Leveraging AI in contact centers is not just about increasing automation. It is necessary to meet customers’ growing expectations for fast, efficient, and personalized service. Note that a customer's first interaction with a contact center is critical.
First-call resolution and successful self-service are essential for businesses to stand out. Instead of overwhelming human agents or spending more on hiring and training, contact centers can integrate AI to reduce workloads and wait times while improving operational efficiency with high-quality customer service.
Today we are exploring the top use cases of AI in contact centers, showing how businesses of all types can enhance their customer service to build more loyal, satisfied customers.
While traditional contact centers rely on human agents to manage all customer interactions, AI contact centers use artificial intelligence to automate routine tasks for faster, smarter, and more efficient customer service.
Both still exist as central hubs to bridge customers with businesses, but AI-driven tools enhance a contact center's capabilities to improve customer service across multiple channels without adding extra work to human agents.
An AI contact center excels at handling customer inquiries around the clock to reduce wait times and improve customer satisfaction, analyzing customer data to anticipate customer needs, reducing the cost of hiring a large support team, and understanding customer intent to give contextual and personalized responses.
Contact centers around the world have used AI to improve their operational efficiency while cutting down expenses. Below are some of the best use cases of AI in contact centers, alongside real-world examples of how prominent brands have transformed their customer service with artificial intelligence.
Unlike traditional chatbots, IVAs use machine learning and natural language processing to converse with customers in a human-like manner. Additionally, the IVA continuously learns from each interaction to further refine and tailor its responses. These capabilities allow contact centers to let IVAs handle routine tasks and answer common queries, saving resources and reducing the workload on live agents.
Bank of America's AI assistant, Erica, handles common queries like recent transactions and account balances. Erica can also suggest actions, such as setting spending thresholds. For complex requests, like financial advice, Erica transfers calls to human agents, streamlining workflows, reducing call volumes, and enabling quick self-service support.
NLP is an advanced core use of AI in contact centers. Its integration allows the AI to understand and respond to human language naturally. This improves customer experience by offering accurate, human-like responses in real time.
Delta Air Lines uses NLP systems to analyze and categorize customer inquiries, understanding and processing natural language in both spoken and written forms. It reduces wait times with automated responses for common queries like confirming flight status, tracking baggage, booking changes, etc.
Driven by NLP capabilities, sentiment analysis allows the AI to detect emotions during customer interactions. This is another powerful contact center use case because sentiment analysis can provide better responses based on how frustrated customers are.
Hilton Hotels' contact centers use sentiment analysis to enhance their guest experiences. The AI can detect emotions such as frustration, satisfaction, and urgency from customer interaction through all channels, moving to offer tailored solutions if a negative sentiment is detected. This also helps Hilton Hotels identify pain points to improve services.
Additionally, sentiment analysis can use NLP to provide appropriate phrases and responses for the live agent in real time by analyzing the ongoing conversation. This data-driven approach is useful for meeting customer expectations and making informed decisions.
ICR considers several factors when routing calls. Instead of finding the first available live agent, ICR uses machine learning to find the most suitable agent before routing a customer. By analyzing real-time data, ICR can identify customers’ preferences, past interactions, and current issues before matching them with each agent's expertise to find the best one available. This drastically improves customer satisfaction ratings.
Vibrant Emotional Health is an excellent example of using Mosaicx's AI-driven ICR to enhance its operations. All calls and messages to the 988 Suicide & Crisis Lifeline are routed on priority to the nearest available mental health counselor in less than 30 seconds based on their location and availability. This approach improves the effectiveness of crisis management by minimizing wait times and ensuring that individuals in distress receive immediate, personalized assistance.
Self-resolution systems are the cornerstones of all the AI use cases in contact centers. IVAs powered by NLP-based sentiment analysis ensure customers can resolve their issues fast without any wait times or involving any live agent.
AI-driven self-service portals understand customers’ issues and guide them step-by-step through the best solution, allowing contact centers to manage large ticket volumes simultaneously while maintaining quality.
United Airlines gets hundreds if not thousands of basic customer inquiries to process ticket refunds, ask about travel credits, confirm flight schedules, etc. Its AI-powered virtual assistants understand each inquiry using NLP and guide customers through personalized solutions without involving any agent. This allows United Airlines to handle large volumes of inquiries and cut down ticket volumes and call durations to improve operational productivity.
