Agentic AI in energy and utility is not a future concept, but a present reality. The scope of its application is broad across the industry. It has proven especially effective in customer service and revenue management, as these domains rely heavily on high-volume, rule-driven, but exception-prone processes, which are ideal for agentic AI.
Besides, agentic AI enables closed-loop automation, in most cases by creating a multi-agent ecosystem.
In this piece, we’ll dive into real-life use cases of Agentic AI in customer service and revenue management. This will give you the clarity to decide whether it’s time to bring this technology into your business, not just to stay afloat, but to lead.
The reality is, your competitors are constantly chasing ways to serve customers better, streamline operations, cut costs, and boost financial results. Agentic AI is exactly the kind of edge that can put you ahead.
So, let’s dive into the subject.
What is Agentic AI for energy and utilities?
Leaders in utilities come with a different level of awareness about Agentic AI, so let us provide a brief introduction to the technology.
Agentic AI refers to autonomous systems that independently choose actions and carry out tasks without human oversight. These are not just tools, but intelligent systems equipped with “thinking” models that possess advanced reasoning capabilities.
This allows them to analyse information, draw logical conclusions, incorporate context and nuance, and make informed decisions before responding.
Unlike previous AI applications that required step-by-step instructions, agentic AI systems can interpret goals, plan actions, and work autonomously.
They move beyond simple automation to become integral to business processes, capable of understanding tasks, making decisions, and taking action, such as responding to customer requests, scheduling field work, or monitoring systems, all without needing someone to tell them every step.
Industry Forecast
The transformation is happening rapidly across the energy and utility sector:
By 2028:
- 33% of enterprise applications will include agentic AI (up from less than 1% in 2024)
- 15% of daily decisions will be autonomously made by agents
Gartner’s prediction: Embedded AI agents will be built into more than a third of all enterprise applications, making natural-language conversation the primary user interface. These agents will handle approximately 15% of routine decisions, freeing people from repetitive tasks and unlocking a new level of productivity.
What makes Agentic AI so helpful in utility customer service and revenue management?
Let’s imagine: you are furious about repeated billing errors. A support rep could either say:
“I’m sorry for the inconvenience. We’ll fix it.” Will it help much?
Or, you might be getting another response:
“I see you’ve already had two incorrect bills in the past three months. That shouldn’t have happened. To prevent this again, I’ve set up a recurring check before your next bill is sent.”
By directly referencing the history (the exact pain point), the rep validates the frustration and provides a tailored solution. Many times, customers shift from anger to relief, even appreciation.
Contextualization for the best relevant results
This leads us to the number one benefit of AI: contextualization for the most relevant results.
Agentic AI systems excel at providing faster, more contextualised responses to queries by:
- Understanding full context: AI systems can access and analyse complete interaction histories, account details, and relevant background information
- Real-time integration: Connecting to live data sources and enterprise systems to provide current, accurate information
- Intelligent analysis: Drawing logical conclusions and incorporating context and nuance before responding
Personalisation at scale – you sell more
The technology enables highly personalised customer interactions through:
- Individual customer history: Incorporating details from purchase history, previous interactions, and service preferences
- Behavioural pattern recognition: Understanding customer preferences and communication styles
- Dynamic adaptation: Adjusting responses and recommendations based on real-time customer data.
AI agents can draft personalised responses to customer queries about new services, automatically incorporating details from their purchase history, previous service interactions, and current account status.
Advanced data handling and enterprise leverage
Agentic AI transforms how businesses handle enterprise data by:
- Breaking down data silos: AI agents enable businesses to more effectively find, understand, and act on their enterprise data, helping to break down traditional data silos and deliver concrete value across various industries.
- Multi-source Integration: Systems with Agentic AI combine advanced reasoning, powerful multimodal search, and enterprise data, enabling teams to discover, interpret, and act on information—while ensuring security, privacy, and compliance at scale.
- Proactive synthesis: From the very first day, employees can interact directly with their organization’s knowledge, turning it into actionable insights and producing a variety of content formats powered by deep data analysis.
