By Richard Arneson
According to a recent study by a London-based research and analytics firm, ninety-one percent (91%) of executives realize the importance of AI and machine learning technologies. However, only slightly more than half of them are currently utilizing it. And less than twenty percent (20%) know how it’s being used in their organization, which makes you wonder if that “slightly over half” figure is really accurate. They know they want it and, as you’ll read below, know how they’d like to use it, but that’s often where the dream dies.
Here’s the problem
At the core of executives’ issues with implementing AI and machine learning is because they don’t really know how to communicate its benefits. They can’t speak to how or why it’s being, or needs to be, used.
These percentages are growing rapidly, but the key decision makers who are in charge of making AI and machine learning happen throughout their organizations are in the same boat. They don’t have enough information or skill sets on board to help match needs and/or adoption levels with AI and machine learning.
It’s one (1) thing to understand that changes need to be made, but another to know precisely where the change is needed. And without fully understanding what AI and machine learning can accomplish, it’s difficult to know what issues it can address. They know adoption is needed to stay competitive in the marketplace, but forking over a lot of dough without knowledge of the technologies and what they can provide is throwing good money after bad…executives don’t like that.
What else they know
The study also uncovered how executives would like to utilize AI and machine learning. Well over fifty percent (50%) want it to enhance employee productivity, which doesn’t come as a surprise. Find an executive who doesn’t want to improve employee output, and you’re looking at an organization that isn’t long for this world. In addition, they want AI and machine learning to help them make better business decision and streamline business processes.
Of those executives whose organizations are currently utilizing AI and machine learning, its ability to automate decisions ranks first, as forty percent (40%) utilize it for exactly that purpose. Second was customer satisfaction and retainment at thirty-six percent (36 percent), with deploying a better way to detect waste and fraud ranking third at thirty-three percent (33%).
Another study, this one (1) from Deloitte based on interviews with executives regarding AI and machine learning, discovered that the notion of putting blind faith in results is terrifying. The executives know that data, and loads of it, is being analyzed, but haven’t the foggiest what’s done with it and, most importantly, whether what comes out the other side is trustworthy.
And part of that fear lies in the word algorithm. When it’s thrown about, it tends to make people squirm. Whether referring to math, SEO or AI and machine learning, many tend to tense up when they hear it, or stop listening altogether. Algorithm may sound cryptic, but it shouldn’t; it’s simply a mathematical equation. It’s the task(s) algorithms perform, and how they calculate answers and results, that can give people a case of tired head. They think back to that C they got in high school algebra and promptly decide that they’re not equipped to participate in any discussion that includes the word or has anything to do with it.
The aforementioned figures will steadily rise, of course, but its rapidity depends on how much executives understand what AI and machine learning can accomplish, and how to explain its benefits to others, like shareholders.
Education breeds Trust…here’s how to get both
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