Chatbots: Definition, Types, and How They Work (2026)

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A chatbot is a software program that simulates conversation to help a business communicate with customers and answer basic questions without human involvement. Grand View Research estimates the global chatbot market at $11.8 billion in 2026, and retail and ecommerce accounted for the largest share by industry in 2025.

In a 2025 Shopify survey of store owners,* 75% said they use AI tools in their businesses. Chatbots are one type of AI tool that customers interact with, whether they’re asking a shipping question or checking in on an order.

This guide covers what a chatbot is, how rule-based chatbots work versus conversational AI-powered chatbots, real product examples, and how to decide which type fits your store’s needs.

What is a chatbot?

A chatbot is a type of computer program that simulates human conversation via text or voice to suit various business needs. Store owners might use these to answer common questions, walk a customer through a task, or route more complex issues or requests to an actual human representative.

For example, Who Gives A Crap uses an AI chatbot on their online store to answer frequently asked product questions and direct customers to email support when a human needs to step in.

As customers communicate with a store’s chatbot, it follows this basic sequence:

1. Input. The chatbot takes in what a customer has typed or said.
2. Intent. The chatbot works out what the customer wants.
3. Response. The chatbot sends back an answer.

There are two ways a chatbot can answer the middle intent question: a rule-based bot that follows a pre-programmed workflow, or an AI-powered conversational bot that interprets a message using natural language processing (NLP). Google Cloud says AI chatbots use natural language understanding (NLU) or NLP and distinguishes them from traditional bots built around pre-programmed conversation flows and responses.

Pew Research found that about half of US adults now use AI chatbots, up from about a third in 2024. That growth means customers are arriving at a store’s chat window already comfortable with communicating via chatbot.

In fact, when customers want immediate assistance, 51% say they prefer interacting with a bot over a human, according to Zendesk.

A few popular applications of chatbots in ecommerce customer service include:

• Greeting shoppers. A chatbot can pop up with information as soon as a customer lands on a brand’s website or app.
• Answering direct questions. A chatbot can handle direct customer requests for information about products, company policies, or recent orders.
• Automating engagement. A chatbot can automate other forms of customer engagement like marketing emails.

What is conversational AI?

Conversational AI is technology that uses NLP and machine learning to understand human language. Conversational AI systems can recognize vocal and text inputs, interpret language, and generate answers that successfully mimic human interactions.

Conversational AI technology powers some AI chatbots, as well as AI writing tools and voice recognition technologies like voice assistants and smart speakers. This approach lets these tools recognize user intent, follow the natural flow of a conversation, and provide unscripted answers based on the tool’s training and knowledge database.

IBM describes NLP and machine learning as a continuous feedback loop, allowing the underlying algorithms to improve their responses over time.

What is the difference between chatbots and conversational AI?

Chatbots are functional tools, while conversational AI is an underlying technology that may or may not power a chatbot. Not every chatbot uses conversational AI technology, and not every conversational AI platform is a chatbot.

Some chatbots also blend the two approaches together to create a hybrid tool, offering a button-based menu for common requests, then switching to conversational AI when the customer types something outside of those available options.

In a customer service context, the two main types of chatbots you can use are rule-based chatbots and conversational AI-powered chatbots. Both use a conversational interface to handle customer interactions, like asking and answering questions. Both also function as virtual support agents, helping businesses extend the capacity of their customer service teams.

The biggest difference is the technology each type uses to respond to customer requests, which affects the complexity of the tasks it can handle. A rule-based chatbot can automate answers to simple questions that it’s been programmed to handle. A conversational AI-powered chatbot can engage with a wider range of questions because it interprets intent rather than checking a script.

Rule-based vs. conversational AI-powered chatbots

According to IBM, rule-based chatbots rely on predefined scripts and decision trees, while conversational AI chatbots use technologies like NLP and machine learning to interpret customer intent and handle more open-ended requests. 

The table below compares how each chatbot type responds to customers, what it would look like to set up one, and where each one fits a business best.

A third category, generative AI-powered chatbots, has become common as large language models (LLMs) have matured. These chatbots generate new responses instead of relying on a fixed answer bank. This lets the chatbot handle more phrasing differences and follow-up questions than earlier conversational AI systems.

In fact, Techtelligence data reported by CX Today found that content tagged “AI agent” generated 2.9 times the audience reach of content tagged “chatbot.”

“Generative AI was the defining hype cycle of 2023 and early 2024,” says Rob Scott, publisher of Techtelligence. “AI Agent is the packaged, outcome-driven application of that technology.”

How do rule-based chatbots work?

Rule-based chatbots, sometimes called declarative chatbots or task-oriented chatbots, are a basic form of chatbot technology. They solve common issues by responding to customer interactions according to a predetermined script.

