The municipal utility of the future with the Voice AI Agent: No more waiting on hold! AI on the phone is changing the service provided by energy suppliers

No more waiting on hold: AI on the phone – A pilot project from Siegburg becomes a blueprint for the industry

The energy transition is pushing many municipal utilities to their limits: Growing demand due to new legislation, solar installations, and dynamic tariffs is colliding with a critical shortage of skilled workers. To withstand this pressure and significantly relieve the burden on service staff, Rheinische Energie AG (rhenag) is taking a pioneering step. By deploying state-of-the-art voice AI agents, standard requests such as meter readings or payment adjustments will be handled fully automatically over the phone – with a natural voice and without cumbersome keypad menus. This ambitious pilot project at the Siegburg location is intended not only to increase its own efficiency but also to serve as a blueprint for the entire German municipal utility landscape. But how can the delicate balance be struck between maximum technological automation and the human customer focus that is so essential for regional energy providers?

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Voice assistants instead of waiting loops – the silent revolution of the municipal utilities

Rheinische Energie AG (rhenag) is pushing ahead with the introduction of voice AI agents in customer service in order to automate standard requests such as meter readings and payment adjustments in the future, thereby freeing up capacity for individual consultation.

A pilot project with symbolic significance for an entire industry

At its Siegburg location, where its energy services and electricity and gas distribution business is based, rhenag is currently testing a voice solution that will be made available to other municipal utilities in the future. The background to this project is anything but trivial: The ongoing energy transition is leading to a noticeably increasing number of customer inquiries for municipal utilities and established energy service providers. In addition to classic requests such as changes to monthly payments or meter readings, increasingly complex questions are arising concerning smart metering systems, photovoltaic systems, balcony power plants, as well as political changes, local construction sites, and current offers. This complex situation is impacting an industry that is already under considerable structural pressure. According to the 2025 municipal utility study by BDEW and EY, 86 percent of the surveyed municipal utilities rate the impact of existing legal and regulatory requirements on their operations as rather to very negative, while the bureaucracy cost index for the energy sector decoupled from the overall economy by around 30 percent between 2021 and 2023.

Especially in times of great uncertainty, long-established, regionally rooted utility companies gain importance for consumers, because trust becomes a scarce resource in times of change. However, this position of trust alone is not enough if operational processing capacity can no longer keep pace with the volume of inquiries. Expanding the service business is one of rhenag's key strategic priorities, and the introduction of a voice AI agent fits logically into this direction.

How a voice bot separates routine work from genuine consultations

The principle behind the new solution is remarkably pragmatic: An AI-powered voice assistant is designed to competently and independently answer and process recurring, standardized requests such as meter readings. This frees up well-trained service staff to dedicate more time to clarifying individual contract-related questions that cannot be forced into a rigid question-and-answer format. The division of tasks within the group follows a clear logic: While the development of standardized IT applications takes place centrally in Cologne, rhenag Energie in Siegburg acts as a practical and pilot user for various solutions. The field test is not limited to a single company but is running concurrently at rhenag's network subsidiaries Rhein-Sieg Netz and Westerwald-Netz, thus providing a broader data basis and more robust insights into the technology's transferability.

The motivation for this approach stems from an experience shared by many municipal utilities: A growing shortage of skilled workers, noticeable cost pressures, and increasing customer demands for rapid processing are industry-wide phenomena that affect every utility. This assessment aligns with independent analyses by the HR consultancy Hays, which indicate that the energy sector could face a shortage of more than 400,000 workers by 2030, while over 70 percent of the current core workforce of utility companies is expected to retire by then. The advantage of an internal pilot phase is clear: Uncertainties and technical challenges during implementation can be identified and resolved more quickly in a controlled environment before a further developed version of the solution is made available to other municipal utilities.

Siegburg as a blueprint for the entire municipal utilities landscape

In this context, rhenag Energie explicitly sees itself as a model municipal utility, implementing new solutions, testing them in ongoing operations, and then distributing the validated version to other municipal utilities. This role as a testbed for the entire industry is economically sound: The companies participating in the pilot project effectively act as a purchasing group for the underlying IT services, allowing interested municipal utilities to benefit from attractive conditions and access applications already proven in practice, instead of starting from scratch. This model is complemented by standard use cases that can be reused directly without costly individual development.

