AI for Business Starts with Tackling the Hype
“AI may be more subject to hype than any other field. It is a discipline which exists perpetually on the brink of science fiction, being sometimes described as ‘cool things that computers aren’t yet capable of’.”
That’s how Luke Dormehl framed the hype around AI in a February 2017 article in WIRED. The book AI for Dummies had its own way of putting it:
“Like the purveyors of the emperor’s new clothes, many of the “hype leaders” are getting by because they are depending on the rest of us being too afraid to ask the tough questions or express any doubt.”
At the same time, nobody is denying that AI is an incredibly powerful tool with very real applications. So how businesses supposed to understand what’s hype and what’s not? First, let’s frame some of the hype around AI superintelligence. For example, you may have seen admittedly shocking charts like this (taken from a famous Wait But Why post on “The AI Revolution: The Road to Superintelligence”)
Okay, but how seriously should you take these predictions? First, bear in mind that these kinds of hyped posts are more likely to travel farther than, say, Kevin Kelly’s takedown of the problematic assumptions behind superintelligence predictions (which you should absolutely read). But many posts like this are arguing about the big picture questions, how should businesses frame the “is this hype?” question?
Sam Ransbotham, writing in the MIT Sloan Management Review, outlined a more practical way of viewing AI’s progress in a business context:
“The growth of AI in business is likely to similarly defy smooth, linear progression. It is difficult to build off of what has already happened to reliably determine what is likely to develop.”
Compacting the article, here’s a list of its three main takeaways:
“For AI in business, benefits from incremental improvement may come at a diminishing rate… efforts required may increase disproportionately as it improves… [and] working within the constraints of goals, timelines, budgets, and expectations will be difficult when experimentation perspectives change.”
In other words, manage those expectations. “Magic bullet” thinking is only going to hamper attempts to integrate AI into practical business applications.
Of course there’s a lot more you can do to get you and your business prepared to unlock AI’s potential. Signing up for our two week course on how to do just that would be a great start. It explains everything you need to know to avoid getting hoodwinked by hype-ladened consultants and get yourself on the road to seeing real results from AI applications.
