How AI can power seamless utility CX

This is the third article in a series about how energy providers can thrive in an AI-powered future.  

Despite massive advances in grid technology, the utility business model still revolves around a fragile, high‑overhead cycle: Measure every kilowatt‑hour, price it exactly, and reconcile it flawlessly. Smart meters have reduced manual effort, but the core meter‑to‑cash process remains complicated, expensive, and a frequent source of customer friction. Complex rate structures, exception handling, demand charges, and seasonal adjustments are opaque to most customers, and metering or billing errors can ripple across utility operations. For a utility, excessive billing exceptions can result in regulatory problems and millions of dollars in settlement fees, internal remediation costs, system corrections, audit expenses, and potential future liabilities from disputed bills and customer complaints—costs that utility customers will ultimately pay. 

In short, the legacy system is onerous, expensive, and prioritizes precision over customer experience (CX). That made sense when alternatives didn’t exist. Today, distributed energy resources (DERs), smart thermostats and appliances, EVs, batteries, advanced metering infrastructure (AMI) 2.0, and AI‑driven forecasting create a different possibility: utilities that deliver a predictable, simple, subscription‑style service while orchestrating and settling energy flows through AI-based algorithms. The process becomes more transparent, efficient, and accurate without forcing customers to understand kWh, time-of-use, or demand charges every month. 

A useful analogy: When telephony abandoned per‑minute billing 

For decades, America’s phone bills were dominated by per‑minute long‑distance charges. With deregulation, IP‑based networks, and nationwide mobile plans, carriers shifted to flat‑rate subscriptions with unlimited calling, because the marginal cost of an incremental minute fell toward zero and customers valued predictability. 
 
Energy is not the internet, and electrons are not packets. Yet a similar pattern is emerging: At certain hours in renewables-heavy systems, the marginal cost of an additional kilowatt‑hour approaches zero. Meanwhile, customer expectations have shifted toward simple, subscription‑like experiences. The lesson from telephony is not that policy changes will be easy; it’s that when technology and customer value converge, billing models follow. 

Why not universal service for energy billing? 

Nearly 100 years ago, telecommunications legislation embraced a national universal‑service paradigm, creating a fund that subsidizes carriers serving rural and low-income customers. This system, extended to cover broadband internet connection, exists to this day. By contrast, electricity infrastructure was built out largely via one‑time rural electrification loans, and mandates to serve rural or low-income customers are set at the state level. The result is uneven bill outcomes by geography and income despite similar necessity in everyday life.  

Data from the Energy Information Administration indicates that 142.5 million residential households in the U.S. spent an average of $144 per month for electricity in 2024. This is a relatively small figure in the grand scheme of a household’s monthly expenses, and most consumers spend little time worrying about their electricity bills. That said, the costs vary widely by state. Lower-income states like West Virginia and Alabama rank near the top in terms of monthly spend, alongside wealthier states like Massachusetts and California. Utah residents spent just $89 per month, while Connecticut residents had to pay more than $200 per month for energy in 2024. Such disparities are not present in the telephony and data world today, where dominant mobile carriers and internet providers compete fiercely for every customer, regardless of where they live or use the service.  

Shouldn’t access to energy offer the same pricing stability and protections as those available for voice and data connectivity? Subscription models will not erase the physics or the cost of local infrastructure, but they can decouple everyday customer experience from the intricacies of tariff engineering—providing price stability and a clearer value proposition across diverse communities. Regulatory reform would be necessary for a national framework, but the AI-enabled utility can begin taking steps today. 

The post‑meter grid: From physically counted kWh to digital consensus 

Imagine neighborhoods where energy flows are managed collectively rather than accounted for one household at a time. In this scenario, AI models predict and orchestrate demand using weather forecasts, feeder and transformer telemetry, appliance‑level signatures, and historical behavior.  

Instead of itemized kWh bills, customers choose a subscription tier: comfort‑based (e.g., maintaining a steady 72°F in the home at all times), convenience‑based (e.g., faster EV charging), or value‑based (e.g., maximizing savings). Each tier provides the utility with AI-enabled flexibility options for grid management and optimization. Behind the scenes, AI manages device orchestration, demand response, and storage optimization to ensure system reliability and cost control—without customer micromanagement. 

How would it work? Fairness without per‑premise metering involves multiple steps and requirements: 

• Multiple independent signals: AI integrates AMI interval data, feeder/phase‑level load, voltage and frequency telemetry, weather, and appliance signatures to infer premise‑ and community‑level consumption. 
• Confidence bands and guardrails: Every subscription is governed by accuracy thresholds (e.g., 95–99% confidence). When AI inferences fall outside the bands, the system flags and resolves via automated audits or temporary reversion to metered settlement. 
• Redundancy and cross‑validation: AI models are continuously checked against known loads (e.g., electric vehicle supply equipment, heat pumps) and peer group norms to detect anomalies, theft, or equipment faults. 
• Transparent, auditable models: All AI inference and settlement steps are logged. Utilities and regulators can reproduce a bill using the same inputs and models, creating an auditable trail comparable to today’s revenue‑grade metering events.
• Customer protections, transparency, and dispute resolution: Customers have the right to request a metered audit, switch tiers, or receive make‑whole credits if model error exceeds thresholds. They receive alerts when consumption exceeds (or falls below) the parameters of their selected tier. 
• Affordability by design: Community‑level normalization and means‑tested assistance can be embedded into subscription tiers so that vulnerable customers benefit from the same predictability without complexity. 

