Examining the Challenges of Implementing Artificial Intelligence in the Water Supply Sector: A Case Study

Abstract

Challenges in the water supply sector have hindered the advanced implementation of artificial intelligence (AI) compared to other sectors. These challenges have not been sufficiently examined in the existing literature. An empirical study was conducted within a public utilities organization in the United Arab Emirates (UAE) to address this gap. An integrated approach combining interpretive structural modeling (ISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was utilized to identify the critical challenges and to model and analyze the relationships among them. The ISM model provides significant advantages by enabling decision-makers to visualize complex interactions, supporting the development of an effective AI implementation strategy. The strategy should prioritize four critical challenges: the lack of technical skills and knowledge, the limited availability of ready-to-use AI solutions, inadequate water infrastructure, and concerns regarding privacy and data security. These challenges were identified based on their positioning at the lowest level of the ISM model and their classification as independent in the fuzzy MICMAC analysis. Addressing these four challenges will help to mitigate the remaining six. The study’s findings and implications are expected to offer valuable guidance for decision-makers in implementing AI technologies within water supply organizations, both in the UAE and in countries with similar environments.