8 AI Use Cases in Contact Centers: Benefits, How to Guide & Examples

AI is transforming business operations by enabling contact centers to:

• Automate repetitive tasks like call categorization, note-taking and post-call dispositioning.
• Provide agents with real-time support and intelligent insights during live calls.
• Offer managers more accurate, actionable insights to assess agent performance.
• Gain deeper insights into customer behavior with Voice of the Customer (VoC) data.

By implementing AI in your contact center operations, you can free up more time for your agents and improve your customer experience.

In this guide, we highlight eight AI use cases for contact centers, with real examples of how industries like food services, healthcare, debt collection, and financial services use Level AI to implement them.

Book a demo with our sales team to see these AI use cases in action.

Two Types of Contact Center AI: Rule-Based vs. True AI Systems

Today, there are many different AI tools for customer service, so it can be difficult to sort through them all and choose the right one(s) for you.

One common issue when implementing artificial intelligence is selecting tools that appear to use AI across their platform but really only use it in a few areas. Instead, they rely on NLP techniques like keyword matching to understand customer interactions, analyze call recordings, gauge customer sentiment, and detect the meaning of a conversation.

However, these approaches often fall short compared to ai speech analytics solutions that deliver deeper insights by capturing the true intent behind customer conversations.

Let’s take a closer look at the two and see how they differ.

Keyword Matching

Tools that use keyword matching require manually defining dozens (sometimes hundreds) of word combinations to capture the different ways customers could express their intent. Then, the tool identifies instances of these preset keywords and classifies the conversation based on what it finds.

For example, in a customer service context, a keyword system will detect words like “cancel” and “account” and flag them as an intent to cancel the service.

This approach has two major downsides. First, it’s labor-intensive and time-consuming, which results in many missed phrases because it’s impossible to think of every keyword variation and conversational nuance.

Second, keyword-based systems struggle to accurately categorize customer intent and face limitations in detecting and interpreting emotions. For instance, these systems fail to capture the full spectrum of human emotions and rely on binary classification (positive or negative) only.

Classifying all calls as either positive or negative fails to capture nuanced emotional states such as worry, anger, or excitement. True AI systems offer a more effective way to gauge how customers feel during interactions.

True AI Tools

True AI relies on advanced technologies like semantic intelligence and natural language understanding (NLU) to analyze conversations. It comprehends not just words but the intent and meaning behind them, eliminating the need for users to manually enter specific keywords.

Unlike rule-based systems, a tool with genuine AI capabilities, like Level AI, uses semantic and syntactic analysis to understand nuances in conversations with near-human accuracy. This allows it to grasp context, tone, and purpose, even when phrased differently.

With its ability to identify intent, true AI delivers more accurate sentiment analysis. It doesn’t just detect emotions, but it understands the reasons behind them.

In contact centers, a tool like this can optimize call routing, offer deeper insights by analyzing customer conversations holistically, and allow teams to automate repeatable tasks like post-call QA.

Below, we’ll show more detailed examples of how using AI vs rule-based models affects the different use cases.

Top 8 AI Use Cases in the Contact Center

1. Automatically Evaluate 100% of Calls for QA

Traditional call center QA processes are often limited by manual reviews, which are time-consuming and only cover 1–2% of total calls. As a result, QA teams struggle to identify recurring issues or effectively coach agents, as most customer interactions go unexamined.

The result is missed opportunities to address customer pain points, frustrated agents being coached on outliers instead of patterns, and a lack of actionable insights to improve customer satisfaction scores and the overall customer experience.

Level AI’s Scenario Engine analyzes conversations to identify intents expressed by agents and customers, and classifies these as scenarios. For example, if a customer says, “I was charged twice for the same transaction,” the system would classify this as a billing dispute or refund request scenario (depending on how the system is set up).

The system also understands intent shifts in conversations. If a customer initially inquires about canceling a subscription but later asks about alternative plans, the Scenario Engine identifies both scenarios as a cancellation request and a retention opportunity, respectively.

Level AI comes with a number of scenarios out-of-the-box, but you can configure your own scenarios according to your business.

