The tools are in place. The workflows have been redesigned. The productivity gains are showing up in the dashboards. And yet many managers sense that something has shifted in their teams. Part of the explanation lies in a tension that is rarely made explicit.
When individuals adopt AI on their own, they tend to follow a motivational logic: they automate what they enjoy least or feel least competent at. AI then becomes a real help, improving not only their efficiency but also the “fun” of their job. This individual adoption is often well ahead of what leaders perceive: McKinsey found that C-suite leaders estimate only 4 per cent of employees use generative AI for at least 30 per cent of their daily work, while 13 per cent of employees report actually doing so.
Organisational AI adoption follows another logic. At macro level, entire workflows are mapped, automation potential is assessed, and processes are redesigned for efficiency, not to address individual “pain points” or what people don’t like doing. The question “who actually enjoyed this task, and for whom did it carry meaning?” is seldom part of the AI business case.
The result can be paradoxical. In research published in Harvard Business Review, Liu and colleagues ran four studies with more than 3,500 participants and found that AI tools improved performance but also left people less motivated and more bored when they returned to tasks without AI. Their interpretation: when gen AI generates much of the content, the process becomes less engaging and people can feel disconnected from the task.
So the same technology that energises someone who chose exactly what to delegate can drain the energy, and the “fun”, of someone who did not choose when and where to use it.
In interviews conducted by Solvay Lifelong Learning this year with executives from large industrial groups, multinationals, scale-ups and SMEs, one pattern stood out: loss of meaning due to the introduction of AI is real, but not universal.
One contrast came up repeatedly. An employee deeply attached to the craft of her work, felt paralysed after the transformation, not by the technology itself, but by losing what made her feel valuable. For other colleagues, by contrast, AI felt like liberation from boring, repetitive tasks. Same change, opposite trajectories in terms of motivation.
Meaning, one of the key drivers of motivation, is largely personal and difficult to engineer “from above”.
This is why managers in organisations that have been through an AI transformation often find themselves facing questions like these:
How do I keep someone engaged when the part of the job he or she loved has been automated?
How do I give recognition when most of the deliverable was produced with AI?
What does “creating impact on the job” mean now, in my team when AI is doing most of the job?
Self-Determination Theory (Deci & Ryan, 2000) offers a robust lens: sustainable motivation rests on three basic psychological needs, Autonomy, Belonging and Competence. AI does not remove these needs. It changes the conditions under which they are met, and it can work in both directions: a joint MIT Sloan Management Review and BCG study found that individuals benefit from AI when it enhances their competence, autonomy and/or relationships.
Autonomy. When an algorithm recommends, who decides, and who is accountable? Should people be free to choose their level of AI assistance, even if it creates uneven practices within the team? How much room is there for disagreement with tools the organisation has chosen? And when execution accelerates, how do you keep priorities clear enough to avoid scattering effort?
Belonging. How do you make someone’s contribution visible when the output is co-produced with AI? Should performance criteria now better reward critical judgment and relational quality, and how do you assess those fairly? When asynchronous, AI-mediated work replaces informal exchanges, which moments of connection are worth protecting, and at what cost?
Competence. Where is the line between delegating a task to AI and letting critical skills erode? MIT Sloan Management Review warns of a “capability mirage”: employees may look highly capable precisely because of extensive AI use, while their own skills quietly erode. Should some tasks deliberately stay “AI-free”, even if that is less efficient? And how do you support experienced professionals who suddenly feel like beginners again?
None of these questions has a universal answer. The right response depends on the organisation's AI maturity, its culture, the nature of the work, and the people in the team.
That's why these questions are hard to solve alone. A manager in an industrial group facing an uneven rollout across divisions will not land on the same answer as a CIO running AI agents in production, or an SME CEO who discovered “shadow AI” before structuring adoption. Yet each can learn a great deal from how the others are reasoning.
This is one of the questions at the heart of Executive Programme in People Leadership in the Age of AI, a new programme starting in Spring 2027 at Solvay Lifelong Learning, designed for managers in organisations already transformed by AI. To help participants find answers that are pragmatic, contextual and actually implementable in their own organisation, the programme combines robust frameworks to read the situation, research to confirm or challenge intuitions, and structured exchange with peers facing similar dilemmas in different contexts, across four in-person modules and small-group online coaching sessions.
Which motivational question has AI raised in your team that you're still working through?
Are you interested in joining our first cohort of the Executive Programme in People Leadership in the Age of AI? Find all the information on our programme page.