Complete Guide to Customer Service Chatbots in 2025

1. Better technology

Chatbots used to suck. But you know what? They don't anymore.

Instead of the rule-based chatbots of yesteryear, most chatbots are LLM agents these days. They're powered by the latest LLMs, using state-of-the-art AI to process conversations and make decisions in real time.

Advances in processing power and algorithms as well as increases in the availability of data have led to improvements in speech recognition and NLU. This makes it possible for AI chatbots to answer an infinite variety of customer questions.

And modern chatbots go far beyond simple FAQ bots. They can carry out entire end-to-end workflows using agentic AI. They can troubleshoot IT issues, reset passwords, and use bespoke company knowledge to engage customers.

2. More cost-efficient

Up to 75% of queries can be solved by a chatbot alone. As long as an organization knows how much they're paying for each customer support request, it's easy to calculate the ROI of a chatbot.

More often than not, companies that implement a virtual chat assistant expect to hold the headcount for human agents at the same level – the chatbot is used for scaling demand.

‍3. Easy to set up

If you use a chatbot building software, you can start using your chatbot on day 1. Before chatbots for customer service were commonplace, developers needed to build bots from scratch.

But with a wide market of chatbot builders, your team can easily implement a bot, whether you're brand new to coding or have a team of developers by your side.

Natural Language Processing

Natural language processing (NLP) is an aspect of AI that focuses on the interaction between computers and humans through natural language.

The goal of NLP is to process human language in a way that can provide meaningful understanding and responses. 

One way it does this is through syntactic analysis – it will assess how natural language aligns with grammatical rules. If you’re familiar with AI terminology, this encompasses tokenization, part-of-speech tagging and parsing. 

NLP also involves semantics and pragmatics: knowing the meaning behind a sentence and the intended effect of a sentence. For example, understanding semantics allows a chatbot to know that when a customer says “I’m having trouble,” it means they’re having a problem. 

Pragmatics allows a chatbot to know that a customer who starts a sentence with “I think” is uncertain about what they’re saying. The bot will know that the customer is looking for confirmation and assistance, not just stating a fact.

Artificial Intelligence

Everyone knows the term, but what does it mean? Artificial intelligence AI refers to the capability of a machine to imitate intelligent human behavior. 

It involves creating algorithms and systems that can perform tasks – the kind of tasks that would normally require human intelligence. The most important of these include reasoning, learning from past experiences, making decisions, and understanding language. 

Machine Learning

Machine learning (ML) is a subset of AI that focuses on building systems that can learn from data, identify patterns, and make decisions with minimal human intervention. It’s based on the idea that systems can learn from data, identify patterns and make decisions. 

There are several types of ML, including:

• Supervised learning uses labeled data, meaning the data is already tagged with the correct answer. The model learns to predict the output from the input data. 

• Unsupervised learning means the data is not labeled, so the system tries to learn the patterns and the structure from the data itself.

• Reinforcement learning involves an algorithm learning to perform a task by trying to maximize rewards it receives for its actions – in other words, it learns optimal actions by trial and error.

• Semi-supervised learning uses both labeled and unlabeled data.

• Deep learning – an AI buzzword – is a special form of ML that involves deep neural networks with many layers. It’s particularly effective at processing data with complex patterns (images, sound, etc.)

Customization

Any chatbot worth its salt will be customized to the company it represents. 

The bot should:

• Be integrated with databases in order to provide accurate company-specific information
• Be deployed across multiple channels, including your company’s main channels of communication with customers
• Communicate using a consistent tone, whether it be informative, friendly, casual, or professional

Integration and channelsA strong bot-building software will allow users to integrate their AI chatbot with other systems, like sales catalogs, HR policy documents, and other company-specific data.These are important because most, if not all, routine tasks will involve retrieval of company information. You can check out our guide to integrating a Botpress chatbot.

Privacy and securitySome chatbots are created to handle private customer data. If your AI chatbot will handle sensitive information – like birthdays, passwords, or other personal information – it needs to be up to date with privacy and security regulations.If you’re not using an external platform, you’ll need to ensure that your chatbot developer understands applicable regulations. These will vary depending where you are in the world. You’ll also need to build systems and allow for secure data storage and transmission. When a chatbot is handling sensitive information, your developer must implement strong encryption for both storage and transmission. This is to protect the data from unauthorized sources – when it’s encrypted, it means even if someone accesses the data, they won’t be able to read it.

1. 24/7 availability

Unlike a human, a chatbot works 24/7. While 24/7 support is exceptionally costly with live agents, it’s an automatic feature of chatbots. A bot can provide quick answers and simultaneously serve multiple customers, cutting down on wait times.

