incremental automation?

Together, we developed plausible AI futures for retail and built an AI-native vision rooted in brand, growth, and economy. This vision was translated into five strategic programs with C-level ownership, explicit value targets and sharp tradeoffs. The goal: focus on structural benefit, not AI activity.

‍

From Strategy to Scale: Building an AI-Native Operating Model for Growth

A vision without an engine won't get off the ground. That's why we focused the second phase on building the right organization and infrastructure:

• an enterprise-wide AI roadmap focused on workflows that needed to be completely redesigned

• an investment model for data, platforms, tooling and skills

• a clear build-versus-buy framework

• an AI operating model with governance, decision rights and collaboration between business and the customer's AI lab

This enabled them to grow from a small core team to more than twenty AI specialists; without losing speed or quality. In addition, we developed shared AI guardrails, an enterprise AI platform and a companywide AI fluency program, including our own AI ambassador community in Paris that is now accelerating and securing the transformation.

‍

Ruben led the process from content and direction, but always with the client's wider team and our own colleagues. From the start, he worked shoulder to shoulder with leaders, domain experts and the AI lab to make the vision concrete and turn it into an organization that is ready for the next wave of scale. He helped clarify the strategic choices, set the AI-native course sharply and, together, translate this into a working operating model. Not by determining it from above, but by taking teams along every time, making choices together and putting everyone in position to succeed. From investment directions to practical decisions in cooperation: it was a joint design supported by all key players.

What he is most proud of?
In a world where AI is developing rapidly, strategy is no longer a static plan, but a continuous system of choices, reprioritization and capital allocation. This changes not only the customer, but also our profession: strategic advice is shifting from direction to designing organizations that can structurally learn, decide and scale faster than the competition.

That transition, from strategy as plan to strategy as capability, is where the real complexity lies, but also where sustainable competitive advantage is being built.

‍