What Is an AI Call Center? 9 Powerful Use Cases

    What Is an AI Call Center? 9 Powerful Use Cases

    Generative AI has already disrupted many industries, including call and contact centers. They’re turning to AI-powered solutions to meet common challenges that are becoming more difficult to handle, like increasing interaction volumes, rising customer expectations and demands, and agent burnout.

    AI is already being widely adopted across the industry, with research from this year showing that 45% of customer support teams are already using AI. And this figure is only expected to grow, with 83% of executives considering AI a strategic priority for their business.

    In this article, we’ll cover what exactly an AI call center is, the benefits artificial intelligence brings to the industry, and how these AI solutions are used, too.

    What is an AI call center?

    Put simply, an AI call center uses the power of artificial intelligence to support and expand its customer service offering and manage a greater volume of interactions across more channels.

    AI can also be used to improve internal workflows, automate administrative tasks like data entry or call analysis, and streamline agent processes.

    They’re able to perform these feats thanks to underlying tech such as:

    • Large Language Models (LLMs): These are advanced algorithms capable of understanding and generating human-like text, making them ideal for creating conversational agents that can communicate effectively with customers.
    • Natural Language Processing (NLP): NLP empowers computers to interpret, understand, and respond to human language in a way that is both meaningful and useful, enabling call center AI bots to handle various customer inquiries.
    • Natural Language Understanding (NLU): A subset of NLP, NLU focuses on comprehending intent and context, allowing customer service AI to grasp the nuances of customer requests and provide accurate, relevant information.
    • Machine Learning (ML): This technology enables AI systems to learn from data and improve over time, ensuring more efficient call center operations as the system gains more experience with customer interactions.
    • Speech Recognition and Synthesis: Integral to modern AI call centers, these technologies enable the conversion of spoken words into text and vice versa, facilitating seamless communication between customers and virtual agents.

    How can I use AI in a call center? 9 examples

    Conversational AI and virtual agents

    AI call center software streamlines customer interactions in several ways. It can power chatbots, interactive voice response (IVR), and virtual agents to handle routine queries that don’t need human interaction or assist self-service tools.

    Additionally, AI-powered agents can be trained on your internal knowledge base to ensure they’re accurate and informative so customers can trust their answers.

    These services are popular, too. 87% of customers are receptive to conversational AI interactions, showing that AI contact centers adopting these new interfaces won’t scare off customers.

    QA auto-scoring

    Scoring calls for QA can be difficult for your evaluators for multiple reasons. Namely, it can be incredibly time-consuming to listen to many calls and evaluate every single one. Plus, it can introduce bias, both intentional and unintentional.

    AI contact centers can use ‘virtual evaluators’ to completely overhaul the scoring process, saving your team hours of time by automatically auditing agent-customer conversations.

    These evaluators can review selected interactions and score them based on custom, predefined criteria, speeding up the process and allowing human evaluators to give more insightful feedback faster.

    Virtual evaluators can eliminate human bias by removing conscious (or unconscious) bias, ensuring your agents get proper, effective feedback. They’re also useful for monitoring agents to ensure they’re adhering to compliance standards, owing to the sheer number of calls they can parse.

    Evaluator assistance

    In addition to helping evaluators score calls, an AI call center can alleviate some of their manual workload. Using the data collected from the scoring process, AI tools can automatically summarize interactions and grade agents on things like resolution, customer sentiment, and even empathy.

    These tools help your quality assurance team save time, which they can then spend working on more meaningful tasks. By automating aspects of their day-to-day, they can dive deeper into evaluation results and get better insights, which can then be used to create more targeted coaching sessions, for example.

    100% interaction analysis

    Analyzing a portion of your interactions can give you fantastic insight into your call center. But what if you could analyze every single interaction across the board? An AI contact center can quickly analyze and collect data from interactions and leverage that for all kinds of use cases.

    For example, an AI call center can use data from all of its agents to find common trends and patterns within agent behavior—persistent negative sentiment, a lack of product knowledge, weak conflict resolution—and enable the QA team to create more personalized training.

