Abstract
— In many developing countries, wastewater is released into the environment without being tested. The wastewater that is legally discharged to decay never reaches a treatment plant. This has negative impacts on human health, the economy, and the climate change. Previous studies reveal that heavy pollution in the Surabaya River makes the quality of the water bad for people to drink. Similarly, increased industrialisation and urbanisation are studied as sources of pollution and wastewater. The inadequate criteria to measure the quantity and quality of water and its usage patterns have been a significant concern. Though AI is studied in many other domains, very little information is explored in the context of the water sector. The study followed a mixed research design approach. An interview session was conducted with 10 managers and employees working in upper management to have their views on the challenges they face and how AI can help with water management decision-making. For this, snowball sampling was used as it is feasible to generate a pool of participants from acquaintances. Thematic analysis was a valuable tool to generate themes by transcribing responses captured from participants to articulate the findings. Findings show that water companies, policymakers, and the government realise the need to integrate AI and relevant advanced technologies for automation and intelligent outcomes that also aid revenue and profitability. Thus, this research is beneficial in exploring the key challenges met by the water sector in managing the usage, scarcity, quality, and quantity patterns and how AI helps with the management.