The following partner insight was authored by Keith Hoffmann, Industry Principal at HCLTech.
Utilities today are drowning in data. From smart meters and IoT sensors to customer billing and call center logs, enormous volumes of information flow through utility systems every day. Yet simply having data is not the same as gaining insight — data without knowledge is useless.
Many utility executives worry about the “unknown unknowns” that are hiding in their operations: those weak points and inefficiencies they don’t even know to look for. Traditional reporting tools and analytics only reveal what you ask them to; they often miss those subtle patterns or emerging issues that aren’t on management’s radar. This leaves utilities with blind spots in both operational performance and customer service. In an era when 74% of energy and utility companies are already implementing or exploring AI in their operations, relying on legacy methods is quickly becoming insufficient.
Utilities need a new approach to unlock actionable intelligence from their data and to shine light on those blind spots, and that is where artificial intelligence (AI) comes in.
Exception Management: From Manual Slog to Intelligent Automation
One traditionally high-effort area primed for AI transformation is exception management in operational systems. “Exceptions” in a utility context are those irregularities or errors that crop up in processes like meter reading, billing and field operations.
For example, a meter reading outside of the expected range would be flagged as an exception, or a bill may fail to generate due to a data discrepancy. In the past (and for many, still today), teams of employees’ slog through these exceptions one by one, investigating each case, finding the cause and fixing the data or process manually. This is slow, tedious, and expensive work. In fact, resolving a single billing or metering exception could take hours of an analyst’s time. With thousands of such exceptions per month, it’s no wonder backlogs build up and important details slip through the cracks.
AI is fundamentally changing this. By deploying AI-driven automation, utilities can handle the bulk of routine exceptions at machine speed, while also uncovering the patterns behind those exceptions. Instead of treating each anomaly in isolation, an AI system can analyze data across millions of records to identify common threads.
For example, a particular model of smart meter is failing or misreading usage in 30% of cases, AI would flag that trend, which is something a human reviewing individual tickets might never notice. Or consider billing errors clustered in one geographic area: an AI might correlate them to a specific substation experiencing power surges. These insights allow the utility to address root causes (like replacing a batch of faulty meters or repairing equipment in that neighborhood) rather than just repeatedly fixing symptoms. In other words, AI helps utilities stop losing sight of the forest for the trees in exception management.
The efficiency gains are dramatic. AI-powered exception management tools have demonstrated they can shrink resolution times from hours down to minutes. Faster, smarter exception handling means customer issues, like billing inaccuracies, are resolved more quickly and correctly, which boosts customer trust. It also reduces costly errors, avoiding misbills, regulatory fines and the bad press that comes with large-scale mistakes.
Internally, automating the “easy” exceptions frees your skilled employees to focus on complex, truly exceptional cases that require human judgment. Instead of expanding headcount or, as some do, loosening the thresholds for flagging errors just to reduce the queue, utilities can maintain high standards and let AI tackle the volume. The result is a more intelligent operations backbone: one that not only reacts to issues faster but continuously learns to prevent them.
Customer Engagement: From Frustration to Proactive Service
Another domain ripe for AI transformation is the customer experience, particularly in utility call centers and support channels. Consider the typical journey of a frustrated customer today: They receive a bill that’s much higher than expected and can’t figure out why. They try the website or app, but not finding clear answers, they reluctantly dial the call center. After wrestling with an automated IVR system and repeating their account details multiple times, they finally reach a live agent. The agent, in turn, scrambles through numerous screens and systems to piece together the customer’s usage history, weather data and account notes to explain the high bill. All this effort, only to determine that, yes, the customer’s energy usage spiked due to a heat wave and the bill is accurate, something that could have been communicated proactively. The process is frustrating for the customer and inefficient for the utility.
AI can fundamentally improve this experience. By analyzing the huge range of data available – from smart meter readings and grid data to individual customer profiles – AI can predict why a customer is calling before an agent ever picks up. In fact, AI could prevent the call altogether by addressing the issue proactively.
For example, AI analytics might detect an unusual consumption spike at a residence and automatically alert the customer through their preferred channel ahead of the bill, explaining the likely cause and even suggesting energy-saving programs or a personalized rate plan. Taking such proactive measures “lowers the barriers to service” by heading off surprises. When customers do need to call, AI can make the interaction smoother and faster.
At HCLTech, we developed the Intelligent Customer Engagement (iCE) framework that extends the standard Call Center tool by aggregating data from the CIS, outage and other edge systems to give the call center agent a 360° view of the customer. This framework uses a simple “red/yellow/green” traffic-light indicator to flag the most likely reason for the call. For instance, a red light might indicate the caller is probably concerned about an unusually high bill or a recent service disruption.
This kind of intelligent agent assist, powered by AI analytics, means the representative doesn’t have to blindly probe or rely on the customer’s vague descriptions. Even a relatively junior agent can quickly validate the likely issue and provide a solution or explanation with confidence. The result: shorter call-handling times, higher first-call resolution, and a more positive experience for the customer.
In fact, AI-driven support in utility contact centers has been shown to lower call volumes, by resolving issues through self-service or proactive outreach, and to speed up call resolution when live agents are needed. That translates directly into reduced operating costs and even improved employee satisfaction, since agents would deal with fewer frustrated callers and can trust the AI to surface the information they need.
Beyond the call center, AI chatbots and virtual assistants are increasingly capable of engaging utility customers in natural language. Unlike the rigid phone menus that often infuriate callers, modern AI assistants can understand free-form questions and provide instant, accurate answers to common inquiries, such as “Why is my bill so high this month?”.
And if the AI cannot solve the issue, it seamlessly passes the context to a human agent, so the customer doesn’t have to start over – thereby reducing friction. Customers get solutions faster and with less effort, and utilities build goodwill by being responsive and easy to do business with. It’s a far cry from the old-school scenario of pressing zero repeatedly just to escape an unhelpful automated system.
In short, AI allows utilities to transform customer engagement from a reactive, and often frustrating, process into a proactive service that meets customers’ needs with intelligence and empathy.
A New Era of Intelligent Utilities
The utility sector is on the cusp of a new era defined by data-driven intelligence and agility. AI technologies are enabling utilities to vastly improve two areas that have traditionally been labor-intensive and reactive: operational exception management and customer engagement.
By harnessing AI to automate routine exceptions and reveal hidden patterns, utilities can significantly enhance reliability, safety and efficiency in their operations, all while reducing cost and risk. By infusing AI into customer-facing processes, they can turn call centers from cost centers into value centers, delivering faster resolutions and proactive service that delight customers. The journey to get there requires more than just tools, it calls for a strategic vision of an AI-first organization, a commitment to modernize data and systems and enlightened change management that brings employees along as co-pilots in this transformation.
Utility leaders reading this should feel encouraged that the building blocks are in place to start unlocking this operational intelligence. Many of your peers have begun exploring or implementing AI solutions, and the technology has matured to a point where tangible benefits are within reach. The key is to start now, with well-chosen initiatives that address real business challenges, and to scale up from there.
Keith Hoffmann is Industry Principal at HCLTech. For more cutting-edge insights from HCLTech, register to attend the SAP for Utilities, Presented by ASUG conference this fall (Sept. 8-10; in Denver, Colorado).