PRODUCT
AI has been part of customer service for years. Chatbots answer common questions, route calls, and support platforms help agents find information faster. AI agents take this a step further. Instead of only answering a question, an AI agent can understand what a customer needs, use relevant information from connected systems, follow a defined process, and complete certain actions. This makes AI agents particularly relevant for customer service operations where a large share of the work happens a
AI has been part of customer service for years. Chatbots answer common questions, route calls, and support platforms help agents find information faster. AI agents take this a step further.
Instead of only answering a question, an AI agent can understand what a customer needs, use relevant information from connected systems, follow a defined process, and complete certain actions. This makes AI agents particularly relevant for customer service operations where a large share of the work happens after the customer asks the initial question.
For industries such as insurance, that difference matters.
An AI customer service agent is a system designed to handle customer interactions and perform tasks within a business workflow.
Imagine a customer contacting an insurance company to ask about the status of a claim. A basic chatbot may recognize the topic and provide general instructions about where claim information can be found.
An AI agent can potentially go further. Depending on how it is configured and what systems it has access to, it can identify the customer and their request, retrieve the relevant claim information, check its current status, provide an appropriate response and, when necessary, pass the case to a human employee with the relevant context already available.
The important difference is that the interaction is connected to the underlying process. This is why AI agents are increasingly discussed as part of customer service automation, rather than simply another communication channel.
The terms are often used interchangeably, but they describe different levels of capability.
Traditional customer service chatbots are mainly designed around conversation. They may answer frequently asked questions, provide predefined information or direct customers toward the right resource.
Chatbot: “Here is information about how to change your policy details.”
AI agent: “I understand what you want to change. Let me collect the necessary information, check the relevant conditions and help move the request through the appropriate process.”
The exact actions an AI agent can perform depend on its integrations, permissions and the rules defined by the company. An AI agent should not automatically have unlimited access or decision-making authority. That distinction becomes especially important in regulated industries.
From the customer's perspective, the interaction can feel simple. They ask a question through chat or voice and receive a response. Behind that interaction, several things may happen.
First, the system needs to identify what the customer is asking. A message such as “I sent the documents yesterday, is anything still missing?” is not simply a request for general claims information. The system needs to understand the intent, context and information required to answer it.
The answer may depend on information stored elsewhere: a CRM, policy administration system, claims platform, knowledge base or another internal system.
Some requests require more than retrieving information. The system may need to collect additional details, check whether required information is available, update a record, create a request, trigger another step or route the case to the appropriate team.
Once the required information and process are clear, the agent can provide the customer with a response or complete an allowed action. What it is permitted to do should depend on the company's rules and the type of request.
Not every customer interaction should be fully automated. A request may involve an exception, sensitive situation, complex decision or simply fall outside the scope the AI agent is designed to handle. In these cases, the right outcome is often escalation rather than another attempt at automation.
High-volume, predictable requests are often the most obvious place to start with customer service automation. These might include checking a status, providing standard information, collecting required details or helping customers through a known process.
Automating part of this workload gives customer service teams more capacity for interactions that actually require human judgment.
Customer service employees often work across multiple tools during a single interaction.
If an AI agent can retrieve relevant information or perform approved actions through integrations, employees and customers do not need to manually move through as many steps. The value here is straightforward: less time spent looking for information and moving it between systems.
Customer experience can vary depending on workload, employee experience and where information is stored. An AI agent working with approved information and defined workflows can help make routine interactions more consistent.
That does not mean every conversation should sound identical. It means the underlying information and process should be reliable.
Customers do not necessarily have questions during office hours. AI agents can handle appropriate requests at any time, while cases requiring human involvement can still be routed to the relevant team.
For businesses with high customer volumes, this can reduce waiting without requiring every type of request to be automated.
AI agents do not only have to work independently. They can also support human customer service teams by gathering information, summarizing previous interactions or preparing relevant context before an employee takes over.
In this model, automation removes some of the preparation work while the person remains responsible for the interaction that requires judgment.
The first question should not be, “How much of our customer service can we automate?”
A better question is: “Which parts of our customer service operation are repetitive, time-consuming and structured enough for automation to be useful?”
Look at the actual workflow. Which requests occur most frequently? What does an employee have to do to resolve them? Which systems do they open? Where do they spend time searching for information? Which steps follow clear rules? Where are delays common? And which situations clearly require human judgment?
This exercise usually gives a much better starting point than choosing an AI tool first and looking for somewhere to use it afterwards.
