The reasons companies miss big disruptions are always the same, and they’re at work with AI. Here are seven of them. Don’t repeat the mistake and miss the AI boat.
Unless you’ve been living on Mars for the past few months, you can’t ignore the enormous media buzz surrounding the development of artificial intelligence (AI). This development has been meteoric, with one feat following the next, with achievements that would have seemed impossible not so long ago. It’s true that the world of technology tends to exaggerate the importance of its inventions, but it can be said with some confidence that this is not the case with AI. After years of ferment, its development has truly entered an exponential phase. And yet, despite the often impressive results being achieved, many remain on the sidelines. In my experience, there are seven reasons for this wait-and-see attitude, each of which corresponds to a well-established, but false, mental model. Let’s examine these reasons to show why they are false.
Reason #1: “AI will massively eliminate jobs. Behind this reason is the age-old fear that automation will eliminate jobs. Historically, this has not been the case. Automation increases productivity, which lowers costs and enables market development, which in turn drives growth and the need for jobs.
Reason #2: “AI will replace humans.” AI is a powerful tool that only works if it’s used well. Humans have always used technology to do certain things better (productivity) and to do new things (innovation). AI will not replace humans; it will find its full potential in the way humans use it. Those who win will be those who learn to use AI well, just as those who won in the past were those who mastered the making of fire and arrows.
Reason #3: “With AI, the less skilled will be left behind.” On the contrary, AI is an opportunity for the less skilled. For example, it allows those who have not been able to learn a foreign language to get by, thanks to an automatic translator. It is an important factor in helping those who were unable to study to catch up. AI is 150 years of education in your pocket.
Reason #4: “AI is just a technology”. The implication is that it doesn’t concern senior management, who won’t even talk about cooking. Big mistake. AI is a technology, of course, but it would be a mistake to simply apply it to improve what already exists. The right approach is to completely rethink your profession or activity based on AI. Like the press twenty years ago, which asked itself: “What is it like to be a newspaper in the Internet age, when you can find the news for free on Google?” AI is a technology, yes, but one of strategic importance.
Reason #5: “AI isn’t ready yet, so we should wait…” No technology is ever ready. We’ve never waited for a technology to be perfected before deploying it. It took years for the automobile to become more or less usable by the average person. But that didn’t stop it from being used to great effect. If you wait for AI to be “perfected,” assuming it can be defined, it will be too late by the time you start.
Reason #6: “We don’t know how AI really works, so it’s dangerous. You hear this a lot, especially from moralists who assume that you can only use something if you understand it perfectly. But that’s not true. Most human innovations have been intuitive, and we often didn’t understand how they worked until much later. Do we know exactly how an examining magistrate or an accountant works? No. And yet they’re very useful. Lady Montaigu spread the practice of variolization, the forerunner of our vaccines, in the early 18th century. She couldn’t explain how it worked, but it worked, and that was all that mattered.
Reason #7: “I don’t understand AI, so I’ll wait for things to become clearer…”. The mistake here is to think that we can only understand something when everything becomes clear. But for something as complex as AI (which is itself a field of multiple innovations), this will never be the case. The only way to get an idea of what AI is is to practice. And practicing doesn’t mean asking ChatGPT a question and telling your neighbors the answer. Practicing means investing time in practicing on real cases, so you can measure the strengths and weaknesses of the technology. It also allows you to better imagine the possibilities (see reason #4).
Quick wins
As I recently told an audience of professionals concerned about the development of AI, it’s not about stopping what you’re doing and going full-time into AI. It’s about spending some time on it, but in a systematic way. For example, ask one of your employees to become “Mr. or Mrs. AI” for a few hours a week. This time must be guaranteed by senior management, and it must be treated as an investment, not a hobby. It’s an affordable loss: if it doesn’t work, fine, because the time is limited from the start (but how could it not work?). If it does, you can decide to invest more. In this way, we proceed by small victories: we move forward, we build something, but we control the risk by taking small steps. Don’t miss the AI tipping point by falling into age-old traps.
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🇫🇷 French version of this article here.