How is AI used in customer service?

Responding late, always repeating the same tasks or not getting to everything without expanding the team is a very common situation in many companies. That’s why AI for customer service has become one of the most useful applications of artificial intelligence in the business environment. Not because it replaces people, but because it helps to make customer service more agile, more orderly and more consistent.

The key is to understand that AI is not only used to answer messages automatically. It is also used to classify queries, prioritize conversations, resolve frequently asked questions, follow up and maintain a smoother service across multiple channels at the same time. Applied well, it improves the customer experience and relieves some of the team’s operational burden. Poorly implemented, it generates unhelpful responses, a feeling of coldness and more work than it promises to save.

Therefore, before implementing any solution, it is important to be clear about what you want to use it for. The question is not only what AI can do, but what specific problem it will solve in customer service.

What does it mean to use AI in customer service?

Using AI in customer service means relying on systems capable of automating a part of the interactions with customers or contacts. That automation can be very simple, such as answering a frequently asked question, or quite a bit more advanced, such as having a conversation, identifying a need and directing the user to the next step.

In practice, this allows a company to better serve its customers without relying exclusively on manual responses. For example, a system can greet, ask for basic information, resolve initial doubts, suggest an action or refer the case to a person. All this reduces waiting times and makes the process more comfortable for both the customer and the team.

It is also important to understand that AI does not only act in the user’s face. Its value often lies in what happens behind the scenes: summarizing conversations, organizing contacts, detecting repeated topics or helping to better prioritize work. In other words, it not only improves response, it also improves management.

How is AI used in customer service?

AI is used primarily in those parts of care where there is repetition, volume or a need for speed. One of the most common uses is the management of frequently asked questions. Schedules, availability, services, indicative prices, status of a request or basic steps in a process are clear examples of information that can be automated quite efficiently.

Another very common use is initial data collection. Instead of having a team member start each conversation from scratch, the AI can ask for the name, reason for the inquiry, preferred channel or any other data needed to better prepare for subsequent care. This saves time and improves conversation continuity.

It is also used to better organize workflow. If a company receives messages via WhatsApp, Instagram, web, mail or phone, AI can help sort out which queries are urgent, which are commercial, which are support and which need referral. That order makes a huge difference in businesses where the main problem is not lack of customer interest, but lack of time to manage everything well.

Another interesting use is follow-up. Many opportunities are not lost because the service is bad, but because no one picks up the conversation in time. AI can schedule reminders, reactivate contacts or leave the next step ready without always relying on the team’s memory or workload.

What AI tool can help improve customer service?

The answer depends on the type of company, the volume of inquiries and the channels through which it interacts with its customers. Still, most useful customer service tools tend to fall into one of these categories: chatbots, conversational AI agentsvoice assistants, omnichannel platforms and automation systems connected to support or follow-up processes.

A basic chatbot may be sufficient when the goal is to solve simple questions or filter the most repeated queries. On the other hand, when a company needs more natural conversations, multiple channels or greater personalization capabilities, it usually makes more sense to go for conversational AI agents.

The main difference is in the depth. A classic chatbot usually works with more closed responses. An AI agent can better understand the context, adapt more to the user’s intent and have a more flexible conversation. This doesn’t mean always choosing the most advanced option. It means that the tool must be aligned with the actual need.

To make the right decision, five points should be considered:

• Ease of implementation
• Quality of experience
• Customization capabilities
• Supplier support
• Possibility of measuring results.

A solution may look very powerful on paper, but if it is difficult to implement or if the team does not use it well, the impact will be low.

Which AI can speak?

When people talk about “AI that can talk,” they are usually referring to technologies capable of voice conversations. This includes virtual call assistants, automated voice systems, and artificial intelligence phone answering solutions.

Its usefulness depends very much on the context. In companies where the telephone is still an important channel, AI can help gather the reason for the call, identify the customer, resolve simple issues or direct the contact to the right department. This reduces waits and prevents the team from spending time on repetitive tasks that could be solved in a more agile way.

However, the AI phone support only works well only works well when it is well designed. If the experience is rigid or prevents you from reaching a person when you need to, the result is often frustrating. That’s why good voice automation should not act as a barrier, but as a useful filter. Its goal is not to complicate access, but to streamline resolution.

At this point, many companies make the mistake of automating too soon or too fast. Before applying AI to calls, it is worth reviewing what types of queries are coming in, which ones are repetitive, which ones need human intervention, and what the customer actually expects when he or she decides to call.

What is an example of AI in customer service?

A simple example would be a company that receives many inquiries via WhatsApp or the web. Without AI, each conversation depends on someone being available, reviewing the message and responding manually. With AI, the system can respond immediately, ask for the basics, resolve frequently asked questions, and leave the conversation ready for the team to act with more context if needed.

Another example would be a company with many similar requests throughout the day. Instead of taking up team time answering the same thing every time, AI can filter those interactions and leave the most important, complex or sensitive cases to staff. Not only does this improve efficiency, it also allows people to focus on tasks where they actually add the most value.

There are also cases where AI helps a lot in business follow-up. For example, it can recall an appointment, confirm interest, retrieve a paused conversation or summarize what has already been discussed so that the next contact does not start from scratch. This type of support is especially useful in SMEs, where the team often takes on several functions at once.

Buying an AI agent: what should a company value?

Buying an AI agent should not be a decision based solely on novelty or the pressure to “go digital”. It makes more sense to see it as an operational decision: what task will you improve, what burden will you reduce, and what experience will you deliver to the customer.

One of the most important aspects is the actual usability. There are tools that are very impressive in a demo, but then they don’t fit in with day-to-day operations. That is why it is important to analyze whether the system will solve a frequent business problem or whether it will simply add another layer of complexity.

It is also important to review the implementation effort. The simpler the implementation, the easier it will be to see results. Added to this is personalization. The tool must adapt to the tone, type of customer and the company’s customer service style. If the conversation sounds generic or unnatural, automation loses value.

Another key point is the ability to continuously improve. Customer service changes, questions evolve and processes are adjusted. It is therefore advisable to choose solutions that allow you to review results, correct errors and optimize performance over time.

How to use AI without losing closeness to the customer

This is one of the points of greatest concern to any company manager. Proximity continues to be an important value, especially in SMEs and businesses where trust has a direct influence on the purchase decision or loyalty.

The good news is that AI doesn’t have to make care cooler. In fact, it often makes it better. When used well, it allows you to respond faster, avoid unnecessary silences, and have the person on the team come to the conversation better prepared. That can give a sense of more careful attention, not less.

The key is not to try to automate everything. It is usually most effective to leave repetitive tasks, predictable questions and first steps to the AI, while people take care of what requires judgment, empathy or decision making. That combination usually yields better results than either extreme.

 

AI for customer service is no longer just a trend, but a real way to respond better, save time and offer a more agile experience without losing proximity. Properly applied, it helps companies better manage their channels, reduce repetitive tasks and provide a more consistent response to each customer.

The key is to find the right balance: automate what brings efficiency and reserve to the human team what needs criteria, context and personal treatment. When this balance is achieved, artificial intelligence ceases to be a promise and becomes a useful tool to grow with more order and better results.

If your company wants to take this step with a practical solution that is well adapted to your operations, Glofera will be happy to help you. You can schedule a demo HERE, write to us at hola@glofera.com or call us at +34 900 600 300 to speak to one of our consultants