From Adoption to Embrace of Generative AI in the Workplace: A Dialogue Between Educational Sciences and Psychology

Chapter 2: The Adoption of Artificial Intelligence in the Workplace: Between the Transformation of Practices, Professional Identity, and Adult Education

September 11, 2026

There is a gap between the adoption of a technology and its integration into daily work—a gap that the humanities and social sciences are well acquainted with. Using a tool does not necessarily mean adopting it. Adoption refers to a deeper process, during which professionals experiment, adjust their practices, redefine their frame of reference, and gradually make sense of new ways of working.

 Beyond time savings and increased productivity, how might AI affect professionals themselves?

Stéphane:To understand what AI actually affects, I believe the ideas and work of several authors in the field of psychology are particularly relevant to draw upon. An initial analysis of the issues at play, based on the work of Albert Bandura (1997), helps us understand that generative AI creates an unprecedented situation. A professional can, in fact, produce a result that they would not have been able to produce on their own. In this context, asking whether this result is still their own is not a trivial question from a cognitive and social standpoint. If they hesitate to defend it or claim it as their own, their sense of competence has just taken a hit that no one measures in adoption surveys. There are a myriad of questions here, each more fascinating than the last. Authors Deci and Ryan (2000), with their self-determination theory, propose that optimal motivation rests on the fulfillment of three fundamental psychological needs: autonomy, or acting by choice; competence, or the sense of mastery; and relatedness, or being part of meaningful relationships. This is a theory I particularly like, because it allows us to formulate a strong hypothesis in this context: an AI that is imposed—without real choice or negotiated boundaries—may be accepted behaviorally while deeply frustrating the needs for autonomy, competence, and relatedness.

Let’s take a moment to consider things from the perspective of an individual’s career. For example, recent academic literature encourages an increasing focus on self-determination, proactivity, and active management of one’s career path, in order to take charge of one’s career in a context marked by uncertainty and constant reinvention (Hirschi & Koen, 2021; Savickas et al., 2009). Research onLife Designparticularly emphasizes the need to construct career trajectories based on dynamic, nonlinear, and narrative processes (Savickas et al., 2009). From this perspective, research onfuture work selves (Strauss et al., 2012) shows that a clear vision of one’s professional future—a specific form of “possible self” that encompasses one’s hopes and aspirations—constitutes an essential psychological resource for adapting to modern, ever-changing careers. However, an interesting interpretation can be drawn from Voigt and Strauss (2024), who show that people with a clearer vision of their future professional self can gain a sense of control in the face of AI, while those with a less clear vision risk, on the contrary, losing control and proactivity. In other words, generative AI not only transforms work but also challenges the way individuals envision themselves in their professional future. If we consider that AI contributes to increasing uncertainty by reshaping skills, work, and employment to varying degrees, the challenge becomes considerable: how can we plan our professional futures in a world where the tool meant to help us act also makes the future harder to grasp?

Joseph: Buildingon Stéphane’s analysis, the introduction of AI into an organization cannot be reduced to the acquisition of new technical skills. For an adult learner, the challenge also lies in connecting new learning with existing professional experience. This experience is an important resource for learning, but it can also be called into question when new technologies change established practices (Knowles et al., 2015).

From this perspective, AI training should be based on real-world professional situations. Experimentation, analysis of results, and reflection on the experience gradually enable the development of new reference points. This approach aligns with the concept of experiential learning developed by Kolb (1984), according to which learning is built, in particular, through the interplay of experience, reflection, and experimentation.

The goal, therefore, is less about teaching a series of skills and more about supporting professionals as they evolve their practices. Training can become a space where participants share their experiences, analyze the challenges they face, and gradually put new approaches to the test.

When it comes to AI, what kind of training are we talking about, exactly?

Joseph: I think we need to distinguish between learning how to use AI and learning how to work with it. In the first case, the goal is primarily technical: understanding the features, formulating a request, or obtaining a result. In the second, it’s about gradually integrating this tool into an existing professional practice.

