A customer submits a meter reading via a service portal.

Self-Service Automation: Standard Requests That Resolve Themselves

Give your customers a self-service solution that handles standard requests such as meter readings or instalment adjustments immediately. AI identifies, checks and processes each request automatically.

Discuss the use case

Industry

Energy & Utilities

Business Unit

Customer Service, Billing

Data Sources

CRM, Billing System, Customer Portal

01 · Initial Situation

Standard Requests Consume Service Capacity Despite Being Rule-Based

Meter readings, payment instalment changes and billing requests account for a large proportion of service contacts for energy providers. While the processes are clearly defined, they are still handled by phone and email, then recorded, assigned and transferred to the billing system manually. This creates delays, transmission errors and contacts that prevent the service team from focusing on cases that genuinely require clarification. For customers, it means waiting for a process they could easily complete themselves.

02 · How the AI Works

From Reported Value to Completed Process

The four steps take a standard request from secure assignment and validation through to automatic posting – without any manual intervention.

01 · Identification

After logging in, the system securely assigns each request to the correct customer, contract and delivery point. This ensures every case is clearly referenced and authorisation-checked from the outset.

02 · Plausibility Check

The reported value is checked against consumption history, expected ranges, the meter reading date and special rules. The system identifies duplicates and outliers before they reach billing.

03 · Dark Processing

For plausible cases, the AI automatically transfers the information to the billing system and waits for its confirmation. Customers receive immediate feedback without a service case being created.

04 · Exception Management

Only implausible values, missing permissions or assignment issues create a case. It reaches the service team in a structured and prioritised format.

03 · Example Output

This Is What an Automatically Processed Meter Reading Looks Like

A customer submits their current electricity meter reading through their energy provider’s service portal.

Client: Andrea Sommer

Customer Number: KN45678

Meter Number: 1ISK0012345

Delivery Address: Hauptstraße 137, 12345 Hauptstadt

Previous Meter Reading: 42,318kWh | Reading Date: 14 January 2025

New Meter Reading: 44,902kWh | Reading Date: 8 January 2026

Consumption: 2,584kWh

Status: plausible

Example illustration generated from a meter reading report and CRM data.

04 · The Insight

The Greatest Impact Is Achieved Not in Contact Management, but in Billing

With the AI agent for self-service automation, calls and emails relating to standard enquiries are eliminated. Waiting times decrease, while the service team gains time for cases that genuinely require advice. At the same time, data quality receives a significant boost. Every automatically processed case writes a validated, plausible value back to the system – in real time and without transfer errors.

Accurate meter readings and advance payments mean fewer errors in billing. Fewer errors mean fewer cases requiring clarification, fewer enquiries and fewer correction cycles. The benefits therefore extend to areas that are no longer directly connected to the original interaction.

However, this impact is only as effective as the underlying rule set. Validation rules, value ranges and exception logic determine which proportion of cases can be processed automatically and which should remain with a team member. When calibrated effectively, the rate of fully automated processing increases over time, as every exception reveals where a rule needs refining. Self-service therefore evolves from a measure that simply reduces workload into a lever that strengthens the data foundation of your customer service with every case.

Components Used