A new PwC survey provides fresh and illuminating data on the burning questions of the day: Are machines going to take over our jobs? And how much do we rely (or over-rely) today on machines, automation, and algorithms?
Experts are confident that machines are going to replace many workers. A much-quoted report from Oxford University has estimated that “about 47% of total US employment is at risk” for being fully automated. The machine threat to employment is even greater in developing economies—a recent report from Oxford estimates that 77% of jobs in China and 69% of jobs in India are “at high risk of automation.”
But maybe estimating the type of jobs that the machines are going to replace is the wrong approach. Tom Davenport, who just published a book on strategies for coping with automation, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (co-authored with Julia Kirby), told the Wall Street Journal recently: “Computers don’t tend to replace whole jobs; they replace specific tasks.”
The McKinsey Global Institute (MGI) agrees: “…a focus on occupations is misleading. Very few occupations will be automated in their entirety in the near or medium term. Rather, certain activities are more likely to be automated, requiring entire business processes to be transformed, and jobs performed by people to be redefined.”
MGI estimates that 45% of work-related tasks can be automated. This finding does not bode well for knowledge workers who were sure their cognitive skills could not be automated and that they will always outrun the machines. Even CEOs, according to MGI, spend over 20% of their time on activities that can be automated with current technology.
What has been missing in this discussion is data on how much we rely (or don’t) on machines today, rather than estimates based on experts’ assessments of how automation-prone are various occupations and activities. Specifically, has the era of big data and increasingly sophisticated algorithms changed the nature of business decision-making? What is the extent by which business executives rely on machines today when they make strategic decisions?
A new PwC survey of more than 2,100 business decision-makers across more than 10 countries and 15 industries sheds new light on these questions. It frames the discussion as follows: “Executives who once relied firmly on their intuition and experience are now face-to-face with machines that can learn from massive amounts of data and inform decisions like never before.”
59% of the decision-makers PwC surveyed say that the analysis they require relies primarily on human judgment rather than machine algorithms. That means that 41% already tend to rely more on algorithms than their own experience, judgement, and intuition. “We are not talking about pricing a seat on an airline,” says (via email) Dan DiFilippo, Global & US Data and Analytics Leader at PwC. “We are talking about big, strategic decisions that almost certainly involve some combination of human and machine, but clearly we see a significant involvement of the machine.”
The most interesting findings are about the type of decisions that tend to be assisted by machine algorithms and the ones that rely more on human judgement. In the chart above, “respondents who answered closest to zero are nearest to the survey’s overall average reliance on analysis from machine algorithms and human judgment. The farther away from the center point, the greater reliance on either mind or machine,” says PwC.
“Shrinking existing business” was deemed by survey respondents as the type of decision that relies most on human judgement and “Investment in IT” as the one relying most on algorithms. “Investment in IT,” says DiFilippo, “can cover many areas including shop floor automation, CRM systems, HR systems, risk management systems, etc., all of which have varying degrees of machine algorithms and can be assessed by machine algorithms.”
The breakdown of results by country offers a striking juxtaposition of China and Japan with the former as the country/region relying more than others on machine algorithms and the latter as the country/region second only to Central and Eastern Europe in its reliance on human judgement. One would think that China and Japan will have similar attitudes toward and use of algorithms in decision making but this is apparently not the case. It’s possible, however, that the results are due to different interpretations of the survey questions. Says DiFlippo: “We don’t have a precise answer or explanation for this—we are still working to gather more on this front.”
Finally, the breakdown of results by industry shows that different economic sectors differ in the degree by which decision makers rely on their own judgement vs. relying on machine algorithms. Conclude DiFilippo: “Involving the machine can help reduce/eliminate bias (at the individual, department or organization level), add more accuracy and/or more computing power to crank through a high volume of scenarios that human can’t do (or can’t do in a timely manner), and importantly—and the data supports this—there is a sense that the machine can help de-risk the strategic decision… we see that those who had a high degree of machine algorithms felt a high degree of managed and known risks.”
So should we search for the right mix of minds and machines in the context of a specific decision or should we succumb to a universal McAfee’s Law and agree that “as the amount of data goes up, the importance of human judgment should go down”? What’s your experience with trusting machine algorithms rather than your own judgement?
Originally published on Forbes.com
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