A 2024 review of rule-based chatbot design methods by researchers Elsabeth Solomon and Surafel L. Tilahun found that rule-based chatbots are comparatively straightforward to implement and can use structured rules to produce consistent responses.

To set one up, you’ll build out an extensive conversation flow chart with a set of if/then conditions. Whenever a customer interacts with your chatbot, it matches their query to a programmed response.

For example, if your customers regularly ask about placing orders, shipments, and speaking to a representative, you’ll want to program your chatbot with these options:

• “Help me place an order.”
• “Track my shipment.”
• “Connect me to an agent.”

Customers can then select one of those options or type in a different question. If they input their own words, your chatbot will work to match the question to a pre-programmed conversation flow.

A rule-based chatbot is limited to what it’s been programmed to read and respond with. It can’t parse intent or respond to input that deviates too far from its scripts. However, you can work around this issue by programming the chatbot to recognize variants, like understanding “nope” as a version of “no.”

To get started with your own rule-based chatbot, apps in the Shopify App Store let you build these flows without needing to write any code. Shopify retailers can also use the free Shopify Inbox app to create up to 100 “instant answers”: predefined questions customers can select to get an immediate response.

How do conversational AI-powered chatbots work?

Conversational AI-powered chatbots process customer language, interpret intent, and generate a response without a provided script. IBM says that NLU helps these systems decipher the meaning of a customer’s input and identify their intent.

The natural language understanding lets the chatbot follow the flow of a conversation and pivot to meet a customer’s changing needs, where a rule-based chatbot must work strictly within the pre-built conversation flow.

Because these chatbots can answer more elaborate questions than more basic chatbots, stores use them to support a wider range of customer service functions.

For example, a conversational AI chatbot can help a shopper with product discovery. Luggage brand Monos uses an AI assistant called Timbit to help shoppers discover products. When asked for its bestselling item, Timbit recommends specific carry-ons, checked luggage, and bags, then asks which category the shopper wants to explore further.

IBM specifically cites personalized advice, product cross-selling, and sizing recommendations as ecommerce uses for conversational AI.

Shopify businesses can see this model in the Shopify Inbox agent, currently in early access for certain merchants. The agent uses a store’s product catalog, policies, pages, and Knowledge Base to answer customer questions in real time, make personalized product recommendations, look up orders, and add recommended products to a customer’s cart.

Product examples: Chatbot vs. conversational AI

A few product examples help visualize the differences between rule-based chatbots, conversational AI-powered chatbots, and hybrid tools that combine both.

ChatPion: A chatbot, but not conversational AI

ChatPion is a flow-builder chatbot tool for Facebook Messenger and Instagram. You can pre-load conversation flows, and ChatPion executes them with each customer who messages you on Facebook or Instagram.

ChatPion also offers ecommerce capabilities, helping customers browse products and even complete purchases while inside Facebook and Instagram DMs. It uses a drag-and-drop flow builder so you don’t need any coding skills to get your chatbot up and running.

While the platform now markets itself as an AI chatbot, ChatPion’s own flow-builder documentation shows how retailers create predetermined interactions by connecting message components and setting trigger keywords, including exact- and string-match rules.This means it can’t generate new responses or deviate from the flow that you’ve previously created.

Grammarly: Conversational AI beyond ecommerce chatbots

Grammarly is a conversational AI platform that can understand written language and generate human-like text, but it’s not a chatbot. Grammarly’s generative AI assistant, for example, can compose, rewrite, ideate, and reply based on a user’s context.

Grammarly now also offers AI Chat, where users can make requests in natural language and receive tailored responses. Unlike an ecommerce chatbot, however, its primary job is helping users write, research, brainstorm, and refine content.

ChatGPT and Claude: Both conversational AI and a chatbot

OpenAI’s ChatGPT is a conversational AI chatbot that uses generative AI technology. OpenAI explains that ChatGPT learns patterns from large amounts of information during training, then uses those learned patterns to generate new responses to a user’s questions and instructions.

Claude, made by Anthropic, is another widely used generative AI chatbot built around a large language model. Anthropic describes Claude as an LLM trained to work as a conversational assistant. As AI chatbots, both tools can hold a back-and-forth conversation and respond to follow-up prompts based on the context available to them.

Shopify Inbox and Sidekick: Both conversational AI and a chatbot

Shopify Inbox is a free chat app that lets merchants manage customer conversations from the Shopify admin. The AI sales associate can answer shopper questions, recommend products, and handle order inquiries using a store’s catalog, inventory, policies, and other store content. When a shopper signs in with Shop, it can also personalize responses using their preferences and purchase history.

Sidekick is Shopify’s AI-powered ecommerce assistant, included with every Shopify plan. Sidekick can help with tasks across your entire ecommerce business, like:

• Generating and editing store content and theme elements from a written prompt
• Editing product photos
• Writing product descriptions
• Setting up discounts and email campaigns
• Analyzing sales, orders, customers, and other store data
• Building custom apps from plain-language instructions

In 2026, Shopify also expanded Sidekick with app extensions that let it answer questions and take actions across third-party tools including Klaviyo, Loop, and Smile. This is because Sidekick has direct access to your store’s Shopify data and can take permitted actions inside the admin. 

