What is AI Chatbot? A Comprehensive Guide 2025
Artificial Intelligence (AI) chatbots have rapidly evolved from a futuristic concept to a ubiquitous tool in modern digital communication. You’ve likely interacted with one recently. These AI-powered assistants are reshaping how businesses interact with customers, streamline internal operations, and provide personalized experiences. In this comprehensive guide, we’ll explore what an AI chatbot is, how it works, its benefits, limitations, real-world applications, key features, and how to build your own. By the end, you’ll understand why AI chatbots are a game-changer for businesses across industries.
What is an AI Chatbot?
An artificial intelligence (AI) chatbot is a software application that simulates human conversation. AI Chatbots are often used to automate tasks or answer questions. Unlike traditional rule-based chatbots that rely on pre-programmed scripts, AI chatbots leverage advanced technologies like Natural Language Processing (NLP), Machine Learning (ML), and large language models (LLMs) to understand user intent and provide dynamic, contextually relevant responses.
Particularly with the rise of large language models (LLMs), the use of AI chatbot has skyrocketed. There are pre-built chatbots that can do anything from provide emotional support to help you find the right used car.
Key features of AI chatbots include:
Natural Language Understanding (NLU): Enables the chatbot to comprehend user queries, even with typos or slang, and decipher the underlying meaning.
Machine Learning: Allows the chatbot to improve its performance over time by learning from interactions, identifying patterns, and adapting to new data.
Conversational AI: Makes interactions feel more human by interpreting context, generating natural-sounding responses, and managing dialogue flow.
Integration Capabilities: The ability to take action into existing systems.
LLM Routing: LLM agents are bots powered by an LLM ‘brain’ ensures your chatbot starts out with a host of advanced features.
Omnichannel Deployment: The most useful AI chatbots are available to use across channels.
Advanced Analytics: It needs success metrics.
Data Security: An AI chatbot needs the proper security and privacy guardrails before being deployed to the public.
Multimodality: Your chatbot software should often AI voice assistants and rich communication services (RCS).
Chatbot Statistics
By the numbers, here are a few quick chatbot statistics:
88% of customers used an AI chatbot in 2022
Chatbots are the fastest-growing communication channel for brands, rising by 92% from 2019 to 2020 (Startup Bonsai)
Chatbots can answer up to 79% of routine queries (IBM)
Chatbots save companies ~30% on customer support costs (Invesp)
The global chatbot market is expected to reach $27.3 billion by 2030 (Grand View Research)
Why are AI Chatbots so Popular?
There’s been a rapid increase in AI chatbots – from e-commerce, to insurance companies, to our workplaces. Their rising popularity is largely due to the recent accessibility of AI technology.
Before OpenAI released ChatGPT in 2022, using conversational AI interfaces was reserved for a small portion of the tech-literate population. But after the arrival of free AI chatbots powered by LLMs, open-source API
On top of that, they’re low-cost, 24/7, multilingual, and customizable. When deployed correctly, AI chatbots are an incredible return on investment for companies.
List of Key Terms
If you’re unfamiliar with artificial intelligence technology, there are a few terms that you’ll need to familiarize yourself with before understanding AI chatbots.
Artificial Intelligence
Artificial intelligence is a branch of computer science focused on creating machines with human-level intelligence. The types of tasks they set out to accomplish are typically related to learning, reasoning, problem-solving, and understanding natural language.
Natural Language Processing (NLP)
Natural language processing (NLP) is a branch of natural language understanding (NLU), which is a branch of AI.
NLP enables machines to understand, interpret, and meaningfully respond to human language. It’s used not only in AI chatbots, but in language translation and speech recognition technology.
Generative AI
Generative AI refers to AI systems that can generate text, images, video, or other output.
Conversational AI
Conversational AI is a branch of AI that focuses on allowing computers to engage in human-like conversations. Through a combination of machine learning and NLP, conversational AI is designed to engage in dialogue with humans, either through text or voice.