Each scenario that’s identified in an interaction is labeled by a conversation tag, such as a “Delivery Issue” or “Billing Question,” so teams can quickly pinpoint conversational moments in call transcripts by searching on a particular tag. These tags help teams systematically review calls with a laser focus on the most impactful issues.

Since a single conversation can include multiple tags, quality assurance teams gain a more holistic understanding of complex customer interactions. For example, if calls tagged with “Pricing Concern” consistently result in lower CSAT scores, QA teams can more easily identify the root cause and provide agents with the right resources or training to handle price objections more effectively.

Case Study: QuinStreet Trading Company Expanded QA Coverage to 100%

Before implementing Level AI, QuinStreet couldn’t manually review enough contact center interactions to provide actionable feedback to its staff, leaving significant coaching and training gaps.

After implementing Level AI, QuinStreet reduced its staff’s manual workload and improved compliance monitoring. They saw significant improvements in agent performance through real-time coaching and actionable insights from automated analysis.

To learn more about proven QA strategies, check out our guide to call center quality assurance best practices.

2. Uncover Real-Time Customer Sentiment at Scale

Traditional metrics like CSAT surveys often provide a limited view of customer satisfaction because they tend to overrepresent extreme experiences. This happens because neutral or moderately satisfied customers are less likely to respond, leaving their voices unheard and creating an incomplete picture of service quality.

A customer might rate their experience as positive, but this doesn’t necessarily reveal unspoken emotions along the way like anger over a delay, worry about future shipping times, or disappointment with a partially resolved issue.

Identifying these underlying emotions is where customer experience analytics solutions can give deeper insight into what truly drives satisfaction or dissatisfaction.

But most tools, whether they use true AI or not, only label calls as positive, negative, or neutral. This level of feedback isn’t very useful because distinct emotions, like disappointment and anger, are often grouped together and labeled as negative, even though they require different approaches.

With Level AI, you can accurately detect a wider range of emotions than any other AI tool on the market. The seven emotions our software identifies include:

• Anger
• Disapproval
• Disappointment
• Worry
• Happiness
• Admiration
• Gratitude

This makes it possible to really prioritize which calls need your attention to address negative emotional patterns before they escalate into churn or other outcomes.

Our advanced algorithms also generate an overall Sentiment Score on a scale of 0 to 10, with 0 being extremely negative and 10 being extremely positive, that reflects the full emotional tone of the conversation.

The software incorporates all emotions expressed during a call but uses a weighted approach to focus on end-of-call sentiments, which are weighted more heavily as they typically reflect the customer’s feelings about how their issue was resolved.

For example, a customer who begins a call upset but feels gratitude after the issue is resolved indicates that the agent successfully addressed their concern.

The platform also generates an iCSAT score that’s blended from the sentiment of customer interactions, customer effort to resolve their issues, and issue resolution status.

3. Automate Call Center Agent Workflows

Contact center agents can struggle to quickly find answers during live calls, and these challenges impact both their performance and the customer experience. For instance:

• They must manually search through extensive knowledge bases, documentation, or internal wikis.
• Traditional search tools are often slow or ineffective, requiring agents to read long articles to find answers.
• New or less experienced agents may lack the expertise to recall policies, troubleshooting steps, or product details instantly.
• Even seasoned agents may struggle to keep up with evolving product updates, promotions, or compliance regulations.

Customers may feel impatient or frustrated during long waiting times as agents put them on hold to find answers, which in turn puts pressure on agents.

Real-Time Agent Assist helps agents resolve issues faster by surfacing contextual suggestions during live calls. It displays relevant FAQ items, recommended responses, and guidance pulled directly from connected knowledge bases.

Because of Level AI's deep understanding of human language and the software's ability to capture conversational nuances, it can assist and automate call center agent tasks. For example, it can:

• Display relevant topics, information, and suggestions to agents during live calls so they can better handle the interaction.
• Help agents quickly find the best answers from your knowledge base.
• Categorize calls to automate dispositioning.

When information is presented, verification features such as source links to relevant knowledge base articles, and voting buttons (thumbs-up or thumbs-down) are included. The voting buttons in particular help to improve the accuracy and relevance.

