All data management professionals with any tenure know the familiar pattern of hype that accompanies new, potentially innovative technologies and software solutions. We hear a buzzword or two at a conference — or some new, exciting vendor suddenly becomes known to us — and in short order, the concept or company is being hailed as the next big thing. This pattern is so familiar that Gartner created its Hype Cycle methodology, featuring phases such as the “Peak of Inflated Expectations” and the forlorn “Trough of Disillusionment.”
While we’ve seen this all before, Artificial Intelligence (AI) seems to be enjoying (or suffering from) the longest-running period of untethered promises in the “hype cycle” in recent memory. It’s not hard to understand why: Tangible AI-generated breakthroughs are occurring daily across many industries and applications, and some very tantalizing advancements appear ever more attainable.
Despite all this potential, however, many organizations have experienced frustrating and costly failures with AI investments.
What explains this chasm between buzzy AI headlines and the real-world disappointment of so many companies? In my view, one of the factors accounting for some of the dissonance is that a number of software vendors have irresponsibly co-opted the term “AI,” and are applying it to technologies that are little more than partially automated business rules.
But there is another, more fundamental reason that AI isn’t meeting companies’ expectations — and it has nothing to do with the technology itself.
Jonathan Hill is Chief Architect of Data & Insights at RRD.