There is a great deal of discussion about AI at the moment, from tools and models to practical use cases. That discussion is useful, but in my experience, the real challenge lies elsewhere.

What matters most is how ready teams are. Not simply in terms of technical skills, but in their ability to learn, question assumptions and adapt. Deploying a powerful tool without properly supporting the people expected to use it is a fundamental mistake. Performance only improves when the foundations are strong.

understand before taking action

The best-prepared companies I see are not the ones that adopt new technologies the fastest. They are the ones that take the time to understand the challenges, the opportunities and what their teams can genuinely absorb. They read trends with judgement, pick up on early signals and turn that understanding into concrete decisions, rather than headline announcements.

I see this quality of attention as a real competitive advantage. It helps organisations ask the right questions before investing, avoid rushed deployments and stay one step ahead by keeping a clear view of what truly matters.

what we are doing in practice

I am part of the steering committee for our AI initiative at Itecor. What I observe there confirms what I also see with our clients: successful deployments are not necessarily the most technologically sophisticated. They are the ones where teams understand the purpose of the change, and have been given the space to do so.

We have set up a dedicated test environment, a space where our people can experiment freely with AI tools, within a secure framework and without immediate performance pressure. The aim is to build familiarity and confidence, and gradually develop a culture in which AI becomes both a useful reflex and a genuine support in people’s day-to-day work.

We have also invested in targeted training in the areas where AI is already reshaping our work in depth:

• project management, including planning and estimation, risk management, and support with communication and documentation;
• quality assurance, including test case generation, synthetic data generation and automated checks;
• business analysis, including requirements modelling, generation of acceptance criteria, validation of user story completeness and mock-up creation.