If for the past year or so there’s been one subject on everyone’s lips at marketing and business events, it’s been artificial intelligence. What awaits this techy buzzword?
Like many other inventions before it, artificial intelligence is a technology that should be subject to the same cycle of adoption, according to Gartner’s now-celebrated hype cycle.
In my opinion, we’re currently scaling the upward slope of inflated expectations. All the symptoms of hype are present, including the following:
Inevitably, this overblown enthusiasm will lead to disappointment. We’ve already seen this type of hype cycle play out in several areas, notably regarding the web over 20 years ago. The same thing happened with virtual reality a few years ago. (Actually I think we’re still experiencing the trough of disillusionment with regard to that particular technology.)
The disillusionment phase obviously brings its share of negative consequences. Projects, programs, and investments are suspended, jobs are lost, and dreams are broken. One could also take the view that these readjustments of the market represent an appropriate return to an equilibrium state where expectations and delivery are more optimally aligned.
In my view, this disillusionment is best expressed by the idea of a group of business managers and researchers who are trying to work together. The managers are looking for a breakthrough concept that will lead to guaranteed profits within a predetermined length of time—generally in the short term. The researchers, however, feel a lot more comfortable with longer term work cycles and indeterminate results, and naturally do not share the same gauge of success. In short, the researchers formulate their hypotheses and see value just as much when those hypotheses are confirmed as when they are negated, as opposed to the managers, who, for a variety of reasons, don’t have a lot of interest in scientifically proving that a particular idea is faulty.
Disappointment will come particularly when these two different realities collide, but also from the process that allows them to get a taste of the advantages of AI. Conferences and discussions surrounding AI focus generally on analysis and data activation, or in other words on insights. But there are two essential phases you need to deploy before you can get there: data capture and engineering. According to three experts invited to a panel discussion on the subject, these two phases represent between 80 and 90 percent of the work in AI.
What we see a lot in digital is that data are basically accessible, but most companies capture data in a very approximate way. The situation is even worse when it comes to engineering, which involves organizing, structuring, cleaning up, and preparing data so that the work everyone wants to accomplish can actually begin. Artificial intelligence will only take off if those managing its emergence accept and trust the process.
People developing AI business use cases need to consider this work as part of their costs before vaunting the benefits of AI if they want to grade the downward slope towards the trough of disillusionment.