AI Agent
AI chatbots overlap with the concept of AI agents. An AI agent is a software that performs tasks on behalf of a user. They can automate processes, make decisions, and intelligently interact with their environment.
Both AI chatbots and AI agents use NLP, LLMs, and vector databases. But they differ in their purpose and capability. Chatbots are designed to interact with humans, while agents are designed to complete autonomous tasks.
Many AI chatbots are unable to take autonomous actions, and some AI agents don’t exist in text-based, user-facing forms.
How Do AI Chatbots Work?
AI chatbots function through a combination of technologies:
User Input: The process typically begins when the chatbot receives a trigger from a user. Often, a user will prompt an AI chatbot by asking a question via text or voice. However, AI chatbots can be designed to be triggered by other events, like receiving an email from a certain sender, or observing a KPI hit a target number.
Natural Language Processing (NLP):
Breaks down the user’s message into components, identify the user’s intent, and extract important information from the request.
Identifies intent, key phrases, and context.
Intent Recognition: After processing the input, the chatbot will determine what the user wants – a product recommendation, resetting their password, or advice on crafting a resume.
Machine Learning Algorithms:
Analyzes past interactions to refine future responses.
Adapts to new data for continuous improvement.
Response Generation: An AI chatbot will then generate a response using machine learning models.
Context Management: As the chatbot interacts with the user, it will keep track of the conversation to maintain context – this ensures its responses remain relevant.
Integration with Databases:
If a user is asking for specific information – like how much a product costs, reviews from other users, or what a company’s HR policy says about vacation days – the AI chatbot will fetch the relevant data.
It might do this by connecting to a database, retrieving information from a Knowledge Base, or making an API call.
Response Generation:
Uses predefined templates or generates responses dynamically based on user input.
Send Response: After identifying the most appropriate, helpful, and relevant response, the AI chatbot will send its generated response to the user. This process repeats until the conversation is resolved.
Must-Have Features of AI Chatbots
With the rapid increase of available technology, there will always remain a lack of quality assurance.
The world changed for the better when AI technology became more widely accessible. But it also increased the number of useless and poorly deployed chatbots.
Integration Capabilities
If chatbots exist in a vacuum, their purpose is drastically limited. A key purpose of chatbots is their ability to take action into existing systems.
For example, a lead generation chatbot needs to be integrated with a company’s customer relationship management (CRM) system, so that they can update records as they find and qualify new leads.
Or take an e-commerce chatbot: it needs to be integrated with Knowledge Bases that contain information about current stock, return policies, and individual items or models.
Most chatbot platforms will have pre-installed integrations. And flexible platforms will allow chatbot builders to build integrations to any system or platform.
LLM Routing
The best chatbots aren’t clunky rule-based chatbots – they’re LLM agents, a bot powered by an LLM.
An LLM ‘brain’ ensures your chatbot starts out with a host of advanced features. All LLM-powered bots are naturally NLP chatbots that understand what a human is trying to say (even with typos).
Omnichannel Deployment
The most useful AI chatbots are available to use across channels. An organization can deploy their chatbot to their website, but also to their WhatsApp or Facebook Messenger.
It’s always best to meet your users where they are, so AI chatbots are particularly useful when they’re able to send emails or SMS messages.
Advanced Analytics
If a chatbot is deployed in order to help achieve a goal – like increase lead generation, make more sales, or handle customer support calls – it needs success metrics.
Whether an organization builds their own chatbot from scratch or uses a platform, they’ll need to set up analytics to measure the outcomes of their chatbot.
Typical analytics will include the number and length of interactions, while advanced analytics can measure any facet of a chatbot’s workflow.
Data Security
Like any software project, an AI chatbot needs the proper security and privacy guardrails before being deployed to the public.
Data security is particularly important for chatbots that handle any personal data – like names, phone numbers, or addresses. If your chatbot handles data from individuals in the EU, it will need to be GDPR compliant.
Multimodality
Chatbots used to be text only. But we no longer live in a text-only world.
Your chatbot software should often AI voice assistants and rich communication services (RCS). It should offer image-based search – like when a customer uploads a photo of a jacket they like and asks for similar products.
