What advice would you give to a leader who wants to accelerate the adoption of AI while managing human, ethical and organisational risks?
. – “Firstly, I would say that the decision to accelerate AI is strategic, but its implementation is operational: it is the teams, processes and day-to-day tasks that are affected. It is therefore necessary to ensure consistency in actions and their effects at all levels of the organisation. Resistance to the adoption of AI is less a question of age than one of meaning and trust. From top management to the field, what makes the difference is a clear ‘why’ and real support.
I would then recommend demystifying AI. It is already everywhere in our personal lives, so why treat it differently at work? Simply explaining its benefits, limitations and risks through awareness-raising and training illustrated with real-life examples allows it to be repositioned as a tool for teams, rather than a technological imperative. Short, measurable pilot projects quickly demonstrate their value: when well communicated, they create a ripple effect where success breeds success.
Finally, establish clear governance, ethical principles and human controls over key uses, then measure the impact in order to adjust what AI should or should not do.
What do you think are the concrete levers for transforming data and AI into measurable gains in productivity and operational performance?
“I would suggest four very concrete levers: people, reusable building blocks, supervised autonomy, and communication.
People, or the importance of involving business teams. Data and AI should no longer be the preserve of a few experts, but should become everyday tools for every department, with specific objectives (time saved, errors reduced, quality improved). It is the teams on the ground who give meaning to AI and who must take ownership of it: that is where success is built.
Reusable building blocks or focusing on generic modules rather than a multitude of customised solutions: internal chatbots, text extraction, translation, knowledge base searches, smart forms, etc. These are standard building blocks that each team can adapt to their needs without starting from scratch.
Supervised autonomy or giving control to business units without losing oversight; encouraging rapid prototyping led by business teams, with IT support to ensure security, data quality and scalability.
Communication plays a key role: sharing initial successes, showcasing concrete use cases, and giving users a voice helps to create a dynamic of emulation and desire, which is much more effective than adoption imposed by management.
What human and organisational factors are essential for successful, sustainable AI transformation, beyond technological tools alone?
“I would say that achieving sustainable transformation through AI depends first and foremost on empowerment and maintaining human meaning. AI can explain the ‘how’, but it is humans who must define the ‘why’, exercise judgement and set ethical limits. Strengthen critical thinking, common sense and controls (compliance, GDPR) to avoid automatic and futile uses. Give each team responsibility for use cases, ensure regular reviews and prioritise human-machine complementarity: the machine executes, humans question and decide. In this way, we retain control, generate real value for customers and keep humans at the centre.”