AI chatbots and voicebots

Artificial intelligence helps us communicate with our devices through natural language – simply by asking questions and formulating commands. AI chatbots, also known as conversational bots or conversational AI are AI-based computer programs that simulate and process human conversations – written or spoken – enabling people to interact with digital devices as if they were talking to a real person. The global chatbot market is projected to reach $454.8 million in revenue in 2027, up from $40.9 million in 2018. That’s a lot indeed.

AI chatbots and voicebots – table of contents:

How do chatbots and voicebots work?

Before you start thinking about which to opt for to help your business grow, let’s answer the question: How does a chatbot work? Artificial intelligence-based text chatbots allow users to ask natural language questions via text and get natural-sounding and meaningful answers. This is because they feature Natural Language Understanding (NLU) and Natural Language Generation (NLG) technologies.

Voicebot, on the other hand, enables callers to navigate the interactive voice response (IVR) system by voice. With them, callers don’t have to listen to a phone menu and press the appropriate numbers on a keypad. They talk to the IVR live, a simplified simulation of an operator call.

This is because they use the following technologies:

Speech Recognition – converting the caller’s voice into text,
Natural Language Understanding (NLU) – analyzing the understanding analyzing meaning units, extracting
Language Generation (NLG) – generating an appropriate response based on the understanding of the query,
Speech Synthesis Technology – converting the response into speech and delivering it to the caller.

Both bots can use large language models (LLMs) as the basis for creating human-like responses to natural language queries. LLMs are computer algorithms that process natural language input and predict the next word based on patterns they recognize. They adopt natural language processing (NLP) and Machine learning (ML) to analyze and generate text or speech.

LLMs provide the ability to deliver genuine, consistent, contextual responses by training on massive amounts of textual data. LLM therefore improves the ability of chatbots and voicebots to understand and generate natural language. For example, LLM can help voicebots handle complex queries or long dialogues.

LLM-based chatbots have many applications in business, such as customer service, sales, marketing, education, health, tourism, and others.

Types of AI chatbots

Chatbots can be divided into types according to the way they communicate, namely text and voice, and by their complexity and applications:

Task-based chatbots – rule-based and task-oriented, the simplest to operate and implement,
Predictive, data-driven chatbots and voicebots– requiring integration with a database or application, whose operation is most similar to a human conversation.

Explaining how a text or voice AI chatbot works depends on the type we discuss. So let’s take a closer look at each.

AI Chatbots and task voicebots

Task-based chatbots focus on performing a single function, such as providing information or finalizing simple transactions. They follow rules, natural language processing (NLP) and a bit of ML to generate responses to user queries that are automated but somewhat resemble a natural conversation.

Rule-based chatbots are highly specialized and their responses must be structured, so they are often used to support customer service and support departments. For example, a task-oriented chatbot can answer questions about opening hours, business scope or process simple orders. Task-oriented chatbots can handle typical questions, but are not very flexible and cannot adapt to new situations.

Similarly, rule-based voicebots follow predefined rules and scripts to handle simple and specific tasks. These could be, for example, booking a flight or checking the weather over the phone. They are easy to build but have limited capabilities and little adaptability.

An example of the difference between the way a rule-based task-based chatbot works and a chatbot using advanced AI is illustrated by the following excerpt from a dialogue:

	RULES-BASED CHATBOT	AI CHATBOT
Bot:	How can I help you? Ask a question by typing “Opening Hours,” “Privacy Policy,” or “Buyer Protection Program.”	How can I help you?
Klient:	What time do you close?	What time do you close?
Bot:	Unfortunately, I don’t understand. Ask a question by typing in “Opening Hours,” “Privacy Policy,” or “Buyer Protection Program.”	Today, Monday, the store is open until 5 p.m. You are welcome!
Klient:	But I just want to know what time you close!	Thanks 🙂

The quoted dialogue shows the flexibility of an AI chatbot – from the short question “What time do you close?” it guesses from the context that the question is about the store’s operating hours and today’s day. Such a chatbot can also be taught to answer in a specific style that sustains the impression of a conversation with a specific person.

Predictive AI chatbots and voicebots

Data-driven chatbots and voicebots use data from various sources, such as:

user profiles,
preferences and settings,
records of user behavior,
feedback

All of this is to provide personalized and relevant answers. They can also use the data to learn and gradually improve their performance and accuracy.

The data is primarily used to predict users’ needs, intentions, emotions and provide proactive-predictive – responses. Chatbots can also use it to generate new ideas and suggestions for users.

Data-driven predictive AI chatbots are the most advanced. They can also be personalized and used as digital assistants that learn user preferences and can initiate conversations on their own. These two types are often combined to create more engaging and intelligent conversational agents.

They use context awareness, natural language understanding (NLU), natural language processing (NLP) and machine learning (ML) to learn over time. For example, a data-driven and predictive chatbot can help users learn languages through interactive dialogues and exercises, or suggest products based on user profiles and past behavior.

Examples of AI chatbots in business

Task-oriented chatbots perform a single function, such as providing information or finalizing simple transactions. For example, a task-oriented chatbot can:

book a hotel room or airline ticket,
order food or groceries online,
check weather or road conditions,
plan meeting,
answer frequently asked questions (FAQs),
customer support.

