Paper Number
ECIS2026-2307
Paper Type
CRP
Abstract
Generative AI promises significant innovation, but many industrial product companies face declining revenues, strict investment caps, and overloaded IT departments. This study asks how such firms can pursue GenAI innovation under resource constraints. We report a case study of a leading European automation company that created a central AI Hub, crowdsourced 147 employee ideas, and filtered them through staged business, risk, and technical evaluations. From this evidence we conceptualize frugal GenAI adoption as a portfolio-oriented process that prioritizes organizational reach over local impact, focuses first on internal process use cases with limited external exposure, and advances low effort high value pilots to create early evidence. The study extends frugal innovation from product outcomes to adoption processes, reframes early GenAI work as portfolio design under constraint, and identifies four mechanisms for inclusive idea collection, exposure shielding, and effort first evaluation that help resource constrained incumbents build GenAI momentum.
Recommended Citation
Paffrath, Vincent; Bauer-Hänsel, Ingrid; Wlcek, Manuel; and Wortmann, Felix, "Frugal Adoption Of Generative AI In Industrial Product Companies" (2026). ECIS 2026 Proceedings. 18.
https://aisel.aisnet.org/ecis2026/is_adopt/is_adopt/18
Frugal Adoption Of Generative AI In Industrial Product Companies
Generative AI promises significant innovation, but many industrial product companies face declining revenues, strict investment caps, and overloaded IT departments. This study asks how such firms can pursue GenAI innovation under resource constraints. We report a case study of a leading European automation company that created a central AI Hub, crowdsourced 147 employee ideas, and filtered them through staged business, risk, and technical evaluations. From this evidence we conceptualize frugal GenAI adoption as a portfolio-oriented process that prioritizes organizational reach over local impact, focuses first on internal process use cases with limited external exposure, and advances low effort high value pilots to create early evidence. The study extends frugal innovation from product outcomes to adoption processes, reframes early GenAI work as portfolio design under constraint, and identifies four mechanisms for inclusive idea collection, exposure shielding, and effort first evaluation that help resource constrained incumbents build GenAI momentum.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.