Application of Artificial Intelligence on Customer Satisfaction and Loyalty Among Deposit Money Banks in Ondo State (Published)
This study examines the application of Artificial Intelligence (AI) in enhancing customer satisfaction and loyalty among customers of Deposit Money Banks (DMBs) in Ondo State, Nigeria. Specifically, it investigates the influence of key AI technologies chatbots, virtual assistants, and predictive analytics on customer satisfaction, and subsequently, the effect of satisfaction on customer loyalty. The research adopts a descriptive survey design, which is appropriate for gathering quantitative data from a broad population. A stratified random sampling technique was employed to select 1428 bank customers across various demographic groups, ensuring comprehensive representation in Ondo State, Nigeria. Data were collected using structured online questionnaires and analysed using relevant statistical techniques. The findings reveal that the deployment of AI tools, particularly chatbots, virtual assistants, and predictive analytics, significantly enhances customer satisfaction. Furthermore, a strong positive relationship was observed between customer satisfaction and customer loyalty. The study concludes that AI applications are effective tools for improving customer experience and loyalty in the banking sector. It recommends that DMBs in Ondo State, Nigeria should increase their investment in AI technologies and promote their strategic use to foster stronger customer relationships and competitive advantage.
Keywords: Artificial Intelligence, Customer Satisfaction, Customer loyalty, Deposit Money Banks, chatbots, predictive analytics, virtual assistants
AI-Driven Cloud Optimization for Cost Efficiency (Published)
AI-driven cloud optimization represents a transformative approach to addressing the significant challenges of cloud resource management and cost efficiency. As global cloud expenditure continues to grow at a rapid pace, organizations face increasing pressure to optimize their cloud investments while maintaining performance standards. This article examines how artificial intelligence technologies are revolutionizing cloud resource management through dynamic allocation, predictive analytics, and automated workload optimization. The integration of machine learning algorithms with cloud infrastructure enables unprecedented levels of accuracy in resource forecasting, automated scaling, and workload classification. These capabilities allow organizations to significantly reduce both over-provisioning and under-provisioning scenarios that plague traditional threshold-based management approaches. The economic benefits of these technologies are substantial and multifaceted, extending beyond direct cost reduction to include improved application performance, reduced downtime, and decreased operational overhead. As the complexity of cloud environments continues to increase, the strategic value of AI-driven optimization becomes increasingly apparent across diverse industry sectors, from financial services to healthcare and e-commerce.
Keywords: Artificial Intelligence, Cloud optimization, Cost Efficiency, Resource Allocation, predictive analytics
Artificial Intelligence and Business Security among SMEs in Abuja Metropolis (Published)
This study investigates the impact of Artificial Intelligence (AI) on business security among Small and Medium Enterprises (SMEs) in Abuja, Federal Capital Territory (FCT), Nigeria. The primary objectives are to assess the influence of AI security protocols, employee AI training, customer data privacy measures, and automated threat detection on enhancing business security. Anchored in the Socio-Technical Systems (STS) Theory, which emphasizes the interplay between social and technical elements within organizations, this research explores how these AI-driven measures collectively contribute to securing SMEs. Utilizing a cross-sectional survey design, data was collected from a representative sample of 379 employees within the Information and Communication sector, derived from an estimated population of 24,832 employees according to SMEDAN (2021). Multiple regression analysis revealed that AI security protocols, customer data privacy measures, and automated threat detection significantly enhance business security, while employee AI training showed no substantial impact. These findings underscore the necessity for integrating advanced technological measures with robust social frameworks to optimize business security. The study’s results align with STS Theory, highlighting the importance of a balanced approach that incorporates both technical and social components for effective security management in SMEs.
Keywords: AI security protocols, Artificial Intelligence, Cybersecurity, SMEs, automated threat detection, business security, customer data privacy, employee AI training