A voicebot is a cost-effective option for routine, repetitive phone calls, such as customer verification, order status enquiries, bookings, frequently asked questions, and outbound reminders. Complex, sensitive, and emotionally charged cases are best handled by a human agent. This article explains which types of calls a voicebot can handle and where its limits lie, how well it understands Czech, what a smooth handoff to an agent should look like, and how much deployment costs. It draws on Daktela’s survey of 500 customers in Czechia and 500 in Slovakia, ContactBabel data, and real deployments for Czech customers.
The phone rings, the voicebot answers immediately, identifies what the customer needs, and resolves simpler requests on the spot. Agents are left with far fewer routine queries. In providers’ presentations, this sounds ideal. But phone conversations are particularly sensitive to the quality of automation. Hesitation, misunderstood questions, or unnatural intonation can put customers off within seconds. It is also harder to recover from a mistake during a call than in a chat.
That is why choosing the technology is only part of the decision. What matters more is deciding which types of calls your AI voicebot should handle. A poorly configured bot will not save you money. It will cost you customers who give up after the first failed attempt. In this article, we explore where a voicebot makes sense, how well it handles Czech, how to ensure a smooth transfer to a human agent, and how much it all costs.
The success of automation depends on the nature of the call, not just the bot itself. The more predictable the request and the clearer the process, the more reliably an inbound voicebot can handle it.
Typical examples include routine tasks such as:
But when a call involves a product claim, a complaint, or an emotionally charged situation, the voicebot should step aside. The caller needs a human touch, and any hint of a robotic script will only add to their frustration.
The figures also show that people turn to the phone for help. According to ContactBabel research, the share of customers who prefer the phone for complex queries rose from 28% in 2018 to almost 47% in 2024. When a customer calls about a complicated issue, they expect real help, not scripted responses. The voicebot’s role is therefore to handle routine requests and smoothly hand complex cases over to agents.
A real deployment at Slevomat shows that this approach works. Its inbound voicebot now handles 63% of calls from start to finish without human intervention. Customers with simple questions do not have to wait in a queue, while agents have more time to handle demanding requests.
Understanding written text in a chat is one thing. Processing speech over the phone is considerably more challenging. A voicebot first needs to recognise words despite background noise, accents, colloquial language, and interruptions. Only then can it work out what the customer needs.
Today’s voicebots built on large language models (LLMs) do more than rely on predefined phrases. They understand natural speech and can ask for additional information when needed. But the result still depends on how well the model has been trained on Czech and the quality of the knowledge base it draws on. With poor source material, it may speak fluently but make things up.
Before putting any bot into service, test it with real calls. Do not be taken in by prepared demos in presentations. Call it yourself, speak naturally, stumble over a word or use slang, and see how it responds.
Handing the call over to a human agent is the most important part of the system. Customers have little patience on the phone. If the bot cannot help and does not transfer them to someone who can, they will simply hang up and remain frustrated.
A Daktela survey of 500 customers in Czechia and 500 in Slovakia highlights where companies most often go wrong: 44% of people had to explain their issue again from the beginning after being transferred in the past six months. More than half found this very frustrating, and for more than 60%, it reduced their willingness to return to the company. A voicebot must therefore pass on both the call and its context. The agent needs to see immediately who they are speaking to and what the customer has already told the bot.
Another essential step is to be upfront from the start of the call. People will recognise a bot’s voice within seconds anyway. More importantly, the EU AI Act requires companies to clearly inform users when they are interacting with AI. Make it clear in the first few seconds that the caller is speaking to a voice assistant.
Turn your AI Act obligation into an advantage. Instead of an impersonal notice such as “This call is being handled by artificial intelligence,” explain the purpose right from the start: “Hello, I’m a voice assistant. I’m here to help you avoid waiting in a queue. If I can’t resolve your query straight away, I’ll connect you with a human agent.” The customer sees an immediate benefit rather than a barrier: they know AI is saving them time, so they have no reason to hang up.
One respondent in Daktela’s survey captures the frustration of a poor bot experience: “I called my service provider’s helpline, but only a robot answered. It refused to transfer me to a real person, and I couldn’t even wait in a queue.” This is exactly the situation you need to prevent. Plan how the bot will hand calls over to an agent before you start writing its responses.
Comparing voicebot prices can be complicated because providers use different pricing models. Some charge a setup fee, a monthly fee, and usage fees per minute. Others offer packages or licences per agent, while some include the voicebot in a contact centre package. Whenever you see a price, check exactly what it includes.
At Daktela, pricing is based entirely on call minutes, and our price list is public:
A voice channel licence costs an additional CZK 750 per user per month.
When planning your budget, factor in additional costs such as preparing call flows, integrating with your CRM or ERP system, and keeping source material up to date. The quality of that material determines whether the bot helps customers or simply wastes their time.
The return on investment is more than theoretical. According to a Deloitte Digital survey of 600 organisations, 39% of companies already report a lower cost per contact thanks to AI, and 43% expect AI to reduce their contact centre costs by at least 30% within three years.
A voicebot does not have to wait for incoming calls. It also works well in outbound campaigns, where it can call contacts from a database, handle a simple task such as reminding someone of an appointment or checking their interest, and transfer the customer to an agent after completing the initial steps.
The main advantages are speed and capacity: a bot can handle hundreds of calls simultaneously. But the same rule applies here. The call flow must be straightforward, and a human agent must be immediately available when a customer asks for more detailed information.
One real-world example is the outbound voicebot at Swiss Life. It calls clients who have not been in touch for some time and books appointments with financial specialists. The bot handles the entire process independently, achieving a sales success rate of 7%, comparable to that of human agents. Monthly calling costs fell by more than 70%, saving the company the equivalent of two full-time roles.
A voicebot’s role is to relieve agents of repetitive work. It makes sense when you handle a high volume of recurring queries, have clear processes in place, and can provide quality data for the bot to draw on. Leave complex or emotionally demanding calls to human agents.
Before making your final decision, test its understanding of Czech with real calls, check how it hands calls over to a person, and calculate your expected usage in minutes. If these three areas meet your needs, a voicebot can save you time and money while maintaining a good customer experience.
