Artificial Intelligence Adoption and Decision-Making Effectiveness in the Public Sector
TL;DR
The study concluded that Artificial Intelligence adoption significantly enhanced decision-making effectiveness in the public sector and recommended increased investment in AI technologies, digital infrastructure, employee training, and responsible AI governance, while maintaining appropriate human oversight.
Abstract
: Artificial Intelligence (AI) has increasingly emerged as an important technology for transforming public-sector operations and improving organizations' capacity to make timely, accurate, and evidence-based decisions. Despite its growing adoption, concerns about technological readiness, skills, data availability, automation, and effective integration continue to influence the extent to which public institutions realize the benefits of AI. This study examined the effect of Artificial Intelligence adoption on decision-making effectiveness in the public sector. The study was guided by the Technology Acceptance Model (TAM) and adopted a positivist research philosophy and a descriptive correlational research design. The target population comprised employees in ministries and extra-budgetary institutions in Kenya, estimated at 236,700 employees in 2024. Using Yamane's formula at a 5% level of precision, the study determined a sample size of 399 respondents. Of the 399 questionnaires considered issued, 327 were completed and returned, representing an illustrative response rate of 82.0%. Data were collected using a structured questionnaire and analyzed using descriptive statistics, Pearson correlation, and simple linear regression. Artificial Intelligence adoption was assessed using AI data analysis, predictive analytics, process automation, and AI decision-support systems, while decision-making effectiveness was assessed in terms of accuracy, timeliness, quality, and efficiency. Descriptive findings indicated a relatively high level of Artificial Intelligence adoption, with an overall mean of 3.79 and standard deviation of 1.16. AI-generated data analysis providing useful information for organizational decisions recorded the highest mean (4.22), while the use of AI to automate routine administrative tasks recorded the lowest mean (3.25). Inferential findings established a strong positive relationship between Artificial Intelligence adoption and decision-making effectiveness (r = 0.746, p < 0.01). Regression analysis indicated that Artificial Intelligence adoption explained 55.7% of the variation in decision-making effectiveness (R² = 0.557). The regression model was statistically significant, F (1, 325) = 408.65, p < 0.001. Further, Artificial Intelligence adoption had a positive and statistically significant effect on decision-making effectiveness (β = 0.746, t = 20.195, p < 0.001), leading to rejection of the null hypothesis. The study concluded that Artificial Intelligence adoption significantly enhanced decision-making effectiveness in the public sector. It recommended increased investment in AI technologies, digital infrastructure, employee training, and responsible AI governance, while maintaining appropriate human oversight. The study contributed empirical evidence on the importance of AI adoption in strengthening decision-making effectiveness within public-sector organizations.