An upvote tells the AI that its suggestion was helpful and accurate. Agents have three options for downvotes: delayed response, irrelevant results, or inaccurate information.

Our AI chat system, AgentGPT, is incorporated in Real-Time Agent Assist’s search feature and auto-fills the search bar according to the topic currently under discussion, suggesting topics for the agent to peruse in order to reduce cognitive load while on a call, and minimize hold times as well.

This feature also helps onboard new agents by showing them relevant topics and at-a-glance summaries to find information quickly, giving them a set of "training wheels" to navigate customer issues.

Level AI also eliminates the need for manual call dispositioning by automatically categorizing and subcategorizing calls.

Our system uses NLU and semantic intelligence to analyze the overall purpose of the conversation and accurately assign categories in real-time. This improves consistency, saves agents time, and reduces post-call fatigue, allowing them to focus on delivering excellent service during live interactions.

Case Study: ezCater Reduced Call Handling Time with Agent Assist

ezCater implemented Level AI's Real-Time Agent Assist to help their agents navigate complex customer interactions more effectively. This led to improved customer satisfaction and more efficient service delivery.

Since ezCater started using Level AI's Agent Assist, they saw a 13% decrease in overall call handling time and a notable improvement in agent confidence during customer interactions.

4. Improve Agent Performance Scoring Accuracy

Consistent, objective performance measurement is necessary for fair agent evaluations. However, without the right tools, assessing agent performance can be a challenge, especially when trying to maintain consistency when assessing agents across a large volume of calls.

Manual call reviews by QA specialists often rely on key metrics like average handle time (AHT) to determine which calls to assess. While thorough, this process can be time-consuming and subjective, which leads to inconsistencies in performance reviews and leaves some calls under-analyzed or skipped altogether.

Level AI auto-scores agent performance across all interactions using InstaScore, which allows you to automatically rate agent performance based on your predefined scorecard. This offers a more consistent and objective picture of how well agents support customers during their interactions.

InstaScore is calculated for 100% of customer interactions and allows managers to quickly view performance metrics or flagged moments during customer interactions to determine where agent performance should be analyzed in more detail.

To help standardize evaluations, Level AI provides both out-of-the-box rubrics and the option to create custom checklists that suit your business requirements so you can create performance assessments that align with your specific goals.

Level AI offers an InstaReview feature that tags conversations that meet specific criteria, such as excessive handling time, a high number of agent-requested assists, or low CSAT scores.

Both InstaReview and InstaScore are helpful for QA teams to quickly identify which calls to review. In the next section, we also show how managers use these features for real-time monitoring and coaching.

5. Monitor Customer Calls in Real-Time

Call center managers rely on AI to gain complete visibility into live customer interactions. This allows them to offer a more high-quality customer experience while proactively supporting agents.

Without real-time insights, supervisors must rely on post-call evaluations, making it impossible to intervene when agents need immediate assistance before the caller hangs up.

Level AI's Real-Time Manager Assist solves this issue by offering live call monitoring capabilities to supervisors, including instant access to key conversation metrics, sentiment trends, and agent performance insights.

Instead of waiting for post-call analysis, managers can detect issues as they happen and take immediate action to improve outcomes.

InstaScore is also integrated into Real-Time Manager Assist. Simply click on the InstaScore value to drill down into the details behind the score and assess specific aspects of the agent’s performance.

Level AI also provides a real-time Sentiment Score with live call monitoring features. Supervisors can use this tool to see emotional shifts as they happen on calls. These insights allow managers to decide when to intervene or provide live on-call assistance to agents.

If a manager needs to step in, they can quickly assess the status of all live conversations and decide whether to intervene directly with call barging or offer real-time advice to the agent with call whispering.

Additionally, supervisors can initiate coaching sessions directly from the call center analytics dashboard to ensure agents receive timely feedback and continuous improvement.

6. Generate More Useful VoC Insights

Traditional surveys are helpful, but often fail to provide a complete picture of customer sentiment. Many customers skip surveys, and those who do fill them out often fall into extremes. They're either highly satisfied or extremely dissatisfied, which leaves the middle majority of experiences underrepresented.

