Empirical Insights and Technical Stakeholder Perspectives: AI for Effective Decision-Making in South Africa’s Water Management Sector

South Africa’s water management sector faces escalating challenges from climate change, population growth, and fragmented governance. This study investigates strategic stakeholder perceptions of artificial intelligence (AI) as a tool for effective decision-making, focusing on five thematic domains: Key Challenges in Water Management (CWM), Impacts of Climate Change and Population Growth (ICCPG), AI for Effective Decision-Making (AIEDM), Recommendations for Improving AI Tools (RIAIT), and Data Privacy and Security (DPS). A structured digital questionnaire using a five-point Likert scale was administered to thirty-five professionals across engineering, planning, policy, and civil society sectors. Purposive sampling ensured strategic-level representation. Quantitative analysis using SPSS v30 included descriptive statistics, reliability testing (α = 0.929), Pearson correlations, and multiple regression modelling. Findings reveal significant relationships between ICCPG and all predictor domains, with CWM (r = 0.756) and DPS (r = 0.697) emerging as the strongest predictors. Technically trained stakeholders demonstrated high alignment between climate/data concerns and systemic water challenges. The relatively lower influence of AIEDM and RIAIT suggests cautious optimism toward AI, tempered by concerns around implementation feasibility and ethical safeguards. To address the “black-box” problem, which is the difficulty in understanding or explaining how AI systems arrive at their decisions, particularly deep learning, the study advocates for the integration of Explainable AI (XAI) to enhance transparency and stakeholder trust. These insights align with South Africa’s National Water Act and Water and Sanitation Master Plan, underscoring the need for integrated governance, digital literacy, and inclusive stakeholder engagement to support AI-enabled water resilience. Keywords: Artificial Intelligence (AI) · Water Resource Management · Climate Change · Stakeholder Perceptions · Explainable AI (XAI) · Decision-Making · Data Privacy