    Teams can also use that information to get more information about customer behaviors and common issues, improve processes, and add new self-service options.

    Speech, text, and predictive analytics

    Speech analytics and text analytics isn’t new, but they’re taken to a whole new level within an AI call center.

    These systems can transcribe and analyze interactions across all of your channels, helping agents provide information when needed or collecting data on customer behavior. They can even be used to improve existing self-serve tools like your IVR, making them capable of managing more complex customer conversations.

    Businesses with AI call center software can use all of the data these tools collect for detailed reporting and analytics. They can help identify critical moments affecting customer satisfaction (CSAT) scores, recommend training to course-correct, or help management make data-driven decisions about operations.

    Enhance CX with Conversational Analytics

    Sentiment and root cause analysis

    Just like it can collect data for agent training, AI contact centers can use NLP/NLU and machine learning to measure and analyze customer sentiment during interactions, such as verbal cues, tone, specific words, and even language patterns. They can analyze the tone, pace, and language used in these interactions to understand how customers feel during a call, whether they’re frustrated, satisfied, or indifferent.

    For root cause analysis, AI can process vast amounts of call data to spot common issues and trends that lead to customer dissatisfaction or queries. It can also detect patterns in speech or specific keywords that frequently appear in negative or positive contexts.

    Intelligent call routing

    AI significantly boosts call center efficiency with intelligent call routing, improving both customer satisfaction and speeding up time spent in the general queue.

    By understanding the context and urgency of each call, AI contact center software can route customers to the right agents or departments. For example, calls requiring specialized knowledge can be filtered out of the general queue, while self-service tools may handle routine issues.

    In addition, AI-driven routing systems can help manage the overall workload within the call center by distributing calls based on agent availability and skill level. This prevents individual agents from becoming overwhelmed or burnt out, helping boost customer satisfaction and agent morale.

    Automatic scheduling

    By using ML algorithms, AI call centers can analyze historical data on call volumes, handle times, and peak hours to predict future demands accurately. This helps call centers to optimize their staffing, ensuring enough agents are available during busy periods without overstaffing during quieter times.

    Additionally, AI can factor in agent preferences, skills, and availability when creating schedules. It ensures that skilled agents are available for complex tasks and helps maintain better job satisfaction among your employees by considering their scheduling preferences.

    AI contact centers can also adapt to rapidly changing conditions. For example, if there’s a sudden surge in call volumes or agent shortages, the system can adjust schedules, call in backup staff, or reassign tasks to meet the new demand.

    Knowledge base management

    Finally, an AI-powered call center can easily manage and maintain its knowledge base.

    With LLMs, contact center generative AI can create new entries that fit your company’s specific format to ensure everyone has access to the same information. This helps ensure that all important information is always current, relevant, and easily accessible.

    Additionally, AI can analyze interactions in real-time to identify gaps within your knowledge base. If multiple agents are searching for information on a new issue that isn’t documented, AI can make a note of it and suggest it to create a new entry or update an existing one with new information.

    AI can even personalize the knowledge presented to agents based on individual customer interactions. It can recommend the most relevant articles based on the customer’s history and the nature of their query, making your agents more efficient and helping them get the answers they need.

    What are the benefits of call center AI?

    Webinar: analyze 100% of agent - customer interactions

    Happier customers

    Adopting AI tools helps speed up your entire operation. From generating faster responses and resolutions to improved self-service options and lower average handle time (AHT), AI call centers can handle many more interactions than before while still maintaining an excellent customer experience.

    It’s further improved by adding better analytics and reporting tools, which can help improve internal workflows and give agents more relevant training sessions. Paired with personalization features brought about by integrating with customer relationship managers (CRMs), customers can have a unique, personal experience that’s memorable and effective.

    More productive agents

    An AI call center can empower its agents with multiple tools and options to streamline their workflows and eliminate tedious manual tasks that can take up their time. It increases efficiency by delegating basic inquiries to chatbots or virtual agents, freeing agents to tackle more complex issues.

