AI skills for IT teams: moving development practices forward.
Building AI into the new software development practices without losing control of quality, security and the application estate.
Atlassian Platinum Solution Partner · Alongside IT departments since 2008 · Paris and Lyon
The problem.
Artificial intelligence is gradually changing the way applications are designed, built, tested and maintained. Developers no longer necessarily write every line of code; their role shifts towards more design, specification, steering, review and validation.
For IT departments, the point is therefore not simply to authorise new tools. It is to move practices forward without losing control.
Development assistants and coding agents are reaching teams fast. Using them can speed up some activities considerably, but it raises new questions: code review, standards, what data may be sent out, security, measuring the gains and dependence on tools or models.
Our answer.
We bring AI skills directly into the IT department's teams and projects. Our consultants bring both development experience and command of the new practices linked to AI.
The aim is not to run a demonstration. It is toexperiment on real projects, to measure what works and to draw repeatable practices from it.
Our five-stage approach.
We favour progress through experience: start from a real context, learn, then generalise what actually adds value.
Observe current development practices, the tools, the architecture and security constraints, and what the teams expect.
Choose a pilot project or scope, define the permitted uses, the quality standards, the security rules and the indicators of gains.
Introduce AI tools and practices on a real project, with support for the developers and adaptation of the design, review and test workflows.
Train, share practices, help teams develop their role and document what genuinely works in your context.
Industrialise the standards, measure the results, develop the rules alongside the tools and transfer the skills to internal teams for good.
What your teams will be able to do.
On controlled scopes, without immediately overturning every practice.
Test new methods on a real project before rolling them out.
Permitted uses, review practices, security, traceability and quality.
More design, specification, review and validation.
So that the new tools become practices under control, not just individual experiments.
In productivity, quality, lead time or capacity.
Of the architecture, the security and the quality of the software produced.
Several engagement models.
A specialist skill joins a team temporarily.
A team able to deliver with the new AI practices built in from the start.
Trial on a pilot project, definition of the practices, then support for their roll-out.
Internal teams building their skills.
Frequently asked questions
A question that finds no answer here is dealt with in a thirty-minute conversation, about your actual context rather than a general case.
Talk to an expertIs this simply training in GitHub Copilot or Claude Code?
No. The tools move too fast for the subject to be reduced to learning them. We work above all on the new development practices they make possible, how they fit into projects and the controls required.
Do you work directly inside our teams?
Yes. This can take the form of an individual reinforcement, a dedicated team or an engagement built around a pilot project.
Can you start with a single project?
Yes, and it is often the best starting point. A real project lets you measure the gains, the difficulties and the new risks before rolling the practices out more widely.
Does AI make developers less important?
It mainly changes their role. The ability to design, understand the architecture, specify, review and arbitrate matters even more when more code is produced automatically.
How do you avoid losing control of the code produced?
Through standards, review, testing, security, traceability and keeping clear human responsibility for the software delivered.
Going further: The Artificial intelligence overview·AI assessment & roadmap·AI for business processes·AI governance