AI adoption: does time saved become team capacity?
AI promises are often expressed as time saved on a task, yet team managers remain accountable for work actually completed. The useful question is therefore precise: how can managers check whether individual acceleration increases team capacity rather than merely shifting the workload?
What the literature establishes
Erik Brynjolfsson and Lorin M. Hitt (1996, Management Science) used firm-level data to identify a positive contribution of information systems spending to output. Their finding challenges the idea that computing is necessarily unproductive. However, it concerns firm performance, not an equivalence between minutes saved on a task and additional team capacity.
The mechanism of organisational complementarities
Timothy F. Bresnahan, Erik Brynjolfsson and Lorin M. Hitt (2002, The Quarterly Journal of Economics) provide evidence consistent with complementarities between information technology, workplace organisation and skills. Technology is therefore not a resource whose effects are independent of how work is allocated. For AI, this suggests a managerial hypothesis to test: accelerating one stage may leave the final outcome unchanged if the next stage cannot absorb the flow.
The premature conclusion
Managers sometimes infer that adding up individual gains is enough to calculate newly available capacity. That conclusion does not follow from these studies: a draft produced faster may require more corrections or wait longer for approval. The relevant management gain concerns the completed process at comparable quality, not just the accelerated task. (our executive and employee training programmes)
What the evidence cannot promise
These studies concern technologies that predate generative AI and draw on observational firm-level data. They establish neither the causal effect of a current tool within a team nor a conversion rate from time saved to additional output. Conversely, the common practice of collecting only users’ self-reported time savings leaves waiting, rework and work transferred to colleagues outside the picture.
A practical check in Lausanne
As part of SHR’s programme « Conduire l’adoption de l’IA dans son équipe », a Lausanne-based team can track a single workflow for two weeks: dossier preparation in medtech, administrative summaries at a higher education institution, operational documents in international sport or commercial proposals at a Lake Geneva region scale-up. For each deliverable, it records every contributor’s working time, waiting periods, rework and acceptance against quality criteria defined beforehand, without entering sensitive data into an unauthorised tool. The main measure is the number of accepted deliverables divided by total working hours, compared with a baseline period involving cases of comparable complexity. End-to-end lead time complements this ratio: together, these measures help check whether the claimed gain extends beyond the AI-assisted stage, without claiming that they alone isolate a causal effect. To go further: explore the Leading AI Adoption in Your Team training in Lausanne, or browse our executive and employee training programmes in Switzerland.
In pictures: Leading AI Adoption in Your Team in Lausanne
- leading ai adoption in your team
- Lausanne
- research
- training lausanne