AI call center trends to watch

Contact centers have always used technologies related to artificial intelligence. Now, new innovations in AI technology are starting to enter the industry. See which trends to look out for in the future.
AI call center trends to watch

Artificial intelligence (AI) has moved from the realm of science fiction into everyday reality. While no machine can match human intelligence (at least not yet), advancements in this developing technology have made it user-friendly and powerful enough for commercial use. AI supports workforce optimization through marketing, customer service, and business analysis in many industries, including outbound sales. But contact centers aren’t exactly new to the trend. They’ve dabbled in finding an AI solution since its earliest developments.

Looking back: The evolution of AI for contact centers

The first call center emerged in 1957, shortly after researchers initiated the study of AI. Early work on AI produced automated machines, and call centers have been using automation to optimize not only workflow, but also the customer experience from the beginning.

In fact, intelligent, algorithm-driven technology arguably launched the call center industry: automatic call distribution (ACD) replaced manual switchboard operators in the 1960s, and modernized versions remain a staple. Another essential call center technology, interactive voice response (IVR), brought human–machine interaction to customer service way back in the 1970s.

Contact center technologies have overlapped with advancements in artificial intelligence for decades, and they continue to do so. That makes call center AI solutions important to watch for clues to the future of outbound sales.

Looking forward: 4 pioneering trends in AI for contact centers

AI takes many forms, but these four features are trending at some future-looking contact centers.

Machine learning

Machine learning is the original AI trend. It refers to the way, through algorithms, computers can be programmed to teach themselves as humans do. That means learning by processing information gained from the customer interaction and automatically adjusting.

The learning process relies on data. Fed with information such as statistics on call volume, talk time, or customer purchase history, AI will recognize patterns and respond. For example, it could generate reports on agent performance, adjust an automated process, or produce insights to help live agents close sales. 

The range of applications is so huge, machine learning factors into all of the following trends, too — the possibilities continue to expand.

Intelligent automation 

ACD put contact centers on the map by automatically directing incoming calls. Today's ACDs are intelligent, drawing on data to pinpoint which call center agent would be the ideal recipient of each call. Predictive dialers are types of outbound dialers that automate outbound calls, intelligently dialing at a pace that matches its team of reps. With AI in the picture, these processes can be fine-tuned to hone in on the best contacts and respond in real time to fluctuations in agent availability.

AI-driven outbound sales automation has the potential to improve the quality and efficiency of calls. Still, there are also countless other complex processes that it can learn to address: answering simple customer service requests, sending templated emails, and additional administrative tasks that customer service agents want off their plates. 

Natural language processing, speech recognition, and IVR

The innovation that shaped call center history in the 1970s, IVR, has seen upgrades over the years. AI research had advanced to the point of commercially usable speech recognition by the 1980s, but it took a long time to make the technology user friendly and accessible.

Now virtual assistants — like Apple’s Siri — show it’s possible for customers to speak naturally with a computer, but the applications for call centers go beyond a streamlined menu interface. Some AI can recognize not only what people say, but also what they feel. Insights into customer sentiment can help agents understand how to approach a conversation. When the technology is applied to their own speech, it can coach them to use the right tone.

But advancements in natural language processing apply off the phone, too. Organizations are starting to use AI chatbots, which are conversational AI-driven robots that can conduct text-based conversations, as an element of customer service. This technology can be trained to provide information and perform simple functions, like booking an appointment or producing account information, to free up contact center agents so they can focus on sales.

Real-time and predictive analytics

Analytics are the cornerstone of operations decisions, and there’s no better business area to apply AI. The process of analyzing past and present data to gain insight, and even to forecast future outcomes (what’s known as predictive analysis), doesn’t require it. However, AI usually makes more accurate predictions than humans do because it can process large quantities of data quickly, iterating, testing, and learning as it goes. 

Real-time analytics make dynamic systems flexible; for example, some predictive dialers adapt to metrics. And there’s a wealth of insight that predictive analytics can surface, from which rep is likely to meet their target to which purchase a customer is most likely to make next.

Time will tell which AI innovations produce the greatest successes for contact centers. While the industry’s tried-and-true methods still make sense, it’s wise to keep an eye on the potential of call center AI solutions.

ReadyMode’s all-in-one, cloud-based call center solution strikes the right balance between proven and innovative technologies. Its intelligent ACD and predictive dialer boost efficiency, while its integrated CRM gathers and centralizes data to produce exceptional metrics. It’s the one thing you need to run a call center.

Photo: tsyhun/Shutterstock Inc.

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