Europe’s Federated AI Strategy Is Taking Over the Office

(4 min read)

Author

Midas AI Research
July 04, 2025

As generative AI expands into the enterprise, the question isn't just what tools to adopt, but how to structure the rollout. A new whitepaper from digital consultancy Inetum, based on interviews with 30 business and IT leaders across Belgium, France, and Spain, outlines the operating models European companies are using to integrate GenAI into daily work. The key takeaway: governance matters as much as the tech itself.

Three models are emerging but only one seems to offer the right balance of speed, scale, and control.

Three Paths to Enterprise AI

According to the report, companies across the region are converging around one of three structural models for AI deployment:

1. Centralized: In this model, the IT or CIO office owns the AI agenda. Centralization brings tight governance, unified tooling, and fewer surprises. But it also creates friction. Individual teams often feel constrained, and “shadow AI” (unauthorized tool use) becomes more likely when innovation is bottlenecked.

2. Decentralized: Business units choose their own tools and build their own solutions. This speeds up experimentation, but with a cost—duplicate work, inconsistent standards, and fragmented data practices. Companies pursuing this path often end up reinventing the wheel in parallel.

3. Federated: The emerging front-runner is a hybrid approach. A central “AI center of excellence” defines guardrails, best practices, and shared infrastructure. Meanwhile, business units are empowered to build and deploy AI use cases that meet their specific needs. According to Inetum, this model is gaining strong traction in France, where firms are finding it strikes the right balance between innovation and oversight.

The Organizational Layer of GenAI

These structural choices aren’t just theoretical. They shape who gets to experiment, how learnings get shared, and how risks are managed. A company that picks the wrong model can waste months building incompatible systems or end up with costly governance gaps.

AI at scale introduces new coordination problems. Tools like GPT-4 or Claude 3 don’t just sit in the background, they become part of workflows, decision-making, and customer touchpoints. That makes alignment between technical and business teams critical.

A federated model offers flexibility with structure. It allows for faster adoption in real-world settings while keeping a consistent security, compliance, and ethics posture across the organization.

Moving from Strategy to Execution

For companies serious about GenAI, this whitepaper’s findings offer a practical blueprint.

First, set up an internal AI center of excellence, one that’s not just staffed with data scientists, but also includes legal, operations, and HR stakeholders. Give it authority to define usage policies, training protocols, and shared tooling.

Then, let business units lead real-world pilot projects with measurable outcomes. Encourage experimentation, but require teams to report learnings and metrics back to the center. This flywheel of experimentation and refinement is already powering scaled deployments in sectors like insurance, telecom, and manufacturing.

In short, the real AI differentiator may not be your model or vendor—but your org chart.