Analyzing Artificial Intelligence (AI) Technology Integration in Project Management: A Case Study of ZESCO Operations in Lusaka

This study investigates the integration of Artificial Intelligence (AI) in project management within ZESCO operations in Lusaka. The study was informed by the following objective the current level of AI adoption, potential benefits, the role of AI in quality control, and barriers to AI integration. Using a mixed-methods approach, data was collected from a sample of 60 participants, including project managers, engineers, technical experts, and senior management. Structured questionnaires were administered to 50 participants to gather quantitative data, and 10 semi-structured interviews provided qualitative insights into strategic implementation, challenges, and AI benefits. Statistical analysis, including descriptive and inferential statistics via SPSS, was employed to interpret quantitative data, while thematic analysis examined qualitative responses. Triangulation strengthened the study’s validity by integrating data from multiple sources and methods. Results indicate that 64% of respondents indicated very low integration of AI in project management at ZESCO, while 26% report low integration. Notably, 80% of employees have not attended AI training in the last two years, leading to limited familiarity with predictive analytics, natural language processing, and machine learning. Key AI benefits identified include productivity gains (36%) and decision-making improvements (24%), although 52% perceive AI’s effectiveness as neutral. Predictive analytics and automated testing are highlighted for enhancing quality control, with a positive correlation (coefficient 0.558) between performance tracking and AI integration. However, significant barriers remain, including resistance to change (32%), high implementation costs (38%), and limited scalability (36%). The study recommends that the government facilitate AI training programs, policymakers establish supportive regulatory frameworks, ZESCO prioritize scalable AI infrastructure, and organizational leaders implement change management strategies to address resistance. These steps would collectively enhance AI adoption, optimize project management, and position ZESCO for greater productivity and efficiency. Limitations include potential response bias and limited generalizability beyond the Lusaka operations.