The energy and utilities sector stands at a unique inflection point. While 78 percent of organizations now use AI in at least one business function, up from 55 percent a year earlier, utilities have maintained a measured approach given the critical nature of energy infrastructure and the industry's emphasis on reliability above all else. According to Latitude Media's 2024 Utility AI Insights report, AI isn't new to utilities. Many have been working with load forecasting and other machine learning applications for some time. But the extent to which AI is being incorporated into grid systems has increased dramatically in recent years. This conservative stance has created both a challenge and an opportunity: utilities now have the advantage of implementing more mature AI technologies while learning from the experiences of early adopters across other sectors.

The question isn't whether utilities should embrace AI; it’s how they can strategically implement Generative and Agentic AI solutions to catch up with other industries while building sustainable competitive advantages without disrupting the reliable operations that customers depend on.

With years of experience guiding utilities through transformational initiatives, I've identified five high-impact use cases where AI can deliver immediate value while positioning organizations for long-term success. These applications represent areas where mature solutions already exist, implementation risks are manageable, and the potential for operational transformation is substantial.

Management Reporting: The Most Dramatic AI Transformation Available Today

Management reporting has seen the most dramatic improvement and development in Generative AI solutions. Several mature solutions exist that can simplify management reporting, reduce the workload of existing IT operations by eliminating custom-developed reports, and provide adaptive intelligent reporting where formats and views can change on the fly. According to PwC's 2025 AI predictions, nearly half of technology leaders say that AI is "fully integrated" into their companies' core business strategy, with a third saying AI was fully integrated into products and services.

The main challenge here is the need for a mindset shift within Corporate Finance functions. They are typically used to a one-click view of static reports and dashboards and will need to transition to having a conversational interaction about data with AI platforms. Many corporate finance leaders currently receive reports via email; this could be replaced with an AI solution that consumes all analytical data and the corp finance interacts with it to gain the right insights over a few prompts.

To put this in perspective a typical management report could require building custom dimensional cubes in an ERP to support multi-level drill down to the source data. And the corp finance functions usually require data in multiple models to slice/dice and analyze. Such reports tend to take several hundred hours of technical and business time to perfect but tend to get obsoleted in a few years owing to organizational changes, technical changes and even market changes. Replace this with prompting and the data is accessible quicker, it is not tied to a specific format therefore the report is agile, and the Gen AI model learns from the users making it scalable inherently.

To summarize, a clear advantage is several layers of reporting could be uncovered without developing customized technical models. Traditional static reporting systems with their rigid formats give way to conversational AI platforms that process vast datasets and provide dynamic, interactive insights through natural language queries that would traditionally require weeks of specialized development work.

Proactive Systems Monitoring: Leveraging IOT-Enabled Hardware Intelligence

There are plenty of mature solutions available that can use the operational data from IOT enabled hardware and share critical insights about system health, equipment usage like meters, transformers, switches and the companies whether electric, water, or gas can all leverage this intelligence to perform system maintenance proactively. According to a recent analysis by DataForest, predictive maintenance in utilities can reduce operational costs by up to 40% while improving equipment efficiency and preventing service interruptions.

Agentic AI could be used to trigger maintenance requests that can be reviewed by an administrator who can then dispatch the crew for updates. As Soracom research indicates, running equipment until it breaks down is 3-10 times more expensive than regular maintenance, with the average cost of unplanned downtime hovering around $260K per hour.

The shift from reactive to proactive maintenance strategies represents a fundamental transformation in how utilities manage their infrastructure assets. The key to successful implementation lies in establishing proper data governance frameworks and ensuring seamless integration with existing work management systems.

Sourcing, Procurement and Inventory Optimization: Expanding Beyond Enterprise Data

These tend to be heavily manual and time consuming processes within utilities. There are several metrics that utilities monitor to get the most optimized procurement deals, reduced cost and increased availability. However, so far they have been constrained by their own enterprise data.