Some chatbots will even generate images or videos to better communicate, like a sales bot at a dealership that changes the color of the car the user wants.
Benefits of AI Chatbots
AI chatbots offer numerous advantages for businesses and users alike:
1. 24/7 Availability
One of the standout features of AI chatbots is their ability to operate around the clock. Unlike human employees, chatbots don’t need breaks, sleep, or vacations. They’re always on, ready to assist customers at any time of day or night.
Their 24/7 availability ensures that businesses can provide support whenever it’s needed, enhancing customer satisfaction and ensuring that no query goes unanswered.
2. Cost Efficiency
By automating routine tasks AI chatbots can reduce costs of running or scaling an organization.
the need for large customer service teams. This means companies can cut down on labor costs while still delivering high-quality service.
AI chatbots can efficiently handle a wide range of tasks, freeing up human employees to focus on more complex issues. The result? Lower operational costs and a more streamlined workflow, making AI chatbots a smart investment for businesses of all sizes.
3. Improved Customer Experience
Chatbots offer instant responses, personalized recommendations, and seamless issue resolution, enhancing customer satisfaction.
4. Scalability
As businesses grow, so do their customer service demands. AI chatbots are highly scalable, meaning they can handle an increasing number of customer interactions without a drop in performance.
Whether a company is dealing with a few dozen customers or thousands, chatbots can manage the load efficiently, ensuring that every customer receives timely support.
5. Data Collection and Insights
AI chatbots don’t just interact with customers; they also collect valuable data from every interaction. This data can be analyzed to gain insights into customer behavior, preferences, and pain points.
Businesses can then use these insights to refine their strategies, improve products, and personalize customer experiences.
6. Consistency
Unlike humans, chatbots never have a bad day. They’re able to deliver a high-quality standard of service every time, never varying on tone or accuracy.
Consistent service ensures that users can receive reliable support, no matter when they need it (or how many of them need it at the same time). It builds a strong brand image and higher trust with users, increasing satisfaction across the board.
Use Cases of AI Chatbots
Due to their flexible nature, AI chatbots can be deployed in nearly any conversational AI use case.
AI chatbots have long been popular in booking processes like restaurants, airlines, and hotels. And a wider range of industries – like gaming, manufacturing, and higher education chatbots – have all grown in chatbot usage across a diverse set of use cases.
While we can’t cover all their applications, here are a few of the most popular AI chatbot use cases:
1. Customer Support Chatbots
Everyone has a customer service chatbot these days. And for good reason – AI chatbots are perfectly suited for customer support use cases.
A customer service chatbot can answer customer questions, dispense information, or share videos about products. They can use AI ticketing to prioritize and route support tickets.
AI chatbots are able to divert significant percentages of calls away from call centers, making them one of the most common use cases.
2. Internal Employee-Facing Chatbots
While most AI chatbots are external-facing, there’s a growing adoption of internal chatbots within enterprises. More and more often, we’re seeing HR chatbots taking vacation requests and IT chatbots troubleshooting an employee’s tech issue.
Internal chatbots often act as the first stage of assistance for employees seeking information about internal procedures. They may interact with a chatbot to schedule days off, call in sick, learn more about their benefits, or get support with a procedural task.
Since internal processes like HR requests take up the time of two employees – the employee and the HR representative – an AI chatbot greatly reduces the cost of internal operations.
3. Sales Chatbots
The majority of chatbots deployed on our platform are part of an organization’s sales process.
A sales chatbot can answer questions, compare models, and give out pricing information. These chatbots are typically part of an AI-enhanced sales funnel, all the way from lead generation to post-purchase follow-ups.
A sales bot might look like a retail chatbot that recommends products based on a customer’s purchase history, or a website widget that facilitates a customer’s payment transaction.
4. Lead Generation Chatbots
A lead generation chatbot is one of the most common use cases for AI chatbots. They often send emails or messages on WhatsApp or Facebook Messenger, as well as sync up information with a CRM (customer relationship management) system.
