What is a chatbot?
Learn about chatbots, which simulate human conversation to create better customer experiences
Discover watsonx Assistant
Black and blue background
What is a chatbot?

A chatbot is a computer program that simulates human conversation with an end user. Though not all chatbots are equipped with artificial intelligence (AI), modern chatbots increasingly use conversational AI techniques like natural language processing (NLP) to understand the user’s questions and automate responses to them.

Related content AI Academy

Learn about putting AI to work for customer service

Subscribe to the IBM newsletter

IBM named a Leader in the 2023 Gartner Magic Quadrant™ for Enterprise Conversational AI Platforms.
The value of chatbots

Chatbots can make it easy for users to find information by instantaneously responding to questions and requests—through text input, audio input, or both—without the need for human intervention or manual research.

Chatbot technology is now commonplace, found everywhere from smart speakers at home to consumer-facing instances of SMS, WhatsApp and Facebook Messenger to workplace messaging applications like Slack. The latest evolution of AI chatbots, often referred to as “intelligent virtual assistants” or “virtual agents,” can not only understand free-flowing conversation through use of sophisticated language models, but even automate relevant tasks. Alongside well-known consumer-facing intelligent virtual assistants like Apple's Siri and Amazon Alexa, virtual agents are also increasingly used to in an enterprise context to assist customers and employees.

Featured products

IBM watsonx Assistant

IBM Cloud Pak for Data

How chatbots work

The earliest chatbots were essentially interactive FAQ programs, programmed to reply to a limited set of common questions with pre-written answers. Unable to interpret natural language, they generally required users to select from simple keywords and phrases to move the conversation forward. Such rudimentary traditional chatbots are unable to process complex questions, nor answer simple questions that haven’t predicted by developers.

Over time, chatbot algorithms became capable of more complex rules-based programming and even natural language processing, allowing customer queries to be expressed in a conversational way. This gave rise to a new type of chatbot, contextually aware and armed with machine learning to continuously optimize its ability to correctly process and predict queries through exposure to more and more human language.

Modern AI chatbots now use natural language understanding (NLU) to discern the meaning of open-ended user input, overcoming anything from typos to translation issues. Advanced AI tools then map that meaning to the specific “intent” the user wants the chatbot to act upon, and use conversational AI to formulate an appropriate response. These AI technologies leverage both machine learning and deep learning—different elements of AI, with some nuanced differences—to develop an increasingly granular knowledge base of questions and responses informed by user interactions. This sophistication, drawing upon recent advancements in large language models (LLMs), has led to increased customer satisfaction and more versatile chatbot applications.

Related links

How to build a chatbot

Chatbots vs. AI chatbots vs. virtual agents

The terms chatbot, AI chatbot and virtual agent are often used interchangeably, which can cause confusion. While the technologies these terms refer to are closely related, subtle distinctions yield important differences in their respective capabilities.

Chatbot is the most inclusive, catch-all term. Any software simulating human conversation, whether powered by traditional, rigid decision tree-style menu navigation or cutting edge conversational AI, is a chatbot. Chatbots can be found across any nearly any communication channel, from phone trees to social media to specific apps and websites.

AI chatbots are chatbots that employ a variety of AI technologies, from machine learning that optimize responses over time to natural language processing (NLP) and natural language understanding (NLU) that accurately interprets user questions and matches them to specific intents. Deep learning capabilities allow AI chatbots to become more accurate over time, which in turns allows humans to interact with AI chatbots in a more natural, free-flowing way without being misunderstood.

Virtual agents are a further evolution of AI chatbot software that not only use conversational AI to conduct dialogue and deep learning to self-improve over time, but often pair those AI technologies with robotic process automation (RPA) in one interface to act directly upon the user’s intent without further human intervention.

To help illustrate the distinctions, imagine that a user is curious about tomorrow's weather. With a traditional chatbot, the user can use the specific phrase “tell me the weather forecast.” The chatbot says it will rain. With an AI chatbot the user can ask, “what’s tomorrow’s weather lookin’ like?”—the chatbot, correctly interpreting the question, says it will rain. With a virtual agent, the user can ask, “what’s tomorrow’s weather lookin’ like?”—the virtual agent can not only predict tomorrow’s rain, but also offer to set an earlier alarm to account for rain delays in the morning commute.

 

Common chatbot use cases

Consumers use AI chatbots for many kinds of tasks, from engaging with mobile apps to using purpose-built devices such as intelligent thermostats and smart kitchen appliances. Business use is equally varied: Marketers use AI-powered chatbots to personalize customer experiences and streamline e-commerce operations; IT and HR teams use them to enable employee self-service; contact centers rely on chatbots to streamline incoming communications and direct customers to resources.

Conversational interfaces can vary, too. AI chatbots are commonly used in social media messaging apps, standalone messaging platforms, proprietary websites and apps, and even on phone calls (where they are also known as integrated voice response, or IVR).

Typical use cases include:

  • Timely, always-on assistance for customer service or human resources issues
  • Personalized recommendations in an e-commerce context
  • Definition of fields within forms and financial applications
  • Intake and appointment scheduling for healthcare offices
  • Automated reminders to for time- or location-based tasks
Benefits of chatbots

The ability of AI chatbots to accurately process natural human language and automate personalized service in return creates clear benefits for businesses and customers alike.

