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Chatbot vs Voicebot: Where to Use Each One in 2024?

Both chatbots and voicebots can enhance customer experience, streamline rule-based business operations, and offer accessibility to the user on multiple channels.

However, executives might not know the intricacies that make each tool suitable for a specific process, leading them to pick the wrong conversational AI bot, and incur:

  • Direct costs
  • Operational disruptions
  • Training costs
  • Replacement costs
  • Opportunity costs

This article explores the differences between chatbots and voicebots, and explains the processes each of these artificial intelligence tools are suitable for.

What is a chatbot?

A chatbot is a software application that enables text-based machine-to-human interaction thanks to natural language understanding (NLU), and natural language processing (NLP). Chatbots’ flexibility makes them deployable across a wide range of use cases in the customer journey and on various communication channels.

What is a voicebot?

Voicebots, often referred to as voice assistants, are software designed to engage with users through spoken words. Voice bots use automatic speech recognition to process and deliver verbal responses to user commands and queries.

What’s the difference between chatbots and voice bots?

Although chatbots and voice bots offer a conversational interface, they differ on numerous axes:

  1. Mode of interaction:
    • Chatbots: Interact with users through text. Users type and receive responses in text form
    • Voicebots: Interact with users through spoken language. Users speak and the bot responds audibly.
  2. Underlying technologies:
    • Chatbots: Rely on text processing, which involves tokenization, intent recognition, and machine learning models (for intelligent and generative chatbots).
    • Voicebots: Voice bots use NLP as well as Automatic Speech Recognition (ASR) to convert speech to text and Text-to-Speech (TTS) to revert text to speech.
  3. Platforms & devices:
    • Chatbots: Commonly found on websites, messaging platforms (like Facebook Messenger, WhatsApp, or Slack), and some mobile apps.
    • Voicebots: Commonly deployed on smart speakers (like Amazon Echo or Google Home), smartphones as voice assistants (like Siri or Google Assistant), and other voice-activated devices, like in-car systems.
  4. User experience:
    • Chatbots: Provide visual feedback and options to choose from, making navigation simpler. They may also incorporate images, videos, and clickable buttons.
    • Voicebots: Offer an entirely auditory experience, which can be more intuitive for some tasks and simpler because it’s hands-free.
  5. Accessibility:
    • Chatbots: Accessible to anyone who can read and type, making them widely usable.
    • Voicebots: Particularly beneficial for visually impaired individuals, providing a more inclusive user experience.
  6. Development considerations:
    • Chatbots: Developers focus on text processing, dialogue management, and possibly integrating rich media (images, videos). Read more about chatbot architecture.
    • Voicebots: Developers need to handle potential issues like background noise, varied accents, speech idiosyncrasies, and ensure smooth voice interactions with clear and understandable synthetic speech.

How does a chatbot work?

Here’s a step-by-step breakdown of how a typical chatbot works:

  1. Receiving input: Users send a message
  2. Processing the input:
    • For rule-based chatbots, the bot pits the input against the knowledge base
    • For AI-powered chatbots, the input goes through several layers. GPT-3, for instance, has been trained with 48 layers, by its own admission (Figure 1).
Screenshot shows ChatGPT's answer to a question. A difference between chatbot vs voicebot is that chatbots have so far been more intelligent than voice bots.
Figure 1: ChatGPT has been trained on models with as many as 48 layers. Source: ChatGPT
  1. Understanding user intent:
    • Rule based chatbots have knowledge bases, keyword matching, decision trees, and “menu-driven” interactions
    • AI chatbots use tokenization, entity recognition, and intent recognition
  2. Response generation:
    • Rule-based bots get a predefined response from the database that matches the command. Or if they should execute a task, they follow a series of steps that correspond to an input
    • AI bots can either select a response from the recognized intent, or generate a new response on the fly using deep learning models.
    • Note: For all chatbot types, if they are integrated with external APIs or databases, they could include those data in their answers

How do voicebots work?

  1. Receiving input: User activates the voice bot, often with a wake phrase like “Hey Siri,” “OK Google,” or “Alexa.” Device’s microphone captures it.
  2. Automatic Speech Recognition (ASR): Voice bot converts the audio into machine-readable text.
  3. Intent understanding: After input is transcribed, through the same process as a chatbot, the voice bot understands it.
  4. Query processing: Depending on the identified intent and entities, the voice bot processes the request. This might be:
    • Fetching information from a database, or the internet.
    • Generating a new answer.
    • Controlling smart devices (like turning on lights or adjusting a thermostat).
    • Performing an action, like setting reminders or making a calculation
  5. Generating a response, Text-to-Speech (TTS) conversion, & output: Once the bot has the information or has performed the task, it formulates a relevant textual response. It’s then converted back to audio with TTS and played to the user through the device’s speakers.

Which processes are chatbots and voicebots useful for?


Because of their overarching function of facilitating conversation, voicebots and chatbots may seem to be deployable in the same use cases. However, not every website that has a chatbot also has a voicebot, and there are various reasons for this:

  • Cost: Voicebots cost more than chatbots because of, for example, their more complex backend technology of speech understanding, using more bandwidth to process the human speech to text, or being easier to train. Learn more about how chatbots are priced.
  • Usability: Chatbots can be considered more accessible and easier to use because typing is already ingrained in our daily lives
  • Input clarity: Chatbots process text, so there’s less chance of misunderstandings. This is unlike voicebots trying to understand different dialects or blocking out the background noise
  • Privacy: With chatbots, there’s data leakage potential. But there’s no possibility of eavesdropping, or picking up users’ conversations
  • Integration: Chatbots can be deployed on websites and integrated with a multitude of messaging apps.
    • That’s not the case for voicebots, where because products like Google Home or Alexa are the most used ones, companies offering a virtual assistant would probably have to partner with a voice assistant company with established market presence. There might be voice bot-equipped websites, but we haven’t come across any good ones.

The combination of these make chatbots more suitable for high-volume, fast-paced, instant problem solving scenarios, like at a contact center.


Through elimination; therefore, we conclude that voicebots are useful for:

  • Hands-free and multi-tasking scenarios where hands are already occupied, like while driving
  • Entertainment and leisure where there’s no hurry and natural sounding conversations are preferred
  • Where the primary mode of contact is with calls. This could either be from the demand side (the elderly, for instance) or the supply side (like emergency response systems).
  • When privacy is guaranteed and there’s no chance of eavesdropping, like at one’s home and using the voice assistant to control the lights, for instance. (Although, as we said, smart speakers have been proven1 to sometimes be on all the time and collecting users’ conversations for advertisement, purposes, for instance)

We should note that the company has started working on a smart speaker prototype that leverages OpenAI’s GPT model to allow a conversational experience of using ChatGPT around the house. But it’s still not finalized yet.

Further reading

To learn more about conversational AI, read:

If you are ready to invest in a conversational AI solution, explore our data-driven lists of:

And reach out to us with questions, if you have any:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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