Gilles Burnier drives AI technology scouting at Groupe Mutuel, one of Switzerland’s leading health insurance providers. As co-lead of the company’s AI initiatives, he helps steer the adoption of emerging technologies, with a focus on aligning innovation with operational impact and digital transformation.

Expertise & Perspective: Burnier brings a pragmatic, results-driven approach to enterprise AI adoption, focusing on measurable productivity gains and practical implementation strategies. His leadership oversees nearly 300 in-house developers who are embracing quickly AI tools as standard practice, making Groupe Mutuel a reference case study in large-scale AI deployment within the Swiss health insurance sector.

Key Insights at Panoramai:

On Strategic AI Implementation: « What we do and actually what I'm in charge of is to bring new technology into the company and of course coding with AI is part of it. » Burnier positioned AI coding within a broader technological transformation mandate, emphasizing systematic rather than ad-hoc adoption.

On Tool Selection Methodology: « You have to, like always, you have to find the right tool and the right model for the right use case, in terms of desired quality, functionality, costs and performance. But limitations, I don't think there are any. » He advocated for empirical testing as the only reliable method for matching AI tools to specific challenges.

On Concrete Productivity Gains: “When assessing concrete productivity gains, we’ve observed that AI-assisted development can significantly accelerate complex technical tasks, such as framework migrations, while simultaneously improving code quality. What would typically require weeks of effort through conventional processes can now be completed in a fraction of the time. Moreover, these tools often surface hidden bugs and enhance test coverage, leading to both efficiency and robustness.”

On Developer Tool Adoption: « With nearly 300 developers in-house, the majority are now coding with AI support on a daily basis. Rather than enforcing a one-size-fits-all solution, the approach emphasizes flexibility—developers are encouraged to choose the most suitable AI tools depending on the nature of the task at hand. This decentralized adoption fosters both autonomy and efficiency.»

On User Empowerment: “We are giving targeted teams access to agentic low-code and no-code platforms, supported by a curated set of preconfigured agents. This setup empowers employees to autonomously experiment and prototype”

On Addressing Developer Shortages: « On one hand, in Switzerland we are facing a lack of good developers. On the other hand, as we are in more and more digitalized world with customers asking more and more personalized interactions, as regulation is increasing every day, there are more and more software development projects. AI-development tools help fill that growing gap» Burnier framed AI as addressing Switzerland's structural developer shortage rather than displacing existing talent.

On Productivity Reality Check: « So they are not 10 times as productive for sure, but they are really more productive. That's true. » When pressed for specific metrics, he provided honest assessment of meaningful but not revolutionary productivity improvements.

On Future Workforce Evolution: « I'm quite convinced that within 10 years, not only for the developers, but for every job in services economy, you won't be hired for your CV and your competencies anymore, but for the crew of agents you’ve been training and you bring with you. IT could become  the next HR . » Burnier offered perhaps the most forward-looking perspective on how AI capabilities might become individual professional differentiators.

On Strategic Imperative: « The biggest risk with AI, not only with AI development, is not to embrace it because if you don't, your competitors will and they will outcompete you. So just do it, try it and have fun. » His closing message emphasized competitive necessity while maintaining an optimistic, experimental approach.

Strategic Positioning: Burnier embodies the pragmatic innovation strategist who has moved beyond AI experimentation toward structured deployment. His focus on AI technology scouting supports a balanced approach—combining measured adoption with strategic impact—guided by tangible metrics and iterative validation.

Management Philosophy: He advocates for bottoms-up tool selection, allowing developers to choose optimal AI assistants for specific tasks while providing governance frameworks and internal marketplaces that democratize development capabilities across business functions.

Industry Impact: Burnier brings a pragmatic, results-driven approach to enterprise AI adoption, focusing on measurable productivity gains and practical implementation strategies. Groupe Mutuel has become a reference case for AI deployment in the Swiss health insurance sector—showcasing how large, regulated organizations can adopt AI at scale while maintaining operational integrity. His insights provide actionable frameworks for executives navigating similar transformations in traditional industries.