    AI tools can also offer real-time suggestions and access to relevant information during calls, further reducing the time agents need to spend on calls. Plus, they can analyze customer interactions to identify patterns and offer insights to agents, letting them better anticipate customer needs and resolve issues faster.

    Many companies are already seeing the benefits of adopting these AI tools. During our recent AI webinar, we found that almost 50% of poll respondents felt “increased efficiency and productivity” would be the biggest impact of deploying an AI contact center.

    A study from the National Bureau of Economic Research has also shown that the staggered introduction of a generative AI-based conversational assistant increased productivity by 14%.

    Easy scaling and cost-efficiencies

    Through technologies like chatbots and virtual agents, AI call centers can handle a much larger volume of interactions without increasing staffing, significantly reducing labor costs. These types of AI tools are able to easily manage routine inquiries regardless of call volume, freeing up agents to work on more complex issues that need a human touch.

    AI contact centers also gain the ability to scale outwards to new markets and locales. They can make achieving an omnichannel contact center much more attainable without hiring extra staff and can offer translation services for a multilingual audience. These translations can even be applied to self-service options, expanding customer support to a global, 24/7 system.

    By collecting and analyzing agent data and customer behavior, training teams can dramatically improve their training and onboarding processes. The adoption of AI allows agents to easily fill in gaps of knowledge between training sessions or during calls, leading to more informed and accurate service.

    Improved reporting and insights

    AI call centers use advanced data analytics and machine learning algorithms to process and interpret massive amounts of data—far more than your average human could ever do. This enables call centers to gain an even deeper understanding of their customers' behaviors, preferences, and needs.

    It also enhances the granularity of reporting by segmenting data based on different demographics, agent performance, and the type of interactions. This helps businesses learn about their agents and their workflow just as much as they learn from their customers, which can then be used to create more effective training.

    In addition, AI contact center software can assess customer emotions, sentiment, satisfaction, and more across multiple communication channels. This provides a more nuanced look into customer satisfaction beyond traditional metrics, helping call centers improve CX and service quality together.

    Reduced compliance risk

    AI can significantly reduce compliance risk in call centers by ensuring that communications adhere to legal standards and company policies.

    Through real-time monitoring and analysis, AI tools can listen to customer interactions and instantly flag any deviation from prescribed scripts or compliance guidelines. This allows supervisors to intervene promptly and correct non-compliant behavior.

    Many AI tools can be integrated with your QA system as well (or come with their own AI features), which can help expand your coverage, too. With the ability to cover 100% of interactions and automatic scoring, evaluators can be notified when there’s noncompliance and create better training sessions to make up for areas where agents may be lacking.


    Adopting AI and implementing it into your call center is a powerful way to boost efficiency and improve the customer experience. And there’s no going back from the widespread deployment of AI within the call center industry—75% of organizations are expected to adopt AI on a mass scale in the next five years.

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    AI call centers have the power of more intelligent and effective self-service options. They can use sophisticated chatbots and virtual agents to assist customers with routine queries while collecting deep insights into their customers and agents. These solutions enable a more impactful and effective contact center.

    Are you curious about how AI tools can benefit your business? With Scorebuddy’s embedded AI, you can paint a complete picture of your call center’s performance and use the power of AI to speed up your evaluation process, automatically score interactions, and more.

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      Will AI replace call center agents?

      AI is unlikely to replace call center agents completely but will work alongside and empower them instead.

      AI automates routine tasks and handles straightforward queries, allowing agents to focus on more complex and nuanced interactions that require human empathy and decision-making skills.

      Enhancing efficiency and providing support tools enables agents to deliver higher-quality service, ensuring that the human element remains vital in customer relations even as technology evolves.

      What is AI in customer service?

      AI in customer service refers to using artificial intelligence technologies, such as machine learning and natural language processing, to automate and enhance customer support functions.

      This includes chatbots for immediate response, sentiment analysis to gauge customer emotions, intelligent routing to direct queries, and predictive analytics for personalized service.
      AI helps streamline operations, reduce response times, and improve customer satisfaction by supporting human agents with tools that enable more efficient and effective service delivery.