- Scalable knowledge access: The technology allows organisations to tap into the collective expertise of their organisation and access the precise information needed to solve problems and work more efficiently through multi-agent systems working together.
The multi-agent approach: agents communicating with each other and third-party systems
The evolution from single conversational tools to multi-agent systems represents a fundamental shift in AI implementation. Instead of having one model that we talk to, we now have multiple agents that collaborate.
You give instructions to one agent, and it reaches out to other agents for specific tasks, creating a network of specialised AI workers.
AI agents’ access to external systems, applications, and data sources creates another level of expertise, workflows, marketing campaigns, etc.
The question everybody asks, but no one knows the full answer yet
It’s a question about safety.
The AI Act defines how AI should be leveraged in the context of customer data and customer rights/privacy protection. You can read the general statements in Artificial Intelligence in energy and utilities: successful business cases & tips for utility CIOs.
The healthy approach for E&U businesses to handle well this “AI implementation safety for customers, in the first place” would be 1) know the questions that regulatory bodies address and ask how your business responds to them, 2) leverage the best practices already existing in the industry.
Let’s take the example of Ofgem, the energy regulator of Great Britain.
The regulatory watchdog emphasises key safety questions that businesses should address:
- Accountability: Who is responsible for the decisions AI is recommending?
- Human oversight: Can humans oversee and override throughout the whole cycle?
- Transparency vs. explainability: How will you explain agentic AI decisions to users and ensure understanding of why those decisions are made?
- Data privacy: Protecting customer information throughout automated processes
- Digital inclusion: Ensuring AI doesn’t create barriers for certain customer segments
Each company needs to address these aspects within its organisation. In the meantime, there are best practices in the industry that will definitely add to the secure approach of AI implementation in customer service operations.
The best practices in the E&U industry to ensure safety
Monitoring hubs
Drawing from real-world implementations, safety in Agentic AI systems requires robust monitoring and human oversight. Some developers’ approach to building AI agents for utilities suggests building out agents with a monitoring hub, so every decision can be tracked by humans and clearly understood.
To be more specific, the monitoring hub architecture tracks every autonomous decision made by AI agents, creating transparency and accountability:
- Decision audit trail: Every action taken by agents is logged and can be traced back to specific inputs and reasoning
- Performance Metrics: Continuous tracking of agent performance, accuracy rates, and outcome quality
- Pattern Recognition: Identifying trends in agent behaviour and flagging unusual decision patterns
Human-in-the-Loop: strategic oversight and control
The most effective implementations maintain human control while leveraging AI efficiency. Let’s define a 3-level approach of human engagement, which altogether minimises risks of AI misuse.
Level 1 – Full automation: Routine, low-risk decisions where agents operate autonomously.
For example, an email lands in the inbox. The automated email triage classifies it as a ‘no need for human review’ email and is already drafting a routine customer response (in utilities, it’s 35% of emails).
Level 2 – Human review required: Complex decisions requiring approval
The agent qualifies an email as the one requiring a human review and sends it immediately for approval.
Level 3 – Human-only decisions: High-stakes or sensitive situations
These could be billing disputes requiring empathy and judgment, regulatory compliance decisions, complex customer escalations, etc.
Continuous learning and safety improvement
Successful implementations create continuous improvement cycles. Businesses can maintain human agents “in the loop” for verifying and sending AI-generated responses and then scale across business functions while maintaining oversight.
Agentic AI use cases in customer service and revenue management
Customer service
a) Empowering employees
Imagine getting thousands of enquiries annually through your inbox where customers raise consumption or billing questions and requests for meter application.
Previously, 20 agents worked full-time handling these requests, with team leaders spending hours daily manually categorising emails.
Today, email triage and processing are handled by two AI agents. One reads texts, analyses attachments, and categorises emails by priority; the second scans email bodies and attachments, condenses information, and feeds data into backend systems for human review.
As previously mentioned, agentic AI excels at providing faster, more contextualised responses to customer queries while maintaining essential human oversight for quality and empathy.
