Artificial intelligence and financial decision-making in manufacturing firms in Nairobi county, Kenya

Abstract

This study investigated the effect of artificial intelligence on financial decision-making in manufacturing firms in Nairobi County, Kenya, addressing the gap in understanding how AI tools influence financial strategies despite their growing adoption in business operations. With manufacturing firms facing intricate financial challenges, AI’s potential to enhance efficiency, accuracy, and planning remains underexplored locally. The general objective was to establish AI’s impact on financial decision-making, with specific objectives to assess the influence of predictive analytics, evaluate the role of machine learning models, examine the impact of automated financial reporting, and determine the effect of natural language processing tools on these processes. The study was guided by four theories: the Technology Acceptance Model, the Resource-Based View; the Automation Theory; and the Cognitive Fit Theory. The study adopted a descriptive cross-sectional survey design. The target population of this study were all the 2752 manufacturing firms in Nairobi County, Kenya. A sample of 349 was arrived at using Yamane formula. The unit of observation was the finance manager in each firm. Questionnaire was utilized in primary data collection. Data was analyzed using descriptive and inferential statistics, including correlation and regression analysis. The regression results revealed that 77.7 percent of the variation in financial decision-making was explained by the four AI dimensions. The model was statistically significant (F = 207.497, p < 0.05). Regression coefficients showed that all four dimensions had significant positive effects on financial decision-making: predictive analytics (β = 0.325, p < 0.001), machine learning models (β = 0.349, p < 0.001), automated financial reporting (β = 0.206, p < 0.001), and natural language processing tools (β = 0.128, p = 0.001). The study concluded that artificial intelligence significantly enhances financial decision-making by improving budgeting, investment planning, cost management, and risk assessment. The study recommends that manufacturing firms increase investments in AI tools to strengthen decision-making efficiency and accuracy. Managers should prioritize the integration of predictive analytics and machine learning into financial processes while expanding the use of automation for accurate and timely reporting. Firms are also encouraged to adopt NLP tools to reduce cognitive load in financial analysis and improve policy interpretation. Policymakers and industry associations should provide supportive frameworks and incentives to enhance AI adoption across firms, thereby strengthening competitiveness and resilience in the manufacturing sector.