- Work
- AI
Building a culture of innovation to integrate AI in an organization
January 7 — 2026
Integrating artificial intelligence into an organization is often approached from a purely technical angle: which tools to choose? Which licenses to buy? Which models to deploy? But the question does not stop there. Experience demonstrates that the successful adoption of AI relies as much on the technology itself as it does on a solid culture of professional development. AI is learned, practiced, and shared.
Professional development as a foundation
Before even considering the deployment of tools like Copilot or ChatGPT, an organization must cultivate fertile ground for learning. Certain principles of an innovation culture allow teams to absorb change rather than suffer from it.
Fun
This principle comes down to a simple truth: people learn faster and more lastingly when they enjoy the subject. When individuals work on topics that naturally interest them, the notion of effort diminishes in favor of curiosity. For AI, this means letting teams explore use cases that they are passionate about, rather than imposing rigid top-down directives.
Individual responsibility
An organization has a duty to provide the playground—time, budget, access—but each individual is the pilot of their own progress. This is the concept of being "responsible for one's development." In a context of rapid innovation, a passive approach is fatal. The initiative must come from those doing the work; they are the ones who will find the best use cases.
Varied challenges
A linear career forges narrow skills. Conversely, exposure to complex and varied problems is the best engine for growth. AI, with its new capabilities and yet-undefined limits, represents the ideal challenge to stimulate this growth.
Learning by doing
Theory has its limits. Practice makes perfect, and it is by writing prompts that one understands AI. It is not just about consuming passive training, but about integrating experimentation into daily work.
Knowledge sharing
Having a thirst for learning is essential, but transmitting knowledge is crucial. The value of an individual discovery is multiplied when it is shared with the rest of the team. This is what transforms an individual skill into an organizational capability.
Concrete ways to integrate AI
How does one move from theory to action? Certain levers have accelerated this adoption at Mirego. Here they are.
Make innovation a priority (and secure the terrain)
Innovation cannot be a "side-of-the-desk" project. It must be a priority displayed by the management team, which must also assume the initial risks. It is imperative, for example, to remove legal barriers and clarify intellectual property and data confidentiality issues to draw red lines. Given the number of AI users worldwide, it is highly likely that team members are using it anyway (with a personal account), with all the risks that implies. Once these issues are mitigated by leadership, teams can receive the green light to explore without fear. In Mirego’s development team, for example, two strict measures were implemented:
As input (confidentiality): To guarantee that client code is never used to train public models, the use of corporate subscriptions (Zero data retention) has become the norm. For the strictest cases, the use of local LLMs keeps data entirely on the machine.
As output (quality): AI can hallucinate or introduce vulnerabilities. The solution is not technical, but procedural: the software development lifecycle (SDLC) remains unchanged. No line of code generated by AI bypasses human validation, code reviews, or automated tests. AI amplifies the developer; it does not replace them.
Minimize obstacles
To favour adoption, friction must be reduced to a minimum and micromanagement avoided. A particularly effective approach is to grant broad access to tools and let enthusiasts explore, like an all-you-can-eat buffet, with well-defined limits. Instead of dictating use cases, creative chaos is allowed to operate. This diversity of organic uses is the only way to discover the tool's real value.
Introduce feedback loops
Many claims about AI exceed reality. To steer integration, one should not rely on general hype, but on field data. It is crucial to survey the team regularly: Who is really using it? How often? For which tasks? This data makes it possible to distinguish fantasies (what one thinks AI does) from real value (what it actually accomplishes today in the workflow).
Share learnings
The risk of an "all-you-can-eat buffet" approach is the creation of silos: everyone finds their tricks in their own corner. Chaos is good for discovery, but bad for generalizing practices. Sharing groups are a good solution to remedy this and formalize discoveries. Historically, Mirego organized biweekly meetings (called Horizontal Meetups) to break down silos between projects. For the software development team, the recent explosion of tools like Claude Code, Cursor, or Codex required rethinking this approach slightly. The team therefore transformed these meetings to create a "Summer of AI." For an entire summer, these moments of exchange were dedicated exclusively to artificial intelligence. Every two weeks, the team shared what had evolved: a new tool tested, a new prompting method, an observed productivity gain, etc. Realizing that AI integration challenges were shared by the entire industry, the team organized the AI DevCamp, an event opening these discussions to other organizations.
✦
Ultimately, artificial intelligence is merely a revealer of culture. Where a rigid organization sees friction, a learning organization finds a lever for performance. For while technological investment is necessary, it remains insufficient without the human element. The true competitive advantage of tomorrow will not lie in tools, but in a team's capacity to continuously learn and develop its skills to use AI. Innovation is a muscle that must be trained long before technology arrives.