Take one of our customers as an example: Extendly for HighLevel. They provide analytics and support service to a major customer-relationship management system — and one of their top services is 24/7 hour tech support.

But their market was huge: over 10% of this CRM’s small to medium business clients. Running their most popular feature was eating into their revenue. 

Extendly for HighLevel deployed a Botpress chatbot to partially automate their tech support process. Now, every support request starts with a chatbot that can resolve conversations on its own. When a request is highly specific, or coming from a VIP client, customers are able to be served by their human support teams.

After switching to Botpress from their in-house solution, the company was able to divert 500% more chats away from agents. Their bot handles over 30% of all support queries about their highly technical SaaS product from start to finish. 

And with their service teams able to handle the most complex questions and important customer accounts, they’ve never had better customer relationships.

The ability to offer 24/7 support is a huge advantage over competitors. In a global landscape, companies need to be equipped to provide support around the clock. AI chatbots are an easy, low-cost solution for 24/7 support.

2. Scalability

Customer service chatbots allow companies to scale in two ways: in size and across channels. 

There are a finite number of support calls that customer agents can take in a minute – and they can only be on one call at a time. An AI chatbot can handle a large number of simultaneous customer service inquiries. For companies looking to scale, a chatbot for customer service is necessary.

Chatbots can also scale across platforms far more efficiently than live agents. A chatbot can run across a webpage, WhatsApp, and Facebook Messenger simultaneously. Depending on customer communication preferences, an AI chatbot can interact with them via a webpage or a messaging platform.

Customer service processes are no longer limited to the channels of the past – phone calls and websites. If you want to center customer experience, scaling across channels is an easy way to enhance the user journey. 

3. Reduced costs

Chatbots empower companies to scale their operations without ballooning their support costs. Their cost-effective nature is one of their leading benefits.

In order to have the same availability and quality of service with human agents, a company needs to pay for:

• Hourly labor costs
• Overtime or irregular hours pay
• Agent training 
• Hiring and turnover costs

With a chatbot, a company can eliminate nearly 100% of the above costs. Customer service chatbots are often paid month-by-month. Prices will vary based on the chatbot software and the scale of your AI chatbot. 

Another price of a chatbot is AI spend: when you empower a chatbot with AI, you pay a small cost for using the large language models (LLMs) that power it. For example, if you have an AI bot that runs on OpenAI’s GPT-4 Turbo, you pay $10.00 USD for every million tokens (words or sub-words) that are entered into your chatbot.

Most chatbot software companies will charge additional cost for AI spend, but Botpress provides LLM access for no additional cost. If you use GPT-4 Turbo for your chatbot, we charge you the same $10 per million input tokens that OpenAI charges to use their LLM engine.

The cost of a chatbot is far more predictable than a fully human fleet. All training is front loaded and there’s zero labor turnover. A chatbot remains consistent without any extra costs.

4. Faster support speed

Think back to the last time you called your bank or a government agency. Waiting to receive customer support is a notoriously frustrating and painstakingly long process. 

One of the greatest benefits of an AI chatbot is its ability to respond instantaneously. Chatbots largely eliminate wait times, ensuring every user receives an exceptional customer experience. 

In most support scenarios, an AI chatbot can respond to a customer almost instantaneously. 

Humans hate waiting. We’ve designed a plethora of strategies to make waiting time feel like it’s passing faster, from mirrors in elevator lobbies to loading bars on webpages.

Cutting out wait time ensures that customers are in a positive state of mind when they begin their support query – all in all, a better customer experience.

5. Consistent service

While service teams undergo a training process, humans are prone to error. When faced with an unusual scenario, it’s easy to forget the most relevant policy.

Another key difference? Chatbots don’t have bad days. When you’ve had a bad night of sleep and you’re on the phone with a customer that can’t locate their reference number, it’s easy to slip from top-notch customer service into mediocre customer service. 

But chatbots can follow information to a tee, and they can do it with a cheerful persona 24/7. 

Customers interacting with a chatbot to solve their queries will have far more consistent support than those interacting with a varied collection of live agents. An AI customer service chatbot ensures customer inquiries are handled in a consistent manner every time.

6. Asynchronous communication

Chatbots can handle asynchronous communication. This means that it is much easier for the customer to multitask. This is a more convenient mode of solving queries than a synchronous conversation – it matches the pace of the customer without sacrificing any time for a live agent or the chatbot.

Some customer inquiries take longer than others. While we traditionally think of a customer support call as two humans working through a live problem, these calls can run long when accounting for troubleshooting. 

A customer may call a customer service line for a printer company when they find out that, no matter what they try, the printer will only print pictures of chickens. 

A live agent will ask them to try a series of troubleshooting tasks: Did they turn it off, wait 2 minutes, and then turn it back on? Is the printer connected to the right wifi network? Did they open the ink tray to inspect for anomalies? Did they install the latest driver?