Shopify presents proposed changes for you to review before applying them. So it can go much further than a typical customer-facing chatbot while still running as a conversational AI assistant.

Use case example: A rule-based chatbot in customer service

Businesses can use both rule-based and conversational AI chatbots to resolve customer requests and streamline the customer service experience. Although rule-based chatbots are more limited, they can still handle the initial conversation before funneling customers to the proper human agents.

A rule-based chatbot can also walk a customer through a routine task, like starting a return or tracking an order. Clothing brand Shinesty, for example, uses Gorgias Flows to give shoppers pre-scripted, interactive answers based on the options they select. 

The flows can help customers choose products, manage subscriptions, or apply discount codes, with some of Shinesty’s most-used flows resolving more than 90% of interactions without an agent.

From there, you can specify responses to “Yes” and “No” by pointing to where a customer can find their order number or providing a direct link to where customers can initiate a return. If a customer query falls outside the scope of your chatbot’s scripted flow, you can have it route them to a human agent.

Use case example: An AI chatbot in customer service

Since conversational AI chatbots can handle more complex customer service requests, you can use it to support several points along the customer journey, from helping a shopper choose a product to answering a question after checkout.

Room Shop Founder Shelly Horst used Inbox as a “virtual stylist,” answering questions about product measurements, sizes, colors, and restocks. Shelly said the conversations helped customers feel more confident about their purchases.

“Getting a customer feeling really confident about a purchase is Shopify Inbox’s biggest advantage for me,” says Shelly.

There are measurable benefits to partnering AI with human agents rather than replacing them entirely. In a randomized field experiment analyzing more than 250,000 online chat conversations, agents who used AI-based response suggestions saw a 22% drop in response times and a 0.45-point increase in customer sentiment on a five-point scale.

In the experiment’s report, Shunyuan Zhang, associate professor of business Administration at HBS, recommends against using AI as a one-size-fits-all solution in your business, even for customer service. The researchers concluded that AI “currently works best as a complement to human intelligence, rather than a replacement.”

If you’re not sure which chatbot best fits your store, use these guidelines:

• Choose rule-based when most of your incoming customer questions are repetitive, like order status, shipping policies, or store hours
• Choose conversational AI when shoppers tend to ask more varied, unscripted questions, like product comparisons or sizing advice
• Combine both when a rule-based script can take on basic questions before handing off more complex questions to a conversational AI tool or a human agent

Benefits of chatbots for ecommerce

Chatbots offer several concrete benefits for an ecommerce customer service strategy:

• Always available. A chatbot can answer a question instantaneously, whether it’s 2 a.m. or 2 p.m. That increasingly matches customer expectations; Zendesk’s 2026 CX Trends research found that 74% of consumers now expect customer service to be available 24/7 because of AI.
• Instant responses that scale customer service. A chatbot can hold hundreds of conversations at once, something you’d have to massively scale a human team to match.
• Extends team capacity. A chatbot resolves repetitive questions so a smaller support team can cover more complex customer issues. With routine requests taken care of by AI, staff have more time for refunds, complaints, and other issues that require a human touch.
• Supports product discovery. A conversational AI chatbot can help a shopper find more personalized options and help narrow down purchase decisions. Those conversations often happen at a high-intent moment. Shopify reports that 70% of Shopify Inbox conversations involve customers who are making a purchasing decision.

According to Deloitte Digital, AI adoption in customer service organizations rose from 46% in 2023 to 61% in 2025, with chatbots the leading AI capability behind the increase. Deloitte found the clearest benefits in faster resolution times, higher customer satisfaction, and reduced costs. The report cites a correlation between high employee satisfaction and high customer satisfaction.

However, a 2025 Shopify survey* found that content generation was the most common AI use case among store owners, at 69%, while fewer than a third used AI for customer service. But store owners earning more than $100,000 were significantly more likely to use AI for customer service, automation, and scaling with small teams.

For example, silk apparel brand SilkSilky doubled the number of emails their customer service team could manage in a day after adding the AI-driven Re:amaze Live Chat Helpdesk CRM app from the Shopify App Store to their workflow.

While SilkSilky saw success, AI chatbot results vary by store, and outputs are only as accurate as the data and policies behind them. Review AI-generated responses to ensure it is presenting the correct brand tone and information.

*Based on a 2025 survey of 500 Shopify merchants conducted in English across Australia, Canada, the United Kingdom, Ireland, New Zealand, and the United States. Respondents were established merchants with two or more years on the platform. Results reflect the experiences of this specific sample and may not be representative of all merchants.