This distinction relates to the concept of reflective practice developed by Schön (1983). Professionals do not simply apply the knowledge they have acquired through training; they also learn by analyzing the situations they encounter and adjusting their actions accordingly. Experimenting with AI can thus serve as a vehicle for professional development, provided that the training allows participants to take a step back and reflect on how AI is used and its effects.

Training programs would therefore benefit from drawing on real-world professional situations. This involves, in particular, analyzing how the use of AI changes a given situation, the opportunities it offers, the challenges it may pose, and the adjustments it requires. This approach promotes learning that is directly rooted in professional practice, rather than a mere accumulation of technical knowledge (Kolb, 1984; Schön, 1983).

Adult education can thus serve as a bridge between technological innovation and professional experience. It is not solely aimed at acquiring an additional skill, but also allows individuals to take a step back and reflect on their practices and, when necessary, to expand their frames of reference (Mezirow, 1991).

Stéphane:It sounds like a joke, but learning to prompt effectively is much faster than learning not to prompt! In the relationship I’m building with AI, my judgment must lead me to decide if and when I want to do without it. This requires each of us to have rebuilt a clear understanding of what aspects of our work are worth protecting from the systematic use of AI. This process of redefining is an internal struggle that generic training programs rarely address head-on. By teaching AI as a tool—while repeating that it is much more than that—these programs often leave people to grapple alone with the decisive question: What do I still want to do myself, and why? This question fundamentally touches on an existential dimension of work. As Meyerson (1949) noted, human beings do not work merely to occupy their time but to construct and embody their behaviors in works. From this perspective, while generative AI automates tasks, it forces us to redefine which works we still want to carry out ourselves. What a training program should offer is not a list of features but a space where professionals can, together, articulate what can be automated, what can be enhanced, and what must remain a deliberate professional act.

Bibliography

• Bandura, A. (1997). Self-Efficacy: The Exercise of Control. H. Freeman.
• Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.https://doi.org/10.1207/S15327965PLI1104_01
• Hirschi, A., & Koen, J. (2021). Contemporary career orientations and career self-management: A review and integration.Journal of Vocational Behavior, 126, Article 103505.https://doi.org/10.1016/j.jvb.2020.103505
• Knowles, M. S., Holton, E. F., III, & Swanson, R. A. (2015). The Adult Learner: The Definitive Classic in Adult Education and Human Resource Development (8th ed.).
• Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Prentice-Hall. KKoha+1
• Meyerson, I. (1949). Behavior, Work, Experience, and Achievement.L’Année psychologique, 50, 77–82.https://doi.org/10.3406/psy.1949.8426
• Mezirow, J. (1991). Transformative Dimensions of Adult Learning. Jossey-Bass.
• Savickas, M. L., Nota, L., Rossier, J., Dauwalder, J.-P., Duarte, M. E., Guichard,
• Schön, D. A. (1983). The Reflective Practitioner: How Professionals Think in Action. Basic Books.
• Strauss, K., Griffin, M. A., & Parker, S. K. (2012). Future work selves: How salient hoped-for identities motivate proactive career behaviors.Journal of Applied Psychology, 97(3), 580–598.https://doi.org/10.1037/a0026423
• Voigt, J., & Strauss, K. (2024). How future work self-salience shapes the effects of interacting with artificial intelligence.Journal of Vocational Behavior, 155, Article 104054.https://doi.org/10.1016/j.jvb.2024.104054

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Joseph Baud-Grasset

Training Coordinator
ISFB

Stéphane Bonzon

Career Counselor
ISFB

AI that is imposed—without any real choice or negotiated parameters—may be accepted behaviorally while deeply frustrating the needs for autonomy, competence, and a sense of belonging

Stéphane Bonzon

Training can become a space where participants share their experiences, analyze the challenges they’ve faced, and gradually put new approaches to the test.

Joseph Baud-Grasset