This article is part of our series that explores thebusiness of artificial intelligence.
These changes provide opportunities for unprecedented growth across different sectors of the economy.
But its adoption will also face a period of lull, also known as the J-curve.
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And thats where a lot of our societys biggest challenges and problems and some of our biggest opportunities lie.
In many areas, machine learning is reducing costs and accelerating production.
In other areas, machine learning can help create applications that did not exist before.
For example, generative deep learning models are creating new applications forarts, music, and other creative work.
Weve all invested in technologies that are allowing us to adapt to a more digital world.
The J-curve
The productivity potential of machine learning technologies has one big caveat.
Historically, when these new technologies become available, they dont immediately translate into productivity growth.
Often theres a period where productivity declines, where theres a lull, Brynjolfsson says.
Brynjolfsson calls this the Productivity J-Curve and has documented it in apaperpublished in the American Economic Journal: Macroeconomics.
These investments and changes often take several years, and during this period, they dont yield tangible results.
During this phase, the companies are creating intangible assets, according to Brynjolfsson.
For example, they might be training and reskilling their workforce to employ these new technologies.
At first glance, it seems that costs are increasing without any return on investment.
When these changes reach their turning point, they result in a sudden increase in productivity.
Most firms arent making the transition correctly or lack the creativity and understanding to make the transition.
Various studies show thatmost applied machine learning projects fail.
Only about the top 10-15 percent of firms are doing most of the investment in these intangibles.
This is not just the big tech firms.
This is within every industry, manufacturing, retail, finance, resources.
In each category, were seeing the leading firms pulling away from the rest.
Theres a growing performance gap.
Some of them are in business schools and academia.
A lot of them are in consulting companies.
Some of them are journalists.
And there are people who are describing which practices work and which dont.
We think of all the jobs as this mathematical space.
We can understand how they can relate to each other, Brynjolfsson says.
We now have them for social sciences and business to have this kind of visibility.
Thats allowing us to make a transition a lot more rapidly than before.
However, Brynjolfsson warns that not many companies are using these kinds of tools.
And at the center of this strategy should be the correct use of human capital.
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