Paper Type
Short
Paper Number
PACIS2025-1244
Description
Artificial intelligence (AI) adoption is becoming increasingly widespread and essential for many organisations. As AI technology continues to evolve, there is a growing societal expectation for businesses to use AI not only effectively but also responsibly and ethically. While various responsible AI (RAI) frameworks exist, they are often broad and difficult to apply, posing challenges for SMEs that lack resources and AI expertise. To address these challenges, this study aims at investigating how SMEs can implement RAI effectively and how RAI contributes to business value in SMEs. By integrating RAI into existing AI capability frameworks, this research develops a RAI capability framework based on theoretical and empirical insights. The study will also provide SMEs with practical guidelines and tools for RAI adoption. Therefore, this study will offer theoretical contributions and actionable strategies to help SMEs build organisational capabilities for RAI adoption and enhance business value.
Recommended Citation
Lee, Maggie C. M.; Terziovski, Mile; Scheepers, Helana; and Corbel, Pascal, "Developing Responsible Artificial Intelligence (RAI) Capabilities for Small and Medium-Sized Enterprises (SMEs)" (2025). PACIS 2025 Proceedings. 5.
https://aisel.aisnet.org/pacis2025/aiandml/aiandml/5
Developing Responsible Artificial Intelligence (RAI) Capabilities for Small and Medium-Sized Enterprises (SMEs)
Artificial intelligence (AI) adoption is becoming increasingly widespread and essential for many organisations. As AI technology continues to evolve, there is a growing societal expectation for businesses to use AI not only effectively but also responsibly and ethically. While various responsible AI (RAI) frameworks exist, they are often broad and difficult to apply, posing challenges for SMEs that lack resources and AI expertise. To address these challenges, this study aims at investigating how SMEs can implement RAI effectively and how RAI contributes to business value in SMEs. By integrating RAI into existing AI capability frameworks, this research develops a RAI capability framework based on theoretical and empirical insights. The study will also provide SMEs with practical guidelines and tools for RAI adoption. Therefore, this study will offer theoretical contributions and actionable strategies to help SMEs build organisational capabilities for RAI adoption and enhance business value.
Comments
AI ML