Best Contact Center AI Use Cases

Artificial Intelligence (AI) is transforming how organizations utilize data. How can your contact centers adopt AI solutions to improve business workflows, boost revenue, and more? 

Simply adopting an AI-driven solution and expecting immediate ROI is not enough. You need to know how to apply AI to target your contact center's pain points. MiaRec has helped hundreds of contact centers across retail, financial service, and government sectors boost revenue and customer loyalty with its AI-driven Voice Analytics and Auto Quality Management solutions. 

In this article, we will explore how AI is currently used in contact centers and why you should consider adopting AI for your organization. By the end of this article, you will know how to best utilize AI for your contact center’s needs and what best practices and next steps you should consider to guide your contact center’s AI journey.

To help you navigate the AI market, we have compiled the most popular AI use cases in contact centers:

• Gaining Valuable Customer Insights
• Automating Post-Call Work
• Automating Compliance and Quality Management (QM) Processes
• Improving Customer Experiences

What Is An AI-Driven Contact Center Solution?

At its core, AI enables machines to think. According to IBM, "Artificial Intelligence is a field, which combines computer science and robust datasets, to enable problem-solving.” This broad definition means that AI-driven solutions cover a wide variety of use cases. Modern contact centers have adopted AI solutions to get customer insights, improve quality management processes, better utilize their data, and more. 

Most AI-based contact center solutions use a combination of Machine Learning (ML) and Natural Language Processing (NLP). According to Columbia’s School of Engineering, Machine Learning is a subset of AI that “uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions.” In other words, Machine Learning tools learn to recognize patterns and insights that can be used to drive business decisions, improve processes, and more.

Meanwhile, NLP is a branch of AI that helps machines understand text and speech similar to how a person would. NLP-based contact center solutions can understand and analyze human speech. Popular NLP-based applications include Speech-To-Text (STT) transcription, Sentiment Analysis, and chatbots.

As AI continues to evolve, its range of applications will only continue to grow. Contact Centers are enthusiastic about the future of  AI; Gartner predicts that Conversational AI tools could reduce agent labor costs by $80 billion in 2026. Generative AI is currently generating a lot of buzz for its potential to improve text-based conversations and to better support agents during live calls. This makes it especially beneficial for real-time Agent Assist, automated call summaries, and chatbots. 

Why Adopt An AI Solution?

You can reduce operational costs in the long run, personalize customer experiences while improving agent performances, and more by adopting AI solutions.

For example, you can use AI to automate repetitive processes by creating call summaries or automating call scoring. By automating contact center processes, your workers can have more for high-value tasks, you will gain a more comprehensive view of contact center operations, and customers can enjoy a better experience. 

It is important to emphasize that AI tools are meant to enhance agent interactions, not replace them. A majority of customers still prefer speaking to agents for more complicated inquiries. The future of AI is bright, but only if it is used properly.

If you are new to implementing AI-based solutions for your contact center, or even if you are a seasoned AI-user, we highly recommend checking out our AI Maturity Model. This model can help you to assess where you are in your AI journey and provide you with recommended next steps to further enhance your AI capabilities. You can download the maturity model as a mini 6-slide presentation here.

Contact Center AI Use Cases

Gaining Valuable Customer Insights

Contact Center AI solutions often offer Voice Analytics features to transcribe and analyze calls for meaningful insights that will improve contact center processes. In this section, you will learn how to use Voice Analytics to understand consumer behavior, measure agent performance, and improve customer experiences.

Turn Call Audio Into Accessible Transcripts

Most Voice Analytics solutions offer Speech-to-Text (STT) transcription. These solutions use AI to turn your audio into call transcripts. Supervisors can then skim the call transcripts to quickly understand agent calls, rather than having to listen to the entire audio.

Gain A Deeper Understanding Of Customer Sentiment 

Most contact centers offering Sentiment Analysis will offer either rule-based or NLP-based Sentiment Analysis. Unlike rule-based sentiment analysis, NLP-based Sentiment Analysis offers a more nuanced analysis by measuring context. By analyzing context, NLP-based Sentiment Analysis is able to better determine customer sentiment throughout the conversation. With NLP-based Sentiment Analysis, you can understand how customers felt during their call with the agent. These insights can help you better understand how to meet customer expectations. 

Image: Screenshot of MiaRec Plaform showing sentiment analysis by department.

Organize Calls To Detect Trends, Prevent Problems From Escalating, And More

You can use Topic Analysis to organize calls by topics such as products, competitor mentions, and more. It uses Generative AI technology, which is very flexible and allows you to effortlessly configure what specific areas of interest are important to you right now.This powerful tool provides a clear and organized visualization of the key factors driving your calls, allowing you to take immediate action and drive improvement across your entire organization.

