This post is co-authored by Mikhail Naumov, President & Co-Founder, DigitalGenius
Artificial intelligence in the workplace is all the hype right now, but the search for examples of AI in the work place continues.
It just so happens that one of the main places where practical artificial intelligence lives right now is in customer service, and there are some fantastic examples of AI transforming the contact center already. Both the customer experience and the at-work experience of customer support agents have been drastically improved thanks to the rise of technology.
Stop the brain drain
AI’s benefits are most practically fulfilled when it can definitively automate the repetitive, mundane, often mind-numbing tasks that frequently define the modern workplace.
Contact center agents know all too well what this means: There’s always a small handful of repeated inbound customer inquiries that turn into the same tickets to be resolved with the same answers, day-in and day-out. Examples include refunds, account lookups, cancellations, upgrades, and the like.
Most people overlook the reality that “conversations” – real, actual conversations with customers over email, chatbots, phone calls, etc. – are a mere 30% of the average customer service experience. The other 70% is a series of actions and processes that customer support agents must perform manually each time a customer asks them for a refund, or the status of an order (or insert your company’s annoyingly repetitive example here).
What if we turned practical AI loose to automate these repetitive (and often expensive) tasks, allowing employees to focus on the complex, higher-level work their human brains were built for?
The good news is that it’s already happening in contact centers worldwide.
The future is now: Examples of AI transforming the contact center
Contact centers are one of the first business functions completely transforming thanks to AI. Here’s why: An abundance of historical customer service logs provides the necessary training data, as the repetitive nature of customer support requests makes the perfect use case for the predictive capabilities machine learning provides.
AI’s deep learning algorithms can be trained upon historical customer service logs; things like chat logs and email transcripts. After this training is complete, the machine learning model can predict answers to new incoming questions, even if phrased in new and unexpected ways.
This then manifests itself in a couple of interesting variations. First, there’s “Conversational AI” or an “agent assist” model, which has been in existence for a couple of years. The AI determines the objective of the customer, based on the words and phrases used, as well as an intent determination based on the customer’s sentiment and other predictive case intelligence.
All of this information is passed along to the human agent in the form of a ready-made answer, which is already “scored” as meeting the customer objective at a high-enough confidence threshold. The agent selects the recommended answer, and “that is that.” Thereby significantly cutting down transaction time, while increasing customer satisfaction exponentially.
A much newer approach blends process automation with conversational AI to produce smart process automation. This is where the concept of the “Intelligent Enterprise” really takes flight.
Quick resolutions and happy customers: What more could you ask for?
Customer service interactions require two critical factors: Conversations with customers, and the back-end processes needed to resolve them (often in as many as 5-8 systems).
Smart process automation seamlessly integrates the two, using APIs to connect multiple systems to each other, providing the capability to process massive amounts of repetitive queries without the need for human involvement. This effectively places repetitive customer service tickets in autopilot mode, allowing agents to tackle more difficult, complex, and interesting customer challenges.
Early results have shown that smart process automation provides the potential to significantly impact a customer support team’s average handling time, its customer satisfaction scores, and its first-contact resolution metrics.
Customers don’t contact customer support because they have no one else to talk to. They reach out because they have specific issues such as refund requests, subscription upgrades, or a problem with the product.
In this problem-focused domain, the two most important factors are speed and quality. Providing a fast and accurate resolution to repetitive inquiries goes much further than providing a real “human agent level” of comfort that can sometimes force the customer to wait for three days.
This is why practical AI is singularly transforming the modern contact center, and why every single company in the world will have some form of AI in their contact center within the next few years.