Frost & Sullivan’s recent analysis of data Analytics and AI Boost Accuracy to Drive Global Smart Water and Wastewater Leak Detection Solutions Market, discover that the wastewater leak detection market witnessed a significant rate of innovation and digital transformation.
Internet of Things (IoT) sensors, Machine Learning (ML), Artificial Intelligence (AI), and cloud or edge-based data analytics platforms are boosting the market. By 2026, the market is estimated to reap a revenue of $1.99 billion from $1.23 billion in 2020, up at a compound annual growth rate (CAGR) of 8.4%.
Paul Hudson, Energy & Environment Research Analyst, Frost & Sullivan, said: “The high rate of urbanization in most developing countries increased the pressure on existing water and wastewater infrastructure pushing the demand for leak detection solutions. It is partly to improve asset efficiency and partly to meet water conservation goals. To tap into this growth prospect, leak detection solution providers should integrate their technologies and customize services to meet customers’ demands and exploit investments made for the development of Smart Cities and resilient infrastructure.”
Hudson added: “The increasing adoption of cloud-based data analytics, ML and AI is transforming the industry’s business model from product-based solutions to leak detection services. Further, utilities’ emphasis on a ‘one-stop solution provider’ for leak detection in both their water and wastewater networks is encouraging solution providers to embrace new business models such as technology-as-a-service (TaaS) and leak (or non-revenue water (NRW)) management-as-a-service (LMaaS). TaaS enables service providers to fully control and strategically expand and enhance their technology offerings, whereas, LMaaS could help focus on the growth and market penetration of smart solutions such as continual leak monitoring and proactive prevention.”
The move toward a circular economy and holistic sustainability will present immense growth opportunities for market participants, varying considerably, depending on the region.
