Thesis:

Influence of artificial intelligence technologies adoption on the operational performance of commercial banks in Kakamega town, Kakamega county, Kenya.

Due to their rapid evolution, Artificial Intelligence (AI) technologies are transforming the global financial sector, making it crucial to understand their effects on operational performance, especially in developing nations. This study examined the operational performance of commercial banks in Kakamega County, Kenya, after adopting AI technologies like ML, NLP, RPA, and CV. Using a conceptual knowledge of technology adoption and organizational performance, the study examined how these AI technologies benefit banking operations. A causal-comparative study examined Kakamega County commercial bankers. Stratified random sampling was used to choose 39 employees from an estimated target population of 100. Primary data gathering was standardized surveys, with restricted interview schedules for qualitative insights. The research instruments' Cronbach's Alpha values above 0.80 for all constructs, indicating strong reliability. Expert review and a comparable geographical pilot research proved their validity. Data was analyzed using SPSS version 27, including descriptive and inferential statistics such multiple linear regression and correlation. The descriptive data revealed that AI technology's perceived influence and acceptance differed by banking function. ML and RPA were thought to have a stronger influence on IT, financial, and customer service operations, improving efficiency, fraud detection, and back-office activities. NLP worked well in customer service and product promotion. Computer vision was used for real-time suspicious behavior monitoring and identity authentication. All AI technologies had a far smaller perceived impact on credit assessment, supply chain management, and human resource management. The inferential analysis showed that the regression model, which included Machine Learning, Natural Language Processing, and Robotic Process Automation, did not significantly predict Computer Vision applications, the proxy for Operational Performance (F=1.29, p=0.29). The statistical significance of ML (p=0.32), NLP (p=0.48), and RPA (p=0.10) was equally insignificant. This crucial study shows that the collective adoption of these AI technologies does not yet predict operational effectiveness, despite their apparent benefits. This may be due to early implementation, contextual factors specific to Kakamega County, or undiscovered mediating variables. The study found that while Kakamega commercial banks are investigating AI, its impact on operational performance is not yet prevalent or statistically significant. Banks should establish a strategic AI integration strategy supported by a strong data infrastructure and focused skill development. Future study should examine mediating/moderating elements, operational performance measures, and longitudinal studies to capture AI's developing impact.