AI: Creating the Conditions to Scale

Mission

For a major French civil engineering division, part of a leading European construction and concessions group, Artificial Intelligence had become a strategic question within its 2026–2030 roadmap.

With close to 30,000 employees and more than €6bn in annual revenues, the division delivers complex infrastructure projects across transport, energy and maritime sectors, from ports and tunnels to major engineering structures. Its performance is built on technical rigour, reliable processes and disciplined execution across a decentralised, project-driven organisation.

In such an environment, AI introduced a distinctive tension. Its potential lay in the ability of teams to identify useful applications close to the reality of projects, engineering methods and day-to-day operational challenges. Yet any emerging use of AI had to meet the standards of a business where reliability, system integration and alignment with Group principles are essential.

The Executive Committee therefore wanted AI to contribute to operational excellence and project productivity. Not as a disconnected innovation topic, but as a practical lever for the business. The challenge was to create the conditions for relevant uses to emerge from the field, while providing enough structure to discuss, prioritize and govern them coherently.

The mission aimed to define a shared framework for AI opportunities across civil engineering activities and support functions, connect local exploration with Group-level coordination, and lay the foundations for secure, scalable development.

Solution

A senior European working group was mobilized to define the principles and governance needed to hold this demanding balance: enabling useful AI applications to emerge from the field, while providing the structure required to develop them safely, coherently, and at scale.

Bringing together business teams, support functions, and AI change expertise, the group positioned AI as a concrete lever for transformation: improving productivity, supporting project teams, accelerating access to knowledge, and strengthening decision-making.

The approach defined the conditions for governed, scalable development: principles for data use and confidentiality, integration with existing information systems, consistency with Group-level guidelines, and alignment with AI coordination at Group level.

This was a transformation challenge — but not a conventional change program. With AI, the destination cannot be fully prescribed from the outset. Use cases emerge progressively, the technology evolves rapidly, and their relevance becomes clearer as teams test, challenge, learn, and adapt their practices. The intervention therefore focused as much on stimulating initiatives from the field as on strengthening the organization’s ability to learn as those uses mature.

The approach created a bridge between bottom-up exploration and Group-level steering: open enough to capture useful applications from the field, structured enough to guide their development safely and coherently.

The work gave emerging AI opportunities a pragmatic, governed development framework — anchored in operational reality and designed to learn as it scales.