Artificial Intelligence Adoption Practices and Project Performance of Fintech Firms in Nairobi City County, Kenya

Abstract

Despite the evident potential of AI technologies, fintech firms in Kenya, particularly those operating in Nairobi City County, continue to face persistent project performance challenges, notably in areas of timely completion, budget adherence, and achievement of strategic goals. As such, the purpose of this study was to evaluate the effect of artificial intelligence practices on project performance of Fintech firms in Nairobi City County, Kenya. The specific objectives of the study were; to assess the effect of automation of operations on project performance of Fintech firms in Nairobi City County, examine the effect of predictive analytics on project performance of Fintech firms in Nairobi City County, establish the effect of AI-driven customer service on project performance of Fintech firms in Nairobi City County and evaluate the moderating effect of institutional factors on the relationship between AI adoption practices and project performance Firms in Nairobi City County, Kenya. The anchor theory of this study is the Technology-Organization-Environment (TOE), which is supplemented by Technology Acceptance Model (TAM), Resource-Based View (RBV) and Institutional Theory. This study employed a cros-ssectional-explanatory research design. The study target population included 73 senior managers within 23 fintechs in Nairobi City County who were directly involved in AI-driven projects. The adopted a census of the 73 senior managers where primary data was collected using a structured questionnaire. A pretest was conducted on 8 respondents from selected fintech firms located in Kiambu County. Data collected was analyzed using Statistical Package for Social Sciences (SPSS) version 29.0. Descriptive statistics such as mean and standard deviation summarized the data,, while inferential statistics such as correlation and regression analyses were used to test the hypotheses and establish relationships among variables. Descriptive statistics revealed consistently high mean, reflecting strong agreement among respondents on the centrality of AI adoption in enhancing project outcomes. Regression results indicated that automation of operations (R²=0.477, p<0.05) and predictive analytics (R²=0.469, p<0.05) were the strongest predictors of project performance, while AI-driven customer service (R²=0.288, p<0.05) had a moderate effect. Institutional factors demonstrated a significant direct influence (β=0.281, p<0.05), raising the explained variance of project performance to 61.3%, but their moderating effect on the AI–performance relationship was statistically insignificant (β=-0.228, p=0.081). The findings affirm that AI adoption significantly improves project efficiency, timeliness, and stakeholder alignment in Fintech firms, with automation and predictive analytics being the most impactful. The study concludes that leadership and culture operate more as direct enablers rather than moderators. Practical recommendations include increased investments in automation technologies, capacity building for predictive analytics, integration of AI-driven customer service into core systems, and leadership initiatives to foster an innovation-driven culture. The findnings align with Kenya Vision 2030, the Africa Agenda 2063, and the UN Sustainable Development Goals (SDGs), by demonstrating how digital transformation enhances financial sector efficiency, promotes innovation, and supports sustainable economic growth. Future research is recommended to adopt longitudinal designs and sector-specific analyses to capture the dynamic and evolving impacts of AI adoption on performance.