Key points
- AI-powered M2C transformation boosts billing accuracy, predicts delinquencies, and streamlines collections—enhancing cash flow and customer trust.
- Human-in-the-loop frameworks ensure regulatory compliance, empathy, and oversight—training AI models for smarter, context-aware decisions.
- Sutherland’s proven impact combines automation, analytics, and global operations to deliver up to 30% cost savings and industry-leading customer satisfaction.
The meter-to-cash (M2C) cycle is the core financial process for utilities. It encompasses all steps, from collecting meter data to billing customers, managing payments and collections, and resolving any disputes.
When it works well, customers enjoy a seamless experience and revenues flow predictably. When it falters, the consequences ripple across the business: disputes mount, service costs rise, and regulators take notice. Despite decades of investment in billing systems and customer service platforms, many utilities still grapple with inefficiencies that frustrate customers and erode margins.
The urgency to fix this challenge has never been greater. Demand for electricity is climbing sharply, driven by the electrification of transport, the growth of AI-intensive data centers, and a resurgence of onshore manufacturing. At the same time, utilities are under pressure to modernize grids, integrate renewable energy, and comply with increasingly complex regulations. In this context, clunky and error-prone M2C processes are no longer sustainable. They must evolve into intelligent, adaptive, and customer-centric systems that keep pace with a rapidly changing energy landscape.
The AI Advantage in M2C
Artificial intelligence is playing a pivotal role in this transformation. No longer confined to experimental pilots, AI solutions are now being scaled across utilities to bring new levels of accuracy and efficiency. In billing, advanced analytics make it possible to detect anomalies in consumption patterns and flag potential errors before they ever reach the customer, reducing disputes and building trust. In collections, machine learning models can anticipate which accounts are most at risk of delinquency and recommend proactive engagement strategies. These kinds of interventions have delivered tangible outcomes: in one U.S. engagement, Sutherland helped a client exceed its collection targets by more than 50 percent and improved the roll-forward ratio by 4.5%, increasing cash flow and reducing write-offs.
Equally important is the role of automation in streamlining the everyday interactions that weigh down utility operations. Robotic process automation (RPA) and conversational AI agents are already deflecting routine calls, managing payment requests, and handling standard account queries. This frees agents to concentrate on more complex issues, lowers average handle times, and contributes directly to higher customer satisfaction.
But AI on its own is not a panacea. Energy transactions often involve nuance, empathy, and regulatory interpretation—areas where a purely automated response can create more problems than it solves. That is why the future of meter-to-cash lies not in AI alone, but in a carefully designed balance between automation and human expertise.
Human-in-the-Loop: The Missing Ingredient
Human-in-the-loop (HITL) frameworks provide exactly that balance. They ensure that while machines handle scale and speed, people remain responsible for oversight, judgment, and care. Within meter-to-cash, this might mean a human expert validating an AI-generated billing adjustment before it is applied, or stepping in when a case involves complex tariff structures or vulnerable customers. It also means continuously feeding human feedback back into AI models so they become more accurate and context-aware over time.
At Sutherland, we have seen how HITL builds confidence in digital systems. For regulators, it provides assurance that billing and collections processes meet compliance standards. For customers, it brings empathy and fairness to what might otherwise feel like cold, machine-driven decisions. And for the utilities themselves, it creates a virtuous cycle: as human interventions train the models, the models get smarter, and the reliance on manual effort gradually diminishes.
Sutherland’s Track Record
Our work with utilities around the world demonstrates the power of combining AI with HITL. One U.S. regulated natural gas utility modernized its contact center operations with Sutherland Connect, our AI powered Next Gen CCaaS omni-channel platform. The results were striking: 93 percent customer satisfaction, 97 percent agent satisfaction, and quality scores consistently above 95 percent. These weren’t abstract gains; they translated into faster resolution times, happier customers, and a more motivated frontline workforce.
In another engagement, we led a comprehensive customer-to-cash transformation for a leading energy provider. By designing a global operating model that integrated front- and back-office operations, establishing nearshore and offshore support, and embedding intelligent automation and process analytics, we reduced backlogs, improved service order handling, and delivered measurable cost savings. In fact, by reimagining processes with advanced cognitive assets and analytics, Sutherland has helped clients achieve annual cost reductions up to 30% while simultaneously expanding service capabilities and meeting regulatory requirements in all the operating states. These examples illustrate a consistent theme: Sutherland doesn’t simply implement technology. We orchestrate people, processes, and platforms to deliver outcomes that matter—better financial performance, stronger compliance, and an elevated customer experience.
Why Now Is the Moment
The case for transformation gets reinforced as the broader industry dynamics change. In 2024 alone, U.S. utilities invested a record $174 billion, now it is estimated to spend over $212 billion in 2025 across grid modernization, advanced metering infrastructure, smart grid technologies, DERMS (Distributed energy resource management systems) and transmission expansion. These investments must be matched with efficient revenue realization, which means retooling M2C processes. Policy incentives and environmental regulations are also reshaping the economics of utilities, making transparency and accuracy in customer billing more important than ever. Meanwhile, technologies such as digital twins, cloud platforms, and IoT are reaching maturity, creating opportunities to embed intelligence directly into billing, forecasting, and customer engagement workflows.
Utilities that cling to legacy approaches will struggle under the weight of these changes. Those who embrace AI and HITL together will be better equipped not just to keep up, but to lead.
A New Standard for Utilities
The future of meter-to-cash is neither human-only nor machine-only. It is the thoughtful integration of both, where AI provides scale, speed, and predictive power, and humans bring judgment, empathy, and oversight. Sutherland calls this the new standard for utilities. It transforms M2C from a cost center into a strategic capability, enabling operational efficiency, regulatory confidence, and an improved customer experience. As electrification accelerates, as decentralized energy reshapes grid dynamics, and as regulators demand ever-greater transparency, utilities that reimagine their core processes now will be best positioned to thrive. At Sutherland, we are proud to be leading this shift—helping utilities harness AI and HITL to deliver not just better processes, but stronger customer relationships and a more sustainable future.
