What is a chatbot?

Chatbots are available on websites, messaging apps, SMS, WhatsApp, customer service portals and many other digital channels.

Not all chatbots use artificial intelligence (AI). Traditional chatbots follow predefined rules, decision trees and scripted conversation flows. AI chatbots use technologies such as natural language processing (NLP), generative AI and large language models (LLMs) to understand user intent, generate more natural responses and adapt to a wider range of conversations.

Traditional chatbots work well for predictable requests but struggle with questions that fall outside their programmed responses. Today’s AI chatbots can do much more than provide answers. Many connect with business applications, databases and knowledge bases so they can retrieve information, complete transactions or guide users through a process. They can also support human agents by resolving routine requests and transferring more complex conversations to a human when more assistance is needed.

Organizations use chatbots to support customers, employees and partners across industries. They can help with customer service, sales, technical support, internal knowledge sharing, appointment scheduling and many other everyday tasks.

While every chatbot has different capabilities, the goal is the same: to make it easier for people to get information and accomplish tasks through human conversation instead of navigating menus or searching through content.

Chatbots are important because they help organizations deliver faster support, improve access to information and scale service across digital channels. They reflect a broad change in how digital experiences are delivered.

Today’s customers expect faster, more personalized omnichannel interactions. Employees also want quick access to company knowledge and internal resources. Chatbots help meet these expectations by making information and services available through a familiar conversational interface.

The idea of computers carrying on conversations with people dates back decades. In 1950, Alan Turing proposed what became known as the Turing Test to evaluate whether a machine could exhibit human-like intelligence. In the 1960s, ELIZA became one of the first well-known chatbots by simulating conversations with users through simple pattern matching, identifying keywords and responding with predefined replies rather than understanding meaning.

Chatbots are now far more advanced, but they build on the same goal of enabling computers to communicate through natural language. They play a central role in how organizations interact with people. Instead of relying only on phone calls, email or static web pages, businesses can provide conversational experiences that help people find information and complete tasks more directly. In fact, conversation has become a common way for people to interact with technology.

Organizations are finding new ways to use chatbots as they evolve. They have become an important part of customer engagement, employee support, customer experience and digital transformation.

Many people assume that the word chatbot always refers to an AI-powered chatbot, but chatbots existed long before generative AI.

Conversational AI is also sometimes used in place of chatbot, but the two are not the same. Conversational AI is a broad category of technologies that enables systems to understand and respond to human language, while a chatbot is one application of that technology. In other words, conversational AI powers many chatbots, but it can also power AI assistants, AI agents, virtual assistants and other conversational systems.

Some organizations use virtual agent interchangeably with chatbot, often in customer service. Others use virtual agent to describe more advanced chatbot systems that can access business applications, complete tasks or support more complex interactions. Because there is no universal definition of virtual agent, the term can vary by vendor or product.

Every AI chatbot is a chatbot, but not every chatbot is an AI chatbot. Both traditional and AI chatbots communicate through conversation, but they differ in how they understand and respond to users.

• Traditional chatbots, also called rule-based chatbots, follow predefined rules, decision trees or scripted responses. They work well for predictable interactions but have limited flexibility. Many businesses use traditional chatbots on their websites to answer frequently asked questions (FAQs) or guide users through common tasks.
  
  
• AI chatbots use AI models to interpret natural language, maintain context and generate responses. They can handle a wider variety of questions and conversations. Well-known examples include ChatGPT and Claude, which allow users to ask open-ended questions and receive conversational responses.

Many AI assistants include chatbot capabilities, but not every AI chatbot functions as a full AI assistant. Both use conversational AI, but they have different goals.

• AI chatbots are designed primarily as conversational chatbots that interact with users through natural conversation. They answer questions, provide information and help users complete specific tasks.
  
  
• AI assistants have a broader role. In addition to answering questions, they can help users manage calendars, set reminders, create content, search for information and work across multiple applications. Examples include Amazon Alexa, Apple’s Siri, Google Assistant, Google Gemini and workplace assistants such as Microsoft Copilot.

AI chatbots and AI agents can both communicate through natural language, but AI agents are designed to take a more active role in completing work.

Some AI agents include a chatbot interface, but conversation is only one part of their function.

• AI chatbots respond to user requests and guide conversations. They typically answer questions or help users complete specific tasks one step at a time.
  
