Types of AI in the Contact Centre (And How They’re Used)

Alexa Schmitt Bugler at Capacity explores the different types of AI used in contact centres, how they are applied, and which solutions are best suited to different use cases.

Types of AI in the contact centre include machine learning, artificial neural networks, natural language processing, generative AI and large language models, and agentic AI.

All these different types of AI work together in the contact centre context to handle customer interactions, support and work alongside agents and ultimately optimize operations.

AI has many applications in contact centre operations, from AI agents for self service deflection to internal agent assist tools to conversational analytics and even outbound campaigns.

If you’re a customer service leader trying to understand the applications for AI in the contact centre, read on to learn:

• The different types of AI used in contact centre operations and how they’re used
• How AI can improve contact centre outcomes
• How to combine different types of AI to achieve better contact centre metrics

What AI Terms Do Contact Centre Leaders Need to Know?

Artificial Intelligence (AI)

A broad field of technology that deals with the ability of machines to solve complex problems. As the name suggests, it involves machines simulating human intelligence.

Machine Learning

A branch of AI that involves using machines to “learn” or adapt to new data. Machine learning algorithms are designed to find new mathematical relationships within data sets and to solve the unknowns of a problem that is often not well defined.

An example of machine learning in the contact center might be a model that predicts whether a customer is satisfied or dissatisfied with their service interaction (such as Creovai’s CSATai).

Artificial Neural Networks (ANNs)

Part of machine learning models. Artificial neural networks work to find patterns and make decisions in a way similar to the human brain.

ANNs can be trained to identify patterns in large data sets, with human supervisors correcting mistakes so that the model continues to “learn” and become more accurate.

Natural Language Processing (NLP)

A branch of AI that uses machine learning to enable computers to understand written or spoken language.

Some examples of natural language processing applications in the contact center include interactive voice response systems, chatbots, sentiment analysis, conversation intelligence, and real-time agent assistance.

Generative AI

A branch of AI based on machine learning and ANNs, generative AI creates new content based on provided data sets. While generative AI is still relatively new, 86% of contact centre leaders say they are planning for GenAI investments.

Example use cases include generating call summaries, augmenting chatbots, recommending next best actions to agents, and responding to questions or prompts about customer data.

Large language Models (LLMs)

A part of machine learning and AI specifically focused on processing and generating human language. LLMs aren’t necessarily a subset of generative AI but often are (you’ve likely heard the two phrases used together).

They are trained on large data sets of text and are good for NLP tasks such as translation, summarization, and simulating human-like conversation.

Agentic AI

Connected and autonomous AI systems that can act independently to achieve specific goals.

An example of agentic AI might be a chatbot that captures customer information and writes it back to a CRM, or AI agents that analyze what is being said during a call and provide recommended knowledge articles to human agents in real time.

How Are Contact Centre Leaders Using AI?

AI is now a crucial part of contact center services. In a fast moving industry, using tools like AI-powered analytics or AI agents for self-service is table stakes. Here’s a bit more about common capabilities in contact center technology:

Conversation Intelligence

Conversation intelligence software is a technology that uses machine learning and natural language processing to analyze customer conversations and uncover trends and insights.

Conversation intelligence helps contact centers identify areas for operational or product improvements, points of friction in the customer journey, agent training needs, signs of churn risk, and more so they can improve the agent and customer experience.

QA Automation

One major benefit of AI in the contact center is its ability to automate repetitive and time-consuming tasks, freeing managers and agents to focus on higher-value activities.

Quality assurance (QA) automation uses the same techniques as conversation intelligence to review 100% of customer conversations for objective QA criteria.

This reduces the time managers spend on manual QA reviews and gives them greater visibility into agent performance.

It also helps agents improve by giving them targeted insights into their performance and highlighting data-backed opportunities for improvement.

Real-Time Agent Assistance

Real-time agent assistance often uses machine learning, natural language processing, and generative AI to guide agents through conversations with customers, ensuring the agent completes the appropriate steps and efficiently resolves the customer’s issue.

Agents may see prompts on their screen with the next best action, a dynamic checklist that shows when they have completed the required steps, and recommended resources or information to share with the customer.

Predictive Analytics

Predictive contact center analytics uses AI to analyze large data sets and make predictions using mathematical relationships within the data.

Example use cases include predicting hold times, predicting call volumes, and using customer data to route calls to the agents best suited to handle them.

Predictive analytics can also help contact center leaders predict how customers would have rated their service interactions, even if they don’t complete a post-interaction survey.

After-Call Work Automation

After-call work typically includes summarizing calls, updating customer records, and taking other administrative actions to resolve customer issues.