A live agent can only handle one of these calls at a time – they must devote a full hour of their time to troubleshooting one printer. 

But the length of a support interaction doesn’t matter for a bot. They’re not held up by a single task. If a customer wants to turn off their printer, check on their baby upstairs, then check the wifi connection, and then get the laundry out of the dryer, there’s no time wasted for the support chatbot.

7. Customer self-service

Thanks to chatbots, businesses can offer their customers goods, services, and help without having to lift a finger.

In the era of digital self service, chatbots offer customer experiences almost indistinguishable from a customer support team, while also being able to handle multiple customer queries at once.

Younger generations have made clear their preferences for self service options, from self-checkout lines at the grocery store to ordering food delivery through an app. Exemplary customer context involves knowing how your customers prefer to get in contact with support agents. Conversational AI allows for a customer experience that reflects changing communication preferences.

8. Multilingual support

Imagine the cost of employing customer service agents that speak over 100 languages. But with an AI chatbot, multilingual support is no extra cost (at least with platforms like Botpress).

AI chatbots offer multilingual support in a way that isn’t possible for customer service reps. For companies with international presence, looking to expand globally, or in areas with diverse language demographics (like India and the United States), AI chatbots are a necessity for satisfactory customer service interactions. 

9. Integration capabilities

Unlike a human brain, chatbots can be integrated to company-specific databases and applications.

A well-rounded AI software will provide seamless integration to company knowledge bases. For example, Botpress users are able to connect to documents, tables, or entire URLS.

If you’re selling items out of a clothing store, you’ll want to connect a chatbot to your inventory of items in stock. If you’re selling a product or service, you may want to connect a chatbot to your CRM (customer relationship management) system.

While a human agent needs to manually check databases for relevant information, a chatbot can automatically retrieve it for the customer. As long as your database is updated, it will provide immediate, accurate information.

Integrations also mean that your customers can access help across channels – like Slack, SMS, or Whatsapp. A chatbot means a wider array of support options for your customers, increasing ease of accessibility.

10. Automatic analytics

Live agents are sometimes tasked with recording key information from their customer support calls. What demographic has the hardest time learning the product? Which service are most external complaints about?

If you’re using an equipped chatbot software, an AI chatbot provides you with advanced analytics about your customer support interactions. They can also allow direct user feedback, so you’re able to identify customer preferences and improve all your chatbot interactions with small tweaks.

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Apple

Apple offers the best of both worlds to its website visitors. Their users can type in any contextual information about their problem, and the bot will identify which pieces of content are a closest match to their needs.

Apple's customer service chatbot also allows users to choose whether to start a query with the bot or a human — and it provides real-time feedback on the expected waiting time for each. This makes it easy to choose which path best suits a user's needs.

Optimum

Optimum is an internet provider — so they knew they couldn't provide a wifi-dependant chatbot. What if someone needs to ask how to set up their wifi?

This SMS customer support bot uses NLP to understand and troubleshoot its users' issues. It's available 24/7, so that users can get assistance any time they need it.

Domino’s Pizza

Dom has been around since 2016, serving the pizza-eating masses. And Dom knows how to get down with its audience. It uses emojis and converses over Facebook Messenger, just like the teenagers ordering from it.

The Domino's chatbot can remember past orders, so it can even further streamline the ordering process. If a customer wants to ask their Alexa or smartwatch to order a pizza for them, Dom can take that order, too.

H&M

One of the first-generation chatbots lost to emerging technologies, the H&M Kik chatbot was one of the early examples of a personalized customer support enterprise chatbot.

The Kik bot provided styling advice using personalized feedback. It would communicate its suggestions to customer via images of H&M products from their catalog.

This low-cost bot was a free service for the store's customers, complementing their main source of revenue (i.e. selling clothes).

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AI Agents

An AI agent is an autonomous system that processes information, makes decisions, and takes action to achieve a goal.

Unlike AI chatbots, which respond to user inputs, agentic AI refers to software that is capable of autonomous decision-making. It’s often used to automate complex workflows, like customer service, data analysis, or coding assistance.

That means AI agents can eliminate the need for human involvement in certain tasks, or support employees in their day-to-day tasks.

AI Chatbots

Most customer service chatbots these days are AI-powered. They use machine learning and natural language processing to flexibly answer customer questions. 

Because of their artificial intelligence, they can handle complex interactions, adapt and learn over time, and provide a personalized service for customers. If you’ve interacted with a chatbot recently, it was almost certainly an AI chatbot.

These chatbots can engage in human like conversations, connect to knowledge bases (like company products or regulations), and handle customer interactions in a helpful manner.

