AI on the job: saving time without shifting the work

In Basel’s pharmaceutical, life sciences and Rhine logistics businesses, a deliverable often passes through several professional functions before it becomes usable. The managerial question is precise: does the time its author saves with AI reduce total work, or increase the burden on its recipients?

What the literature establishes

Thomas W. Malone and Kevin Crowston (1994, ACM Computing Surveys) propose understanding coordination as the management of dependencies between activities. Their review shows why analysing an isolated task is insufficient: its execution depends, among other things, on shared resources and outputs produced elsewhere. Applied to AI, this perspective directs attention not only to producing a document, but also to the conditions under which someone else can use it.

The mechanism: making a deliverable usable

Gerardo A. Okhuysen and Beth A. Bechky (2009, Academy of Management Annals) identify three integrating conditions for coordination: accountability, predictability and common understanding. A quickly drafted document may leave these conditions unmet if its recipient does not know what is expected, when, or for what purpose. The relevant mechanism is therefore not simply drafting speed, but the work required to make the result usable.

The premature conclusion

One might conclude that a single format should be imposed on all AI-assisted documents. These studies do not justify such a rule: the dependencies to be managed differ between an international R&D memo, a logistics update and a pharmaceutical dossier. A common format can facilitate a handover, but it can also add sections that nobody uses. (our executive and employee training programmes)

What the evidence does not establish

Both articles are theoretical reviews, not trials measuring the effects of generative AI in Basel businesses. They provide an analytical framework, not an estimate of time saved or shifted. Recording drafting time alone would not settle the question either, since it would exclude requests for clarification and downstream rework.

A practical check in Bâle

Within SHR — Swiss Human Resources’ “IA on the job” programme, a useful exercise would be to track a non-regulatory internal memo about a Rhine logistics delay affecting supplies to an international life sciences R&D team in Basel. For comparable cases, with and without authorised AI assistance, record active drafting, reading, clarification and revision time until the recipient accepts the memo, without changing required approvals. The primary measure would be the sum of these times per accepted memo, retaining the breakdown between author and recipients. A reduction in the author’s time accompanied by an increase in the total would indicate a shift in work rather than a collective gain. To go further: explore the AI on the Job training in Basel, or browse our executive and employee training programmes in Switzerland.

In pictures: AI on the Job in Basel

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