What Are the Use Cases for Contact Center AI?
Contact center AI can be difficult to explain because it isn’t just one thing. It’s an accelerator that amplifies valuable CX outcomes, like accessibility, speed, efficiency, consistency, personalization, and learning.
That said, we’re just scratching the surface for AI’s potential in the contact center. As AI – especially generative AI – becomes more customized and readily accessible at scale, we’ll no doubt see further innovations.
The following is a list of capabilities that’s sure to grow, yet still represents some of the big places where we already see AI having a real impact in the CX space today:
Automate simple/repetitive tasks: Automatic transcription, call summarization, and other AI-enhanced automations can free up your contact center agents to focus on harder-to-handle customer inquiries and give them time for other higher-value tasks.
Chatbots and virtual agents: Chatbots and virtual agents powered by conversational AI are designed to interact with customers conversationally by using natural language processing (NLP). These bots can handle more complex inquiries than their predecessors, which were rules-based chatbots and only able to respond to specific inputs. They can also operate across channels and improve their responses over time.
Interactive Voice Response (IVR) Systems: When built using conversational AI and NLP, today’s IVR systems allow callers to simply state the reason they’re calling rather than listening to a laundry list of reasons. The IVR can then use intelligent routing capabilities to direct the caller to the right agent or simply resolve the request.
Sentiment Analysis: How can a computer tell how a customer is feeling? Sentiment analysis. Using natural language processing (NLP), sentiment analysis detects positive or negative cues in customer conversations in speech or text. Understanding how customers feel allows automation systems or agents to respond more appropriately.
Intelligent Routing: Also known as skills-based routing, intelligent routing technology powered by AI collects customer inquiries through voice, digital, or social channels, and then applies rules to route the inquiry to the agent best equipped to answer the question or resolve the issue.
Personalization: Showing your customers that you know them and understand them is one of the bedrocks of great customer service. With AI, businesses can do this at scale. AI systems can gather data from many sources, including customer profiles, purchase history, browsing behavior, social media activity, and more to identify patterns and create a comprehensive understanding of each customer. This data can then be used to proactively predict customers’ needs.
Fraud Detection: One of the main ways contact centers can use AI for fraud detection is through voice biometric authentication. By using a person’s voice to authenticate them, businesses not only have another secure way to determine a person’s identity, they can also improve the customer experience by reducing the friction that comes from answering endless security questions. AI algorithms can also aid in fraud detection by analyzing historical customer interactions and comparing new interactions with known fraud cases to flag suspicious activities.
Translation: By using neural machine translation (NMT), AI translation goes beyond word-for-word translation to better understand full phrasing, context, and complex sentence structures. When applied to the contact center, AI translation has the ability to allow agents to communicate effectively with customers even when they don’t speak the same language.
Predictive Analytics: While predictive analytics have long been a staple of contact center operations to help anticipate future outcomes and proactively prepare, this discipline gets a big boost from AI. With AI-enhanced predictive analytics, contact center operations can analyze more data more quickly, recognize patterns in data, and enable real-time decision making.
Learning and Development: AI-powered knowledge centers allow contact centers to greatly reduce the amount of time it takes to onboard new agents. Using existing knowledge bases, manuals, FAQs, case notes or other guides, generative AI can consume all of that content and use it to generate answers to just about any question an agent might receive.