  
• AI agents can plan, make task-specific decisions and carry out multi-step workflows with limited human involvement. They often use tools, interact with other software and adapt their actions based on changing conditions.

For example, an AI chatbot might help a customer exchange a product or answer questions about employee benefits. An AI agent might complete a broader goal by coordinating the exchange, updating inventory, notifying other business systems and confirming the transaction with minimal user input.

The line between chatbots, AI assistants and AI agents is becoming less distinct. Many platforms now combine elements of all three, although their primary purpose still determines how they are classified.

Traditional chatbots rely on predefined rules, while AI chatbots combine multiple AI technologies to understand requests, generate natural responses and complete more complex tasks.

The following technologies typically complement one another throughout a conversation rather than working independently. For example, an AI chatbot might use NLP and natural language understanding (NLU) to interpret a user’s request and retrieve relevant information, then use an LLM with generative AI to produce a conversational response.

Algorithms: Every chatbot relies on algorithms to process information and determine how to respond. Traditional chatbots typically follow predefined algorithms based on rules and decision trees, while AI chatbots combine algorithms with machine learning and language models to generate more flexible responses.

Generative AI: Generative AI enables modern chatbots to create original responses instead of selecting only from predefined answers. This technology allows AI chatbots to respond more naturally and adapt to a wider range of questions.

Large language models (LLMs): LLMs are the AI models that power many chatbots. They are trained on large amounts of text so they can recognize patterns in language, understand prompts and generate responses that support more human-like conversations.

Machine learning: Machine learning allows AI systems to recognize patterns during training and improve how they perform. Many modern AI chatbots also rely on deep learning, a type of machine learning that helps language models recognize complex patterns in text and generate more natural responses.

Natural language processing (NLP): NLP helps chatbots interpret written or spoken language. It allows chatbots to identify the meaning of a user’s request, even when questions are phrased in different ways.

Natural language understanding (NLU): NLU is a specialized area of NLP that focuses on interpreting user intent and context. Rather than simply recognizing keywords, NLU helps AI chatbots understand what a user is trying to accomplish.

Whether a chatbot follows predefined rules or uses AI, every conversation follows the same basic process. The computer program receives a user’s message or voice command, interprets the request, selects the appropriate response and then replies or performs an action. The difference lies in how each type of chatbot understands the request and decides what to do next.

A traditional chatbot compares a user’s input against predefined rules, keywords or conversation flows. If it recognizes the request, it returns a programmed response or guides the user to the next step. For example, a customer might select “Track an order” from a menu or answer “yes” to continue through a scripted conversation. If the request falls outside the chatbot’s programmed responses, it often cannot continue without redirecting the user or transferring them to a human representative.

AI chatbots follow a more flexible process. They can understand the intent and context behind a user’s request. They can also remember information from earlier in the conversation, allowing users to ask follow-up questions or change topics naturally.

For example, a customer might type or say, “I ordered a jacket last week, but I’d like to exchange it for a different size.” Instead of asking the customer to choose from a menu of options, an AI chatbot can understand the request, ask for the order number, retrieve the purchase details and guide the customer through the exchange process.

Many AI chatbots also integrate with various data sources like knowledge bases and business applications. These connections enable them to answer questions, complete tasks and take action. They might create support tickets, schedule appointments, update customer records or retrieve order information, all within the same conversation.

Organizations can deploy chatbots across many departments to improve interactions with customers and employees. Most of these use cases apply to both traditional and AI chatbots, although AI chatbots are better suited for more complex conversations.

More advanced AI chatbots can automate parts of these workflows. AI agents can often complete entire workflows with even less user involvement.

Customer service remains one of the most common uses for chatbots. Chatbots answer questions, provide guidance and help customers resolve issues without always needing to contact a support representative. Common tasks include:

• Answering questions about products, services or policies
• Helping customers track orders, process returns or manage accounts
• Providing troubleshooting guidance
• Routing complex requests to the appropriate support team

For example, a retailer might use a chatbot to help customers check order status, while a software company might use a chatbot to answer basic technical questions before connecting users with a support rep.

Within organizations, chatbots can serve as a central point for company resources and support. These chatbots help employees access information and complete common workplace tasks. Typical uses include:

• Answering questions about company policies and benefits
• Supporting employee onboarding
• Helping with common IT requests
• Connecting employees with internal resources

An employee might use a chatbot to find information about vacation policies, or a new hire might use it to receive guidance on setting up company software.