The real bottleneck this strategy aims to address is not a general lack of personnel, but rather their misallocation. Highly qualified service staff, who want to develop customized solutions for customers' homes, actually spend a significant portion of their working time on repetitive, standard requests such as adjusting monthly payments. This misallocation can be illustrated with concrete figures: rhenag Energie alone receives around 100,000 inquiries per year concerning meter readings and monthly payments. The company anticipates around 400,000 processing transactions and approximately 200,000 telephone calls by 2026. These figures demonstrate the automation potential inherent in seemingly trivial routine processes when considered in relation to the total number of customer interactions.

Also noteworthy is the benchmark against which customer expectations are now measured. Service standards are no longer defined by one's own industry or local competitors, but rather by the way large e-commerce retailers treat their customers, significantly shaping expectations of 24/7 availability. Energy suppliers measured against this benchmark can hardly afford to offer service hours from 9 a.m. to 5 p.m. on weekdays without risking customer dissatisfaction. An analysis by TMT Labs underscores this finding, revealing that German energy suppliers handle over 120 million customer contacts annually, with 41 percent of customers dissatisfied with the availability.

Human connection despite artificial intelligence – the fine line

For the technological implementation, rhenag is already successfully using a solution from the providers Parloa and logen.ai at its network subsidiaries Rhein-Sieg Netz and Westerwald-Netz, which will now also be used at rhenag Energie. The crucial difference to classic text-based chatbots lies in the voice quality: The voice AI agent offers a human-sounding voice that conveys the impression of a genuine conversation, instead of confronting customers with recognizably synthetic robot language. A built-in safety mechanism is essential: As soon as the system detects that a request deviates from the predefined standard, it immediately forwards the conversation to a human service representative.

This routing logic is not a technical footnote, but the core of the entire strategy. The underlying conviction is that artificial intelligence should support, not replace, humans. Customers should feel that they are dealing with a company where real people are there for them and have time for them, because when it comes to individual and complex questions, very few people want to speak to a system that seems like a robot. This assessment aligns with the experiences of other energy providers: In EnBW's flagship project, for example, it was shown that voicebots can currently resolve around 20 percent of cases completely automatically, while 80 percent are still referred to human advisors. This illustrates that voice AI in the energy sector currently functions less as a complete replacement and more as an intelligent pre-filter and efficiency driver in the background.

The actual rollout at rhenag is proceeding in deliberately phased steps to minimize risks and systematically build up experience. Initially, the voice bot will be used for the majority of applications, such as meter readings, which are particularly frequent in the run-up to the annual billing. In the second step, the voice AI agent will handle changes to monthly payments. The third expansion phase at rhenag Energie will activate a concierge service that intelligently pre-sorts customer inquiries: Callers simply need to describe their issue in their own words and will then be automatically forwarded to the appropriate department. Cumbersome, rigid voice menus with instructions such as pressing specific number keys are completely eliminated. Particularly sensitive topics, such as fault reports, are explicitly excluded from the system and immediately forwarded to human employees or directly to the responsible control center, demonstrating that security aspects clearly take precedence over pure automation logic in the system's design.

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The economic calculation behind the cross-group cooperation

Even during the ongoing development phase, rhenag maintains close contact with interested municipal utility partners to tailor the solution to their specific needs from the outset. The service offering extends beyond pure software development to include setup, hosting, and technical integration with the existing IT systems of the respective partner companies, making early communication about local conditions particularly important. Simultaneously, internal development of various modules for different application requirements is underway, indicating a modular platform strategy that can be flexibly adapted to the size and IT landscape of each municipal utility.

