Given that NLP and other associated technologies like generative AI are the building blocks of using AI for customer interactions, there is obviously a mismatch here. So, what is the difference? Well, one question asked about AI specifically, and the other didn’t use the term at all. Instead, the latter question focused on the specific technologies that fall under its umbrella. This discrepancy points to a major issue institutions have when thinking about and talking about AI — they do not have a clear idea of what it actually is.

Taking advantage of the AI opportunity requires understanding the technologies that it involves and how those tools can be used. For instance, machine learning is very good for deriving insights from large, tabulated datasets. It’s also been around for a long time. As noted in our latest report, machine learning has established use cases in financial services in areas like anti-money laundering, fraud detection and prevention, underwriting, analytics, biometrics, and automated document processing. Meanwhile, chatbots today, which represent one of the most obvious uses cases for AI are generally built using NLP, and more recently, generative AI. These are the kinds of technology that would be applicable to customer interactions, as noted above.

As executives contemplate this area, it is important to remember a few things. First, AI is a catch-all term for a set of tools that can be applied to different things. Second, you have to know what you are trying to solve for in order to apply these tools effectively. And finally, you do not need to use all of the tools, nor do you have to use any of them. In getting started, the smartest thing executives can do is educate themselves on the differences between these technologies, how they are used — and critically, what is already proven versus what is actually new.