How Call Centers Can Improve Their Operations by Implementing AI?

The cutting-edge technology—Artificial Intelligence (AI) and Machine Learning (ML) have impacted several industries and their processes, including banks, HR processes, hospitals, and many more. Gartner suggests that 37% of organizations have globally implemented AI technologies in some form within their operations.

One such area AI has revolutionized and has a significant impact is the call centers. Long gone are the days when customers and users had to wait over calls to talk to an agent and get their queries resolved.

Artificial Intelligence in call centers has improved several operations, streamlined workflows, and boosted efficiency—making things easier for both the call center agents and the customers.

In this article, we’ll see some AI use cases to improve call center operations and their greater impact on the revenue and overall customer experience. Let’s get started!

How does AI Improve Operational Efficiency in a Call Center?

The need for call center automation to improve operational efficiency and offer a better customer experience is at a high.

Here are some exciting ways AI helps call center operators deliver exceptional results and earn more revenue:

1. Intelligent call routing

AI intelligent call routing uses self-learning algorithms to analyze customer personality models, behavioral data, and past call histories.

Based on this gathered data, it then determines whether to send the routine and simple requests/queries to bots or route the call to an agent if the query is complex and requires a proficient expert to answer it.

AI uses the data and information at its disposal to route the call to the right agent who’s well-equipped to handle specific types of requests or questions—ensuring quick and effective responses and closing of the tickets.

This efficient and successful match-making strategy and automated call answer system helps handle customer requests much more effectively and allows agents to respond to the customers much better.

2. Data collection and analysis

One of the most substantial applications of AI in a call center is the collection and analysis of user data. Every customer interaction and conversation offers a wealth of information, and an AI call center captures it well to analyze and use this data intelligently to drive better results.

AI-driven data analytics processes include:

  • Sentiment analysis
  • Advanced sentiment analysis
  • Natural Language Processing (NLP)

Call centers can use the data gathered through AI-driven analytics engines to optimize processes in impactful ways.

For instance, have you ever had automated suggestions pop up on your screen while entering details like your name, or phone number, email address, or while filling a form or contacting a business? How easy and convenient it gets to enter the data, right?

AI retrieves customer details and suggests them while entering the information, so they don’t have to enter the details themselves—improving customer experience manually.

In addition, simple and advanced sentiment analysis and NLP provide hints on the customer’s feelings about the brand, products, or particular services and whether they’re happy, satisfied, or angry—allowing call centers to adjust their strategies and deliver better customer service.

3. Facilitating self-service support

AI call center automation technology eases agents’ workflow and boosts operational efficiency by using AI tools to empower and facilitate self-service.

Virtual assistants and live chats are great examples of routine, repetitive, and simple questions to assist customers quickly. This has two benefits:

  • It makes it simpler and quicker for customers to resolve their own queries via chatbots and other services available 24/7.
  • The agents no longer have to answer questions like, “How to place an order?”, “How to pay a bill?” etc.—so they can invest their time to handle many complex calls.

Accenture’s report suggests that 57% of businesses agree that they can yield significant ROI with minimal efforts using chatbots. Moreover, ML-driven chatbots are observed to get better over time by learning from customer interactions and improving their responses in the future.

4. Monitoring agent’s performance

AI call center process automation isn’t about replacing human agents altogether—but assisting agents and supporting their performance boost through solutions like sentiment analysis and Real-time Speech Analysis (RTSA).

RTSA analyzes customers’ and agents’ speed in real-time using AI—providing live feedback to the agents or other senior officers on the call. This feedback can entail instructions regarding what to say in the call and how to say it.

In addition, this solution can also provide relevant information to the agents to help handle the calls much better.

On the other hand, sentiment analysis helps agents determine the caller’s mood by analyzing factors like length and volume of pauses within the calls.


The potential of AI in the call center operations and overall environment is boundless.

Though AI helps call centers in a wide range of way, the primary use cases of AI call center automation revolves around these three major areas:

  • Automating routine processes to free up agents’ time and save efforts.
  • Automating data collection and analyzing data to gain valuable customer insights and refine strategies accordingly.
  • Supporting self-service to improve response quality and time.

Our platform allows banks and credit unions to enhance call center productivity, engagement, and revenue with zero call wait times using intelligent AI solutions. Check out our services to transform your call centers into revenue centers.