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

Abstract

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.