Managing Artificial Intelligence Deployment in the Public Sector

Abstract

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 shortterm actions.

The Adoption and Implementation of Artificial Intelligence Chatbots in Public Organizations: Evidence from U.S. State Governments

“…This lack of technological complexity results in manageable implementation challenges, which confirms perceptions of chatbots' ease of use. We also argue that this is an interesting finding given that most literature on AI has widely recognized the complexity of AI systems and, therefore, of implementing AI projects (e.g., Campion et al, 2020Campion et al, , 2022, which may point to the existence of different types of drivers of adoption for different types of AI systems. Through comparative qualitative studies, further research could explore the extent to which the type of project and its related complexity determine the factors that influence adoption decisions.…”

“…First, it shows that data and information-related factors as well as technological factors play an important role in the implementation of chatbots, even more important than organizational and institutional factors. This is particularly interesting for research on digital government has shown a different trend over time: as the use of technology by public organizations has matured and institutionalized, organizational, and institutional factors have become more important than data and information and technology-related ones (e.g., Campion et al, 2020, Gichoya, 2005Glyptis et al, 2020). We argue that these differences are related to the nature and characteristics of chatbots: as a specific type of AI-based systems, chatbots are highly technological and heavily based on data (Campion et al, 2022).…”

“…That is, once the decision to adopt a technology-driven innovation is made, state agencies may have to pay attention to very different issues in its implementation process. Thus, as Campion et al (2020Campion et al ( , 2022 indicate, when taking the overall deployment process into account, organizations need to consider long-term issues, such as potential benefits and creation of public value, as well as short-term actions that may contribute to addressing specific implementation issues.…”

Technology 3.0: Police Officers’ Perceptions Towards Technology Shifts

“…Public management research is addressing growing attention to the recent and vast technological shift to “system” bureaucracy (Andrews, 2019; Busuioc, 2021; Vogl et al, 2020). However, this work thus far has mainly been theoretical and conceptual, and empirical work is still being determined (Campion et al, 2020; Sun & Medaglia, 2019). Specifically, we need to understand how these shifts matter for SLBs interacting with citizens face to face (Janssen & Kuk, 2016; Bullock, 2019).…”

Knowledge Sharing in AI Services: A Market-Based Approach

“…for instance [4]. Despite its simplicity, data sharing is often impractical due to multiple reasons, including privacy concerns related to the source data [5] or the problematic process of finding an agreement between different parties [6].…”