Abstract
Artificial intelligence (AI) has the potential to enhance every component of information systems (IS) at the individual, organizational and societal level. However, AI technologies are being developed and commercialized at an unprecedented speed making it hard for IS researchers and practitioners to keep up with these technologies and how they can enhance IS. The technologies have evolved so fast in the last decade that many companies have tried and failed to implement AI without truly understanding what it is. Therefore, understanding AI from the perspective of the leading developers of related technologies is crucial for its adoption, use and impact on IS. In this paper, we provide a systematic review and synthesis of practice-based literature on AI, highlighting what leading industry entities and experts understand by AI. We use these findings to propose an AI adoption, use and impact classification framework for IS research and propose a corresponding research agenda.
Recommended Citation
Bawack, Ransome Epie; Fosso Wamba, Samuel; and CARILLO, Kevin, "Artificial Intelligence in Practice: Implications for IS Research" (2019). AMCIS 2019 Proceedings. 2.
https://aisel.aisnet.org/amcis2019/ai_semantic_for_intelligent_info_systems/ai_semantic_for_intelligent_info_systems/2
Artificial Intelligence in Practice: Implications for IS Research
Artificial intelligence (AI) has the potential to enhance every component of information systems (IS) at the individual, organizational and societal level. However, AI technologies are being developed and commercialized at an unprecedented speed making it hard for IS researchers and practitioners to keep up with these technologies and how they can enhance IS. The technologies have evolved so fast in the last decade that many companies have tried and failed to implement AI without truly understanding what it is. Therefore, understanding AI from the perspective of the leading developers of related technologies is crucial for its adoption, use and impact on IS. In this paper, we provide a systematic review and synthesis of practice-based literature on AI, highlighting what leading industry entities and experts understand by AI. We use these findings to propose an AI adoption, use and impact classification framework for IS research and propose a corresponding research agenda.