Generative AI: The Secrets of European Companies for Boosting Productivity

A Belgian doctor quickly enters a few details about their patient’s condition. In just seconds, generative AI provides a structured medical report, using terminology tailored to the specialty. Gone are the long minutes spent composing notes: the practitioner can focus on their diagnosis. This scene, that played out at UZ Brussel hospital, perfectly illustrates how certain European professionals are managing to boost their productivity with generative AI.

Yet, more than two years after the arrival of ChatGPT, the promised revolution is still slow to materialize in many companies. According to the International Labour Organization (ILO), nearly a quarter of jobs worldwide have some degree of exposure to generative AI. In high-income European countries, this figure rises to 34%. Yet this theoretical exposure does not automatically translate into tangible gains.

“The trap of poorly mastered, time-consuming new tools is lurking,” underlines the Inetum white paper “How to scale GenAI,” which questioned about thirty decision-makers in Belgium, France, and Spain. Without proper support, some projects are abandoned or users become discouraged. However, some companies are proving themselves as models to follow.

The Applications that Truly Create Value

The reasons for these disappointments? First, expectations were set too high. "There was a setback due to overly high initial expectations," said Nele Philips from De Watergroep in Belgium. Next, a lack of training: "The lack of training is seen as a cause of project failures," underscores the white paper.

But above all, an approach that is too focused on technology at the expense of people. "Mindset is the biggest bottleneck for AI," argued up Ben Vicca, Solutions Director at Inetum Belgium.

Yet, companies that succeed are demonstrating tangible results. At UZ Gent hospital, researchers use generative AI to produce synthetic data—intentionally fabricated but derived from real cases. "This could help share research data without violating confidentiality rules," explained Christiaan Polet, Chief Information Officer.

The Conditions for Success: Governance and Training

In education, a Belgian institution is guiding students in their study choices with the help of conversational AI, assisting them in better self-assessment. In Spain, automating administrative tasks is freeing up time for teachers: “Users need to get used to digital tools, and from there, the use of GenAI could be considered, especially to help with paperwork.”

In software development, GitHub Copilot is proving its worth. In Spain, developers interviewed use it for straightforward coding, testing, and debugging. “They find it convenient to generate code snippets and suggestions based on their comments,” notes the study. The same goes for France: “Developers use GenAI for simple functions, particularly to draft documentation, but not for complex features,” confirmed Lionel Genix, Managing Director of Air Liquide Medical Systems.

This accepted limitation paradoxically becomes a success factor: generative AI excels at automating well-defined, repetitive tasks, without claiming to replace human expertise.

A Return on Investment of 17 million Euros in Nine Months

Financial gains are beginning to materialize. The most spectacular example: a company invested 500,000 euros in an AI tool for managing bids, generating a return on investment of seventeen million euros in just nine months, reported Dina Capelle, analyst for IDC.

At Heineken, investments in AI appear to be confirmed by “good financial results for the group in 2024,” suggesting a positive return on investment. On a smaller scale, Thales has nonetheless seen a 4% increase in productivity thanks to the digitization of its industrial processes.

“Mass adoption of AI is essential if Europe does not want to lag behind the United States and China,” concluded Dorian Vacher, an expert from Inetum’s GenAI unit. European case studies show that, beyond the headlines, success relies on a pragmatic approach: structured governance, tailored training, and a balanced operational model.

Ultimately, the recipe for success is not technological but organizational. The companies that manage to boost their productivity with generative AI are those that have turned a technical challenge into a human-centered project.

Download the “GenAI at Scale” White Paper 

Contact : Olivier Serfaty