Chatbots can make it easier for people to find information stored across websites, documents and internal systems. This use case has become more common as AI chatbots’ understanding of natural language questions has improved. Common uses include:

• Finding answers in company documentation
• Searching product documentation, technical resources or knowledge bases
• Locating internal policies and procedures
• Summarizing information from documents

A customer might use a chatbot to find specific information in a product guide, while an employee might ask it to locate a company policy.

Chatbots can support sales teams by helping customers learn about products and services, providing recommendations and guiding them through the buying process. Chatbots help by:

• Answering product questions
• Recommending products or services
• Qualifying leads and collecting customer information
• Scheduling meetings or product demonstrations

An e-commerce company might use a chatbot to help shoppers choose products. A software provider might use one to answer questions from potential customers.

Instead of only providing answers, some chatbots help users complete routine actions. These chatbots often connect with business systems to assist with common requests. Tasks might include:

• Scheduling appointments
• Making reservations
• Updating account information
• Completing forms or submitting requests

For example, a healthcare provider might use a chatbot to schedule appointments, while a bank might use one to help customers update account information.

More advanced chatbot software can automate parts of business processes by collecting information, connecting with other systems and completing specific tasks. These might include:

• Creating support tickets
• Routing requests to the appropriate team
• Updating business records
• Starting approval processes

An HR chatbot might collect information from a new employee and begin the onboarding process, while an IT chatbot could automatically create a support ticket and route it to the appropriate team.

Some AI chatbots automate parts of these workflows, while AI agents can often handle them more autonomously.

Traditional and AI chatbots both improve how organizations interact with users. Regardless of the technology behind them, chatbots offer several common advantages.

Consistent information: Chatbots respond using approved content and business rules, which helps provide more consistent answers across conversations.

Faster responses: Chatbots can provide immediate answers to common questions, reducing the time people spend searching for information or waiting for assistance.

Greater availability: Unlike teams with limited business hours, chatbots can respond at any time of day. This flexibility allows organizations to support users across different time zones and schedules.

Improved employee productivity: Employees can quickly find policies, procedures and other internal resources without searching through multiple systems or contacting colleagues for routine questions.

More efficient customer support: Chatbots handle routine requests, which frees support teams to focus on issues that require more time, expertise or personal attention.

Support for business growth: Chatbots enable organizations to handle growing customer inquiries and employee requests without proportionally increasing support staff, improving operational efficiency.

The conversational abilities and connectivity of AI chatbots can provide more benefits, including:

Actionable insights: AI chatbot conversations can reveal common questions, recurring issues and information gaps. These insights can be used to improve products, services and support resources.

Automation of routine tasks: Many AI chatbots can connect with business systems to complete actions such as scheduling appointments, updating records or creating support tickets. This ability reduces manual work and helps streamline common processes.

More personalized interactions: AI chatbots can tailor responses based on user preferences, previous conversations or available account information. This customization creates more relevant interactions without requiring users to repeat information each time.

Chatbots can provide significant value, but they also introduce important considerations. Organizations should think beyond the technology itself and establish processes for governance, maintenance and human oversight. With the right planning, many common challenges can be reduced or avoided.

Bias and fairness: AI chatbots can reflect biases found in training data or the information they access. Regular testing, monitoring and updates help identify potential issues and improve the quality of chatbot responses over time.

Complex requests: Chatbots can’t handle every conversation. Some situations require human judgment, empathy or specialized expertise. A well-designed chatbot should recognize its limitations and provide a clear path to a human representative when needed.

Incorrect or misleading responses: AI chatbots can occasionally generate responses that are inaccurate, incomplete or misleading. These responses are sometimes referred to as hallucinations in AI, and are more likely when a chatbot lacks access to reliable information or is asked questions outside its area of knowledge. Connecting chatbots to trusted data sources and regularly reviewing their performance can improve accuracy.

Ongoing maintenance: Chatbots require regular updates as business information, policies and customer needs change. AI chatbots also benefit from continuous monitoring and refinement to maintain accuracy, improve performance and support new use cases.

Privacy and security: Chatbots often process customer, employee or business information. Organizations should establish policies for protecting sensitive data, controlling access to business systems and complying with applicable privacy regulations.

User trust and transparency: People should know when they are interacting with a chatbot and understand what data it can access. Setting clear expectations helps build trust and creates a better user experience.