Conference Agenda

The emergence of artificial intelligence (AI) within public administration is anticipated to be a catalyst for significant improvements in operational efficiency and service quality, especially at the local level. Specifically, local government can leverage AI to identify trends, forecast outcomes, and make evidence-based decisions, resulting in more efficient functioning and enhanced responsiveness to citizen needs. However, successful AI integration requires comprehensive organisational readiness, with public managers fully aware of all possible internal and external constraints that may affect AI readiness.

This research tries to understand the successes and challenges faced by early adopters of AI and underscore the critical role of AI in local governments. The research, thus, will focus on identifying the determinants that enable or constrain the AI readiness of local governments based on the Technology-Organization-Environment (TOE) framework. Data will be obtained from validated questionnaires distributed to public managers in Japanese local governments. The complex relationships between the determinants (e.g., direct benefits, financial costs, organisational innovativeness, government pressure, citizen pressure, and government incentives) of AI readiness and its components (e.g., technical skills, business skills, data, technology, and basic resources) will be examined using statistical techniques such as regression analysis, factor analysis, and structural equation modelling.

The paper explores the first set of results from the two countries’ comparative research between Japan and Slovenia, but focusing mostly on the Japanese survey data. The research is supported by JSPS-MESS bilateral research funding (research ID: JPJSBP 120255004; 2025-2028).

Since the current research, which has just started, would not produce significant results by the time of the conference, the paper will be mostly based on the survey results of the previous comparative research (funded by JSPS-MESS bilateral research funding, research ID JPJSBP 120205004; 2020-2023, titled “Public administration models and principles: Slovenia and Japan in a comparative perspective”) to understand the tendency of AI readiness in local governments from the surveyed data on digitalization-related questions.

Indeed, in the previous survey, the public managers assessed digital era governance elements as more prominent on both countries’ local than state levels. The results confirmed that the activities of e-government are not progressing in Japan. The Digital Agency was established in September 2021 at the national level, while in the past, each ministry, agency, and local government has been promoting digitalisation separately and the Covid-19 pandemic highlighted such practice as ineffective, according to the Digital Agency (2021). However, digitalisation in Japan is lagging at the national and local levels. In the survey, more than 70% of national public employees disagree with the following statement: “Advances in information technology have greatly improved your workloads”. It may be said that digitalisation is progressing, but not in a user-friendly manner in Japan.

In conclusion, the paper tries to address the obstacles, issues, and opportunities identified through the survey to understand the trends in AI readiness in local governments.

As artificial intelligence (AI) advances, its strategic importance in public administration (PA) becomes increasingly evident. AI enables data-driven decision-making, personalised service delivery, and enhanced internal management, promoting more transparent and efficient governance. Despite AI's rapid technological progress, many PAs face challenges in developing the organisational, strategic, and institutional capacity required for effective deployment. This highlights the crucial concept of AI readiness - the preparedness of public organisations to adopt AI systematically to generate public value.

Despite growing interest in AI in the public sphere, the concept of AI readiness remains unclear and underexplored. Existing studies often focus on general digital innovation or private-sector models, offering limited insights into the unique conditions of PA. As a result, there is no established framework for assessing public organisations' readiness to engage with AI, a gap that is critical for effective AI adoption.

This study presents a new framework to measure AI readiness in PA, aimed at enhancing performance and supporting hybrid governance. It enables institutions to assess and align their AI readiness strategically. Based on the Technology-Organization-Environment (TOE) model, it adapts the technology dimension with a layered AI model, covering application, functional, and infrastructure layers. The organisational dimension uses the extended Leavitt Diamond model, focusing on structure, people, processes, and culture, while the environmental dimension includes legal, political, economic, demographic, and cultural factors affecting administrative capacities.

The study's objectives are to test the framework, identify necessary improvements, and assess the AI readiness of Slovenian PA. Empirical testing of the proposed framework will be conducted through a nationwide online survey among public managers in Slovenia, such as general directors of ministry directorates and their subordinate public agencies, institutions, and other bodies. The questionnaire operationalises AI readiness through several core domains: Technology, Structure, Culture, People, Processes and Data, Environment, and AI Potential. The latter, focusing on three key areas of administrative activity: internal management, service delivery, and policymaking, was added to capture the extent to which public organisations perceive AI's transformative potential in these areas. This approach provides both a conceptual and empirical assessment of AI readiness within the specific administrative context of Slovenia. The collected data will be empirically analysed using inferential statistics, with hypotheses developed to evaluate relationships between AI readiness domains and to test the validity of the framework.

