Polish company Netwise S.A. joins the collana Group
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Paul M.
13 August 2026
AI in customer service means that an AI application independently understands and processes incoming customer enquiries: by telephone via an AI voicebot, via text via AI chatbots, or in the background through automated prioritisation and summarisation. Unlike traditional menu-based systems, today’s AI systems recognise natural language and the intent behind a query, rather than following a rigid set of rules.
The expectations placed on artificial intelligence in the service sector are high, and so are the challenges involved in its implementation. That is why this article focuses less on the theory and more on the practical aspects: how an AI voicebot works from a technical perspective, what it has achieved in a real-world project, what AI cannot do, and what you should bear in mind when implementing it.
After a Bitkom survey from March 2026 In Germany, 41 per cent of companies with 20 or more employees now use artificial intelligence, whilst a further 48 per cent are planning to use AI or are discussing its use. Customer service is one of the areas where the benefits of AI are most quickly apparent, because the volume of enquiries is measurable and a large proportion of customer enquiries are recurring.
In customer service, two types of AI tools are used, which are often lumped together. AI chatbots process text – via website chat, messaging apps or email. An AI voicebot processes spoken language, usually over the phone. Both AI tools can access the same data, but the voicebot solves the problem that most support teams actually face: the telephone helpline.
From a technical perspective, several components work together in an AI voicebot:
Real-time speech recognition (speech-to-text) converts spoken words into text whilst the caller is still speaking
An LLM-based dialogue control system recognises what the conversation is about and decides on the next step in the conversation
A workflow engine carries out the actual task: checking the status, completing the process, updating the data
Text-to-speech returns the answer in natural language
The key difference between AI and a traditional IVR system of the „Press 1 for …“ variety lies not in the voice, but in the freedom of input. An AI voicebot allows for natural language input, recognises intentions and context, and operates using customisable workflows. The traditional system guides users through a rigid menu structure, operates on a rule-based system and offers only limited customisation. For customers, this makes the difference between getting immediate answers and experiencing frustration due to lengthy navigation.
AI is only as good as the data it is allowed to access. A stand-alone solution can provide information, but it cannot carry out any tasks. Only by connecting to the core system – such as an ERP system like Microsoft Dynamics 365 Business Central – can the AI recognise the caller by their phone number during the call, assign an order status, retrieve invoice details or view master data. What starts as a simple enquiry then becomes a fully completed transaction.
With over 1,000 vehicles in more than 40 cities, Carl und Carla is one of Germany’s leading providers of flexible van hire. As the business grew, so did the strain on customer service: particularly at weekends and outside business hours, the volume of calls rose dramatically, reaching up to 200 calls a day. Most customer enquiries were recurring: return procedures, vehicle usage and general queries. The service team was constantly stretched to the limit; genuine 24/7 availability was not feasible, and waiting times and operating costs rose.
Rather than automating the entire customer service process, a single procedure was singled out: vehicle return, one of the most common and time-sensitive use cases. The AI service agent ILAI identifies callers by their telephone number or number plate, retrieves location-based vehicle data – such as GPS position and fuel level – and guides them through the return process via natural language dialogue. Once the process is complete, ILAI can even lock the vehicle remotely.
Within six weeks, around 90 per cent of the returns processes had been fully automated. The average call duration was halved, and the cost per call fell to less than one euro. Since then, customer service has been available round the clock, in multiple languages, without overburdening the team.
„Thanks to the AI-powered call centre agent, our customers can now return their vehicles in just a few minutes, with no waiting time at all. This takes a huge load off our team, as routine enquiries are handled automatically and reliably.“ Gregor Wendt, CTO at Carl and Carla.
The lesson to be learnt from this AI project is less about the percentage and more about the scope: it was a success because a clearly defined, high-frequency process was chosen – rather than customer service as a whole.
The proven areas of application for AI vary more according to the type of process than according to the sector. They are particularly well suited to customer enquiries that occur frequently, are clearly structured and require system access:
E-commerce and wholesale: placing orders, initiating returns, checking delivery status
Production and Manufacturing: Helpdesk for internal IT enquiries, supplier communication
Healthcare: Booking appointments and responding to common patient enquiries – in other words, administrative tasks, not medical advice
Availability with no waiting time. AI processes enquiries even outside business hours, without a shift rota and without queuing.
Taking the strain out of routine tasks. Recurring tasks tie up fewer staff, leaving more time for complex issues. This is particularly relevant in situations where service centres are difficult to staff.
Parallel processing during peak loads. An AI system handles several conversations at once, meaning that peak demand at weekends or following a campaign no longer automatically results in long waiting times.
Consistency and traceability. Every process follows the same steps and is documented. This facilitates evaluation and quality assurance and measurably improves the quality of service.
AI is no substitute for a service team, and providers who promise otherwise are selling an expectation that does not hold up in practice. Based on practical experience, there are three key limitations.
Emotional and unusual issues require people. Complaints, claims and exceptional situations: what counts here is sound judgement, not strict adherence to procedures. A well-designed AI system recognises this and escalates the matter rather than trying to resolve it itself.
A proper handover is a requirement, not a matter of convenience. When the AI hands over, the context of the conversation must be carried over. Otherwise, the customer has to start from the beginning – and the result is worse than if there were no automation.
Without access to the data, we are limited to providing information. If you don’t integrate AI tools with the leading systems, you’ll end up with a better voice announcement system, not automation.
What matters is not the label ‘GDPR-compliant’, but what the AI actually does: In which data centre is the processing carried out? What data is actually collected – is a telephone number and a reference number sufficient, or is a profile created? Are the AI’s actions documented and auditable? At ILAI, for example, processing takes place in a German data centre and only the data required for the transaction is processed.
Both options are possible. The choice should be based on the sensitivity of the data being processed, not on a matter of principle. For environments where data must not leave the premises, running local AI models is the appropriate option.
A single, high-frequency use case provides reliable figures more quickly than an AI programme across all channels. The example of Carl and Carla began with just one transaction.
Artificial intelligence in customer service is changing roles. Those in the service team who understand that it is routine tasks that are being phased out – and not their own roles – will support the introduction of AI.
Before you begin, decide how you will measure the benefits of AI – or, in this case, an AI voicebot: the proportion of automated processes, call duration, cost per enquiry, and availability. Without baseline figures, you will later lack a basis for comparison and thus the foundation for deciding whether to implement further use cases.
No. AI handles repetitive, clearly structured tasks. Human support remains essential for unusual or emotionally charged issues. Good AI systems, such as a voicebot or service agent, forward such cases to the appropriate person whilst passing on the context of the conversation.
AI chatbots process text, whilst a voicebot processes spoken language – usually over the telephone. Technically, both essentially use the same language models; the voicebot incorporates speech recognition and text-to-speech capabilities.
That depends on the specific use case and the system integration. In the project with Carl and Carla, around 90 per cent of the returns processes were automated within six weeks – provided that the process was clearly defined and had existing data interfaces.
The decisive factor is not the size of the business, but the volume of recurring customer enquiries. If a single process regularly ties up the team, the investment often pays off even with a moderate number of cases.
As well as the language model itself, you need speech recognition, text-to-speech, a workflow engine and, above all, interfaces to the systems where your customer data is stored. A single tool is rarely enough – it is the interaction between them that is crucial.
A few key performance indicators: the proportion of fully automated processes, average call duration, cost per enquiry and availability outside business hours.
Whether an AI voicebot is worthwhile for your customer service depends on a specific process, not on the technology itself. If you’d like to find out which use case might be suitable for your business, we’ll work with you in the AI Discovery Workshop to identify the first viable use case.
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