There are a couple of immediate use cases where artificial intelligence has the potential to be an enabling and disruptive forece.
. Let’s start with service transactions. From B2C perspective, I’m closely watching the intersection of AI driven Machine Learning (ML) and Natural Language Processing (NLP) to support Asychronous Messaging. To keep this simple, AI represents a broader spectrum of artificial machine intelligence. Machine Learning is a specific method of AI, and NLP is a specific method of Machine Learning that enables bots to communicate in a language that is natural to humans. Bots need training, but as they learn to process information faster, they deliver faster responses leading to greater adoption by the consumer. At ADP, we’re currently training our digital assistant, Rosie, to handle simpler payroll transactions and answer questions in real time without human assistance. She has handled tens of thousands of client interactions without a handoff, but we have humans there to jump in if Rosie gets stuck. Our long view for Rosie is to introduce Asynchronous Messaging wherein continuous conversations happen via a text-like experience that toggles between AI bot assistance and humans based on where the need is best served. This kind of support will break down barriers between businesses and their customers creating more personal engagement, but with digital solutions. It enables true real time connectivity for businesses to serve their customers any time on any device based on what the customer wants.
There are solutions already in market today for retail, travel, finance, and entertainment. More on that later. As consumers gravitate to these channels, bots get smarter from these continuous interactions. They will get better and better at providing the right experience and answers, and over time, I don’t think people will be able to tell if they are talking to a machine or a person.
A second, less interactive use case is the case for more secure access through biometric authentication. Biometrics like face, voice, or fingerprint authentication will make it more difficult to break down security protocols and gain access to private customer information. Most financial firms are already leveraging these kinds of solutions, and we can expect the same for other industries as consumers come to expect easy, highly secure access methods for their products and services. People don’t want to have to remember PINs or Passwords, and these access methods frustrate clients. We can and should do better.
While the immediate use case is for security, biometric solutions can also be used for assessing customer and employee sentiment or mood and recommending actions for a sales associate or manager to improve their interactions. So there is potential here to increase overall customer and employee satisfaction and retention, and that’s a pretty compelling opportunity.