Managing with AI: automate or augment judgement?
When a team adopts AI, the decisive question is not only what the tool can do, but how it redistributes judgement, accountability and the right to challenge.
The central paradox
Sebastian Raisch and Sebastian Krakowski (2021, Academy of Management Review) describe a paradox between automation and augmentation. Their article is theoretical: it organises propositions rather than proving causal effects.
What workplace studies add
Katherine Kellogg, Melissa Valentine and Angèle Christin (2020, Academy of Management Annals) show that algorithmic control can guide, evaluate and discipline work, while also prompting resistance. Their review spans heterogeneous settings and offers no universal recipe.
A common managerial error
Presenting AI as a simple efficiency gain hides new choices: which recommendations may be challenged, which data matter and who answers for an error? Without explicit rules, assistance can become silent delegation of judgement. (our executive and employee training programmes)
Defensible conditions
Responsible adoption separates repetitive tasks from ambiguous decisions, preserves human appeal and records disagreement with the tool. These practices do not guarantee performance; they make accountability observable.
A practical check in Geneva
For two weeks, record three AI-assisted decisions, the recommendation, any managerial departure and its rationale. If no departure can be explained, the team may not yet be genuinely steering the tool. To go further: explore the Managing in the Age of Artificial Intelligence training in Geneva, or browse our executive and employee training programmes in Switzerland.
In pictures: Managing in the Age of Artificial Intelligence in Geneva
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