A lead gen chatbot might offer advice or information to users – like explaining which laws are relevant in a legal dispute, or suggesting the best country to travel to based on their interests – and then offer further services based on a user’s response.
They might also chat back and forth with a website visitor to qualify a lead before booking a sales meeting.
5. Booking Chatbots
Bookings are typically a straightforward process. A booking chatbot can handle an entire booking flow from start to end – no employees needed.
For a restaurant chatbot, a customer just needs to enter their name, contact info, and select a time and day. They can even be built as simple FAQ bots.
But some bookings are more complex. Take a hotel reservation: Guests can investigate the available room options, look at amenities and services, and book their room. It’s a task that’s easily offloaded to an AI chatbot.
That’s why chatbots for hotels are skyrocketing in popularity – they can handle bookings, streamline housekeeping requests, and sell extra services. Our partner organization has used AI chatbots to solve 75% of guest requests without human involvement and sell extra services to 20% of guests before they arrive at the hotel.
6. Government Chatbots
Government services have traditionally lagged behind the speed and quality of private services – and some are looking to improve the gap with AI chatbots.
Government chatbots have been deployed to help guide citizens through forms and applications, provide status updates, provide voter registration and election information, and provide information about public health or public transport.
7. Chatbots for Healthcare
Healthcare is another industry that is rapidly adopting chatbots. Medical chatbots often assist patients by answering basic medical questions, scheduling appointments, and providing information on symptoms and treatments.
By handling routine inquiries, the best healthcare chatbots are able to reduce the burden on medical staff, while simultaneously improving access to information for patients.
8. Finance Chatbots
Banking chatbots are nothing new: Bank of America’s Erica has been around since 2018, monitoring subscriptions, guiding spending behavior, and assisting with account and transfer information.
But not all finance chatbots are bound to banks. Some finance chatbots assist in compliance monitoring for financial professionals, track expenses for businesses, or onboard customers to a fintech app. Chatbots for insurance might help with claims processing, guidance on policies, or fraud detection.
9. Real Estate Chatbots
Real estate has one of the most widespread adoption rates across industries, due to its high volume of conversational interactions and the need for constant, up-to-date information.
Real estate chatbots can suggest properties, keep track of paperwork, and manage client relationships. They can also coach real estate agents on pitching individual properties or neighborhoods, and qualify leads before setting up a meeting with a realtor.
Limitations of AI Chatbots
Despite their benefits, AI chatbots have some limitations:
Lack of Emotional Intelligence:
They cannot fully understand or replicate human emotions, which may lead to impersonal interactions in sensitive situations.
Limited Context Understanding:
Chatbots may struggle with complex queries requiring deep contextual comprehension.
Dependence on Training Data:
The quality of responses depends on the data used for training; biased or outdated data can lead to inaccurate answers.
Integration Challenges:
Some chatbots may not seamlessly integrate with existing business systems, limiting their functionality.
Security Concerns:
Poorly designed bots can be vulnerable to cyberattacks or misuse of sensitive data.
Best Practices for Implementing AI Chatbots
To maximize the effectiveness of AI chatbots, consider the following tips:
Define Clear Objectives:
Identify specific tasks the chatbot will handle (e.g., customer support, lead generation).
Choose the Right Platform:
Select a chatbot platform that aligns with your business needs and integrates well with your existing systems.
Train the Bot Effectively:
Use diverse and high-quality training data to improve accuracy and reduce bias.
Monitor Performance:
Regularly analyze chatbot interactions to identify areas for improvement.
Provide Human Backup:
Ensure a seamless handoff to human agents for complex queries that the bot cannot resolve.
How to Build Your Own AI Chatbot in 7 Steps
With all the free chatbot technology on the market, it’s never been easier to build an AI chatbot of your own.
A project that was once reserved for developers, now it’s possible for anyone with a computer to build an AI chatbot.
