Benefits of AI in Customer Service

Advanced chatbots were already changing customer service when we first wrote this post in 2019. They were no longer limited to a simple script written by a developer with assumed questions and answers: Natural language processing (NLP) and machine learning let them understand differently worded questions and learn from past conversations. Since OpenAI launched ChatGPT in November 2022, large language models (LLMs) have pushed this much further. A modern assistant reads the company’s help center, policies and order data, writes the answer in plain language and, more and more often, carries out the request itself.

In various forms, AI now improves quality or cuts the cost of customer service in almost every industry. Large companies trust AI chat and voice systems with real volumes, and small teams get the same tools built into the help desk and CRM software they already use.

We cannot expect salespeople, call center agents and employees in other customer service roles to study the entire customer history before each call. Artificial intelligence makes it possible: it pulls up past orders, earlier tickets and the last conversation in a second, and tells the agent what the customer probably wants.

Here’s how AI applications change customer service:

• Chatbots: chatbots are conversation agents based on artificial intelligence that are used in many different client engagement scenarios. They have been designed to simulate human interaction and provide immediate, personalized 24/7 responses. This removes frustrating delays in customer service, particularly for simple questions and complaints.
• Virtual Assistants: virtual assistants use artificial intelligence to execute commands or answer questions, by text or by voice, in an app, on a smart speaker or on the phone line.

Examples of AI in customer service

Chatbots and voice assistants are the visible part. Much of the value in 2026 comes from AI that works behind them:

• AI agents: the step after chatbots. An agent does not only answer, it acts in company systems. It checks an order, issues a refund within set limits or changes a booking, and hands the case to a person when it falls outside its rules.
• Agent assist: AI next to a human agent that suggests replies, finds the right help article and summarizes a long email thread before a handover.
• Routing and triage: models read each incoming message, detect its topic, language and urgency, and send it to the right queue.
• Sentiment analysis: the system flags angry or anxious customers so that a person can step in early.
• Voice AI: phone lines that understand a spoken request instead of making callers press 1, 2 or 3.
• Knowledge management: AI drafts help articles from solved tickets and shows which questions the help center does not answer yet.
• Quality monitoring: instead of a manager reviewing a small sample of calls, AI scores every conversation and points to coaching needs.

The best known example of scale is Klarna. In February 2024 the payments company said its AI assistant, powered by OpenAI, handled 2.3 million conversations in its first month, two-thirds of its customer service chats, which it described as the work of 700 full-time agents. By its own figures, the time to resolve a case fell from 11 minutes to under 2. What happened next is just as useful, and we come back to it below.

Benefits of AI in customer service

There are many benefits to using AI in customer service. These are the most important of them:

Availability for customers

Enterprises must offer support whenever customers need it, sometimes all year round, without breaks. This is where Chatbots based on AI help most. They hold intelligent conversations 24/7 without getting tired: no holidays, no sick days, no frustration, and the same patience at 3 a.m. as at noon. A company can rely on a trained assistant to serve customers across time zones and in many languages at once: Klarna’s assistant covered more than 35. AI also gives every customer fast, accurate and consistent answers in real time, and it does not leave the company and take its knowledge with it.

More efficiency in less time

Every company looks for ways to raise revenue and cut costs. AI that handles customer support lowers the cost per contact. It can automate the workflow, serve more customers in less time, free up people and turn more questions into sales. Its capacity scales with a growing customer base and with more complex queries, and it deals with many clients and their different problems at the same time. Customers get help with ordering a service, finding a product, escalating a problem, finalizing transactions, obtaining information and more, without waiting in a queue, and the company does not have to hire extra staff for every peak.

The best evidence comes from agents who work with AI rather than being replaced by it. Erik Brynjolfsson, Danielle Li and Lindsey Raymond studied 5,172 support agents at a Fortune 500 software company after it rolled out an AI assistant that suggested replies, which agents were free to ignore. Agents resolved 15% more issues per hour on average. The least experienced and lowest-skilled agents gained the most, in speed and in quality, while the most experienced barely changed. The authors’ explanation: the assistant passed on what the best agents already did.

Fewer mistakes, more consistency

A well-trained AI system makes fewer routine mistakes than a tired person at the end of a shift. AI Chatbots collect and analyze every conversation, and that stored information helps them answer each client precisely. Companies can make sure the assistant knows the product, the customers and the policies, and it gives the same answer to the same question in chat, by email and on the phone. Updating one knowledge base updates every answer at once, which is faster and cheaper than retraining a whole team.

The old promise that AI makes no mistakes if we teach it well did not survive generative AI, though. A language model can be fluent and wrong. In February 2024 a Canadian tribunal ordered Air Canada to pay a customer C$812.02 after the chatbot on its website said he could claim a bereavement fare after booking, which the airline did not allow. Air Canada argued that the chatbot was responsible for its own actions. The tribunal called that “a remarkable submission” and said the airline is responsible for all the information on its website. What makes AI reliable is answering from approved content, testing before launch and checking the transcripts afterward.

