3 types of AI you need to know if you work in customer service

Keeping up with the rapid advances in artificial intelligence can seem impossible. This post provides a quick and clear overview of the various types of AI for customer service currently available in the contact centre solutions market.

TYPES OF AI USED IN CONTACT CENTRES

1. CONVERSATIONAL AI

It’s the technology that allows machines to interact with humans in a natural way. It is trained on large volumes of data (texts, recordings, video calls) to understand the semantics of human interactions.

Despite the name, it’s not limited to chat channels: it can also be used for voice, without pressing buttons or using specific commands.

The most well-known conversational AIs among the general public are assistants like Siri or Alexa, although these are less sophisticated than the conversational AIs available today.

HOW DOES CONVERSATIONAL AI WORK?

1. The user makes a request (prompt)
2. The AI analyses the message
3. It identifies the user’s intention
4. It generates and delivers a response

EXAMPLES OF CONVERSATIONAL AI IN CONTACT CENTRES

In the contact centre industry, conversational AI is used in voice channels as a replacement for traditional IVRs. Its main advantage is that it allows users to freely state the reason for their enquiry, instead of choosing from a limited menu.

In text channels, we find bots or virtual assistants that, just like in voice, are able to understand open-ended questions.

Regardless of the touchpoint, what sets conversational AI apart from older intent-detection technologies is that it doesn’t rely on commands or keywords. It understands full messages, can interpret the user’s emotional state and responds accordingly—as a human agent would.

While a machine can’t feel empathy, it can generate empathetic responses like: “I’m sorry your order hasn’t arrived yet.”

2. GENERATIVE AI

GenAI is the technology that allows machines to create content from scratch (text, images, code, etc.). For example, ChatGPT.

It is trained on large amounts of data (articles, posts, images…) to recognise patterns and generate new content accordingly.

A conversational bot may include a generative AI model, allowing it to interact in a more creative, natural and personalised way.

HOW DOES GENERATIVE AI WORK?

1. The user makes a request (prompt)
2. The AI understands what kind of content it needs to create
3. It generates a new text (such as a summary or a reply)
4. It delivers the content ready to use

EXAMPLES OF GENERATIVE AI IN CONTACT CENTRES

An example of generative AI in contact centres is speech analytics solutions. Based on the transcription of a call or chat history, the AI can generate a title and summary of what was discussed, categorise the conversation, assess the customer’s satisfaction and score the agent’s performance.

3. AGENTIC AI

Unlike generative AI, agentic AI is proactive: it doesn’t wait for instructions. It can make decisions autonomously, set priorities, and perform actions without direct human input.

This means it doesn’t need a user prompt to operate. It reacts automatically when its environment changes.

HOW DOES AGENTIC AI WORK?

1. It detects an event or need (without being asked)
2. It interprets the situation and defines a goal
3. If the task is complex, it breaks it into smaller steps
4. It checks the conversation history
5. It takes action in external systems (CRM, email, etc.)
6. It resolves the task and updates the system (e.g., logs it in the CRM)

EXAMPLES OF AGENTIC AI IN CONTACT CENTRES

A clear example of agentic AI in contact centres is copilots integrated into contact centre software. While the agent is speaking with a customer, the system detects that the issue is related to a delivery. It then proactively checks the CRM, sends an alert to the logistics team, and suggests a response so the agent can inform the customer that the issue is being handled.

The challenge with these agently powered copilots is that they can feel intrusive to customer service professionals. That’s why many software providers choose to integrate them in a less proactive way, allowing the agent to activate the flow manually, for example by pressing a button.

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