AI is not meant to replace human agents, only to enhance them. These AI-powered tools help agents by reducing their workload to focus on more complex matters, deliver fast and more accurate responses with real-time suggestions, offer data-driven insights for informed decision-making, etc.
JPMorgan Chase uses AI to assist human agents during customer interactions by providing real-time support and relevant information. The AI analyzes each conversation to understand the context before suggesting responses, recommending actions, and offering key customer data, allowing agents to respond accurately and efficiently to banking inquiries.
One of the biggest AI use cases in contact centers is delivering personalized customer support. By analyzing customer data such as past interactions, preferences, common queries, and other histories, AI can tailor interactions accordingly to offer relevant recommendations.
Even if a live agent is handling a customer, AI can suggest an appropriate course of action based on the customer's history such as scheduling a technician visit only on the weekends. This creates a seamless customer experience across all channels.
Sephora uses an AI virtual assistant that keeps a record of all customer interactions. It helps users with product recommendations based on past purchases, preferences, and online behavior. Customers with a history of hovering over a specific skin product on the business portal get tagged as intent. If they reach out for skin product recommendations, the AI can recommend the same product they were hovering on, increasing the likelihood of sales. This creates an experience that feels unique to each customer.
AI-powered contact centers record every single customer interaction in real time for data analysis. This data collection can include dozens of metrics such as customer preferences, behaviors, etc, enabling the AI to give tailored responses.
The data collection and analysis also identify areas of improvement so a contact center can make informed decisions. If the call abandonment rates are increasing for a specific product, the sales team can quickly move in to find the root cause. This critical insight enables AI contact centers to address pain points and dynamically adapt their customer service.
American Express uses AI to collect and analyze data from every customer interaction across all channels, identifying recurring topics and recognizing trends. This allows American Express to anticipate customer needs more effectively, and use the same data to train their agents for improved customer satisfaction. If the AI notices a sudden surge in customer queries about credit card fees, the appropriate team or department will be alerted automatically as a potential area of improvement. They can then either adjust their policy or find ways to communicate better. Any changes made will also be monitored by the AI, confirming if customer sentiment scores are improving.
AI makes it easy to optimize the complete customer journey: from the initial contact to the final decision. By integrating AI systems to collect and analyze data to get insights into customer interactions and behaviors, contact centers can map out every step a customer takes. This helps identify pain points and improvement opportunities and refine sales and engagement strategies. Combined, AI contact centers can look forward to delivering more personalized experiences across all touchpoints.
HSBC is one of many global banking institutions using AI to assist customer journey mapping. Its AI system is always gathering data across all channels to detect patterns. For example, the customer journey map might point out a surge in questions related to credit card billings. The appropriate department can then look into the area to see how that friction can be reduced.
Another reason how AI systems are improving customer service is the ability to proactively reach out to customers after their initial interactions. Using both voice calls and text messages, IVAs can follow up on customers to gather feedback, confirm if their issues have been resolved, offer additional assistance, etc. Hence, contact centers can further reduce the load on human agents.
Mount Sinai Health System uses AI to automatically send personalized follow-up messages to remind patients about their upcoming appointments, and any related instructions such as lab tests, medication, etc. The AI also reaches out to patients to gather feedback about their visit and treatment, even scheduling a follow-up consultation if required. These automated follow-ups not only improve patient engagement but also reduce the burden on the administrative staff.
Every contact center can make great use of AI translation and transcription tools for real-time customer support. Businesses can enter new markets without worrying about hiring large teams of local language speakers and instead rely on AI translation tools for immediate customer interactions. NLP also works here to not only understand speech, but also identify context and intent. Simultaneously, transcription tools generate real-time text records of conversations for quality purposes and analytics.
Mayo Clinic uses AI-driven translation and transcription technologies to break language barriers for patients who speak different languages. All patient calls are automatically converted into text, making it easier for staffers to review them. Additionally, the AI translates real-time conversations between patients and agents, removing the need to hire translators or agents who speak different languages. This allows Mayo Clinic to serve a diverse market while reducing wait times and minimizing miscommunication for quality health care.
Mosaicx offers advanced conversational AI solutions to transform every customer interaction into a seamless experience. Using natural language processing and machine learning capabilities, you can expect your business' customer interactions to become faster, smarter, and more personal.
From mundane to complex queries, Mosaicx’s IVAs deliver accurate resolutions and human-like responses that take into account customers' behavior, journey, and preferences. Schedule a demo today and watch your contact center evolve into a proactive hub with data-driven decision-making that can anticipate customer needs before they even make contact.