GenAI: Powering the Future of Utilities 

The rise of GenAI has caused a scramble in every industry to quickly capitalize on this exciting new technology. In theory, GenAI can address numerous challenges faced by utilities, helping the industry navigate the energy transition and respond to changing customer preferences amid a rising cost-to-serve. But to date, use cases for utilities companies have been elusive.

Many utilities are approaching GenAI from a technology perspective, asking: “How can we begin?” However, this sets them up for failure. The more important question is: “Where should we begin?” The right approach must be domain-driven and business-first, identifying the business use cases and then using GenAI to drive business outcomes and transform into a utility of the future.

Breaking Down Utilities GenAI Use Cases

Many GenAI use cases will make an impact across the utilities landscape. Simple GenAI use cases like automated document processing and email responses can be implemented equally across electricity, gas, water, and waste management, and they can be applied in both internal and customer-facing contexts.

Across all asset types and utilities products, GenAI will refine the outputs of cognitive AI, machine learning, deep learning, and data science initiatives, making insights and predictions from these technologies more targeted and user-friendly. GenAI will help utilities more efficiently manage the software development cycle and will play an advanced role in areas like predictive maintenance for assets and demand forecasting across all products. 

Some of the most impactful GenAI use cases will be more niche, driving value in areas such as:

• Grid optimization: GenAI will it enable digital assistants for grid operators that improve the speed and impact of their decisions when dispatching generators or taking renewable energy sources offline.
• AR asset maintenance: GenAI will translate asset information into AR environments that will advance remote and digitally augmented asset maintenance (and will similarly supercharge other Industry 4.0 technologies).
• Energy transition readiness: GenAI will synthesize data to aid in forecasting energy usage and renewable energy generation, thus playing a pivotal role in balancing supply and demand, reducing losses, enhancing grid stability, and optimizing distributed renewable generation.
• Customer billing: GenAI will deliver intelligent automation, responding to queries that were previously too complicated to automate. For example, GenAI will be able to automate responses to “high bill” enquiries, analyzing historical usage and previous charges to generate an explanation in language that the average consumer can understand. 

Across many use case themes (see figure below), utilities have reason to be enthusiastic about the potential of GenAI. 

A Business-First Approach to GenAI

Utilities need to take a “business-first” approach to fully realize the benefits of GenAI. After identifying the business problems that will benefit from GenAI technologies, they can begin by prioritizing use cases based on business impact, complexity/time-to-market, and enterprise-specific factors that may affect feasibility. Utilities also need to keep in mind the fundamental changes taking place in the energy industry — such as the energy transition, the shift to net zero, the energy crisis, and the decentralization of energy systems — to select the use cases that will best equip them to address the future of energy.

The top use cases can be taken forward to the proof-of-concept phase. At this stage, the focus should be on identifying the datasets required to support the use cases, as well as the required technology solutions and any challenges to integrating those solution in the existing application landscape. 

As they refine proofs-of-concept and the associated business cases, utilities will need to address data accuracy, quality, and reliability; navigate the complexities of GenAI models; and ensure that they can continue meeting data privacy regulations while breaking down data silos. As they zero in on identifying the ideal datasets, they will also need to increase their focus on integrating them for use by GenAI models.

As GenAI projects move from proofs-of-concept to pilot implementation, gathering robust customer feedback during the pilot phase will be critical to ensuring an eventual large-scale rollout. Measuring the business KPIs of the pilot, meanwhile, will help forecast the anticipated impact of enterprise-wide GenAI transformations and inform the most important KPIs to track in the future. Such KPIs might include customer satisfaction, average call handling time, grid frequency stability, and equipment availability.  

A full-scale solution rollout will need a strategy for reducing know AI risks. For use cases that leverage customer data, for example, utilities will need to enable appropriate customer communications and enhanced privacy frameworks. Enterprises also need to be prepared to pivot quickly based on emerging enterprise-wide KPIs. GenAI is an extraordinarily iterative technology, and as data changes in real time, so too will the results of any GenAI model. 

GenAI Outcomes for Utilities

According to McKinsey’s detailed analysis, GenAI is poised to contribute as much as $4.4 trillion to the economy annually, largely in the areas of customer operations, marketing and sales, software engineering, and R&D. Recent utilities-specific data suggests that the current market for GenAI services in the industry stands at $534M and will grow to $8.6B by 2032 — a CAGR of more than 33%. 

If utilities can streamline their legacy data systems, they will be able to unlock a great deal of value from GenAI across all of the major GenAI use cases. But even without solving every problem in their data estates, most utilities have pockets of high-quality data that are custom-made for GenAI-driven optimization. By landing on use cases that match the power of GenAI with the most appropriate data and the most profound business cases, they will be able to build the initial GenAI pilots that lead to large-scale GenAI transformations.   

About the Authors

Shirish Patil
Head of Consulting – Utilities, ECO, and GIS

Shirish has worked in the utilities industry for more than 28 years. He has championed and architected many large transformation deals with clients across the power, gas, and water sectors globally, including in Australia, the UK, Germany, the US, and the Middle East. As an industry leader for Wipro’s Utilities, ECO, and GIS sectors, Shirish’s priority is to help customers develop and operationalize digital and operational technologies for business transformation, data monetization, and new business models. 

Diptarka Sensarma
Enterprise Transformational Leader – ENU

Diptarka has worked in the utilities industry for more than 20 years. He has led large engagements across business transformation, strategic consulting, and architecture, working with utilities clients from the UK, Canada, Australia, and Germany. He now leverages this experience to assist utilities in maximizing value while transitioning to the digital utilities of the future.