ECIS 2026 (2026) AI Processed Human Approved
MANAGING ARTIFICIAL INTELLIGENCE IN PUBLIC SECTOR ORGANIZATIONS:A DYNAMIC CAPABILITIES PERSPECTIVE
This study investigates how public sector organizations can build and leverage dynamic capabilities to effectively adopt and continuously adapt artificial intelligence (AI) technologies. The researchers utilized a qualitative multiple-case study design, gathering semi-structured interview data and secondary documentation across five German public sector organizations.
Problem
Public sector organizations face significant barriers when adopting AI, driven by complex technological, regulatory, and organizational uncertainties. Existing literature frequently takes a static approach to technology adoption and primarily focuses on private sector environments, leaving public sector managers without clear frameworks to handle ongoing socio-technical shifts.
Outcome
- The study empirically supports that AI adoption in public organizations is hindered by high levels of technological, regulatory, and organizational uncertainties, which require proactive management.
- The research empirically identifies and supports a framework of specific organizational microfoundations across three key dimensions: sensing (e.g., tracing employee expectations and monitoring regulatory landscapes), seizing (e.g., establishing AI governance and launching pilot projects), and transforming (e.g., active change management and promoting AI literacy).
- The findings empirically show that employee representation bodies (such as staff councils) often play a critical, gatekeeping role that can delay or hinder AI implementation decisions.
- The study acknowledges limitations, including its geographical focus on the German public sector and a limited observation timeframe from April 2024 to October 2025.
- The research empirically identifies and supports a framework of specific organizational microfoundations across three key dimensions: sensing (e.g., tracing employee expectations and monitoring regulatory landscapes), seizing (e.g., establishing AI governance and launching pilot projects), and transforming (e.g., active change management and promoting AI literacy).
- The findings empirically show that employee representation bodies (such as staff councils) often play a critical, gatekeeping role that can delay or hinder AI implementation decisions.
- The study acknowledges limitations, including its geographical focus on the German public sector and a limited observation timeframe from April 2024 to October 2025.
What it means for you
- CIO / IT Executive: Send an email calendar invite to the chair of your organization's Employee Representation Body (Staff Council) to establish a joint, bi-weekly 'AI Governance and Alignment Committee' to co-create deployment guidelines and prevent approval bottlenecks.
- IT Manager: Draft a 1-page project charter for a highly constrained, 4-week pilot project using a low-risk internal AI tool (e.g., automated document summarizing) to test technical capability and establish baseline performance metrics.
- Business Strategist: Design and launch a 5-question MS Forms survey to all department heads to map their teams' current AI literacy levels and identify specific anxieties, using the results to structure next month's targeted change management plan.
- Researcher: Draft a research proposal and reach out to two international public sector research partners to replicate this qualitative dynamic capabilities study in a non-German jurisdiction (e.g., the US or UK) to address the geographical limitations of the original study.
- Policymaker: Create and distribute a standardized 'Public Sector AI Regulatory Compliance Checklist' to provide local agency leaders with clear, step-by-step guidance on data privacy and municipal regulations, directly reducing their regulatory uncertainty.
Transcript
Host: Welcome to A.I.S. Insights - Turning IS Research into Business Action. I'm your host, Aaron Ivan Sterling.
Expert: And I'm Ava Irene Solis. It is great to be here to share insights from recent research.
Host: Today, we are looking at a study presented at the European Conference on Information Systems 2026. The title is "MANAGING ARTIFICIAL INTELLIGENCE IN PUBLIC SECTOR ORGANIZATIONS: A DYNAMIC CAPABILITIES PERSPECTIVE." Ava, could you give our listeners a quick summary of what this study is about?
Expert: This study investigates how public sector organizations can build and leverage dynamic capabilities to effectively adopt and continuously adapt to artificial intelligence technologies. The researchers used a qualitative multiple-case study design, gathering data from five German public sector organizations between April 2024 and October 2025.
Host: Let's start with the real-world problem. Why is adopting AI so challenging for public sector organizations in particular?
Expert: Public sector organizations face significant barriers when adopting AI, driven by complex technological, regulatory, and organizational uncertainties. For instance, they must comply with evolving regulations like the EU AI Act, manage high public accountability, and address employee concerns about job security and shifting roles. The authors of the study point out that much of the existing literature takes a static approach to technology adoption and primarily focuses on private sector environments. This leaves public sector managers without clear, dynamic frameworks to handle ongoing socio-technical shifts.
Host: That makes sense. Public institutions have very different stakeholder demands compared to private firms. How did the researchers approach this problem to find a solution?
Expert: To capture the real-world complexity, the researchers conducted a multiple-case study. They interviewed key personnel—including Chief Information Officers, HR leaders, and IT managers—and analyzed secondary documentation across five distinct public sector organizations. They then analyzed this data using dynamic capabilities theory, which focuses on how organizations sense opportunities and threats, seize those opportunities, and transform their processes and structures over time.
Host: This brings us to the core of the study. What did the researchers find?
Expert: The study empirically supports that AI adoption in public organizations is hindered by high levels of technological, regulatory, and organizational uncertainties. To address these, the study identifies a practical framework of specific organizational microfoundations across three dimensions: sensing, seizing, and transforming.
Host: Let's break those down. What does "sensing" look like in practice according to the study?
Expert: Sensing involves identifying and assessing opportunities and threats. For AI, the study shows that organizations must trace employee expectations and anxieties, monitor technological developments, and closely track regulatory landscapes. This helps managers anticipate challenges before they arise.
Host: And "seizing"? How do organizations act on what they sense?
Expert: Seizing is about mobilizing resources to address those opportunities and threats. The study identifies key microfoundations here, such as establishing AI governance—like setting up an AI ethics advisory board—and developing internal guidelines. It also involves launching pilot projects and creating in-house AI competence centers to build experience without relying immediately on cloud-based solutions, which often present privacy concerns.
