“For technical support, press 1. For billing inquiries, press 2. To hear about our latest promotions, press 3.” Once you’ve chosen technical support, you’re prompted to enter your account number using the keypad. You carefully input the long string of digits, only to be told, “Sorry, we didn’t recognize that account number. Please try again.” Finally, after several failed attempts, you’re given the option to speak with a customer service representative. Relieved, you eagerly select this option, only to be met with a recorded message: “Due to high call volumes, all of our representatives are currently busy. Your estimated wait time is… thirty minutes.” Show of hands if you’ve ever been through this menu madness!
IVRs have been an essential, and commonly used, tool in contact centers for a long time—in fact, the first IVRs were commercially available in the 1960s. However, they’re definitely showing signs of their age and have serious limitations that fail to meet the needs of modern customers, leading to frustrating experiences that jeopardize, rather than enhance, customer satisfaction and loyalty. Generative AI changes all of this. Instead of scripts and established processes based on rules that always have a predictable outcome, generative AI’s conversational approach learns and adapts to customer needs in real time.
Limitations of traditional IVR systems.
Traditional IVR systems offer a one-size-fits-all approach to customer service. One important drawback is their lack of natural language understanding. They have basic troubleshooting and rely on pre-defined and rigid pathways that only allow customers to navigate through a set of specific options. But voice prompts are not flawless either. Diverse linguistic variations, such as different accents, speech issues, or background noise often result in misinterpretation of customer inputs, leading to customer frustration instead of swift resolution. Navigating through labyrinths of “press or say 1 for this and press or say two for that” traps customers in a cycle of confusion that often ends up with calls being routed to the wrong department or abandoned altogether.
IVR systems also have very limited personalization and contextual awareness, for example, they can’t adapt answers based on individual customer preferences or historical interactions. Instead, customers receive a generic answer that usually doesn’t meet specific needs.
Due to these limitations, IVR systems add to the frustration experienced by both customers and agents. Confusing menu options that don’t go anywhere useful, and the long wait times escalate their dissatisfaction with the service. Likewise, agents are overwhelmed by the amount of repetitive inquiries that could be automated with the assistance of AI technology. The IVR’s inefficiency increases interaction time and strains the contact center resources, leading to a reduction in productivity and an increase in operational costs.
How generative AI transforms customer service workflows.
Natural language understanding is one of the most important transformations of generative AI. Customers and automated systems can now communicate seamlessly, removing all barriers of traditional IVR systems and scripted interactions—most importantly, the maddening decision trees that are the hallmark of bad customer experiences! With generative AI you only need to instruct the system about the type of requests your business entails and actions to it, all the steps in between will be determined by the system.
To take this out of the abstract, if you were to set up a workflow for an autobody shop using generative AI for customers that want to schedule an appointment, all you would need to do is define a general customer inquiry (e.g., schedule an appointment) and then the endpoints (e.g., automated scheduling, escalation to an agent, etc.). The large language models (LLM) powering the system can understand all iterations of that request (e.g., I want to book an appointment, I want to schedule a meeting, I need to meet someone in person, etc.) without any additional training and make decisions based on the context of the conversation. For example, if a customer called in and told the system, “I need to come in right away. I’m driving, and my engine just started smoking,” it would immediately escalate you to an agent instead of trying to funnel you into a self-service experience.
A traditional IVR system, even augmented with traditional natural language processing (NLP) capabilities, would require defining 10-20 different iterations of the same phrase to capture all possible requests, and then building a rigid flow to drive customers to the appropriate endpoint. There will likely be unnecessary steps so as to accommodate the largest number of possible scenarios. Customers are unhappy and administrators are tired of building long decision trees.
The future of customer experience: From IVR to hyperpersonalization.
Modern customers demand swift and deeply personalized interactions that the IVR can’t offer. Generative AI goes beyond the scripted, one-dimensional interactions of the past. It understands human language, solves complex problems, and supports unique, hyperpersonalized interactions. This is further enhanced by the capability to analyze customer emotions at a deeper level through the interaction. It goes beyond the traditional categories (positive, negative, neutral) and captures other sentiments (gratitude, annoyance, or relief) to give each interaction a holistic context, enabling companies to address customer issues with greater precision and accuracy.
Whether it’s analyzing vast amounts of data and customer emotions in real time, anticipating needs, offering custom recommendations, or seamlessly resolving issues generative AI ensures that each customer experience is better than the last. It doesn’t just improve customer service—it reinvents customer experience—to ensure that every interaction is memorable, hypersonalized, and seamlessly executed.
