Determinants of Artificial Intelligence (AI) Readiness and Adoption in Public Sector Governance

Artificial intelligence (AI) is increasingly recognized as a critical driver of digital transformation and improved public sector governance. Despite national initiatives such as the Philippine National Artificial Intelligence Strategy Roadmap, the adoption of AI within National Government Agencies (NGAs) and Local Government Units (LGUs) remains uneven and at an early stage. This study assesses the determinants of Artificial Intelligence (AI) readiness and adoption within the Philippine public sector, specifically focusing on National Government Agencies (NGAs) and Local Government Units (LGUs). Despite national initiatives like the National AI Strategy Roadmap, a gap persists between digital transformation goals and the actual capacity of public institutions to implement AI effectively. The research employed a quantitative descriptive-correlational design using a structured survey questionnaire. Data were gathered from 128 respondents, including government officials, ICT staff, and administrators in the Province of Bataan. The study utilized the Technology–Organization–Environment (TOE) Framework, Diffusion of Innovation (DOI) Theory, and Institutional Theory to analyze variables such as technological, human resource, and organizational readiness.
The findings reveal a satisfactory "Good" level of overall AI readiness (Composite Mean = 2.67), with technological readiness ranking highest. However, the level of AI adoption was rated as "Fair" (Composite Mean = 2.43), indicating that while foundational elements exist, they are not yet robust enough for extensive implementation. A significant digital divide was identified: NGAs (Mean = 2.75) scored statistically higher than LGUs (Mean = 2.36) across all readiness dimensions. Financial and logistical support emerged as the most significant organizational barrier to adoption.
The study concludes that higher readiness levels directly correlate with more successful AI adoption. While policy frameworks are emerging, a critical "policy-implementation gap" exists due to inadequate technical infrastructure and a shortage of skilled personnel. Proposed strategic interventions include institutionalizing AI governance structures, mandating continuous upskilling programs rather than isolated seminars, and formalizing public-private partnerships to bridge internal capacity gaps.

Artificial Intelligence, Public Sector Governance, AI Readiness, AI Adoption, NGAs, LGUs, Digital Transformation.

1. Alhosani, K., & Alhashmi, S. M. (2024). Opportunities, challenges, and benefits of AI innovation in government services: a review. Discover Artificial Intelligence, 4(1). [Google Scholar] [Crossref]

2. https://doi.org/10.1007/s44163-024-00111-w [Google Scholar] [Crossref]

3. DICT (Department of Information and Communications Technology). (2024). Draft Joint Memorandum Circular on the Principles for an Ethical and Trustworthy Use of Artificial Intelligence (AI) in Government (draft). [Google Scholar] [Crossref]

4. European Union (AI Watch - Joint Research Centre). (2024, November 25). AI adoption in the public sector: a new study on key influencing factors and two frameworks for competencies and governance. [Google Scholar] [Crossref]

5. Greenwood, R., & Meyer, R. E. (2008). Influencing Ideas: A Celebration of DiMaggio and Powell (1983): A Celebration of DiMaggio and Powell (1983). Journal of Management Inquiry, 17(4), 258-264. https://doi.org/10.1177/1056492608326693 (Original work published 2008) [Google Scholar] [Crossref]

6. Hwang, S. (2025). A review on AI Government Service Cases and Types: Suggesting strategies for successful planning and implementation interactions. koreascience.kr. [Google Scholar] [Crossref]

7. https://doi.org/10.22693/NIAIP.2025.32.3.003 [Google Scholar] [Crossref]

8. Joint Research Centre. (2024). AI adoption in the public sector: a new study on key influencing factors and two frameworks for competencies and governance. European Union. [Google Scholar] [Crossref]

9. Kankanhalli, A., Hahn, J., Tan, S. H., & Gao, G. (2019). Big data and AI–enabled transformation of government: A conceptual model. Government Information Quarterly, 36(4), 101–113. [Google Scholar] [Crossref]

10. Khogali, S., & Mekid, S. (2023). The potential for job losses in the government sector due to massive automation. International Journal of Social and Economic Problems. [Google Scholar] [Crossref]

11. Neumann, O. (2024). Exploring artificial intelligence adoption in public organizations: Challenges, capacities, and governance implications. Public Administration Review, 84(2), 345–360. [Google Scholar] [Crossref]

12. NSW R. (n.d.). Using machine technology. [online] Revenue NSW. [Google Scholar] [Crossref]

13. https://www.revenue.nsw.gov.au/news-media-releases/using-machi ne-technology. Accessed 23 Feb 2024. [Google Scholar] [Crossref]

14. Philippine Institute for Development Studies (PIDS). (2021, December 28). Lack of coordination between nat'l and local gov'ts during devolution period could affect service delivery—DILG official. (Citing Bonagua's discussion). [Google Scholar] [Crossref]

15. Public Service Digitalization in the Philippines. (2024). Towards a national program to capacitate digital frontliners [Policy Paper]. ResearchGate. [Google Scholar] [Crossref]

16. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.Sadeghi, M. (2024). AI adoption in HR: Resistance, readiness, and the role of change management.Journal of Marketing & Social Research. [Google Scholar] [Crossref]

17. Selten, C. P. M., & Klievink, B. (2024). Adapting AI to existing organisational structures, improving employee skills and ensuring strategic alignment are key to the successful integration of AI into public administration. (Conceptual basis from search result). [Google Scholar] [Crossref]

18. Selten, F. J., & Klievink, A. J. (2024). Organizing public sector AI adoption: Navigating between separation and integration. Government Information Quarterly, 41(1), 101885. [Google Scholar] [Crossref]

19. Selten, C., & Klievink, B. (2024). Organizational AI Readiness for Public Administration: A Comprehensive Review and Framework for Conceptual Modelling. International Journal of Electronic Business & Emerging Issues, 17(1), 101–120. [Google Scholar] [Crossref]

20. Tornatzky, L. G., & Fleischer, M. (1990). The process of technology innovation. LexingtonBooks. [Google Scholar] [Crossref]

21. Wirtz, B. W., et al. (2020). With the rapid development of AI combined with strict institutional regulations, public administration is struggling to keep pace and ensure adequate governance. (Conceptual basis from search result). [Google Scholar] [Crossref]

22. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial Intelligence and the Public Sector—Applications and Challenges. International Journal of Public Administration, 42(7), 596–613. [Google Scholar] [Crossref]

23. Wirtz, B. W., Weyer, M., & Rösch, M. (2021). A structured literature review of artificial intelligence in the public sector. International Journal of Public Sector Management, 34(7), 896-918. [Google Scholar] [Crossref]

24. Yigitcanlar, T., David, A., Li, W., Fookes, C., Bibri, S. E., & Ye, X. (2024). Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations. Smart Cities, 7(4), 1576-1625. https://doi.org/10.3390/smartcities7040064 [Google Scholar] [Crossref]

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