For example, Topic Analysis can be used to gather customer reviews and feedback. If you wanted to see what customers were saying about a specific product, you could use Topic Analysis to sort calls that only mention that product. 

Image: Screenshot of MiaRec Software showing the calls categorized by Generative AI-based technology.

Support Agents During Customer Calls 

AI can be used to support agents during calls. Some Voice Analytics solutions provide real-time Agent Assist services that can provide recommended next steps, suggested scripts, and more during the call. This can help agents provide better customer experiences while reducing call times.

Automating Post-Call Work

Traditionally, contact center agents would take notes throughout the call. These notes would cover why the customer was calling, how the call was resolved, and any additional key information. These insights would then be turned into a call summary. Supervisors, other agents, and your quality assurance team would then use the call summary to review the call, complete any necessary follow-up, and more. 

An AI-based Automatic Call Summary tool can streamline this process. Rather than taking notes throughout the call, your Auto Call Summary solution would use your call transcript to create a call summary for you. This allows agents to better focus on the customer.

Image: Screenshot of MiaRec AI Call Summary feature demonstrating structured call summary with call type categorization and key facts extraction.

Automating Compliance and Quality Management (QM) Processes

Your contact center has a Quality Management (QM) process to make sure all contact center conversations are up to your organization's standards. Here are a few AI tools you can use to get a more comprehensive view of how your contact center is operating. 

Ensure Your Agents Are Always Compliant

Without Automated Data Redaction, most contact centers require agents to manually pause and resume calls to prevent their customers' sensitive information (SSIN, birth dates, etc.) from being recorded. Manually redacting data leaves room for human error. 

An AI-based Automatic Data Redaction solution analyzes interactions for potentially sensitive information and redacts it from the call audio and transcript. This ensures all of your calls meet compliance regulations and standards, allowing agents to focus better on the customer. 

Automate Call Scoring For Faster And More Accurate Insights

Call scoring is when contact center supervisors review agent calls to measure the agent's performance and review script effectiveness. Most contact centers are only able to manually score less than 5% of their calls. 

By automating call scoring with an AI-based tool, contact centers can grade 100% of their calls automatically. This allows for a more accurate representation of their agent’s performance and allows supervisors to give agents more personalized and meaningful feedback. 

Image: Screenshot of MiaRec Auto Score Card, showing script adherence and call evaluation scores. 

Improving Customer Experiences 

In this section, you will learn how AI can improve customer experiences while decreasing agent workloads. Discover AI-driven tools that will support agents before and during their customer calls.

Support Agents In Real-Time During Customer Calls

Some Voice Analytics solutions provide real-time Agent Assist services that can provide recommended next steps, suggested scripts, and more during the call. This helps agents respond to customers confidently and quickly and provide customers with helpful resources.

Provide Customers with Self-Service Options

Conversational AI tools are AI-driven tools that interact with customers. It is typically associated with Chatbots and Interactive Virtual Assistants, both of which can answer repetitive and basic customer questions. 

An Interactive Virtual Assistant (IVA) is a virtual assistant that automates call center processes. It uses customer data to provide personalized, human-like interaction. An IVA solution typically includes chatbots and text-to-speech recognition to route customers to the best channel that will answer their questions.

Chatbots are a valuable customer service tool. It gives customers the option to interact with your business without having to face an agent. Customers can find answers to basic questions on their own, reducing agent workloads. 

Improve Your Customer’s Self-Service Calling Experience

Standard Interactive Voice Response (IVR) systems have a set of predefined rules: greet customers at the beginning of inbound calls and then present a menu. Anytime you have been on call and heard “Press 2 for Spanish”, that is an example of an IVR. 

However, Conversational IVRs, or AI-based IVRs, provide a more personalized and helpful experience. With an NLP-based Conversational IVR solution, consumers could simply state their reason for calling and be directed to the appropriate self-service or agent channel. 

It may decide on the best agent for the call based on expertise or personality, depending on how your contact center decides on the determining metrics. AI-powered Call Routing can also provide agents with insights into customer behavior and needs, so that agents can personalize calls and effectively address the customers' issues.

Conclusion: What AI Solution is Right for My Contact Center?

Deciding on what AI tools your contact center needs can be difficult, especially when different contact center solutions offer different tools and services. The right AI-based contact center solution should align with your business goals.

For example, MiaRec is a Conversation Intelligence platform that provides Voice Analytics and Generative AI-powered Automated Quality Management solutions. We are a great choice if you want to analyze agent calls for customer insights, automate quality management processes, and ensure compliance workflows with AI. We also help automate post-call workflows with our powerful AI-based Automatic Call Summary.  

Free feel to contact MiaRec's sales team to learn more about how your contact center can adopt AI tools to improve customer experiences and agent performances. Alternatively, check out the rest of our blogs to learn more about AI use cases, Voice Analytics best practices, and more.