For a long time, text-based chatbots and digital assistants were in the spotlight – but now AI telephone agents are rapidly conquering the market. International studies and observations show that companies are increasingly relying on voicebots in customer service to improve telephone accessibility and reduce costs.

In German-speaking regions, telephone automation was long considered a niche solution, but that is changing rapidly. A current survey shows that hundreds of medium-sized companies in Germany and Austria have introduced AI telephone assistants within a few months.

Nevertheless, many companies still have catching up to do. According to a study, almost 45% of German SMEs had no contact with AI technology at all by 2025 – ignorance and reservations are accordingly widespread.

But the tide is turning: Success stories like the mentioned solar company are generating attention. Those who dare to take the first steps with telephone AI today can secure an innovation advantage in their own market.

Initial concerns that customers might react negatively if they realized they weren't speaking to a human have largely not materialized. On the contrary, many companies report that customers often don't even notice they are talking to an AI – provided the voice quality is good and the dialogue is intelligently conducted.

The voice now sounds natural, and the agent responds context-aware. Dialects or highly emotional customers are still a challenge – intensive development is underway to cover such cases as well.

A crucial advantage of the AI solution is its scalability without linear cost increases. Personnel would have to be maintained for peak times or compensated with overtime – AI scales dynamically, eliminating overload and waiting times.

Frequently recurring questions are excellently suited for AI phone agents. Such routine inquiries can be answered quickly and correctly by an AI, without customers being stuck in waiting loops.

AI agents can process hundreds of telephone contacts per day, significantly scaling marketing or sales campaigns. They call prospects who have requested information online to assess their needs and timing (qualifying). Or they reactivate cold leads: old customer contacts are called again to check for current interest.

The solar company in the example generated 80–200 fresh customer inquiries per day in this way, and six-figure revenues from contacts that had been dormant for years. This volume was unattainable for human sales teams – AI made it possible.

These components must be orchestrated, meaning a seamless flow of conversation logic must be ensured. In practice, many providers offer integrated orchestration: you get a platform where you can configure prompts, flows, and integrations, and the platform handles real-time communication between the modules.

A decisive factor for the technology stack is its integration into the existing IT landscape. The voicebot should be connected to CRM, ERP, ticketing systems, etc., to exchange information.

The multitude of platforms makes selection a challenge. It is advisable to proceed comparatively. As a rule of thumb: don't just look at the per-minute price, but at the overall package.

The successful introduction of an AI voice agent requires not only technology but also coordinated project management. Below, we outline an exemplary 30-day plan (approx. 4 weeks) for implementation that has proven successful in practice.

For an AI telephone agent to act reliably and in line with the company's objectives, the design of its "personality" and policies is crucial. In generative models like GPT, the so-called prompt (system prompt or initialization text) plays a central role.

The system prompt should clearly state who the agent is and in what context it operates. A consistent identity ensures the AI reacts authentically in every situation.

It is often helpful to provide examples or explicit instructions in the prompt on how to handle specific situations. For instance, exemplary question-and-answer pairs can be added to the prompt. This helps the model understand the expected level of detail and tone.

An important part of the conversation policy is how to handle ambiguities and errors. One should define: What does the bot do if it doesn't understand something?

Especially in the German market, it is advisable for the bot to transparently clarify from the outset that it is an AI, without disturbing the flow of conversation. Often a phrasing like: "Good day, this is the digital assistant from Company X. How can I help you?" is chosen – this informs the customer.

The prompt should therefore ensure that the agent never makes false claims about its identity. If a customer asks "Are you a robot?", the AI should answer truthfully ("I am an AI-based telephone assistant here to help you.").

Many Voice AI agents allow the use of tools – e.g., a knowledge base query, web search, or calculations. The prompt can define when the AI should use which tools.

After the go-live of an AI phone agent, it is essential to continuously monitor its performance and quality. This is the only way to ensure that the intended goals are achieved and the user experience is satisfactory.

This KPI indicates how many calls could be fully resolved by the AI agent without human intervention. A high automation rate means cost savings and efficiency – however, it must be interpreted with caution: 100% is not realistic and not even desirable, as in some cases, human intervention is intended.

In customer service, FCR is a key metric – it measures whether a customer problem was definitively resolved in the first call. For AI agents, this means: Could the bot (possibly with handover to a human) resolve the issue in the same call?

It is therefore also considered successful if the bot forwards the call, but the customer achieves their goal in that same phone call without having to call again. FCR can often be improved through AI, for instance, because the AI immediately provides the relevant information or, if it forwards the call, already informs the colleague (no need for repeated problem description).

The satisfaction of callers is perhaps the most important quality indicator. It can be classically measured via surveys – e.g., at the end of the call: "How satisfied are you with our service (scale 1–5)?" or follow-up SMS/Email.

For existing customers or inquiries from interested parties, the AI agent can, of course, be used, but even there, the following applies: for fully automated calls (without human intervention), it should be clearly stated at the beginning who is calling and for what purpose.

Furthermore, the Telecommunications Act stipulates that for telephone contacts, the number display must be correct and the consumer must not be deceived. Therefore, it is best to use your official phone number as the Caller ID.

A medium-sized solar company had collected around 10,000 leads over ~12 months – e.g., people who had expressed interest in solar systems at trade fairs or online. Due to limited sales capacity, these contacts were never systematically followed up. They lay dormant as "cold" entries in the CRM.

The figures from the first few weeks were impressive: Per day, the AI agent generated 100 to 300 fresh customer inquiries (qualified leads who were interested and wanted an appointment). Over a few weeks, this led to over €140,000 in additional revenue for the company.