With the help of GenAI, utilities can expand this data set multifold to tighten their negotiations, get more accurate reliability metrics associated with suppliers. Just in time procurement contracts could be made faster and agentic AI can help build the POs, create inventory movement transactions, develop the right sourcing strategy for the enterprise and go beyond to compare the same with competitors. As McKinsey research demonstrates, generative AI has already started to drive measurable impact in procurement through generating content, enabling synthesis, augmenting engagement, and accelerating software programming.

AI-powered systems can perform real-time competitive analysis, ensuring utilities achieve optimal pricing and supplier terms. The transformation extends beyond cost savings to strategic supply chain resilience, enabling utilities to better navigate market volatility and supply disruptions.

Staffing, Resource Planning and Human Capital: Weather-Aware Workforce Intelligence

HCM has long been the backbone of these labor intensive operations. With the help of Gen AI utilities can monitor workforce quality, project resource demands against dynamic market, macroeconomic, and weather trends and build a stronger foundation for accurate staffing.

The current metrics, for example, do not combine weather data with labor hours. With the help of Gen AI this could be achieved. Similarly storm damages are inefficiently tracked in the system by simple non-intelligent flagging of requests related to storm repair. With gen AI utilities can do without the manual tagging of requests and use publicly available weather information combined with enterprise data to get more accurate zip code level maintenance info with little additional effort.

According to a market research conducted by DemandSage, AI recruitment can reduce hiring costs by 30% per hire. The driving factor for these is the changing skills and labor market landscape. Access to a greater talent pool with ‘remote’ working models enables AI driven recruitment process to scan wider talent pool with higher accuracy for a match within a shorter turnaround time. 

Such intelligence can help better plan for overtime, labor contracts and equipment availability. This intelligent approach to workforce management becomes particularly valuable during extreme weather events, where rapid resource mobilization can significantly impact customer service and operational costs.

Project Planning: Automating Complex Operations Management

Major utilities manage operations through the construct of well defined projects with standardized structures. This project creation and maintenance tends to be cumbersome and labor intensive. With the help of Agentic AI, with sufficient training, the project creation and maintenance can be significantly automated.

Consider a simple prompt like 'Create a project similar to the 2019 hurricane related maintenance work that impacted the entire state of Mississippi where XXX company performed the electric substation repairs'. When such approaches are followed, the operations efficacy can increase manifold, so procurement isn't waiting for approvals for days before they issue POs for the associated, instead the operations are taking place at a much faster pace.

The AI system can automatically establish project parameters, resource requirements, procurement needs, and timeline estimates based on historical data and current conditions. This acceleration in project management allows utilities to respond more rapidly to grid modernization needs, storm recovery efforts, and infrastructure expansion requirements.

Strategic Implementation - How to Balance Innovation with Operational Reliability

Successfully implementing these AI use cases requires a thoughtful approach that respects the industry's reliability requirements while also capturing the competitive advantages that AI provides. Organizations should prioritize use cases based on three main criteria: the maturity of available AI solutions, the potential for measurable business impact, and the ability to implement without disrupting critical operations.

Management reporting and procurement optimization often represent ideal starting points, as they can deliver immediate value while building organizational confidence in AI capabilities. The key is to begin with use cases that offer high value potential with minimal operational risk, typically back-office functions and analytical processes that don't directly impact real-time grid operations.

In order to thrive, utilities must recognize AI is a disruptive force to be feared, but a strategic enabler that can help them catch up with other industries while building sustainable competitive advantages. By focusing on these five high-impact use cases, utilities can begin their AI transformation journey with confidence, knowing they're implementing proven solutions that respect the industry's operational requirements while positioning their organizations for future success.

Kunal Saxena is a senior management professional with over 14 years of experience in the energy, power, and utilities industry, specializing in optimizing business processes to reduce operational costs. As an advisor to C-Suite executives on technology innovation and industry trends, Kunal provides leading design and process recommendations to drive true operational transformation that prepares companies for future technologies like Generative AI. He is particularly focused on helping organizations address key industry challenges including decarbonization, utility decentralization, and creating efficient service delivery models that enable companies to scale through M&A activities.