Algorithmic fairness of AI must be governed like any safety‑critical system. That means model versioning, approval workflows, third‑party validation, bias detection and remediation, red‑team exercises, and continuous monitoring. Cybersecurity hardening is essential across cloud, edge, and device layers, with zero‑trust architectures and secure device onboarding. Regulators can certify model classes (e.g., load disaggregation, non‑wires orchestration) and require test suites, performance thresholds, and consumer‑protection remedies before broad deployment. 

In 2021, several U.S. utilities—including Duke Energy in North Carolina and AES Indiana—piloted flat‑rate subscription billing. Customers paid a fixed monthly fee while granting utilities limited control of thermostats and flexible loads. Reported outcomes included an average 31% reduction in peak usage when the subscription was bundled with demand response—demonstrating how the subscription model’s benefits can extend to grid operators, especially if new generation or peaker plant construction can be avoided.  

In Europe, utilities are increasingly bundling fixed monthly service fees with equipment (heat pumps, EV chargers) and managed consumption, reflecting an energy‑as‑a‑service trajectory that shifts value from commodity kWh to outcomes like comfort, resilience, and carbon reduction. 

Where will trajectories like these take us? The scenario below looks ahead a little more than a decade, visualizing what a fully realized subscription model might look like for an AI-enabled utility and one of its customers. 

 

Linking the vision to the AI-native utility stack 

Realizing and scaling the frictionless, automated, intelligent experience depicted in the above scenario will require modernizing integration, intelligence, and orchestration—the middle layers of the hardware-software stack of an AI-native utility.  

The first step is building a unified integration layer capable of absorbing data from grid-edge sensors, DERs, AMI 2.0, and community-level telemetry into a secure, interoperable fabric. Utilities must modernize data management and legacy systems with APIs, semantic models, and cybersecurity mesh architectures so that device behavior, local grid conditions, and customer-level preferences can flow seamlessly across platforms. 

With this foundation in place, the intelligence layer becomes the engine of the service-based model. Here, utilities must invest in forecasting, load-disaggregation models, anomaly detection, and causal-inference algorithms that continuously calibrate a digital twin of every home and feeder. This is what allows the system to optimize without metering every kWh. Utilities will also need rigorous AI governance—including versioning, auditability, and performance thresholds—to maintain trust for customers and regulators. 

Finally, the orchestration layer turns intelligence into real-time action. Utilities must develop automated flexibility markets, device-level control signals, and outage-avoidance algorithms that balance the system while preserving customer comfort. This is how the utility ensures a seamless experience: with silent, continuous orchestration that aligns household needs with grid stability, all within a transparent, predictable subscription service. 

An incremental path to regulatory and financial feasibility 

Such a major paradigm shift will take time, and the journey to a post-meter-to-cash model will be incremental. Guidehouse envisions a three-phase process: 

• In the near term (2025–2035), AI will augment existing metering systems, validating and reconciling data to reduce errors and improve efficiency. Community billing pilots can test collective accountability models, laying the groundwork for broader adoption.  
• Between 2035 and 2045, hybrid systems will emerge, with some customers billed on inferred usage and participation credits.  
• From 2045 onward, service-based pricing will dominate, and AI-driven consensus will replace per-meter billing entirely. This timeline reflects not just technological readiness but also the pace of regulatory adaptation and cultural acceptance. 

Specific steps that utilities and regulators can take right away include: 

• Starting opt‑in: Launch targeted, opt‑in subscription tiers that cater to customer priorities (e.g., comfort, value, resilience) in territories with AMI and flexible load penetration. Maintain a metered backstop for program evaluation during the learning phase. 
• Defining settlement standards: Publish model documentation, accuracy thresholds, audit procedures, and consumer protections. Establish a joint utility‑regulator governance board for rapid iteration. 
• Aligning incentives: Use performance‑based regulation to reward peak reduction, reliability, and equity outcomes. Allow utilities to earn on orchestration platforms and verified DER flexibility, not just on volumetric sales. 
• Protecting revenue and cost recovery: Structure subscriptions with capacity components and locational adders; use reinsurance‑like pools for extreme weather; and maintain risk corridors during transition. 
• Measuring what matters: Shift KPIs from bill accuracy alone to a balanced scorecard: CX simplicity, outage minutes avoided, peak shaved, emissions reduced, affordability, and inclusion. 

The ultimate utility CX is seamless, dependable, and predictable 

As utility rate structures get more complex (TOU, peak demand), utilities are challenged to manage the customer experience. Simplifying the rate structures to subscription based pricing will make CX more natural and intuitive. In a well‑orchestrated home or business, temperature, lighting, EV charging, and backup power align automatically with user preferences and system needs.  

Many pieces of this puzzle already exist: smart thermostats that optimize daily, batteries that charge on price and carbon signals, EVs that modulate charging speed—all of it pointing to a world where energy is ambient, not administratively demanding. 

As direct interactions between the utility and its customers recede, trust becomes paramount. Utilities will compete on service quality with tiered offerings and a plethora of attractive use cases. Regulators will evolve from price micromanagement to algorithmic oversight, ensuring proper balancing of both system needs and utility economics. 

The AI‑native utility is not a distant dream; it is a strategic choice that utilities can start pursuing now. By investing in the cognitive grid layer, codifying algorithmic fairness and auditability, rewiring customer intelligence for service‑based models, and working with regulators to design incentives that pay for outcomes, the utilities that move first will set the standard for the next century.