The study is expected to reveal that AI readiness in PA is shaped by a combination of internal and external factors. Organisational flexibility, an open and innovative culture, and skilled, forward-thinking staff are likely to support AI adoption, while rigid structures and skill gaps may present challenges. Efficient processes and the ability to collect, manage, and use data effectively will also play a crucial role. At the same time, external influences such as policy support, funding, and societal attitudes may either enable or hinder progress. Together, these factors will determine the overall potential for successfully integrating AI into PA operations. This study will bridge the gap between AI scholarship and practice by offering a comprehensive framework along with a diagnostic tool for AI-driven transformation.

This chapter examines how artificial intelligence (AI) is adopted, implemented, and institutionalized in the public sector. Drawing on institutional theory, it argues that AI adoption should be understood not through discrete and variable-based models, but through the lens of institutional logics and isomorphic forces: coercive, mimetic, and normative. In this sense, the adoption of AI is not a purely technical decision, but an organizational process deeply shaped by institutional logics.

The first contribution of this chapter lies in shifting the analytical lens from traditional innovation models to a logic-based perspective. While prior frameworks emphasize individual motivation or organizational readiness, they often overlook how what counts as “innovation” is itself institutionally. By foregrounding the role of institutional logics, this chapter explains why AI innovations in the public sector differ significantly in form and purpose.

Second, the chapter shows how different isomorphic pressures lead to distinct innovation pathways. Mimetic logics tend to drive symbolic innovations that rebrand institutions without reforming them. Coercive logics promote compliance-driven deployment, often leading to bureaucratic reconfiguration without substantially changing service delivery. Normative logics, meanwhile, offer more potential for well-embedded innovation, though they can still reinforce technocratic ideals if not critically engaged. By tracing these divergent pathways, the chapter highlights that AI is not a singular reform but a reflexive process, influenced by organizational histories and field-level expectations.

Third, the chapter emphasizes that AI adoption does not occur in a vacuum. Rather than being inherently disruptive, AI tends to amplify existing logics and failures in public institutions. Institutional theory provides not just a framework for understanding adoption, but a diagnostic tool for identifying how prior organizational failures persist and evolve through digital systems.

Overall, this chapter makes the case for a logic-based and context-sensitive understanding of AI adoption. It contributes to current debates in digital governance by showing how AI innovations are translated, interpreted, and routinized depending on institutional conditions, rather than being linearly diffused. In doing so, it offers a more grounded and contextualized account of how AI technologies affect and are affected by public institutions.

The increasing importance of digital transformation in the public sector has prompted the emergence of new leadership roles, such as Chief Information Officers (CIOs) and most recently Chief Data Officers (CDOs). While these roles are established in the private sector, their institutionalization in government remains nascent and fragmented. This paper investigates how CDO roles are defined and enacted within Dutch government organizations at national, regional, and local levels. Drawing on semi-structured interviews with CI/DOs and related experts, the study explores three key questions: (i) how public sector organizations define CDO responsibilities; (ii) how CDOs relate to other digital leadership roles; and (iii) which factors enable or constrain their ability to manage digital transformation. Inspired by socio-material perspectives, the paper conceptualizes digital leadership as emerging from entangled institutional and infrastructural dynamics.

This paper examines digital public service innovation in developing democracies. Using set‐theoretic comparative analysis of 97 countries, it identifies a sufficient condition for digital transformation: the combination of strong governance steering and an open bureaucracy. Despite structural constraints—including weak state and fiscal-extractive capacities—the analysis demonstrates that these mechanisms foster digital innovation. Two case studies, Argentina and Jamaica, further illustrate how such processes operate in practice. The findings challenge prevailing assumptions that structural conditions of developing countries inevitably hinder (digital) public service innovation. The paper contributes with novel insights to the literature on public sector innovation and digital governance.