It’s vital, but it can also be time-consuming–and the more time agents spend on after-call work, the less time they have to spend assisting customers.

Fortunately, AI can automate many common after-call work tasks. For example, AI solutions can automatically update customer information in a CRM, generate a call summary, and trigger criteria-based actions such as scheduling a follow-up call.

AI Agents

It used to be chatbots—now it’s AI agents. AI agents for support have become a popular application of AI in the contact center industry.

They use natural language processing and machine learning algorithms to interpret customer queries and respond to them autonomously.

Contact centers can provide customers with quick and responsive service 24/7, improving customer satisfaction rates while reducing operational costs.

AI agents can also be customized for specific industries and use cases, such as product recommendations, account assistance, or scheduling appointments. Additionally, chatbot conversations can be analyzed using conversation intelligence software to identify areas for improvement.

Combining Generative AI and Machine Learning in The Contact Centre

As a contact center leader, you may be wondering whether you should be investing in generative AI or machine learning-based technology to increase efficiency and help your agents and customers.

Each type of AI has its strengths, and each is best-suited to different use cases.

Generative AI

Generative AI is best for creating original outputs based on prompts and may be useful for generating call summaries, recommendations for agents, or insight highlights based on conversation data. However, there are a few caveats to keep in mind.

One key consideration is the computing power necessary for processing prompts with varying degrees of complexity and context.

The more context you put into a prompt, the more computational resources are needed to generate relevant responses–and the higher the cost to run the model.

Additionally, due to the risk of a generative AI model generating inaccurate information, it’s important to have humans review outputs–especially outputs that will be shared publicly.

Contact center leaders should also keep in mind that large language models are not specifically trained for contact center applications.

Machine Learning

Machine learning is best for rule-based, objective tasks. While machine learning models can’t be used for the same creative tasks as generative AI, they can increase efficiency and deliver valuable insights to the contact centre.

Use cases where machine learning shines include identifying specific call events or categories based on the words and phrases a customer or agent uses, completing objective fields in a QA scorecard, and predicting how customers would have rated their interactions.

Many contact centre solutions now use both generative AI and machine learning models to perform different tasks and improve the overall experience for users.

What is Agentic AI in The Contact Centre?

Agentic AI for contact centres to connected AI systems that can take autonomous action across tools and channels to complete a goal, not just respond to a question.

In the contact centre, agentic AI goes beyond chatbots that answer FAQs. An AI agent might capture a customer’s issue, look up their account in a CRM, initiate a return, send a confirmation email and log the interaction — all without a human agent involved.

Traditional bots follow fixed decision trees and conversation flows. Agentic AI reasons through a problem, determines the right steps and executes them dynamically.

That’s what makes it the most significant shift in contact centre technology since the introduction of NLP-based chatbots.

Contact centers deploying agentic AI typically see deflection rates increase significantly, because the AI can actually resolve issues end-to-end, not just triage them.

What Type of AI is Right For My Contact Centre?

Use Cases	Best-fit AI type	Why
Call Summarisation	Generative AI	Requires contextual understanding of open-ended conversation
QA Scorecard Completion	Machine Learning	High-accuracy, rule-based scoring with low tolerance for error
Customer Sentiment Analysis	NLP + Machine Learning	Identifies patterns and emotional signals in language
Next-Best-Action Prompts	Generative AI + Machine Learning	Contextual reasoning layered on structured routing rules
Call Volume Forecasting	Machine Learning (Predictive Analytics)	Pattern recognition across historical data sets
Chatbot/Self-Service Deflection	NLP + Agentic AI	Language understanding combined with autonomous action across channels
After-Call Work Automation	Generative AI + Automation	Handles summarization, CRM updates and triggered follow-ups

The right AI type depends on the task. Machine learning and NLP excel at structured, high-accuracy work. Generative AI handles context-heavy tasks where the output varies. Agentic AI takes action, not just answers. Most modern contact center platforms combine all three.

AI to Improve The Agent and Customer Experience

As you evaluate different types of AI solutions for your contact center, keep your focus on the desired outcomes: increasing efficiency while improving the agent and customer experience.

It’s not about adding technology for the sake of technology; it’s about choosing tools that will help your people succeed in their roles and ensure your customers’ needs are met.

Achieving these outcomes will feed into long-term benefits for the entire company: lower employee turnover, greater customer satisfaction and loyalty, and greater customer lifetime value

This blog post has been re-published by kind permission of Capacity – View the Original Article

For more information about Capacity - visit the Capacity Website

Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: Capacity
Reviewed by: Robyn Coppell