Rule-Based Chatbots

When we think of the simple (and frusteraing) chatbots of yesteryear, we’re usually picturing a rule-based chatbot. 

A rule-based chatbot operates on a predefined set of rules. They can only respond to the specific questions that they have been designed to understand. They follow a simple logic based on ‘if-then’ programming.

Their scope is very limited – they can handle basic queries, but cannot respond meaningfully to questions outside of their predefined knowledge base. Their answers are always predictable, since they are programmed to give specific, unchangeable responses. 

Most importantly, rule-based chatbots don’t learn from previous interactions. Without manual updates, they cannot improve their ability to understand questions or handle customer complaints.

If you want a chatbot that functions similarly to a FAQ page, then a rule-based chatbot is your best bet. These kinds of bots are unable to understand customer context or provide high-quality customer interactions.

Human in the loop

This is a bot’s ability to escalate queries to a human customer service agent in case it can respond on its own. Not only is this functionality useful by itself, but it also allows chatbot designers to minimize the customer frustration caused by an ineffective bot. 

Customer service teams can rely on the bot to solve the most common queries while they handle the more complex ones. And through learning implementation, a bot can learn from a query it wasn’t able to fulfill in order to better assess the same question in the future. AI customer service chatbots tend to have some human in the loop capability. This means that customer support teams have a way to access a back end where they can respond to escalated queries.

Learning

Machine learning is a part of AI developed to help software applications process information in meaningful ways. Through this competency, AI chatbots can learn from customer interactions and improve their customer service performance without any other human input.

Custom conversation flows

More often than not, a customer will not provide a chatbot with enough information for it to fulfill a query. When this happens, the bot can use a custom conversation flow to ask for the information it needs to provide a solution.

Integration

When processing a query, AI chatbots can send or receive information to and from multiple internal or third-party systems. Chatbots can provide users with a friendly interface to multiple systems.

Seamless integration is a key selling point of chatbot software platforms. When a chatbot can retrieve information from your database, it can better serve customer requests.

Context

Information like user details, device, previous communication, and data leveraged from other systems can provide the bot with the context it needs to make better decisions. Bot designers can use this data to manually design conversation flows. Likewise, the AI can learn what to do or say in the appropriate context.

‍Actions

A chatbot is capable of taking action based on the conversation at hand. It is able to produce digital widgets, graphics on a screen, or similar interactions. It can communicate with internal or third-party systems for this purpose. 

The best AI customer service chatbots will have some of the features mentioned above. These operate in narrow topic domains where they have a very high probability to respond usefully to every customer query. This way, they are capable of offering the best customer service experience.

AI is getting better

As companies like OpenAI, Microsoft, and Google continue their race to the AI ceiling, the technology behind chatbots is getting better every month. As large language models (LLMs) add more features, multimodality, and speed, the capabilities of support bots extend. 

In a few years, we’ll look back at the chatbots of today the same way we look at the rules-based chatbots of the 90s and 2000s.

More real-world uses

As chatbots become the status quo, we’ll see an expansion of case studies. It’s the norm to use chatbots in e-commerce, but many industries haven’t yet fully welcomed customer service chatbots. 

As more companies use AI customer service chatbots, we’ll see more unique and dynamic examples of how they can increase customer satisfaction. More AI chatbot technology will be used to automate routine tasks, more messaging apps will feature a virtual assistant, and chatbots for customer service will be ubiquitous. 

Higher popularity and acceptance

A decade ago, chatbots had a bad rap – and for good reason.

But as bots get better, they become more accepted by the general public. Now companies know that a chatbot for customer service actually enhances the customer experience, instead of diminishing it. They can more efficiently complete routine tasks, support customers across multiple channels, and securely process customer data.

In future service models, chatbots for customer service won’t be optional. They’ll be expected.

What's the best customer service chatbot?

The best customer service chatbot will vary based on your needs, business model, and developer capacity.

The capabilities of a chatbot are determined by its complexity. A chatbot designed to provide information may only be able to access pre-recorded data when solving customer queries. In contrast, a more powerful chatbot can read through vast volumes of data and use NLP techniques to offer fluid and comprehensive answers. 

A chatbot’s design is just as important as the technology platform used to design it. A succinct, intuitive, and well-designed chatbot will function much better than a complex chatbot that has been poorly designed. Since a chatbot will be responsible for handling most customer interactions, it’s important for them to be as refined as possible.

How do I choose a customer support chatbot?

You should choose an AI customer service chatbot based on your needs and ability. Options range from a no code chatbot builder to making your own from scratch. 

Companies considering implementing a chatbot must assess the economic impact of their chatbot solution. Likewise, they must also consider if and how the chatbot will integrate with their existing systems and processes. 

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