Customer service in the energy industry is undergoing profound change. Energy suppliers face a multitude of simultaneous challenges: increasing regulatory requirements, complex market processes, growing data volumes, and a significant rise in customer service and communication expectations on the customer side. At the same time, a shortage of skilled workers and cost pressure are exacerbating the situation in the service units.

In many organizations, it is becoming apparent that evolved structures and historically developed system landscapes can only meet these requirements to a limited extent. Customer concerns are spread across different channels, information is available in other systems, and processes are often not consistently digitized. This makes it challenging to process service cases quickly, consistently, and transparently.

Customer service is thus increasingly developing from a pure processing area into a strategic success factor - with direct effects on customer satisfaction, efficiency, and the reputation of energy supply companies.

Many energy suppliers rely on established CRM systems for customer service, which were initially designed for other industries or for simpler business models. Although these systems offer basic functions for customer and process management, they often reach their limits as soon as the specifics of the energy industry come into play.

Typical challenges include:

• the mapping of energy industry data structures such as metering points, metering locations, delivery points, or contracts,

• the linking of network, sales, and service processes,

• compliance with regulatory deadlines and requirements,

• as well as the integration of external specialist systems, for example, for billing or market communication.

In practice, this often leads to a high level of manual effort, media disruptions,ons and a lack of transparency. Employees spend a considerable amount of time researching, maintaining data, and coordinating across systems - time that is diverted from actual customer support.

Artificial intelligence is increasingly seen as a means of scaling existing customer service processes in the energy industry. This is less about replacing human labor and more about providing support for recurring, time-consuming tasks.

Typical fields of application are

• the automatic classification of incoming requests,
• the summary of transaction histories,
• the prioritization of cases based on defined criteria,
• as well as support in processing through structured action suggestions.

Used correctly, AI can help shorten processing times, reduce errors, and relieve employees in targeted ways. However, this requires close integration with the energy industry's existing processes and data models.

The quality of address and contract data directly impacts customer service. Returns, incorrect assignments, or failed deliveries not only cause additional costs but also increase processing costs and customer dissatisfaction.

Automated checking and validation mechanisms can provide support here by continuously checking and providing data. The aim is to identify sources of error early and make subsequent processes more stable. Regulatory requirements can also be better adhered to when deadlines and responsibilities are displayed more transparently.

Customers in the energy industry are highly willing to switch. Price comparisons are easily accessible, and tariffs are increasingly interchangeable. Against this backdrop, customer service is coming more into focus as a differentiating feature.

Customer loyalty is not created solely by traditional loyalty programs, but by consistent, reliable, and appreciative interactions across all contact points. Personalized communication, proactive information, and a smooth service process help to build trust and stabilize long-term relationships.

To meet these requirements, energy suppliers are increasingly relying on industry-specific CRM approaches that incorporate energy industry processes and data models from the outset. Such solutions aim to expand standard CRM functionality with industry-specific logic and to integrate them more closely with backend systems.

One example of such an approach is the AEP.EnergySuite is based on Microsoft Dynamics 365. It combines an energy industry data and process model with automation functions and AI-supported customer service support. These include automated inbox processing, support for case processing, and mechanisms to ensure data quality.

What is important here is not so much the specific product as the underlying approach: the consistent alignment of CRM systems with the real requirements of the energy industry and close integration into existing system landscapes.