The agent:
- Summarises entire customer interaction history for context
- Auto-drafts responses in natural language
- Suggests specific actions (e.g., requesting meter readings)
- Operates with full platform context, including lifecycle and product data
The system adapts tone and language preferences automatically, ensuring culturally appropriate communication across different customer segments.
b) Digital inclusion and self-service
Utility customers are different: boomers, Gen X, Gen Z. Some prefer phone calls and emails, others would opt for web portals where agentic AI explains invoices and provides consumption recommendations. That’s why multi-channel service is essential.
While Gen Z is enjoying customer support by phone thanks to quick and relevant responses, the savvy generation benefits from an AI-powered virtual assistant. Here’s what AI agents do:
- Contract services online, interact via multiple digital channels, and instantly retrieve personalised account details.
- Manage billing queries, service requests, or account updates without waiting in call queues.
- Escalate automatically when empathy or complex decision-making is required, transferring context-rich information to human staff for continuity.
Wekiwi’s AI-powered virtual assistant not only automates routine questions but also captures data on energy use and preferences. This creates tailored product recommendations, boosting digital inclusion and conversion rates (>10% in six months).
c) Onboarding with digital enrollment and ID verification
Traditional utility onboarding involved lengthy registration forms and manual document verification. Agentic AI transforms this into a seamless, image-based process.
Plenitude’s Automated Onboarding (Italy/Netherlands)
Plenitude partnered with Google Cloud AI to revolutionise customer onboarding:
Image-based bill processing:
- Customers upload images of old energy bills
- Optical Character Recognition extracts all text from bills
- PaLM AI identifies relevant customer details for subscription
- Forms are automatically filled in seconds instead of manual data entry
ID verification automation:
- Document AI automatically recognises and validates customer ID information
- Processing time reduced from weeks to minutes
- Fraud risk reduction through automated verification
- Manual effort elimination while maintaining security
IOANA’s guided enrollment process:
- Requests a picture of ID, the latest utility bill, and the property contract
- Automatically fills required data from uploaded documents
- Asks users to confirm information accuracy
- Creates virtual client files with all documentation stored
d) Automated documentation
Agents streamline back-office paperwork:
- Extracting and classifying contract data, invoices, and meter reports.
- Storing it in customer files automatically with full compliance traceability.
- Generating customised reports and alerts for customers.
Casa Dos Ventos (Brazil) uses Vertex AI to automate document analysis and instruction files, reducing human effort and unlocking efficiency in repetitive documentation tasks.
Revenue management
e) Product upselling, cross-selling, and recommendations
Intelligent revenue generation through personalised offers
Agentic AI transforms product recommendations from generic marketing to personalised, contextual offers based on comprehensive customer data analysis.
Wekiwi’s virtual assistant demonstrates sophisticated commercial funnel automation:
Data collection and analysis:
- Collects data on energy usage, location, and preferences during interactions
- Enables personalised product recommendations based on customer profiles
- Identifies leads for new services during routine support interactions
f) Intelligent contract and proposal generation
Agents move beyond templates to generate contextualised contracts and proposals:
- Pulling verified customer data directly into proposal docs.
- Using reasoning models to adapt terms, tariffs, or bundles based on customer profiles.
- Automating renewals, ensuring compliance with the latest regulations.
This transforms sales and B2B contracting into a near-instant workflow where humans only need to validate or sign off.
g) Negative-price alert bot
When wholesale prices go below zero, AI agents watch next-day prices and alert customers who can shift load (EV, heat-pump, battery) with simple incentives in their preferred language.
In negative-price hours, the market pays suppliers to take energy; suppliers pass a portion as customer bill credits. This creates win-win scenarios where customers save money while utilities reduce balancing costs.
