Artificial Intelligence (AI) In ICT Management and Performance of Commercial Banks in Nairobi City County, Kenya

Purpose: The general objective of the study is to assess the effect of artificial intelligence (AI) in ICT management on performance of commercial banks in Nairobi City County, Kenya. Specifically, the study sought to establish the influence of machine learning and natural language processing on performance of commercial banks in Nairobi City County. Methodology: The descriptive research design was employed. The unit of analysis for the study was 40 commercial banks in Kenya (CBK, 2024) while the unit of observation was management employees. The accessible population was 246 individuals comprising of 41 top managers, 82 middle level managers and 123 lower-level managers. The study used Krejcie and Morgan (1970) formula to arrive at the sample size. The study sample size was therefore 152 employees. Stratified random sampling was applied to get the respondents. Data was collected using a self-administered semi-structured questionnaire. Data obtained from the field was coded, cleaned, and entered into the computer for analysis using the SPSS version 25. Inferential statistical analysis used was multiple regression and correlation analysis. Findings: The study found that machine learning has a positive and significant effect on performance of commercial banks in Nairobi City County, Kenya. In addition, the study showed that natural language processing has a positive and significant effect on performance of commercial banks in Nairobi City County, Kenya. Unique Contribution to Theory, Practice and Policy: Based on the findings, the study recommends that commercial banks in Kenya should invest in the adoption and integration NLP technologies to enhance customer engagement and operational efficiency. By deploying NLP-powered tools such as chatbots, automated customer support systems, and sentiment analysis platforms, banks can provide faster, more accurate, and personalized responses to customer inquiries while reducing service costs.