Leveraging Artificial Intelligence for Enhanced Operational Efficiency in the Telecommunications Industry: A Case Study from Zimbabwe

This study uses one telecommunications company as a case study to investigate the potential of artificial intelligence (AI) to enhance operational efficiency in Zimbabwe's telecommunications industry. Despite global advancements in AI adoption, its integration within Zimbabwe remains limited, particularly in addressing inefficiencies such as high operational costs, poor service quality, and outdated infrastructure. The research is grounded in the TOE framework, the RBV model, and the DOI theory. A quantitative approach was adopted, and data were collected from 117 respondents using structured questionnaires. Analysis was conducted using descriptive statistics, factor analysis, Pearson correlation, regression modelling, and ANOVA to assess AI adoption levels, its impact on efficiency, and the barriers to integration. Findings indicate that while AI adoption is still emerging, it has already led to improved service delivery, reduced downtime, and enhanced resource utilisation. However, several barriers persist, including financial constraints, regulatory uncertainty, infrastructure deficits, and limited technical expertise. The study proposes a five-pillar strategic framework focusing on technological readiness, supportive policy, capacity building, financial planning, and stakeholder collaboration to guide sustainable AI implementation. In conclusion, the research underlines that with targeted strategic investments and institutional support, AI can significantly transform operational efficiency in Zimbabwe's telecom sector. The findings offer practical insights for industry leaders, policymakers, and researchers seeking to drive digital transformation in emerging economies for operational efficiency.