Real-life scenarios:
– Battery Fleet (Netherlands): “Charge 02:00–03:00 (price below zero). We’ll credit you automatically.” Aggregated charging removes MW of curtailment risk
– Water-Tank Preheat (Spain): Prosumers pre-heat DHW tanks during negative hours; credit appears as “Surplus Hour Bonus”
– Multilingual Push (Germany): Sends Ukrainian first, retries in German if unopened; higher participation rates
h) Collections and payment optimisation
Collections whisperer
AI segments overdue accounts by hardship risk and writes empathetic messages in the customer’s language, offering clear instalment choices. The scenarios can be repeated in energy and utilities with solid indebtedness.
Proven Results:
– WhatsApp Plan (Poland): 45-day overdue parent accepts 6-month plan in minutes with auto-confirmed first payment
– Senior IVR (Greece): Local-dialect IVR with simple options reduces complaints while maintaining recovery rates
– Winter Amnesty (Romania): Two-month pause with catch-up from April achieves higher total recovery by June versus the previous year.
i) Cutting peak balancing costs
Most customers don’t change their consumption habits, even when dynamic tariffs are available. This means utilities continue to face costly peak balancing, grid stress, and underused demand-side programs.
Agentic AI reads yesterday’s meter data and sends simple, friendly nudges with one-tap switches (plan or device schedule). It detects and uses the best language for the user automatically.
Real-life examples:
- EV Night Shift (Italy): “Charge after 22:00 to save ~€4 tonight.” One tap reschedules the wall box.
- Landlord View (Czechia): Weekly digest highlights wasteful flats and auto-drafts messages in CZ/UKR/RU.
- Heatwave Comfort (Spain): Suggests “pre-cool 13:00–15:00, ease off at 18:00” with a small credit, reducing peak strain without complaints.
Agentic AI turns demand flexibility into tangible cost savings for utilities while making energy use simpler and smarter for customers.
j) Policy navigator against fines and refunds
Utility rules and regulations change constantly, but late or incorrect updates result in refunds, fines, and lost allowed revenue. Manual monitoring and implementation can’t keep pace with the speed and complexity of regulatory change.
Agentic AI reads official updates overnight, maps each change automatically to affected tariff fields and customer segments, and drafts ready-to-send customer notices in the appropriate language and tone.
It provides a clear impact map showing which classes, contracts, and dates are affected and integrates with billing/CRM systems for bulk price-update previews and legal sign-off.
Real-life scenario:
When the EU introduced the inframarginal revenue cap (emergency rule capping generator revenues at €180/MWh), suppliers had to update their tariff logic immediately.
Missing this deadline meant utilities ended up overcharging customers, which led to costly refunds and additional administrative work, while at the same time delaying allowed pass-throughs and losing valuable revenue opportunities.
The Policy Navigator ensures utilities stay compliant, avoid costly penalties, and capture every euro of allowed revenue while keeping customers transparently informed.
k) New revenue stream creation
Creative new-revenue designer
AI finds micro-segments and drafts ready-to-launch bundles with pricing, copy, and onboarding steps for fee-based services not tied to kWh consumption. These services work best in affluent regions (Switzerland, Germany, the Netherlands, Denmark, Norway), metro DSOs, and industrial hubs.
Service bundle examples:
- Senior warmth care for seniors/families: Weekly warmth checks plus thermostat guardrails providing peace of mind for elderly customers or their families
- Prosumer battery booster for PV + battery homes: Home batteries timed to optimal price hours; homeowners keep most extra earnings while utilities take a small management fee
- Apartment EV Co-op for apartment residents: Shared chargers for condominiums with member rates and revenue sharing from public sessions
MaxBill AI billing and product catalog
MaxBill has launched the AI-native product suite designed for utilities to streamline processes that previously needed weeks for implementation. Now, companies don’t need the implementation project. They can churn out quickly, while being innovative and compliant.
Whether it’s a new regulation on the horizon, innovative pricing strategies, or ESG reporting becoming routine, AI has you covered.
Get to know about MaxBill AI billing and product catalog at AI-Native Billing and Product Catalog Software.
MaxBill AI billing and product catalog via real-life use cases at How Renewables, EV Charging, and Water churn out quickly with AI billing.
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