The global artificial intelligence (AI) in energy and utilities market was valued at USD 10.56 billion in 2023 and is projected to expand at a compound annual growth rate (CAGR) of 22.9% during the forecast period, reaching USD 45.78 billion by 2030. Over the past decade, the utilities industry has undergone significant evolution. The methods for generating, delivering, and utilising energy have transformed from the practices of the early 2000s. This transformation includes the completion of enterprise resource planning (ERP) rollouts, which have enhanced data processing and integration capabilities. The widespread rollout of smart meters has improved real-time data collection and accuracy in monitoring energy and water usage. Additionally, adopting asset management (AM) systems and customer relationship management (CRM) systems has led to more efficient operations and better customer service.

Central to these advancements is the increasing reliance on data, which has significantly enhanced the management of electricity and water access and improved the balancing of supply and demand. Furthermore, the focus on renewable energy and the integration of distributed energy resources (DERs) have become pivotal in the energy security of countries and the transition to sustainable energy systems in industries. This shift towards renewables and distributed resources supports energy security by diversifying energy sources and improving resilience against disruptions.

In addition to these technological and operational advancements, changing customer behaviour driven by economic fluctuations and climate change presents a critical dimension. Economic ups and downs influence consumer energy consumption patterns and demand for services, while climate change introduces new challenges and pressures on utility operations. Consumers are increasingly seeking sustainable operations and cost-effective solutions, prompting utilities to adapt their power generation and transmission strategies and customer centric programs. With data explosion from meters and sensors, the adoption of AI is becoming crucial for addressing changing demand, optimising energy management, enhancing grid reliability, and supporting the overall energy transition while responding to the dynamic landscape shaped by economic and environmental factors.

Challenges of AI adoption

Implementing AI in utilities is fraught with challenges, largely due to the diverse and sometimes conflicting understandings of AI across various departments - business and IT. Each department may have its own interpretation of what AI can achieve, leading to passing of dashboard and reporting needs and potential duplicate demands for AI solutions. These disparate needs and interpretations often prevent the unification of AI efforts under a common strategic context. Without a cohesive approach, different departments may select and implement different AI technologies, resulting in a fragmented and inefficient technological landscape. This lack of alignment not only complicates integration but also impedes the overall effectiveness of AI initiatives.

Additionally, the lack of a dedicated team for enterprise digital footprint consolidation and rationalisation exacerbates these challenges. Without a central team to oversee the standardisation of AI requirements and the integration of digital resources tailored to specific AI value propositions, utilities struggle to develop a cohesive AI strategy and architecture. This oversight is further compounded by the absence of a multi-year AI transformation roadmap, which hinders the ability to plan and execute AI initiatives in a structured and strategic manner. As a result, there are significant gaps in defining AI use cases and a lack of a comprehensive AI value framework, impeding the effective deployment and realisation of AI’s potential across the organisation.

Navigating change with an AI transformation roadmap

Digital footprint rationalisation should provide a clear AI transformation roadmap. It must comprehensively address core initiatives to ensure a robust approach to AI integration into the business processes delivering value to the organisation. Core initiatives are those critical to the utility’s primary operations and include revenue protection efforts such as smart collection analytics, reducing losses such as reducing non-technical losses or minimising energy cost with application of AI.

Additionally, focus should be on application of AI to enhance short– medium– and long-term demand forecasting accuracy and associated peak load management, which are essential for balancing supply and demand. Core applications also involve grid maintenance through predictive analytics, intelligent spare parts management, and sustainable operations, involving fuel demand forecasting, fuel supply chain optimisation and health, safety and environment (HSE) analytics with the aid of AI and drones. Advanced machine learning techniques can be deployed for non-intrusive load disaggregation (NILD) based energy management and or demand response management using highly granular meter or data logger data.

Non-core initiatives, while not directly tied to primary operations, are crucial for overall organisational efficiency and support. These initiatives include continuous monitoring for spare parts contracting and procurement and advanced human resources (HR) analytics, which help streamline processes and improve cost efficiency. Although they may not directly impact the core functions of energy and utilities, non-core AI applications enhance broader organisational capabilities and support comprehensive AI integration. A well-rounded AI strategy would include both core and non-core elements in the AI transformation roadmap that maximises AI’s value across all areas of the organisation.

The transformative power of a data to decisioning AI platform

Having a data to decisioning platform that runs on modern and scalable architecture is quintessential to maximising on the strategic prowess of AI, resulting in substantial value realisation across both core and non-core areas of utility operations. In core areas, a robust platform can provide AI solutions that could deliver highly accurate large scale forecasting automation, real time process or energy optimisation deployment, intelligent decisioning workflows for deploying department strategies built from AI models, insights from unstructured data and optimised decisions with scenario planning. In non-core areas, a robust platform supports continuous monitoring for processes such as procurement to pay and recruitment to retiral, which drive operational and cost efficiency. By leveraging a cloud-native AI platform built on Kubernetes architecture, utilities can seamlessly integrate and analyse data from diverse sources, ensuring a unified approach to addressing both strategic and operational challenges. Such a holistic platform empowers utilities to embrace a new era of smart, predictive, and efficient solutions, driving transformative outcomes across their organisations.

The integration of AI within the utilities sector marks a pivotal shift towards smarter, more predictive, and efficient operations. As the industry evolves, driven by advancements in technology and changing market dynamics, leveraging AI becomes essential for navigating the complexities of modern energy and water management. The transformative potential of AI not only enhances core functions such as resource management, predictive maintenance, and revenue protection but also supports non-core activities like procurement integrity and HR analytics. By adopting a comprehensive AI transformation roadmap, utilities can align their AI initiatives with both strategic and operational goals, ensuring a unified approach that maximises value across all areas. Embracing these innovations enables utilities to adapt to economic fluctuations, climate change, and evolving customer expectations, ultimately fostering a more resilient and sustainable energy infrastructure. Through thoughtful implementation and strategic planning, the utilities sector can unlock new levels of efficiency and effectiveness, driving progress in the transition to a smarter, more connected future.

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