Here’s a step-by-step walkthrough of how to build your own customized AI chatbot:
1. Define your scope
The first step to create an AI chatbot is simple – scoping. What’s your chatbot going to accomplish? Before building, your team needs to develop a chatbot strategy that includes the predicted ROI of your chatbot.
The purpose of your AI chatbot will determine what capabilities it will need, which will determine the platform you use.
If you use an extensible platform, the world is your oyster. A well-designed AI chatbot can take on any conversational AI task you can dream of.
Once you have your scope down pat, it’s time to pick a platform.
2. Pick a platform
There are plenty of free AI chatbot platforms, for any need or use case. You can check out our list of the 9 best AI chatbot platforms for a rundown.
There are a few considerations to keep in mind when selecting a platform for your project. Make sure you pick a platform that:
Has a broad swath of educational resources. There’s always going to be a learning curve, so ensure you’re well-equipped for it.
Matches your intent. Don’t pick a conversational AI platform that specializes in customer service if you want a sales bot.
Includes a free tier, so you can test it out before (or without) making a financial commitment.
3. Build your AI chatbot
You’ve made it: you settled on an idea for a chatbot, you found a platform, and you’re ready to build your own AI chatbot.
The AI chatbot that you build is going to be entirely unique – you have your own vision and your own needs. Part of the process will involve familiarizing yourself with your platform and applying your understanding to your unique roadmap.
4. Integrate
If you’re looking to connect your AI chatbot to another system or platform – like Hubspot, WhatsApp, or your website – then part of your building process will include integrating your bot to the necessary systems.
If your AI chatbot is going to give out information about which products your company has in stock, you’ll want to connect it to your internal source of truth, typically known as a Knowledge Base.
A Knowledge Base can be a table, document, or website that includes the information your AI chatbot will draw from.
For example, an HR chatbot will use a company’s key policy documents as its Knowledge Base. When an employee asks how to handle a specific situation, the chatbot can use the policy documents to inform its answer.
5. Testing and iteration
Once you’re done building your AI chatbot, it’s time to make it better. Some builders forget to account for testing and iteration time, but this is a crucial step to deploying a successful chatbot.
Whichever AI chatbot platform you choose, it should have a simulator within the studio that allows you to practice conversations with your chatbot. This is the first step of testing that you’ll use throughout the building process.
Once your build is complete, you’ll be able to send a sample version of your AI chatbot to your friends or colleagues using a URL. You should do this to test out your bot’s functionality before officially deploying it.
6. Deploy
Once your bot is in a final form, you can release it unto the world. They are a few options for AI chatbot deployment:
One of the most common deployments is via webchat, typically found on a company’s website
An SMS chatbot that can send text messages
An email chatbot that sends
7. Monitor and Improve
Once your AI chatbot is deployed, your work isn’t over. Continuously monitor its performance, gather user feedback, and identify areas for improvement. Regularly update the training data, refine the conversational flow, and add new features to ensure your chatbot remains effective and meets the evolving needs of your users.
The Future of AI Chatbots
The future of AI chatbots looks promising as advancements in technology continue to push boundaries:
Generative AI Integration:
Tools like GPT models enable more natural and creative responses, making interactions even more engaging.
Voice-Powered Bots:
Voice recognition technology will allow chatbots to handle spoken queries effectively.
Hyper-Personalization:
Advanced algorithms will enable bots to offer highly tailored recommendations based on user behavior and preferences.
Cross-Platform Functionality:
Chatbots will become more versatile, operating seamlessly across multiple platforms like websites, mobile apps, and social media channels.
Enhanced Security Measures:
Future bots will incorporate robust security protocols to protect sensitive user data.
Conclusion
AI chatbots are revolutionizing how businesses communicate with customers by offering efficient, scalable, and cost-effective solutions. While they have some limitations, their benefits far outweigh the drawbacks when implemented correctly. As technology evolves, AI chatbots will continue to play a pivotal role in enhancing customer experiences and driving business growth.
Whether you’re a small business owner looking to improve customer support or a large enterprise aiming to optimize operations, investing in an AI chatbot is a step toward staying competitive in today’s digital landscape.
