Adopting AI: should people develop their own ideas before consulting it?
In SHR — Swiss Human Resources’ programme “Leading AI adoption in your team”, adoption is about more than frequency of use. One specific management question deserves attention: should employees formulate their own options before seeing those proposed by AI?
What the literature establishes
David G. Jansson and Steven M. Smith (1991, Design Studies) show that, in design tasks, exposure to an example can steer subsequent solutions towards that example’s features, including its flaws. This fixation effect suggests that a starting point is not necessarily a neutral aid. An AI proposal presented first could play this role, but that extension remains a hypothesis, not a finding of their study.
Group discussion does not guarantee diversity
Michael Diehl and Wolfgang Stroebe (1987, Journal of Personality and Social Psychology) investigate productivity losses in brainstorming groups and demonstrate the role of production blocking: while one person speaks, others cannot express their ideas simultaneously. Gathering a team around an AI response therefore does not, by itself, ensure that every alternative will be voiced. Fixation on an example and constraints on speaking are distinct mechanisms that may limit exploration.
The premature conclusion
It would be tempting to conclude that access to AI should always be delayed. These studies do not justify that prescription: an example can also provide a useful reference point, and not every task requires a wide variety of solutions. The issue is to distinguish situations in which the team needs genuinely different approaches from those in which it must follow an established procedure. (our executive and employee training programmes)
What these data cannot establish
These studies concern design and brainstorming, not contemporary generative AI use in Swiss teams. They establish neither the optimal order for consulting a tool nor the quality of the resulting decisions. Counting ideas is also insufficient: several formulations may describe the same option, while an original proposal may be impractical.
A practical check in Sion
In Sion, a team working in hydropower, healthcare, viticulture or alpine tourism could compare two approaches using comparable planning problems without sensitive data: individual idea generation before consulting AI, or consulting AI before individual idea generation. Counterbalancing the order of the approaches across participants and retaining proposals before group discussion would make the comparison easier to interpret. The primary measure would be the number of distinct options judged feasible against a predefined assessment framework, evaluated without identifying which approach produced them; time spent would be recorded separately. This protocol would provide a practical foundation for SHR’s programme: choosing a working sequence based on local observation, without claiming to establish a general rule. To go further: explore the Leading AI Adoption in Your Team training in Sion in the canton of Valais, or browse our executive and employee training programmes in Switzerland.
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