Level AI goes beyond this limitation by analyzing every interaction for valuable insights into customer behavior, preferences, and unmet needs.

Level AI’s Voice of the Customer Insights automatically identifies trends and patterns in customer interactions. These insights go beyond typical satisfaction metrics, giving you actionable data to improve both the customer experience and your contact center’s efficiency.

For example, Level AI can identify repeated patterns in customer interactions, such as frequent requests for help on a specific product feature, which might have gone unnoticed previously.

It also detects, for instance, underutilized features, recognizes when customers disengage at certain points in the support process, and identifies trends in how customers prefer to use different channels (e.g., chat, phone, email) for specific inquiries.

Using VoC insights, which also derive standard VoC metrics like CSAT, NPS, and FCR from customer interaction data, provides a clearer understanding of how customers feel and what they expect from the customer experience.

Case Study: Financial Institutions Find Big Savings with VoC Insights

A major contact center provider that offers support for finance companies decided to implement AI to improve operations and reduce overhead costs without sacrificing customer service.

They used VoC data to find recurring themes that were costing them money, such as flight cancellations. The product team implemented a new system that rerouted flight cancellations to a website link, automating the process and saving them millions annually.

They estimate a total savings of over $3 million, driven by reductions in average handling time (AHT), optimized workflows, and deflected calls.

7. Answer Customer Inquiries Faster with AI Chatbots

There are many situations where customers want a fast answer and don’t want to be bothered with talking to a real human. This is where AI chatbots can work well.

They’re good for handling simple inquiries and free up human agents to handle more complex situations where customers want to talk to a real human. Customers receive faster response times and a better overall customer experience because they get answers through their preferred channels faster, while agents can focus on providing exceptional service to the calls that need it.

It also results in a better employee experience. Employees don’t have to feel rushed when answering the same simple questions hundreds of times each day. Instead, they can focus on getting really good at solving the more interesting, complex issues.

Adding AI chatbots to the contact center reduces agent workloads and helps deliver fast, intelligent, and round-the-clock customer support. Unlike basic chatbots, which rely on keyword-based algorithms and prescripted responses, AI-powered chatbots use NLU to comprehend customer inquiries and provide accurate, conversational answers.

AI-powered bots and virtual assistants can resolve a wide range of inquiries, from order tracking to troubleshooting, without needing a live agent. During peak times, chatbots efficiently manage a high volume of inquiries to prevent long wait times and ensure consistent service.

Chatbots retain conversation history and share it with live agents when escalation is needed. This helps agents stay on track and avoids the pain of asking customers to repeat their issues.

Unlike human teams, chatbots are always available. They ensure support across different time zones and cater to customers at any time. Implementing this AI technology into your contact center can elevate the customer experience while lightening the workload for agents.

8. Improve Call Handling with Interactive Voice Response (IVR) AI Agents

Traditional IVR systems typically rely on menu-based choices like "Press one for sales," but AI-driven IVR agents use natural language processing (NLP) to understand customer inquiries in real-time.

Call centers relying on these virtual agents can automatically route self-service calls based on the context of the conversation, improving handling accuracy and reducing wait times. These AI-powered IVR systems can also seamlessly integrate with your internal systems, such as CRMs, marketing tools, and ticketing platforms. This unlocks instant access to relevant customer data, which allows for highly personalized and context-rich responses.

For example, a generative AI-powered IVR can respond to complex customer issues, such as a customer experiencing network trouble, by suggesting highly specific solutions, all while learning from the interaction.

This system goes beyond simply routing calls to the next available agent. It analyzes the caller’s language, sentiment, and intent to ensure that the customer is directed to the most qualified agent.

Because AI-powered IVRs can remember past interactions across channels, customers don't have to repeat themselves during the call or when they’re routed to a different department. Contact centers rely on IVR to reduce wait times and increase first-contact resolution rates, resulting in better customer satisfaction scores.

Enhance Your Call Center Operations with AI

Level AI's contact center software allows you to streamline operations, deliver more personalized customer experiences, and improve agent productivity.

Ready to see the impact AI can have on your contact center? Schedule a free demo today and discover how Level AI can transform your customer experience and operational efficiency.