Adopting AI: can people still recognise their own work?
Introducing AI can turn writing or design into an activity of selecting and editing. The managerial question is specific: how much scope should employees have to reshape an output so that they can still recognise their contribution?
What the literature establishes
Jon L. Pierce, Tatiana Kostova and Kurt T. Dirks (2001, Academy of Management Review) propose a theory of psychological ownership: the feeling that a tangible or intangible object is “mine”, regardless of legal ownership. Their framework identifies control over the object, intimate knowledge of it and personal investment as routes to this sense of ownership. Applied to AI, it suggests a hypothesis rather than an established finding: approving a proposal without being able to meaningfully reshape it may not be enough to feel ownership of it.
Automation and contribution are not simple opposites
Sebastian Raisch and Sebastian Krakowski (2021, Academy of Management Review) analyse the tensions between automation, which assigns tasks to machines, and augmentation, which brings humans and AI together to perform them. Their theoretical contribution shows why these approaches need to be considered together rather than as independent options. For a manager, the question is therefore what human contribution is actually preserved, not merely whether a final approval step exists.
The premature conclusion
One might conclude that every AI output must be rewritten by a human. These papers do not justify that rule: mandatory editing can become a formality without giving people meaningful control over the result. The issue is instead to enable substantive choices, including the choice to retain a proposal considered appropriate. (our executive and employee training programmes)
What the evidence cannot settle
Both articles are theoretical contributions, not trials measuring the effect of generative AI on psychological ownership of work. They therefore cannot establish that greater freedom to edit necessarily improves quality or adoption. A sense of authorship must also remain distinct from professional responsibility: recognising oneself in a document guarantees neither accuracy nor compliance.
A practical check in Fribourg
Within SHR — Swiss Human Resources’ programme “Leading AI adoption in your team”, an exercise could use non-sensitive drafts: an agri-food process sheet, a bilingual industrial instruction or an administrative document from Fribourg’s university sector. For each draft, retain the initial proposal and the final version, then ask the employee to name a substantive choice they were able to change and indicate whether they recognise their contribution in the result. The proposed measure is the proportion of drafts for which the workflow confirms this scope for choice and the employee recognises a contribution of their own. This record does not establish causality, but it can help verify whether the team has the power to reshape outputs or merely an approval button. To go further: explore the Leading AI Adoption in Your Team training in Fribourg in the canton of Fribourg, or browse our executive and employee training programmes in Switzerland.
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