AI adoption: which objective takes priority when instructions conflict?
A team may be instructed simultaneously to use AI, shorten turnaround times and strengthen its checks. The managerial question is then not simply how to encourage adoption, but which objective should prevail when a particular assignment makes it impossible to satisfy every requirement.
What the literature establishes
Edwin A. Locke and Gary P. Latham (2002, American Psychologist) synthesise research showing that specific, difficult goals can support performance under certain conditions, including commitment, ability and feedback. Goals direct attention and effort towards activities relevant to achieving them. Applied to AI, this mechanism suggests examining not only how clearly a speed target is defined, but also which aspects of work it might push into the background.
What the organisation actually encourages
Drawing on organisational examples, Steven Kerr (1975, Academy of Management Journal) analyses contradictions between the behaviours organisations hope for and those their reward systems encourage. Management may therefore ask for caution while primarily recognising on-time delivery. In AI-assisted work, this contradiction could make thorough verification less attractive, even when it remains an official requirement.
The premature conclusion
One might conclude that adding more objectives is enough to restore a balance between speed, quality and caution. These studies do not establish that accumulating objectives resolves conflicts between requirements. One managerial option is instead to specify a priority rule for situations in which meeting a deadline means foregoing a check considered necessary. (our executive and employee training programmes)
What these studies do not demonstrate
These publications do not address generative AI adoption and do not demonstrate that an explicit hierarchy of objectives improves its use. The goal-setting review covers varied situations, while the rewards article offers an analysis illustrated by examples rather than a single experimental test. Their contribution is to frame a testable hypothesis, not to prescribe a universal priority between speed and verification.
A practical check in Genève
As part of SHR’s programme « Conduire l’adoption de l’IA dans son équipe », a Geneva-based team could spend two weeks reviewing assignments where a deadline conflicts with verification: a private banking note, a briefing for an international organisation, luxury watchmaking content or a commodity trading analysis. For each case, it would record the conflicting requirements, the chosen priority and the manager’s approval, without including sensitive data. The measure would be the proportion of these cases in which a priority was explicitly approved before delivery, with the total number of cases also reported. This record would not prove a performance gain, but would reveal how many trade-offs are still implicitly left to employees. To go further: explore the Leading AI Adoption in Your Team training in Geneva in the canton of Geneva, or browse our executive and employee training programmes in Switzerland.
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