Delivering clean water and reliable energy are top priorities in today’s modern society. However, utility companies are facing a growing storm of challenges. Aging infrastructure, strained by a growing population and climate extremes are making it difficult for utilities to meet fluctuating demand and offer reliable services.
Water utilities are grappling with pollution and a predicted 40% global water supply shortage by 2030 (UN). In the energy sector, surging electricity demand (IEA) and limited renewable energy sources are driving up costs for both consumers and suppliers. Technological advancements within the sector are also adding another layer of complexity as data management becomes a burden and cybersecurity poses a new risk.
Aging infrastructure, strained by a growing population and climate extremes, are making it difficult for utilities to meet fluctuating demand and offer reliable services.
Aging infrastructure stands as a major barrier to operational efficiency. In the US, over 70% of the power grid is over 25 years old and costs the economy an estimated $28 billion to $169 billion annually (American Society of Civil Engineers). To combat this, utilities are using AI to analyze sensor data, which then foreshadows equipment failures and enables predictive maintenance to minimize downtime, extend equipment life, and save costs for all. For example, a major UK water utility recently developed a Hydraulic Network Risk Tool which leverages data from thousands of sensors across their network. This AI-powered tool helped them predict and prevent a major outage, saving them £7 million and minimizing disruptions for customers.
With global electricity demand expected to rise at a faster rate (3.4% annually) over the next three years (IEA), optimizing the electricity grid has become a top priority. Efficient grid management requires real-time data on energy demand, weather, and renewables. Unfortunately, manually analyzing these massive, ever-growing datasets is inefficient and error-prone. This is where AI steps in, offering utilities the power to unlock valuable insights that allow utilities to predict energy demand, maximize grid reliability and seamlessly integrate renewables through intermittency prediction. This translates to reduced reliance on expensive peak power plants, lower energy costs for consumers, and streamlined operations and outputs for suppliers.
Traditional water meters offer limited data, making leak detection a reactive and time-consuming process. However, smart meters equipped with AI for real-time analysis are able to track water usage patterns at a granular level, allowing utilities to identify anomalies that might signal leaks. In fact, studies conducted by the Alliance for Water Efficiency show that smart meter leak detection can reduce losses by up to 40%. This early detection minimizes water loss, reduces infrastructure damage and also reduces costs for both utilities (who lose less treated water) and consumers (who pay less for water they don’t use).
AI is helping to create a more sustainable and resilient future for utility companies by optimizing operations, predicting maintenance needs, improving customer service, and creating new pathways for revenue growth.
As climate change becomes a growing concern for many, the utility industry is facing increasing pressure to play their part in reducing emissions. In England, water companies have pledged to reach net zero on operational emissions by 2030. To achieve these ambitious targets, companies are adopting innovative approaches and technologies. For example, Severn Trent Water partnered with Sand Technologies to build a digital twin – a real-time, AI-powered model of a wastewater plant – to optimize operations and minimize carbon emissions. This award-winning tool empowers stakeholders by providing a comprehensive assessment of a plant’s footprint, enabling targeted adjustments for a cleaner future.
These are just a few examples, and the potential applications of AI in utilities continue to evolve. As AI technology matures and integrates further, we can expect even more transformative solutions for the utilities sector, paving the way for a more efficient, sustainable, and resilient future.
While AI offers immense potential for the utilities sector, implementing these solutions comes with its own set of challenges. First and foremost, is the big data challenge. Utilities often have data scattered across various systems, making it difficult to consolidate and prepare the large datasets needed for effective AI training. Consolidating and preparing these disparate sources requires significant investment in data management infrastructure and standardization. However, cloud-native technologies offer a solution for massive data-storage and analysis at low costs, ultimately, enabling utilities to unlock the true potential of their data.
Another key hurdle is the evolving legal landscape and infrastructure surrounding AI and data use. While frameworks such as the US’ AI Bill of Rights offer guidelines, clear regulations around the world are still taking shape. This uncertainty can hinder investment in AI solutions. Furthermore, integrating AI with aging infrastructure raises cybersecurity concerns and therefore requires more robust data protection measures to curb cyberattacks. To navigate these challenges, utilities need to develop a well-defined AI governance strategy that integrates seamlessly with existing operational frameworks and ensures the responsible development, deployment, and management of AI systems
Finally, the utility industry also faces a shortage of talent with the necessary expertise in data science, AI development, and deployment. Building these skill sets within existing teams or attracting new talent is imperative but yet, many organizations are struggling with this. According to a recent EY survey, while 85% of utility empowers recognize the need for employee reskilling, only 57% of them have developed a concrete strategy to implement this. Bridging the skills gap is key for utilities to unlock AI’s full potential.
Predictive maintenance minimizes downtime, extends equipment life, and saves costs for all by analyzing sensor data to foreshadow equipment failures.
Implementing AI in traditional sectors such as utilities requires careful navigation. Partnering with an experienced AI implementation partner like Sand Technologies can be transformative. We empower leading utilities around the world to address their biggest challenges through customized AI solutions. Our comprehensive support spans the entire journey – from crafting a strategic roadmap to building customized solutions and equipping your workforce with the skills to manage this cutting-edge technology.
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