From an economic perspective, this approach follows a pattern that has long been established in the platform economy but is still relatively new in the energy sector with its traditionally fragmented municipal utility structure. Instead of each individual utility developing its own AI solution independently, with its own budget and risk, the pooling of demand creates a purchasing group that can realize economies of scale in licensing costs, implementation effort, and ongoing support. For smaller municipal utilities, which often lack both the necessary IT personnel and the development budget for an independent AI strategy, this model offers pragmatic access to technologies that would otherwise be reserved for larger corporations. At the same time, by positioning itself early in the market as a provider of a proven solution, rhenag secures a strategically advantageous role in the changing competitive landscape of the industry, where service businesses are gaining increasing importance.

From a cross-industry perspective, the investment in voice AI can also be interpreted as a response to structural cost pressures that extend far beyond individual companies. Market analyses of AI-supported customer service applications show that automated systems can resolve a support request for a fraction of the cost of a human agent, enabling companies to achieve significant cost savings and a noticeable reduction in the workload associated with routine inquiries. If such efficiency gains can be transferred, even partially, to the energy sector, this would represent a considerable economic leverage, especially considering the aforementioned inquiry volumes of several hundred thousand cases per year for a medium-sized regional utility alone.

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Between efficiency gains and cultural reticence – a critical assessment

Despite the justified enthusiasm for efficiency potential, this development also deserves a sober, critical examination. Historically, the energy sector is a conservative, highly regulated, and reliability-oriented industry where trust has traditionally been built through personal, long-term customer relationships. The introduction of voice AI in this sensitive environment therefore carries a certain reputational risk should the technology fail to deliver the promised user experience in practice or lead to frustration with more complex issues. The conscious decision to use the bot exclusively for clearly defined standard cases and to immediately forward any deviation to a human operator is thus less a technical limitation than a strategic risk mitigation strategy that deliberately prioritizes long-term trust over short-term automation gains.

At the same time, a look at other market participants shows that rhenag is by no means alone in pursuing this strategy. Numerous specialized providers of voice and chat AI for the energy sector, for example in the areas of meter reading, tariff information, and fault management, have emerged in recent years, indicating a broad, industry-wide convergence toward conversational AI. Large corporations like EnBW have also already achieved measurable results with their own voicebot and agent-assist solutions, such as automated call summaries that save around 45 seconds of processing time per call and, with several hundred thousand calls annually, result in significant cumulative efficiency gains. These examples suggest that the question of whether voice AI will be used in customer service within the energy sector is no longer whether the technology will be deployed, but rather how and when it will be implemented.

Another critical aspect concerns job security and acceptance among existing service staff. Official communication consistently emphasizes that the move is not about job cuts, but rather about redistributing personnel resources towards higher-value, more individualized consulting tasks. Given the documented shortage of skilled workers in the energy sector, confirmed by numerous independent studies, this argument seems quite plausible, as the industry currently lacks not so much tasks requiring qualified personnel as a sufficient number of qualified personnel themselves. Nevertheless, it remains to be seen whether this redistribution logic can actually be implemented in practice over the years, or whether economic pressure will ultimately lead to actual staff reductions in the affected service departments once the technology is sufficiently advanced to handle more complex inquiries independently.

Will the voice chatbot become the new standard at municipal utilities?

The rapid spread of voice AI solutions in the German energy sector strongly suggests that automated voice assistants are likely to become an integral part of customer service at municipal utilities within the coming years. The combination of a structural shortage of skilled workers, increasing regulatory requirements, growing inquiry volumes due to the energy transition, and changing customer expectations regarding accessibility is creating economic pressure that few utilities can permanently avoid. The rhenag model, with its combination of internal piloting, group-wide scaling, and a deliberately conservative, trust-based automation philosophy, provides a remarkable blueprint that differs significantly from purely technology-driven approaches.

Crucial for the long-term success of such projects will be whether it is possible to maintain a balance between efficiency gains and personal interaction, rather than gradually shifting it towards pure automation under cost pressure. The coming years will show whether the model municipal utility strategy can actually be transferred across the board to other municipal utilities, or whether individual IT landscapes, differing customer structures, and regional characteristics will ultimately impose stricter limits on this scaling ambition than currently assumed.

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