Businesses today are in a difficult bind. On the one hand, customers expect high-quality customer service and will abandon companies if they don't receive it. At the same time, companies are having an extraordinarily tough time filling support roles, with 63 percent of contact center leaders facing staffing shortages.
Businesses have increasingly recognized the advantages of integrating artificial intelligence-powered support within contact center operations over outdated ticketing systems. As the standards for quality customer service continue to rise, these contact centers have already or are gradually moving toward optimizing their operations with AI-powered automation, increasing customer satisfaction while making life dramatically easier for agents.
In fact, agent productivity can increase by 50 percent when supported by AI-powered agent assist solutions. Understanding the advantages of this fast-emerging technology is a crucial first step if you want to overhaul your customer service operations.
The advantages are manifold, but the most significant might be the automatic handling of routine queries.
The vast majority of queries the average contact center fields will typically conform to a handful of predictable patterns. These queries require little to no problem-solving ability on the part of agents despite generally occupying the majority of their days. The negative consequences here are twofold. First, the repetitive nature of these tasks increases the likelihood of employee burnout. Second, the sheer energy expended on these routine queries siphons time and resources from more complex customer challenges.
This is why having AI-powered large language model-based chatbots as a first line of defense can be so transformative. These chatbots, which can converse in a multitude of languages at any time of the day or night, can automatically handle these routine queries at scale. Owing to rapid advancements in LLM technology, they can do this without the robotic quality so familiar to users of first-gen chatbots. With the simpler queries automatically covered through self-service, agents can then reroute their energy toward more complex customer support issues that only they can handle.
However, in customer-agent interactions, AI also plays an essential role in steering customer interactions toward success. Previously, to properly navigate customer support calls, agents would have to quickly collate reams of relevant data on the fly, toggling through multiple tabs to understand the context of any complaint. AI eliminates this work, instantly providing agents with everything they need to know about customers and their issues through automated ticket summarizations. Instead of trying to make sense of 100 disparate data points quickly, agents can outsource this part of the job to AI and focus their efforts on bringing customer interactions to satisfactory conclusion. Expanding from this foundation, AI can optimize every aspect of the conversation, seamlessly interpreting customer sentiment and generating contextually relevant response suggestions aligned with the best-suited tone.
This matters for agents, who benefit from reduced stress levels (thus decreasing the likelihood of churn). It also matters for customers because few things are more irritating than having to explain a problem repeatedly to a succession of different customer service agents. What emerges is a virtuous cycle where AI bolsters support teams, resulting in more efficient customer interactions, heightened employee satisfaction, and ultimately, improved customer experiences. This continuous cycle not only drives revenue but also creates a win-win scenario for businesses and their customers alike.
Crucially, AI doesn't just make the lives of agents easier. It also makes the lives of their managers easier. In an AI-optimized contact center, all conversations are fed into a single source, where they are automatically sorted and analyzed. This means that contact center managers can sort employee analytics in various ways, digging deep into individual employees or taking a bird's-eye view of the entire organization. Via AI-enhanced insights, pain points can be instantly identified and remedied. Better yet, much of this can happen automatically. AI is self-learning, which means it is constantly evaluating past performance and adjusting accordingly.
Fundamentally, this technology is designed to maximize human potential. In a field historically defined by long wait times, intense frustration and stress (for both agents and customers), and an excess of numbing busywork, AI presents a more utopian possibility. By unlocking agents' productivity while keeping customers happy, this technology stands to revolutionize the contact center as we know it, driving profit in the process.
Raghu Ravinutala is CEO and co-founder of Yellow.ai.