AI on the job: improving efficiency without closing off options
In SHR — Swiss Human Resources’ “IA on the job” programme, AI use also deserves to be examined through the options a team keeps open. The management question is specific: how can we prevent improvements to existing practices from crowding out exploration?
What the literature establishes
James G. March (1991, Organization Science) distinguishes the exploitation of existing knowledge from the exploration of new possibilities. His model shows how their different time horizons and returns can favour exploitation, potentially undermining long-term adaptation. This is not a finding about AI, but a framework for distinguishing two activities that a general productivity objective tends to conflate.
The mechanism: learning can narrow the search
Daniel A. Levinthal and James G. March (1993, Strategic Management Journal) analyse the myopias of organisational learning, including the tendency to overlook distant horizons and failures. An organisation can therefore reinforce what it already does successfully without sufficiently examining other paths. Applied to AI, this framework suggests a hypothesis to test: reusing prompts and formats that provide immediate satisfaction could reduce the search for different approaches.
The premature conclusion
One might conclude that teams should systematically ask AI for more ideas. Yet multiplying formulations guarantees neither diversity of assumptions nor their testing. The useful distinction concerns what the team actually tests, not how many variants it obtains. (our executive and employee training programmes)
What these studies cannot establish
These articles offer models and a theoretical analysis of learning; they do not measure the effects of today’s generative assistants. They therefore establish neither an optimal share of time for exploration nor an expected effect on innovation. Monitoring only time savings would nevertheless leave a central question unanswered: does the team still try anything beyond its usual solutions?
A practical check in Lausanne
For a Lausanne team in medtech, higher education, international sport or a Lake Geneva region scale-up, SHR can propose a two-week check within “IA on the job”, restricted to non-sensitive, reversible tasks. For each selected task, record the usual approach, an approach based on a different assumption, and the test conducted with its evaluation criterion defined in advance. The measure is the proportion of recorded tasks for which this alternative approach was actually tested, supported by a reviewable record of the test. This rate does not demonstrate improved innovation; it checks that exploration has an observable place in everyday work. To go further: explore the AI on the Job training in Lausanne, or browse our executive and employee training programmes in Switzerland.
In pictures: AI on the Job in Lausanne
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