Host: Finally, what about "transforming"?
Expert: Transforming is about continuous renewal. It requires active change management, promoting AI literacy across the entire workforce, and fostering cross-departmental collaboration. However, the findings show a critical challenge here: employee representation bodies, such as staff councils, often play a gatekeeping role. If not involved early, they can significantly delay or even block AI implementation decisions.
Host: That is a very practical detail for managers to keep in mind. Are there any limitations to the study we should be aware of?
Expert: Yes. The study's focus was geographically limited to the German public sector, and the data was collected over a specific timeframe from April 2024 to October 2025. While the regulatory and organizational environments in other countries might differ slightly, the core principles of managing these uncertainties remain highly applicable.
Host: Let's talk about the key business takeaways. If you are a professional in a highly regulated industry—or the public sector—what should you do with this knowledge?
Expert: The biggest takeaway is that managing AI is not a one-time project; it is an ongoing process of strategic and operational adaptation. First, do not just focus on the technology itself. Establish clear governance structures and internal guidelines early on. Second, proactively involve employee representation bodies and staff councils from the start to prevent bottlenecks. Finally, invest in continuous change management and AI literacy programs to reduce employee resistance and foster a collaborative environment.
Host: Thank you, Ava, for breaking down this study and providing such clear, actionable insights for our listeners.
Expert: It was my pleasure, Aaron.
Host: And thank you to our listeners for tuning in to A.I.S. Insights - Turning IS Research into Business Action. We will see you next time.
Expert: And I'm Ava Irene Solis. It is great to be here to share insights from recent research.
Host: Today, we are looking at a study presented at the European Conference on Information Systems 2026. The title is "MANAGING ARTIFICIAL INTELLIGENCE IN PUBLIC SECTOR ORGANIZATIONS: A DYNAMIC CAPABILITIES PERSPECTIVE." Ava, could you give our listeners a quick summary of what this study is about?
Expert: This study investigates how public sector organizations can build and leverage dynamic capabilities to effectively adopt and continuously adapt to artificial intelligence technologies. The researchers used a qualitative multiple-case study design, gathering data from five German public sector organizations between April 2024 and October 2025.
Host: Let's start with the real-world problem. Why is adopting AI so challenging for public sector organizations in particular?
Expert: Public sector organizations face significant barriers when adopting AI, driven by complex technological, regulatory, and organizational uncertainties. For instance, they must comply with evolving regulations like the EU AI Act, manage high public accountability, and address employee concerns about job security and shifting roles. The authors of the study point out that much of the existing literature takes a static approach to technology adoption and primarily focuses on private sector environments. This leaves public sector managers without clear, dynamic frameworks to handle ongoing socio-technical shifts.
Host: That makes sense. Public institutions have very different stakeholder demands compared to private firms. How did the researchers approach this problem to find a solution?
Expert: To capture the real-world complexity, the researchers conducted a multiple-case study. They interviewed key personnel—including Chief Information Officers, HR leaders, and IT managers—and analyzed secondary documentation across five distinct public sector organizations. They then analyzed this data using dynamic capabilities theory, which focuses on how organizations sense opportunities and threats, seize those opportunities, and transform their processes and structures over time.
Host: This brings us to the core of the study. What did the researchers find?
Expert: The study empirically supports that AI adoption in public organizations is hindered by high levels of technological, regulatory, and organizational uncertainties. To address these, the study identifies a practical framework of specific organizational microfoundations across three dimensions: sensing, seizing, and transforming.
Host: Let's break those down. What does "sensing" look like in practice according to the study?
Expert: Sensing involves identifying and assessing opportunities and threats. For AI, the study shows that organizations must trace employee expectations and anxieties, monitor technological developments, and closely track regulatory landscapes. This helps managers anticipate challenges before they arise.
Host: And "seizing"? How do organizations act on what they sense?
Expert: Seizing is about mobilizing resources to address those opportunities and threats. The study identifies key microfoundations here, such as establishing AI governance—like setting up an AI ethics advisory board—and developing internal guidelines. It also involves launching pilot projects and creating in-house AI competence centers to build experience without relying immediately on cloud-based solutions, which often present privacy concerns.
Host: Finally, what about "transforming"?
Expert: Transforming is about continuous renewal. It requires active change management, promoting AI literacy across the entire workforce, and fostering cross-departmental collaboration. However, the findings show a critical challenge here: employee representation bodies, such as staff councils, often play a gatekeeping role. If not involved early, they can significantly delay or even block AI implementation decisions.
Host: That is a very practical detail for managers to keep in mind. Are there any limitations to the study we should be aware of?
Expert: Yes. The study's focus was geographically limited to the German public sector, and the data was collected over a specific timeframe from April 2024 to October 2025. While the regulatory and organizational environments in other countries might differ slightly, the core principles of managing these uncertainties remain highly applicable.
Host: Let's talk about the key business takeaways. If you are a professional in a highly regulated industry—or the public sector—what should you do with this knowledge?
Expert: The biggest takeaway is that managing AI is not a one-time project; it is an ongoing process of strategic and operational adaptation. First, do not just focus on the technology itself. Establish clear governance structures and internal guidelines early on. Second, proactively involve employee representation bodies and staff councils from the start to prevent bottlenecks. Finally, invest in continuous change management and AI literacy programs to reduce employee resistance and foster a collaborative environment.
Host: Thank you, Ava, for breaking down this study and providing such clear, actionable insights for our listeners.
Expert: It was my pleasure, Aaron.
Host: And thank you to our listeners for tuning in to A.I.S. Insights - Turning IS Research into Business Action. We will see you next time.