AI: From Local Sparks to Company-Wide Capability

Mission

Many organisations are no longer asking whether AI matters. They are asking how to turn scattered use cases into something they can trust, govern and scale.

For a major French civil engineering business, part of a leading European construction and concessions group, this question was directly connected to its 2025–2030 strategic roadmap. The Executive Committee wanted AI to support operational excellence and improve project productivity — not as a disconnected innovation topic, but as a practical lever for the business.

The entity operates at significant scale, with close to 30,000 employees and more than €6bn in annual revenues. Its teams deliver complex infrastructure projects across transport, energy and maritime sectors, from ports and tunnels to large engineering structures. In such an environment, complexity is not an exception. It is the business itself.

This made AI both promising and difficult to structure. The most relevant opportunities were likely to emerge close to the work: in projects, engineering routines, methods, functional processes and day-to-day operational irritants. At the same time, the organisation needed to ensure that emerging use cases were aligned with Group guidelines, data governance, confidentiality requirements and existing information systems.

The challenge for the Executive Committee was therefore clear: create enough openness for useful AI opportunities to surface from the field, and enough structure to assess, prioritize, govern and scale them.

The mandate was to move from fragmented initiatives to a clear, actionable AI roadmap: a set of use cases tailored to civil engineering activities, a shared prioritization framework and the foundations for secure, scalable deployment.

Solution

A senior team combining strategy, transformation and AI change expertise supported the Business Unit in building this balance between emergence and structure.

The work began by anchoring AI in the operational excellence agenda. Rather than treating it as a separate innovation or IT topic, AI was framed as a business transformation lever: a way to improve productivity, support project teams, accelerate access to knowledge and strengthen decision-making.

The first step was to make emergence productive. Working sessions with operational and functional stakeholders helped surface concrete use cases from the business: where teams lose time, where knowledge is difficult to access, where information is fragmented, and where AI could support engineers, project managers or functional teams without replacing professional judgement.

These opportunities were then structured through an AI Use-Case Compass, combining six lenses: business value, feasibility, data availability, implementation complexity, system integration and alignment with Group standards. This made it possible to move from scattered ideas to a prioritized portfolio of actionable opportunities, balancing quick wins with more transformative initiatives.

In parallel, the initiative defined the conditions for governed and scalable deployment: data usage principles, confidentiality requirements, quality control, integration into existing tools and workflows, and connection with Group AI coordination.

This was also a change challenge — but not a conventional one. With AI, the target is not fully known at the beginning. Use cases evolve, technology moves fast, and adoption depends on the ability of teams to test, challenge, learn and adapt. The work therefore focused not only on what to deploy, but on how the organisation could keep learning as AI use cases matured.

The initiative laid the foundations for an adoption model combining AI ambassadors, feedback loops from operational teams, shared learning mechanisms and central coordination. This created a bridge between bottom-up exploration and Group-level steering: enough openness to let relevant uses emerge, enough structure to make them safe, consistent and scalable.

The work helped translate AI from a promising field of experimentation into an organizational capability: governed, pragmatic and ready to learn from the field as it scales.