Artificial Intelligence, Work And Jobs: Preparing For AI's Uncertain Future – Forbes

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I’ve been thinking a lot recently about the many ways in which artificial intelligence may change our lives.

One of the biggest impacts may be on jobs, not only on the nature of work itself, but on the availability of work. Some crystal ball gazers are predicting that AI (working in concert with its older sibling, automation) will trigger massive job losses; others see AI producing a net gain in employment. Both views can be supported both logically and empirically; but they both can’t be right.

As Andy Kessler put it in a June 17 Wall Street Journal column, “The future happens, just not the way most people think.” Kessler then walked readers through a shopping list of past predictions that turned out to be way off the mark: “megamistakes,” he called them. One group of AI prognosticators is heading in that direction; we just don’t know which one.

So what do we know about AI? We know that AI algorithms—which are intended to trigger various responses by workers or machines—are created by humans, and are therefore subject to human error, bias, and a host of other potential flaws the techies would rather not talk about. AI is no more infallible than you or I.

Predictions about AI are therefore equally suspect. We don’t know what we don’t know.

What we do know, however, with some degree of certainty, is that AI’s smart technologies likely will impact the labor market, as all new labor-substitution technologies do, affecting some occupations more than others.  The Brookings Institution suggests that the occupations most at risk include those involved in food preparation and food service, production operations, office and administrative support, farming, fishing and forestry, transportation and material moving, and construction and mining.

Frontier Economics , in a September 2018 analysis prepared for the Royal Society and British Academy, points more generically to “jobs typically performed by workers with relatively low levels of formal education” as most at risk.

A BCG team led by Andrea Gallego, Matt Krentz, Miki Tsusaka and Frances Brooks Taplett, in another recent report, “How AI Could Help—or Hinder—Women in the Workforce”, suggests the occupations most at risk are those stereotypically held by women: bank tellers, clerical and administrative positions, teachers. Brookings, on the other hand, suggests that jobs typically performed by men, truck driving and working on factory assembly lines, for example, are slightly more at risk.

Don’t let the jumble of conflicting analyses and opinions confuse you. The important point—whether you’re looking at the BCG report, the Brookings study, or the Royal Society/British Academy analysis—is that millions of today’s jobs, thanks to AI and other technologies, won’t exist in the future, or won’t exist as we know them today.

But this phenomenon is hardly new. The same sentence could have been written at the beginning of the industrial revolution, after Henry Ford introduced the high-speed assembly line, or at the beginning of the computer age. Over time, work, like everything else, evolves.

While some of the lost jobs— maybe all—will be replaced by new and/or different jobs, the operative question is not What and how many jobs will AI make obsolete? Rather, the question is What needs to be done to prepare the workforce for the jobs of tomorrow?

This we can answer without hesitation.  There’s little disagreement here.

For those currently in the labor force the key words are “reskilling,” “upskilling,” and lifelong learning.

The BCG report on AI and women is upbeat about the possibilities here, noting that an earlier analysis had “found that 95% of at-risk U.S. workers could be successfully retrained for jobs that pay the same as or more than their current positions and offer better growth prospects.”

Longer term, the answer is STEM—science, technology, engineering and math—education.

Women, especially, aren’t benefitting in large numbers from the AI and digital revolution because the majority of those who attend college study the social sciences, rather than the hard sciences. “Women hold 56% of university degrees overall,” Gallego and her colleagues write, “but just 36% of STEM degrees.” As a result, women comprise just 25% of the STEM workforce and just 22% of AI professionals.

Altering these dynamics, both for the benefit of women and the workforce at large, will require a collaborative effort, involving companies, government, and the individuals themselves.

But what should you, as a business leader do? Wait until others figure it out for you?  Of course not. The reality is that the talent pool in the STEM fields, including AI, is limited and will remain so for the foreseeable future. Much of what you will need from the labor market, others will want as well. So you, as a senior leader, will need to take charge and develop your workforce—and do so on an industrial scale.

It’s clear that there’s peril ahead. The coming AI revolution will disrupt work as we know it. A word to the wise: Be Prepared.

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