ARTIFICIAL INTELLIGENCE: KEEPING IT REAL IN THE CONTACT CENTER
I don’t think I’ll surprise anyone by saying that, as an industry, we sometimes tend to get a bit ahead of ourselves.
Vendors try to offer products to a market that’s not ready for them, and buyers may postpone purchases of solutions today in favor of waiting for technologies and products that are nowhere near ready for delivery.
And we’re not alone. It happens in other industries too.
Look at Internet of Things (IoT), for example. Some people, and companies, are so excited about IoT that they can hardly contain themselves. They look forward with great anticipation to the day that their refrigerator can communicate with their computer so it can proactively order a replacement for a burned out light bulb. I wonder, though, if these same people would be equally as excited at the prospect of someone stealing their personal and financial information by hacking into their bank account through that same intelligent refrigerator. Sometimes it’s important to think these things through.
In the contact center industry, the latest ripples of excitement are about artificial intelligence (AI) and the potential AI has to radically change customer service. Many of the articles and blogs that cross my desk every day tout conversational AI that will help whisk customers effortlessly through their CX journey, dazzling them with robotic intelligence and leaving them breathlessly happy at the other end, cheerfully smiling and waving over their shoulder to the beaming contact center basking in the glow of a job well-done.
Not so fast.
Call me a skeptic if you like, but the scenario described above is not realistic. Despite all we hear about AI in the contact center today, the fact is, what you’re actually hearing about is machine learning technology being applied in customer service solutions. The difference between AI and machine learning is subtle, but significant.
Artificial intelligence is defined as an intelligent agent that perceives its environment and takes actions that maximize its chance of success at some goal. AI actually mimics human cognitive functions, essentially allowing it to think as a human would think.
Machine learning, on the other hand, gives a computer the ability to learn without being programmed to learn. Machine learning relies on algorithms and pattern recognition that it can learn from in order to make data-driven predictions or decisions. While AI functions in cognitive terms, machine learning functions in operational terms.
Let’s put this idea to work in a customer service scenario at one of my favorite stores to waste time in: Tractor Supply Company. When I walk into a Tractor Supply store, I’m typically greeted by a human customer service or salesperson who usually asks, “How can I help you?” In response, I can say virtually anything and this salesperson will be able to respond in one way or another. If I say I want to buy hay for the horses, they can direct me to the hay barn. If I want to buy a flashlight, they can direct me to the hardware section, and so on. That’s human cognitive function.
Now let’s say I’m greeted by a machine-learning driven bot when I walk into Tractor Supply. It will not be a conversational experience but rather a directed dialog. The bot will understand basic statements or questions within its domain only. So, instead of asking, “How can I help you?” it would more likely ask me something like, “How can I help you with your hay purchase today?” If I ask about flashlights, I likely won’t get an answer but rather be redirected to a resource with cognitive function; i.e., a live agent.
I often hear about customer service professionals who are holding off making a purchase decision to acquire a technology solution today in favor of seeing what AI-based solutions are coming down the pike. To them and anyone like them, my advice is to keep it real.
It’s easy to see how the industry has been captivated by the allure of AI, but it’s important to maintain a realistic perspective regarding AI in the contact center. It is in its earliest iteration and while AI has enormous potential, don’t get ahead of yourself. While machine learning and AI continue to evolve, don’t overlook the multitude of great customer service solutions that are functional and deliverable today.
Wednesday, November 20, 2019
Monday, July 29, 2019