Originally published in May 2022
Updated in January 2026
Chatbots now surround users on all online channels, whether it is Instagram DM, Facebook Messenger, or a company website.
Chatbots are among the most common and promising tools for businesses over the coming years. A bot is a program that performs actions automatically based on defined intents and algorithms. Customers can talk to bots via a chat window or by voice, just as they would with a human.
By 2026, more than 78% of brands will use chatbots for customer support and lead generation. The global market for chatbots is expected to reach around $11.5 billion in 2026, up from $9.6 billion in 2025. Analysts estimate a compound annual growth rate of around 17% between 2021 and 2026.
For a long time, researchers have been fascinated by the idea of talking to computers and getting answers quickly. Even before the term “chatbot”, they began working on machines that interacted with humans through natural language.
ELIZA and A.L.I.C.E
The first chatbots and development systems appeared quite a long time ago. In a nutshell, it all started with Alan Turing in the 50s; the Eliza programme in the 60s; linguistics, and machine learning research in the 90s.
ELIZA, the mother of all chatbots, was developed in 1966 at MIT.
It answered some very simple decision tree questions and imitated a conversation using “pattern matching” and substitution techniques, which gave users the illusion of understanding on the part of the bot.
On November 23, 1995, Richard Wallace introduced the A.L.I.C.E. (Artificial Linguistic Internet Computer Entity), also called Alicebot or simply Alice.
It was inspired by Joseph Weizenbaum’s classic ELIZA programme. It is one of the strongest programmes of its kind, and it won the Loebner Prize for humanoid talking robots three times, in 2000, 2001, and 2004. The programme uses an XML Schema called AIML (Artificial Intelligence Markup Language) for specifying the heuristic conversation rules.
The next big thing in the conversational AI industry was the advent of voice assistants. Siri was launched in 2010 and set the tone for all other virtual assistants that have appeared in this decade.
In 2012, Google launched Google Now.
This assistant was developed based on Google’s “voice search” feature. It stood out because it could remind the user of events, based on information received via email or calendar.
In 2014, Amazon released Alexa.
Alexa can perform many of the standard functions of a virtual assistant. It can also order items from Amazon and control smart devices, making it a home automation system.
These days, the situation in this area has changed noticeably. Due to the development of algorithms that detect semantic similarity and machine learning solutions. This, in turn, has made approaches to text categorisation and NLU model training fast and convenient.
Natural language understanding (NLU) and natural language generation (NLG) are the most promising among them, and these areas have the highest growth rates.
Mordor Intelligence: The global NLP market was valued at $10.72 billion in 2020 and is expected to be worth $48.46 billion by 2026, registering a CAGR of 26.84% over the forecast period (2021-2026).
The rapid development of Generative AI and the accessibility of new technologies for ordinary users have made AI popular and widely discussed. As people evaluate the potential of AI services like ChatGPT, they are becoming more aware of the importance and impact of artificial intelligence on our everyday and professional lives.
Chatbots today live on platforms such as Facebook Messenger, WhatsApp, Instagram DMs, Slack, WeChat, or even a website. Like regular apps, chatbots have an application layer, a database, an API, and a conversational user interface.
There are currently three core types of chatbots
- Rule-based chatbots
- Intellectually independent chatbots
- AI-powered Chatbots
A rules-based chatbot uses a tree-like flow to help guests with their queries. Such chatbots guide users through scripted questions to eventually help them find the right answer. The conversation in this case is controlled by a ready-made database of frequently asked questions.
The whole methodology is called the “rules-based approach”. It is based on finding conventionally meaningful particles of phrases, encoding those phrases, and creating a scripting language to create scripted conversations.
The latest development mechanisms are complex systems that include
- NLU part that contains intent recognition and named entity recognition.
- Linguistic modules
- Dialogue management modules
- Integrations and external APIs
We have to admit, a great deal of conversational solutions based on that system is quite labour-intensive. You’ve got to put a lot of effort into a chatbot to make it communicate on a broad range of topics or to cover a specific discipline with profound knowledge.
Intellectually independent chatbots use machine learning (ML), which helps them learn from data and user queries. This then helps chatbots recognise certain patterns and make decisions with minimal human involvement.
Instead of relying on a predetermined human-designed result, chatbots with artificial intelligence first understand what a question is about. Let’s see how a user-chatbot interaction pattern may be presented.
- A user sends their query into one of the accessible channels, such as speakers, phones, smartwatches, etc. Behind each query lies intent — the user’s wish to get the correct answer or to get the service, product, or some content like music or video.
- There may be further processing or conversion of the message format. Dialog platforms use text recognition techniques, while some channels may only consider voice. A conversational dialog system consists of an automatic speech recognizer (ASR), a text-to-speech synthesiser (TTS), and integration systems.
- The query, converted into text, is then passed to the dialog platform. The goal of the platform is to capture the basic semantics of a given word sequence, retrieve the intent, process it correctly, and give the correct answer or action. Examples of such external systems can be any CRM system, contacts databases, or services like Deezer or Google Play Music.
- After receiving the data, the dialog platform generates a response – a text, a voice message (using TTS). Then it triggers content streaming or notifies about the performed action (e.g. adding a purchase to cart).
Therefore, AI chatbots are not suitable for everyone because they require a training period and tend to require more effort to get started. However, once the training period is over, AI bot can become incredibly powerful.
Generative AI chatbots represent a new generation of conversational agents. They rely on advanced deep learning models trained on vast text corpora to generate responses that feel natural and context-aware. Businesses use them for product recommendations and personalised engagement at scale. As models improve, we will see more realistic and adaptive interactions shaping customer experience.
In 2026, analysts predict the generative AI chatbot market will grow to around $13.2 billion, up from $10.05 billion in 2025, marking a year-on-year rise of over thirty per cent. Leading providers such as ChatGPT command roughly 68% of that market, while Google Gemini has surged to 18%. Meanwhile, around three-quarters of enterprises plan to deploy generative AI chatbots by 2027 for customer service and sales support.
How does a chatbot work and what is its purpose?
A chatbot is a program that chats with users, often using AI to help resolve problems or direct customers to a live agent for further troubleshooting and resolution.
How do Gen AI chatbots work?
Generative AI is a type of artificial intelligence that enables the creation of original content that often looks human-made. To achieve this, generative models are trained on large datasets.
How do you make an AI chatbot?
Tovie helps businesses build AI chatbots and voice bots. We offer ready-made solutions for SMBs and custom options for larger enterprises. Our Tovie Platform makes it easy to launch and scale AI tools quickly.