Adoption and Implementation of AI in the Public Sector Minitrack

Description

Artificial intelligence (AI) is increasingly integrated into public sector organizations as part of broader digital government transformation efforts. Advances in machine learning, automation, and generative AI promise improvements in efficiency, service delivery, and decision support, while enabling new forms of personalization and proactive public services. At the same time, AI deployment in public contexts raises fundamental questions about how algorithmic systems reshape governance, reconfigure power relations, and redistribute risks and benefits across different populations.

Despite growing interest and investment, AI adoption, implementation, use, and impacts in the public sector remains uneven. Many initiatives struggle to move beyond experimentation, encounter integration challenges with existing information systems, or generate unintended impacts on work practices, decision-making, and service delivery. These challenges point to the importance of understanding AI adoption, implementation, use, and impacts not only as a technical innovation, but as an organizational and socio-technical process shaped by institutional contexts, data availability, human capacity, and operational constraints.

This minitrack invites research that examines the adoption, implementation, use, and impacts of AI in public sector organizations. We welcome studies that investigate how AI systems are designed into public services, how they interact with existing processes and roles, and how public organizations develop the capacity to use AI effectively and sustainably. Contributions may address both benefits and risks associated with AI use, including implications for frontline work, decision-making, service quality, trust, and long-term viability, as well as success or failure stories.

The minitrack encourages diverse theoretical and methodological approaches, including qualitative, quantitative, design-oriented, and mixed-methods research. While the primary focus is on public administration, submissions related to broader public sector domains (e.g., health, social services, policing) are also welcome (although we will not consider papers related to AI in national security and the military).

The goal of this minitrack is to advance understanding of how AI is actually adopted and implemented in public sector settings, and what conditions enable AI to contribute meaningfully to digital government and societal outcomes. Among the dimensions of AI in government that might be addressed are:

• AI adoption and implementation
  1. Adoption, diffusion, and scaling of AI in public sector organizations
  2. Organizational readiness and capacity for AI implementation
  3. Managing AI projects and programs in public administration
  4. Determinants of successful and failed AI implementations
• AI in public services and operations
  1. AI-enabled service delivery and public service innovation
  2. Proactive and anticipatory public services supported by AI
  3. Integration of AI with existing public sector information systems
  4. Interoperability challenges in AI-enabled public services
• Work, decision-making, and human–AI interaction
  1. Impacts of AI on public sector work practices and roles
  2. AI and street-level bureaucrats: discretion, judgment, and oversight
  3. Human–AI interaction in frontline and back-office settings
  4. Behavioral effects of AI on trust, motivation, and decision-making
• Data, infrastructure, and sustainability (non-governance)
  1. Data quality and data readiness for AI implementation
  2. Infrastructure and platform dependencies in public sector AI
  3. Sustainability of AI systems in public organizations (economic, operational, environmental)
  4. Vendor dependence, lock-in, and long-term maintenance challenges
• Evaluation and impact
  1. Evaluating AI use and outcomes in public sector contexts
  2. Comparative studies across policy domains or jurisdictions
  3. AI impacts on public value, service quality, and inclusion
  4. Unintended consequences and second-order effects of AI use
• Emerging directions
  1. Generative AI and large language models in public administration
  2. Automation and semi-autonomous systems in government operations
  3. Lessons from pilot projects, experimentation, and scaling efforts

Minitrack Leaders

Antonio Cordella, Ph.D., is an Associate Professor in the Department of Management at the London School of Economics and Political Science (LSE), where he is the Academic director of the Master in Information Systems and Digital Innovation and responsible for the post-graduate course on ICT in the Public Sector. He is also a visiting professor at the Maastricht Graduate School of Governance, UNMERIT, The Netherland. He has published widely in information systems, e-government and public sector associated reforms. An Italian national, he holds an undergraduate degree in Political Science from University of Bologna, Italy, and a PhD in Information Systems from Gothenburg University, Sweden. His current research focuses on ICT in the Public Sector, with specific attention to e-Government and the associated institutional changes.

Anastasija Nikiforova is an Associate Professor of Applied AI and Information Systems at the University of Tartu (Institute of Computer Science, Chair of Software Engineering), as well as the head of Information Systems Research Group. Her research focuses on data and AI governance and digital transformation, with particular emphasis on the responsible adoption of emerging technologies, especially AI. Positioned at the intersection of technology, society, and policy, her work addresses broader challenges of trust, resilience, and inclusivity in complex socio-technical systems. She is a board member of Digital Government Society, member of the European Open Science Cloud Task Force “FAIR Metrics and Digital Objects” and fellow for Digital Statecraft Academy. She is an Editorial Board Member and Associate Editor for several leading journals, including International Journal of Information Management, Government Information Quarterly, IEEE Transactions on Technology and Society, Data & Policy (Cambridge University Press), BMC Research Notes (Springer Nature), and JeDEM. She also chairs tracks and workshops at major conferences, including IFIP EGOV-CEDEM-EPART, dg.o, ECAI2025, CBI-EDOC 2025, IJCAI2025, PRICAI2025, ICA2025, and HICSS2026. She is actively involved in international research and professional communities, including International Federation for Information Processing Working Group 8.5 (IFIP WG8.5 on ICT and Public Administration), Digital Government Society, EDSC (European Digital Skills Certificate), the AIS Women’s Network College, Women in AI, and other professional communities.

Corey Kewei XU is an assistant professor in the Thrust of Innovation, Policy, and Entrepreneurship, Society Hub, The Hong Kong University of Science and Technology (Guangzhou). He obtained his Ph.D. in Public Administration from Florida State University. His main research interest focuses on how technology shapes government decision-making and its impact on the efficiency and equity of public policy and public service. His work has appeared in Public Administration Review, Government Information Quarterly, PNAS, Applied Energy, and other journals. He serves as guest editor for Government Information Quarterly, track co-chair for Dg.o.