Personalization of the service

Personalization is how a company keeps selling to the same customers over time. Customers no longer want an offer for everyone. They are happier when they know the company makes every effort to give them what they want. AI collects and interprets the client’s history to come up with the most appropriate suggestions. While helping a customer with a specific product, a chatbot can recommend better options and complementary products. Based on previous interactions and orders, it can also remind them about the next purchase or a service date and propose the right action.

This works only when the assistant sees the whole customer, not just the current chat: orders, returns, open tickets and preferences in one customer profile. A bot that asks for an order number the company already has is not personal, however friendly its tone.

Better insight from every conversation

Every chat, email and call is data about what customers need, and AI can read all of it, which no team has time to do. It groups conversations by topic, shows which product or policy causes the most contacts, spots a new problem on the day it starts and tells the product team what to fix. Quality teams use the same analysis to review every conversation instead of a small sample. Over time this is often worth more than the time saved on answers, because the cheapest contact is the one that never has to happen.

AI does not replace people, only repetitive tasks

There are many myths about AI replacing people, and because of them some companies hesitate to bring AI into their business. In practice artificial intelligence will replace tasks, not people. It replaces repetitive and unproductive work, such as tagging tickets, copying order numbers and writing the same answer for the hundredth time, with automated workflows.

So, is customer service replaceable by AI? The routine part increasingly is. The rest is not, and customers say so clearly. In a Gartner survey of 5,728 customers carried out in December 2023, 64% said they would prefer that companies did not use AI for customer service, and 53% would consider switching to a competitor if they found out a company was going to. Their top worry was that it would become harder to reach a person.

Companies that tried to go further learned the same lesson. Klarna, which had paused hiring for over a year while it automated, told Bloomberg in May 2025 that it was recruiting human agents again. Its CEO said cost had weighed too heavily in the plan and the result was lower quality, and promised that customers can always reach a person if they want one. In June 2025 Gartner predicted that by 2027 half of the organizations that planned to cut their customer service workforce significantly because of AI will abandon those plans.

Risks of AI in customer service, and how to tell it is a bot

The main risks are easy to list. A model can give a wrong answer with confidence, as Air Canada learned. A bot that loops or hides the way to a person turns a small problem into a lost customer. Personal data in chat logs falls under privacy law such as the GDPR and needs the same care as any other customer record. And a model trained on past decisions can repeat their biases, for example in who gets a goodwill refund.

Customers also have a right to know who they are talking to. In the EU, Article 50 of the AI Act has applied since 2 August 2026: a system that talks with people must be built so that they are told they are dealing with AI, at the latest at the first interaction, unless that is obvious. In California, the B.O.T. Act has since July 2019 made it unlawful to use a bot online to mislead people about its artificial identity in order to sell to them or to influence their vote. Without a label, the signs are instant replies at any hour, identical phrasing for different questions and trouble with anything outside the script. The simplest test is to ask: a well-built assistant answers honestly and offers a person.

How to implement AI in customer service

Teams that get value from AI in customer service usually go in the same order:

• Start with high-volume, low-risk questions: order status, delivery times, password resets, opening hours. Leave refunds, complaints and anything legal for later.
• Fix the knowledge base first. The assistant can only be as accurate as the articles and policies it reads, and outdated content produces confident wrong answers.
• Connect it to the systems that hold the answers: the CRM, the order system and the help desk. This also answers a common question, whether AI will replace the CRM. It will not: AI runs on top of it, and an AI agent is only as good as the customer records it can read and update.
• Keep a clear way to a person at every step, and pass the whole conversation along so the customer does not have to repeat it.
• Tell customers they are talking to AI, as EU law now requires.
• Measure before and after: resolution rate, customer satisfaction, average handling time and how often the bot hands over. A high automation rate means little if satisfaction falls.
• Roll out in phases, read real transcripts every week, and train the team to work with the assistant rather than around it.

The future of AI and customer service

Forecasts about how much AI will affect customer service efficiency and employment change all the time. Partly this is because many of the most interesting applications are recent. It is also because customer service is not easy to translate into numbers.

Gartner, for example, predicts that by 2029 agentic AI will resolve 80% of common customer service issues without human intervention, cutting operational costs by 30%, while the same firm expects half of the companies that planned deep staff cuts to drop them. Both can be true: routine work moves to machines, and people handle what is left, which is harder and matters more.

There is also the question of how this technology creates demand. Companies use artificial intelligence to handle simple consumer interactions and make the reactions faster and cheaper, but also to improve customer service interactions with people. When you do something better and cheaper, however, people expect much more.

Today’s applications of artificial intelligence in customer service take over the most monotonous work and help human agents with the harder problems. As these systems improve, they handle more requests without human intervention, but they are unlikely to replace human customer service agents fully in the near future.

This means companies will probably be able to provide today’s level of service for less money, but that does not mean all of them will spend less on this part of their business. Customers expect better service because better service is possible. For some industries the main effect of AI in customer service may be lower total spending; for others it will be a race to improve quality for the same budget.