Although contact centers have come a long way in recent years (thanks to self-service technology like virtual assistants), one of the more difficult customers to please is the one that prefers (or demands) to speak with a live agent. Upon hearing a robotic-sounding voice, they immediately ask for a human representative. Nevertheless, a readily available human customer service agent is often hard to come by because of ongoing staffing challenges. Today, almost two-thirds of contact centers are understaffed. In fact, contact centers struggle to retain agents, with the average turnover rate being 38% in 2022.
Moreover, the traditional contact center staffed solely with live agents is becoming a relic of the past. Amid today’s economic downturn, contact centers need to save money, and AI-powered virtual assistants are the most effective options. Gartner predicts that by 2026, these automated solutions will reduce the agent labor cost of contact centers by $80 billion.
Businesses cannot boost productivity, reduce hold times, and increase call completion rates without implementing AI-powered automation. While a company may have some customers who prefer speaking with a live agent, maintaining such a contact center is simply untenable practically and economically. Rather than forgoing these customers, businesses can deploy the latest generative AI solutions to enhance the capabilities of their automated customer service bots.
Why People Don’t Like Bots: The Shortcomings of Older Models
Before discussing the benefits of generative AI, it’s helpful to examine why older customer service models garnered disdain. Traditional virtual assistants could only provide customers with pre-programmed answers – essentially, the best choice among various ranked options. Consequently, these interactions followed highly ridged and static decision trees. Due to their inflexibility, if the customer ever asked something “off script,” the chatbot would be unable to answer their question.
Another flaw of legacy models was that the goal of virtual assistants was to collect sufficient information from the customer before handing them off to a live representative. However, customers would typically cut off the machine and ask to speak with a live agent. Even if the customer were patient, the chatbot could wander off-topic or reach a dead end, forcing the customer to start over. Unsurprisingly, customers found these scripted models time-consuming, restrictive, and unnecessary. While the current sentiment toward virtual assistants is understandable, the infusion of generative AI into the contact center will prove the usefulness of such solutions.
Customer Service Benefits of Generative AI
Unlike past virtual assistants, generative AI solutions can create surprisingly life-like and natural-sounding responses to customer questions, simulating actual human conversations. Specifically, generative AI uses large language models (LLMs) to synthesize statistical data and curate the most authoritative answer, rather than drawings from a fixed pool of pre-programmed responses. LLMs give virtual assistants access to millions of human conversations, allowing them to generate responses that emulate real examples of human empathy. Generative AI solutions can also leverage natural language processing (NLP) to analyze the customer tone, language, and emotional cues, adjusting tone, sentiment, and word choice to be just right for them. Instead of appearing as a cold and emotionless machine, generative AI has the potential to infuse virtual assistants with greater compassion toward the customer, enhancing the overall experience.
Likewise, customer service departments can use generative AI to engage with customers in far more personalized ways than what was previously possible through virtual assistants. Notably, companies can utilize communications automation platforms (CAPs) to provide their generative AI solution with customer data collected from various channels spanning the entirety of the customer journey. With this data, the AI solution will have deep insights into customers’ preferences, purchase history and survey feedback, allowing it to craft answers that are not only relevant to the topic but also personalized. Even the most ardent bot-hating customer will resonate with the tailored responses and be impressed by the capabilities of a generative AI-powered customer service solution. Furthermore, generative AI will help virtual assistants become much more dynamic, meaning they can react intelligently to requests, regardless of how far afield. For example, if the customer asks, “What’s for dinner?” The bot can cleverly respond with, “I’m thinking hamburgers, but let’s get back to the status of your package.”
Excitingly, because generative AI solutions “learn” over time, becoming more “acquainted” with the typical customer questions, hang-ups and problems, they’ll continue to enhance the customer experience with each interaction, increasing completion and conversion rates. And although generative AI solutions can make mistakes, with the proper oversight and fine-tuning, their accuracy will constantly grow. As generative AI evolves and familiarizes itself with additional data, it will reach a point where it can just as effectively tackle special cases as a live agent. Should that day come (and it might not be too far off), the customer service department could confidently remain open outside operating hours or during certain holiday seasons, where call volumes are significantly higher, ensuring no caller or query goes unaddressed.
Why Generative AI Needs CAP
The race is on to upgrade one’s customer service environment with the latest generative AI technology; nevertheless, integrating these solutions into, for example, the contact center is a highly delicate and thorough process. In addition to requiring a significant degree of technical know-how and the significant cost to build and iterate as changes are needed, companies should leverage the proper supporting platforms.
Recall that a CAP can provide a generative AI solution with the customer data it needs to create personalized responses. On its own, generative AI can only respond to questions or requests. If a customer gives the AI their credit card information, it can’t send that data to a CRM or point-of-sale solution. However, a CAP can fill the gaps through automation, coordination and orchestration, ensuring all the different AI solutions and communication tools mesh together effortlessly.
To that end, customer service departments should find a CAP offering that can integrate seamlessly with their contact center and other existing infrastructure without forcing any rip-and-replace procedures. Moreover, though companies are eager to try out generative AI, they must implement the proper guardrails and security measures before starting a deployment.
Edited by
Alex Passett
