Enterprise AI Analysis
Artificial Intelligence Adoption in Public Administration: An Overview of Top-Cited Articles and Practical Applications
The adoption of artificial intelligence (AI) in public administration (PA) signifies a transformative shift toward smarter resource management, the provision of customized public services, and enhanced interaction between citizens and public authorities for participatory, data-driven policymaking. Unlike the private sector, the adoption of AI in PA demands a focus on public value, emphasizing effectiveness, efficiency, equity, manageability, and political feasibility. Public value, a multidimensional concept, refers to the capacity of PA to deliver services that align with societal needs, democratic values, and collective well-being. In this context, Wirtz et al. underscore the challenges PA faces in balancing effective governance with the rapid evolution of AI technologies and strict institutional constraints. Similarly, De Sousa et al. highlight that stringent external requirements exacerbate these challenges, making it difficult for PA to keep pace with rapid technological advancements. These hurdles are compounded by unclear conceptualizations and limited critical reflections on AI within PA, which frequently result in fragmented approaches and an overreliance on technological narratives. Additionally, the literature often treats AI in isolation, focusing on either technical or regulatory aspects without adequately exploring its transformative impact on workflows, public services, and governance structures.
Executive Impact Summary
AI in Public Administration (PA) is rapidly evolving, driving significant advancements in operational efficiency, service delivery, and policy formulation. Our analysis of top-cited research highlights key trends and opportunities for organizations looking to leverage AI to create greater public value and enhance governmental functions.
3149+
Documents Analyzed
200+
Top-Cited Articles
88.27
Avg. Citations/Document
2020
Most Productive Year
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The largest category, general reviews, emphasizes the importance of synthesizing the wide-ranging impacts and applications of AI, providing a foundation for future research and policymaking. Policymaking is particularly significant, as AI plays a crucial role in shaping effective and responsive policies. The considerable overlap between policymaking, internal operations, and service delivery highlights the integrative nature of AI research, showing that advances in policymaking are closely tied to operational efficiency and improved service delivery. The smaller number of papers focusing exclusively on internal operations and service delivery suggests that these areas are often analyzed in relation to their impact on policy and service delivery, rather than as independent topics.
| Authors | Year | Document Title | Source Title | Citations per Year |
|---|---|---|---|---|
| Dwivedi et al. [36] | 2023 | "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy | International Journal of Information Management | 296.50 |
| Dwivedi et al. [38] | 2021 | Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy | International Journal of Information Management | 252.75 |
| Gorwa et al. [84] | 2020 | Algorithmic content moderation: Technical and political challenges in the automation of platform governance | Big Data and Society | 59.00 |
| Sun and Medaglia [17] | 2019 | Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare | Government Information Quarterly | 56.33 |
| Wirtz et al. [8] | 2019 | Artificial Intelligence and the Public Sector—Applications and Challenges | International Journal of Public Administration | 52.83 |
Articles on specific areas address individual applications of AI, such as chatbots and disaster response systems. Influential works in this category include those by Androutsopoulou et al. [41] and Jiang et al. [82]. The analysis of general review articles reveals a shared focus on the transformative impact of AI on PA, emphasizing the need for robust research to ensure effective implementation and policymaking.
| Authors | Year | Document title | Source title | Citations per Year | Dimension |
|---|---|---|---|---|---|
| Androutsopoulou et al. [41] | 2019 | Transforming the communication between citizens and government through AI-guided chatbots | Government Information Quarterly | 40.33 | 2 |
| Jiang et al. [82] | 2018 | Urban pluvial flooding and stormwater management: A contemporary review of China’s challenges and “sponge cities” strategy | Environmental Science and Policy | 40.29 | 3 |
| Ragini et al. [85] | 2018 | Big data analytics for disaster response and recovery through sentiment analysis | International Journal of Information Management | 35.71 | 1,3 |
| Lindgren et al. [7] | 2019 | Close encounters of the digital kind: A research agenda for the digitalization of public services | Government Information Quarterly | 34.33 | 1,2,3 |
| Liu et al. [86] | 2017 | Classifying urban land use by integrating remote sensing and social media data | International Journal of Geographical Information Science | 31.75 | 1,3 |
51.1%
of analyzed literature focuses on Machine Learning, indicating its strong role in data-driven decision making.
Enterprise Process Flow
Initial Scopus database (3149 documents)
→
Descriptive analysis
→
Narrowed database of top 200 most-cited documents
→
Descriptive + content analysis (thematic and relational)
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Categorization into General Review & Specific Articles (Internal operations, Service delivery, Policymaking)
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In-depth content analysis of AI applications (Public Sector Tech Watch)
| AI Subset | Key Capabilities in Public Administration | Challenges & Considerations |
|---|---|---|
| Machine Learning (51.1% of literature) |
|
Data transparency, explainability, algorithmic bias, human oversight. |
| Deep Learning (29.2% of literature) |
|
Requires large datasets, high computational power, interpretability. |
| Generative AI (24.1% of literature) |
|
Ethical concerns, data privacy, potential for misinformation. |
Case Study: Dutch SyRI System for Child Benefit Fraud
The **Dutch SyRI (System Risk Indication)** system was designed to detect child benefit fraud using AI, but it faced significant criticism for privacy violations and lack of transparency. Its implementation was ultimately discontinued due to legal challenges and public outcry.
Lesson Learned: Without robust ethical frameworks, transparency, and accountability, AI systems can undermine public trust and lead to unjust outcomes, emphasizing the need for responsible AI adoption that aligns with democratic values and public expectations.
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Strategic Implementation Roadmap
Our phased approach ensures a smooth, ethical, and value-driven AI integration into your public administration. Each phase is designed to build on the last, ensuring sustainable transformation.
Phase 1: AI Strategy & Assessment
Develop a comprehensive AI strategy aligned with public value principles, conduct an organizational readiness assessment, and identify high-impact use cases.
Phase 2: Pilot Programs & Ethical Frameworks
Implement pilot AI projects in selected areas, establishing ethical guidelines, transparency protocols, and accountability mechanisms for AI deployment.
Phase 3: Scaled Integration & Workforce Adaptation
Scale successful pilot projects across relevant departments, invest in data infrastructure, and develop training programs to upskill the public sector workforce.
Phase 4: Continuous Monitoring & Value Optimization
Establish ongoing monitoring and evaluation frameworks for AI systems, regularly assess public value creation, and adapt AI strategies based on performance and citizen feedback.
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