Popular examples of well-implemented task-oriented chatbots:

Expedia’s chatbot – to find and book hotels and flights via Facebook Messenger,
Domino Pizza chatbot – to order pizza and track delivery via Facebook Messenger,
Poncho chatbot – to see weather forecasts and alerts via Facebook Messenger and Slack,
Kayak chatbot – to plan trips and compare prices via Facebook Messenger, Slack and Alexa.

More advanced, data-driven and predictive text chatbots feature in :

language learning or skills – like the Duolingo chatbot, which helps users learn foreign languages through interactive dialogues and exercises in the Duolingo app,
suggesting products or services based on user profiles and past behavior,
generating new ideas or content for creative projects,
assisting in repetitive work tasks, such as managing finances, calendars, emails, etc., such as Google’s Bard, a text-based digital assistant that can generate texts and email them via Google Workspace

Some popular commercial examples of general-purpose predictive AI chatbots are:

Apple’s Siri, a digital voice assistant that can perform various tasks and answer questions via iOS devices.
Amazon’s Alexa, a digital voice assistant that can control smart home devices, play music, order products and more via Echo devices.

Examples of voicebots in business

If a customer calls to block a credit card, a voicebot can help find the way through all the steps without involving a human agent. To provide seamless customer service, voicebots can also help to improve employee productivity by automating tasks such as approving requests, ordering supplies, filling out forms or automating office tasks such as scheduling meetings.

Some of the best market solutions for voicebots are:

Amazon Lex – A service that allows developers to create conversational interfaces using voice and text. Provides speech recognition, natural language understanding, natural language generation and speech synthesis capabilities. It also integrates with Amazon Alexa, Amazon Polly, Amazon Comprehend, etc.
Google Dialogflow – A platform for creating natural and rich conversational experiences using voice and text. It provides speech recognition, natural language understanding, natural language generation and speech synthesis capabilities. It also integrates with Google Assistant, Google Cloud Speech-to-Text, Google Cloud Text-to-Speech, etc.
IBM Watson Assistant – Enables developers to design conversational solutions via voice and text. Provides speech recognition, natural language understanding, natural language generation and speech synthesis capabilities. It also integrates with IBM Watson Speech Services, IBM Watson Text-to-Speech, IBM Watson Tone Analyzer, etc.

AI Chatbots or voicebots – Which one to choose for your business?

Chatbots and voicebots are two types of conversational artificial intelligence that can help companies automate customer interactions and provide better service. However, they have different strengths and limitations depending on the context and user preferences. Here are some criteria for choosing a solution:

User interface – AI chatbots are more suitable for users who need access to visual information, such as images or links. Voicebots, on the other hand, are more suitable for those who need to communicate quickly or, for example, drive a car or operate a machine while talking.
User experience – both rely on natural language understanding (NLU) to process user requests and intentions. Voicebots are more engaging, but their responses need to be truly human-like to perform their function. Voicebots also require speech recognition and synthesis, which can introduce more errors or delays in the conversation. On the other hand, chatbots can provide more feedback and guidance to the user through buttons, menus or emoticons. Moreover, they are easier to train and improve.
Application -both can fit in customer service, sales, booking or information retrieval. However, some may be more functional for one specific duty, depending on its complexity, urgency or sensitivity. For example, text chatbots may be better for duties requiring authentication, verification or confirmation, while voicebots may be better for those that aim at speed, convenience or personalization.

To decide which one will fit better in your business, answer the following questions:

Who are your target customers and what are their preferences and behaviors?

This question will help you understand your customers’ needs and expectations, as well as their preferred method of communication. For example, if your customers are young, tech-savvy and mobile-oriented, they may prefer chatbots to voicebots. If your customers are older, less comfortable typing or have accessibility issues, they may prefer voicebots.

What are your customers’ goals and pain points, and how can you solve them?

This question will help you define the value proposition and use case of your conversational artificial intelligence solution. For example, if customers want to quickly order a pizza or book a flight, they may prefer voicebots to chatbots. If customers want to compare products, read reviews or get detailed information, they may prefer chatbots.

What channels and platforms do customers use to interact with your business?

This question will help you choose the best delivery method and integration options for your conversational artificial intelligence solution. For example, if your customers use social media, messaging apps or websites to contact you, they may prefer chatbots over voicebots. If your customers use phone calls, smart speakers or voice assistants to contact you, they may prefer voicebots over chatbots.

What technical and financial resources do you have at your disposal to develop and maintain your conversational artificial intelligence solution?

This question will help you assess the feasibility and scalability of your conversational artificial intelligence solution. For example, if you have limited resources or expertise, you may prefer chatbots over voicebots. Chatbots are generally easier and less expensive to develop and maintain. Voicebots require more advanced technologies and skills, such as speech recognition and synthesis, which can increase the cost and complexity of the solution.

Conversational artificial intelligence. The future of communication in business

As companies seek to build deeper, more meaningful relationships with their customers, the choice between chatbots and voicebots is not just about technology, but about understanding and anticipating human needs.

Combining artificial intelligence with the ability to have a conversation that resembles that of a human, promises not only efficiency but also a transformation of the way companies interact with their customers. For perhaps herein lies the future of business communication – more intuitive, personalized, and paradoxically, more human.

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Author: Robert Whitney
JavaScript expert and instructor who coaches IT departments. His main goal is to up-level team productivity by teaching others how to effectively cooperate while coding.
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