Traditional Automation Has Reached Its Limit
For years, utilities have been digitalizing their processes with billing systems, field service management tools, and self-service portals that automate a steady volume of repetitive tasks. That model is starting to fall short. Large-scale deployment of smart meters (AMI), extreme weather conditions, disruptive events, and increasingly connected customers are generating more data and more cases than traditional workflows can handle.
The signs are already visible. According to McKinsey, utilities applying agentic AI to exception management, billing inquiries, and data reconciliation have reduced billing- and payment-related calls by 20% to 50% [1]. For executives in the sector, the challenge is no longer to automate more tasks, but to evolve toward autonomous operations.
Where Automation Stops: Exceptions
Traditional automation runs on deterministic logic: “if A happens, execute B.” It is highly efficient for linear, predictable processes. Beyond running the processes in the business cycle, such as billing all customers or generating meter-reading and maintenance orders, a utility deals every day with exceptions, meaning everything that does not go as planned.
Picture a Monday at 8:00 a.m. A meter reports consumption four times higher than usual; a field order fails because of an unforeseen condition, and a customer requests a complex adjustment to their bill. In all three cases the rule breaks, the process stops, and the case lands in an analyst’s queue.
The result is a hybrid model in which technology detects events and analysts resolve the cases. Every exception that reaches an analyst adds time and cost to customer service and opens the door to revenue leakage. This model served the industry for years, but it is not prepared for the volume of today’s operations.
The Era of Autonomous Operations with Agentic AI
Overcoming that limit takes more than adding conditional rules to existing systems. Technology needs reasoning, context, and decision-making capabilities. That is what agentic AI contributes. Unlike rule-based automation, an AI agent can:
- Understand a business objective.
- Analyze dynamic context and real-time data.
- Evaluate multiple solution alternatives.
- Execute the necessary actions across different systems to resolve the case.
The market is moving in this direction. For example, Gartner projects that by the end of 2026, 40% of enterprise applications will include task-specific AI agents, up from less than 5% in 2025 [2]. However, incorporating agents is not the same as operating autonomously. Automation executes predefined rules; in autonomous operations, the system decides and acts within the limits defined by the utility.
Technology alone does not guarantee results, though. According to The GenAI Divide: State of AI in Business 2025, a report by MIT NANDA, 95% of organizations get no measurable return on their generative AI investments [3]. The study attributes this gap largely to tools that do not integrate into workflows or learn from their context. That is why autonomy starts with a solid foundation.
The Starting Point: Unified Data
Installing a language model on top of legacy systems is not enough to operate autonomously. To decide whether Monday’s consumption spike is a leak, fraud, or a measurement error, the agent needs structured, unified, real-time information. If commercial, metering, and field data live in silos, the agent operates blind.
IDC forecasts that by 2027, companies that do not prioritize high-quality AI-ready data will struggle to scale generative and agentic solutions, with a 15% loss in productivity [4].
The Four Pillars of a Unified Operations Platform
To enable autonomous operations, a unified operations platform must incorporate four capabilities. The Monday meter case shows how each one works.
Real-Time Triggers
The platform detects each event the moment it occurs and immediately activates the corresponding resolution workflow, without waiting for overnight reconciliations. At 8:00 a.m., when the AMI meter reports the atypical reading, the system accesses all relevant customer information and can act at that very moment. In a disconnected environment, that same inconsistency would not be analyzed until the early hours of the next day, when the systems manage to synchronize.Agentic Workflows
A traditional workflow follows a fixed path, step by step. It works until a case requires considering more variables and more data and being resolved within a defined timeframe.
An agentic workflow incorporates AI agents configured to understand the utility’s processes and to decide and act within established parameters. Faced with Monday’s consumption spike, the agent reviews the reading history, the week’s weather, and recent maintenance in the area to identify the root cause. Its value lies in operating within the workflow itself, such as handling a customer claim, rather than functioning as an external tool.Closed-Loop Execution
Many automations stop halfway; they detect the error but leave a note for an analyst to fix it. With a closed loop, the agent completes the process. If it confirms a measurement error, it adjusts the reading, recalculates the consumption, and prepares the corrected bill before the customer must call.
This is only possible when information from the entire business cycle, from meter reading and billing to customer service, is connected to a single platform.Human-in-the-Loop Governance
All this autonomy operates within a governance framework defined by the utility. With configurable confidence thresholds, agents resolve low-risk cases on their own. Cases with high financial or regulatory impact reach the supervisor with a preliminary diagnosis for validation. If Monday’s meter adjustment exceeds the established threshold, the supervisor approves it with the analysis already prepared.
In addition, every agent decision must be logged and auditable, a key requirement for regulators and auditors in any market. As a result, human intervention stops being a repetitive operational task and becomes a strategic point of control and approval.
From Concept to Competitive Advantage
Let’s return to Monday at 8:00 a.m. In an autonomous operation, the atypical reading is detected instantly. The agent identifies the cause, corrects the reading and the bill, and escalates to the supervisor only when the case requires it. The analyst stops processing exceptions one by one and applies their judgment to the cases that truly require it.
A utility’s competitive advantage lies in having its entire operation work in a coordinated way. When AI agents are integrated into every stage of the business cycle, from metering and billing to customer service and field operations, the utility reduces manual effort, cost-to-serve, and revenue leakage. People retain the decisions that require judgment, and existing automation remains the foundation on which this next step is built.
To achieve this, AI must be part of the operational core. Smartflex, Open Intelligence’s unified customer operations platform for electricity, water, gas, and telecommunications companies, brings customer information, billing, metering, and field operations together in one place. Alexandria, the AI engine embedded in Smartflex, runs on that foundation and integrates agents into customer service and field operations processes.
Identify which exceptions in your operation can be resolved with agentic AI and what data you need to make it happen.
References
McKinsey & Company (2026). How utilities can rewire customer operations with agentic AI. Available at: https://www.mckinsey.com/industries/electric-power-and-natural-gas/our-insights/how-utilities-can-rewire-customer-operations-with-agentic-ai
Gartner, Inc. (2025). Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025. Available at: https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025
Challapally, A., Pease, C., Raskar, R., & Chari, P. (2025). The GenAI Divide: State of AI in Business 2025. MIT NANDA. Available at: https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
IDC (2025). IDC FutureScape 2026 Predictions Reveal the Rise of Agentic AI and a Turning Point in Enterprise Transformation. Available at: https://www.idc.com/resource-center/press-releases/idc-futurescape-2026-predictions-reveal-the-rise-of-agentic-ai-and-a-turning-point-in-enterprise-transformation/
