The death of the IVR: How generative AI is transforming customer service | Talkdesk (2024)

“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.

The death of the IVR: How generative AI is transforming customer service | Talkdesk (2024)


How AI is transforming customer service? ›

AI, in its various incarnations, gives customer service departments the ability to do more, thus improving the customer experience. Chatbots, for example, can handle multiple queries at once. This is a saving grace for businesses that have busy call centres and struggle with wait times.

What is generative AI for customer experience? ›

Generative AI helps businesses build lasting customer relationships by providing personalized recommendations. It predicts changes in consumer behavior and offers insights based on current user activities, enabling companies to adapt to their customers' evolving needs.

Why is generative AI the future? ›

Generative AI has enabled the creation of photorealistic images from scratch. This has numerous applications in industries like fashion, interior design, and gaming. Artists and designers can use AI to quickly generate concepts and prototypes, saving time and resources.

Is AI going to replace customer service? ›

The short answer is – no. The goal of using AI in customer service today is to complement, not replace, human interaction. When implemented correctly, AI solutions such as virtual assistants, chatbots or automated sentiment analysis can help agents optimise their workload and automate repetitive and mundane tasks.

How can generative AI help customer service? ›

In customer service, generative AI can predict customer needs, enabling proactive and tailored support. It can auto-generate customer replies, assist agents in real-time as they engage with customers, automate notetaking and summarization, and even develop personalized training materials for agents.

How does generative AI affect contact centers? ›

Generative techniques can significantly help agent productivity, improving metrics such as average handling time, after call work, ramp-up time, with solutions like summarization and generative knowledge assist that can drive immediate value and are the shortest time to value.

What is the first generative AI for CRM? ›

Salesforce Einstein GPT: World's First Generative AI for CRM.

What are three ways to improve customer experience using AI? ›

Dynamic Surveys: AI-driven, adaptive surveys for personalized feedback. Predictive Analytics: AI algorithms for trend analysis and customer behavior predictions. Data Integration: Connects various customer data sources for enriched insights. Real-Time Feedback: Quick capture and response to customer input.

What is generative AI in simple terms? ›

Generative AI models can take inputs such as text, image, audio, video, and code and generate new content into any of the modalities mentioned. For example, it can turn text inputs into an image, turn an image into a song, or turn video into text.

What is the downside of generative AI? ›

One of the foremost challenges related to generative AI is the handling of sensitive data. As generative models rely on data to generate new content, there is a risk of this data including sensitive or proprietary information.

What problem does generative AI solve? ›

Overcoming Content Creation Bottlenecks

Generative AI offers a solution to this bottleneck by automating content generation processes. It can produce diverse types of content – from blog posts and social media updates to product descriptions and marketing copy – quickly and efficiently.

What is the next technology after generative AI? ›

Future AI Technologies and Their Potential

The next generation of AI is expected to transform our approach to global challenges and daily tasks alike. Predictive Analytics and Its Expansions The expansion of predictive analytics is set to revolutionize industries by providing insights into future trends and behaviors.

What are the problems with AI customer service? ›

AI implementation for customer service can be costly due to the need for specialized software and hardware, as well as ongoing maintenance and training. Depending on the project's complexity, it could take months or years to implement AI technologies into customer service operations fully.

Will ChatGPT replace call centers? ›

Will ChatGPT replace humans in customer service? The short answer to this question is a resounding no. Period. Not every customer wants to interact with a chatbot, and there are plenty of tasks that should never be automated.

Will the service desk be replaced by AI? ›

In this context, it's common for Service Desk agents to raise concerns about their job security. However, as the following article will show, AI is here to aid them, not replace them. Work at the Service Desk is largely communication-based, and years of automation haven't significantly changed this.

What is the power of AI in customer service? ›

The power of AI in customer service cannot be underestimated. By leveraging AI technologies, businesses can enhance efficiency, improve response times, provide personalized experiences, and ultimately increase customer satisfaction.

What is the AI customer service trend? ›

AI is poised to "transform the customer support industry by automating routine tasks, freeing up agents for more value-added work, enabling self-service options, providing personalized recommendations and assistance, improving response times through predictive analytics, ensuring customers achieve desired outcomes ...

How is AI transforming CRM? ›

Here are some key transformations: Data-Driven Insights: AI-powered CRM systems can analyze massive volumes of data to provide real-time insights into customer behavior, preferences, and needs. This allows for more personalized interactions at an unprecedented scale.

How will future improvements in AI change the customer service experience over the next 15 years? ›

Customers enjoy quicker issue resolution, customized interactions, and proactive support, which leads to improved satisfaction, loyalty, and revenue generation. Brands see lowered service costs alongside increased efficiency. As AI capabilities advance, CX will become even more seamless and tailored to each individual.

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