Improve customer engagement and brand loyalty
Before the advent of chatbots, any customer questions, concerns or complaints—big or small—required a human response. Naturally, timely or even urgent customer issues sometimes arise off-hours, over the weekend or during a holiday. But staffing customer service departments to meet unpredictable demand, day or night, is a costly and difficult endeavor.

Today, chatbots can consistently manage customer interactions 24x7 while continuously improving the quality of the responses and keeping costs down. Chatbots automate workflows and free up employees from repetitive tasks. A chatbot can also eliminate long wait times for phone-based customer support, or even longer wait times for email, chat and web-based support, because they are available immediately to any number of users at once. That’s a great user experience—and satisfied customers are more likely to exhibit brand loyalty.

Reduce costs and boost operational efficiency
Staffing a customer support center day and night is expensive. Likewise, time spent answering repetitive queries (and the training that is required to make those answers uniformly consistent) is also costly. Many overseas enterprises offer the outsourcing of these functions, but doing so carries its own significant cost and reduces control over a brand’s interaction with its customers.

A chatbot, however, can answer questions 24 hours a day, seven days a week. It can provide a new first line of support, supplement support during peak periods, or offload tedious repetitive questions so human agents can focus on more complex issues. Chatbots can help reduce the number of users requiring human assistance, helping businesses more efficient scale up staff to meet increased demand or off-hours requests.

Generate leads and satisfy customers
Chatbots can help with sales lead generation and improve conversion rates. For example, a customer browsing a website for a product or service may need have questions about different features, attributes or plans. A chatbot can provide these answers in situ, helping to progress the customer toward purchase. For more complex purchases with a multistep sales funnel, a chatbot can ask lead qualification questions and even connect the customer directly with a trained sales agent.

Best practices and tips for selecting chatbots

Selecting the right chatbot platform can have a significant payoff for both businesses and users. Users benefit from immediate, always-on support while businesses can better meet expectations without costly staff overhauls.

For example, an e-commerce company could deploy a chatbot to provide browsing customers with more detailed information about the products they’re viewing. The HR department of an enterprise organization may ask a developer to find a chatbot that can give employees integrated access to all of their self-service benefits. Software engineers might want to integrate an AI chatbot directly into their complex product.

Whatever the case or project, here are five best practices and tips for selecting a chatbot platform.

  1. Pick a solution that can accomplish your immediate goals but won’t limit future expansion. Why does a team want its own chatbot? How is this goal currently addressed, and what are the challenges that are driving the need for a chatbot? Does it offer templates to help your organization scale up and diversify chatbot offerings in the future, or will other teams need to develop something else from scratch? Does the pricing allow for efficient internal expansion?
  2. Understand the impact AI has on the customer experience. Chatbots are an extension of your brand. The right AI can not only accurately understand what customers need and how those needs are being articulated, but be able to respond in a non-robotic way that reflects well upon your business. Without the right AI tools, a chatbot is just a glorified FAQ.
  3. Ask what it takes to build, train and improve your chatbot over time. Do you need something simple and ready-made, or sophisticated API access for custom implementation? AI doesn’t train itself. You need a clear sense of what intents and content arrive pre-built and what you’ll need to create yourself. Some chatbots offer the ability to use historical chatlogs and transcripts to create these intents, saving time. Those using machine learning can also automatically adjust and improve responses over time.
  4. Look for ways to connect to, not replace, existing investments. Often, emerging channels or technologies seem like they will replace established ones. But instead, they become just another medium for an organization to manage. A chatbot that connects to these channels and customer case systems can provide the best of both worlds: Modernizing the customer experience while more accurately routing users to the information and individuals that can solve their problems.
  5. Determine if the chatbot meets your deployment, scalability and security requirements. Every organization and industry has its own unique compliance requirements and needs, so it’s important to have those criteria clearly defined. Many chatbots are delivered via the cloud to draw on the learnings and outcomes from other customer conversations, so if you require an on-premises solution or a single tenant environment, the list of available providers is much shorter. It’s also important to understand if and how your data is used, as it can have major impacts in highly regulated industries.
Related solutions
Intelligent virtual agents

IBM watsonx Assistant provides customers with fast, consistent and accurate answers across any application, device or channel.

Explore intelligent virtual agents
Watson Discovery

Find critical answers and insights from your business data using AI-powered enterprise search technology.

Explore IBM Watson Discovery
IBM Cloud Pak for Data

Connect the right data, at the right time, to the right people anywhere.

Explore IBM Cloud Pak for Data
Resources AI for Customer Service assessment

Take this 5-minute assessment to find out where you can optimize your customer service interactions with AI to increase customer satisfaction, reduce costs and drive revenue.

IBV study: The value of virtual agent technology

Learn about how the COVID-19 pandemic rocketed the adoption of virtual agent technology (VAT) into hyperdrive.

How to build a chatbot

Check out our docs and resources to build a chatbot quickly and easily.

AI for Customer Service

IBM Watsonx users achieved a 337% ROI over three years. Improve the customer experience with conversational AI.

Magic Quadrant for Enterprise Conversational AI Platforms, 2023

IBM again recognized as a Leader in the 2023 Gartner® Magic Quadrant™ for Enterprise Conversational AI.

Chatbot design: Streamlining messaging experiences

Explore chatbot design for streamlined and efficient experiences within messaging apps while overcoming design challenges.

Take the next step

IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability. watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently.

Explore watsonx Assistant Book a live demo