dc.contributor.author | Haefner, Naomi | |
dc.contributor.author | Parida, Vinit | |
dc.contributor.author | Gassmann, Oliver | |
dc.contributor.author | Wincent, Joakim | |
dc.date.accessioned | 2024-04-03T13:10:56Z | |
dc.date.available | 2024-04-03T13:10:56Z | |
dc.date.created | 2023-10-20T14:36:27Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Haefner, N., Parida, V., Gassmann, O., & Wincent, J. (2023). Implementing and scaling artificial intelligence: A review, framework, and research agenda. Technological Forecasting and Social Change, 197, Artikkel 122878. | en_US |
dc.identifier.issn | 0040-1625 | |
dc.identifier.uri | https://hdl.handle.net/11250/3124695 | |
dc.description.abstract | Artificial intelligence (AI) will have a substantial impact on firms in virtually all industries. Without guidance on how to implement and scale AI, companies will be outcompeted by the next generation of highly innovative and competitive companies that manage to incorporate AI into their operations. Research shows that competition is fierce and that there is a lack of frameworks to implement and scale AI successfully. This study begins to address this gap by providing a systematic review and analysis of different approaches by companies to using AI in their organizations. Based on these experiences, we identify key components of implementing and scaling AI in organizations and propose phases of implementing and scaling AI in firms. | en_US |
dc.language.iso | eng | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Implementing and scaling artificial intelligence: A review, framework, and research agenda | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2023 The Author(s). | en_US |
dc.source.pagenumber | 11 | en_US |
dc.source.volume | 197 | en_US |
dc.source.journal | Technological Forecasting and Social Change | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.techfore.2023.122878 | |
dc.identifier.cristin | 2186864 | |
dc.source.articlenumber | 122878 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |