AI adoption: why usefulness is not enough
A compelling demonstration can still leave a tool marginal in everyday work. What can adoption research actually explain?
An influential model
Viswanath Venkatesh and colleagues (2003, MIS Quarterly) propose UTAUT: performance expectancy, effort, social influence and facilitating conditions contribute to intention and use. The longitudinal study covered four organisations and technologies predating generative AI.
The organisation matters
Jan Jöhnk, Malte Weißert and Katrin Wyrtki (2021, Business Horizons) identify AI-readiness dimensions through qualitative research. Their framework describes recurring conditions; it does not prove that any one causes adoption.
What one-off training misses
Tool training without changed routines leaves access, data, approval, experimentation time and managerial expectations untouched. Declared enthusiasm is therefore an insufficient measure. (our executive and employee training programmes)
A more cautious rollout
Start with a bounded use case, state what must not be delegated to AI and review experience weekly. This supports learning without confusing deployment with adoption.
A practical check in Lausanne
Select one team routine and track actual uses, abandonments and human corrections for three weeks. Reasons for abandonment provide better evidence than a general interest survey. 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
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