How to survive in a workplace where machines are doing more tasks

pretend-robot-at-work-desk

I do a lot of Google News searches on how certain technologies — particularly artificial intelligence (AI), machine learning, automation, and robotics — are impacting both the workplace and workers.

It quickly becomes clear from results of a search for “jobs and AI” or “jobs and robots” that there are two general schools of thought:

  1. Robots are coming for your job
  2. Relax, robots won’t take your job; they’ll just change how you do it

The latter argument was voiced anew recently by MIT Sloan School of Management professor Erik Brynjolfsson, Tom Mitchell, a professor in Carnegie Mellon University’s machine learning department, and Daniel Rock, a researcher at the MIT Initiative on the Digital Economy.

In a paper published in the American Economic Association Papers and Proceedings, the authors analyzed what specific tasks can be done by machines, rather than analyzing their impact on specific jobs. The researchers developed a 23-question rubric to assess whether a machine can learn to do a task, then applied it to more than 900 occupations in the U.S.

Brynjolfsson tells MIT Sloan’s Tom Relihan:

“In almost every occupation, there are at least some tasks that could be affected, but there are also many tasks in every occupation that won’t. That said, some occupations do have relatively more tasks that are likely to be affected by machine learning.”

This is a very broad statement, but certainly reflective of reality. And I’d argue it has two major implications for enterprise workers.

First, you don’t need an MIT degree to do the math: Less total work for humans eventually means fewer jobs. Let’s say you have a department of 10 people. It could be anything — sales marketing, customer support, finance, whatever. Now let’s say machines are able to do 20% of the tasks done by four of the 10 workers, 30% of the tasks done by three workers, and 50% of the tasks done by the last three workers.

If I’m the CEO of the enterprise or the department head, I just might conclude that only seven employees now are needed to achieve the same level of productivity. I can save money and look good to shareholders or the board of directors! That’s how it always has worked in the real world.

Second, to survive in a workplace where machines are doing more tasks, employees must increase their intrinsic value to the organization one way or another. Whether it’s acquiring more areas of knowledge and expertise, improving their understanding of their enterprise’s strategic goals, or being a master of soft skills that are beyond even the most empathetically programmed machine, it’s imperative that employees recognize and proactively respond to AI and machines in the workplace.

Adapting may not be easy, but it’s better than the alternative.

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