Managing Artificial Intelligence Deployment in the Public Sector

Published in Computer/IEEE, 2020

Recommended citation: Averill Campion, Mila Gasco, Slava Jankin Mikhaylov, and Marc Esteve. "Managing Artificial Intelligence Deployment in the Public Sector." Computer, 53(10): 28-37.

The scarcity of empirical evidence surrounding the organizational challenges and successful approaches to artificial intelligence (AI) deployment has resulted in mostly theoretical conceptualizations. By analyzing policy labs and offices of data analytics across the US to understand organizational challenges of AI adoption and implementation in the public sector as well as to identify successful management strategies to address such challenges, our study moves from speculation to gathering evidence. Our findings show that most challenges are found during the implementation stage and include challenges related to skills, culture, and resistance to share the data driven by data challenges. Further, our results indicate that long term strategies and short term actions need to be put in place to address these challenges. Among the first ones, leadership and executive support and stakeholder management seem to play an important role. Data standardization, training, and data-sharing agreements also seem to be successful specific short-term actions.

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