Artificial Intelligence Adoption and Decision-Making Effectiveness in the Public Sector

Belvin Keitany, Stephene Oloo Magadi

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 2 = 0.557). The regression model was statistically significant, F (1, 325) = 408.65, p

Keywords: Artificial Intelligence adoption, decision-making effectiveness, public sector, predictive analytics, process automation, AI decision-support systems, Technology Acceptance Model

How to Cite?: Belvin Keitany, Stephene Oloo Magadi, "Artificial Intelligence Adoption and Decision-Making Effectiveness in the Public Sector", Volume 15 Issue 9, September 2026, International Journal of Science and Research (IJSR), Pages: 53-61, https://www.ijsr.net/getabstract.php?paperid=SR26831094759, DOI: https://dx.doi.org/10.21275/SR26831094759

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