Like it or not, data-driven artificial intelligence algorithms and other high-tech robotic applications are coming to fill our jobs. An analysis by PwC estimated that up to 38 percent of current American jobs could be taken over by machines within the next 15 years.
Even white-collar jobs aren’t safe, since algorithms are capable of governing sophisticated tasks for machines in ways that previously were unthinkable, such as writing or distributing pharmaceuticals. The transition has given rise to fears.
On a small scale, individuals in potentially obsolete positions are worried they won’t be able to support their families. On a larger scale, some fear that full-scale job automation could lead to economic collapse.
Could there be a solution to these fears in the field of data analysis?
Why Data Analysis Is Safe
Number-crunching software has gotten pretty good at recognizing patterns: highlighting deviations from the norm and identifying trends. In fact, data-analysis algorithms may currently be able to outperform human data scientists.
So why would data analysis be a safe haven for human workers? Because the objective, numerical facet of data analysis represents only one piece of the process.
After the patterns have been identified, you have to figure out what they mean, and which actions to take based on those patterns in order to benefit the company. For the time being, this is too abstract and complex a task for algorithms, so it stays squarely in human minds.
In addition, people will have to be available to monitor the performance of advanced machines and algorithms, to recognize their inefficiencies and recommend changes to improve performance.
This migration could occur in many potential applications. In manufacturing and fabrication, for example, a specialist in plasma cutting could be replaced by a machine that can handle this process on its own.
However, the human worker could land a new position helping to design the algorithm responsible for the job, and monitoring its performance.
Similarly, in a more abstract example, a human journalist could monitor the performance of an algorithm designed to write like a human. He or she could run edits before an article is published, and monitor performance statistics to assess whether the algorithm needs adjustment before future writing assignments.
The ready availability of data analysis seems like a safe haven, but a few potential problems arise in counting on it to prevent human jobs from becoming obsolete.
- Pacing. Once technology gets to the point of being able to update itself, we may see advances in technology that outpace our expectations. At the current rate, in which jobs are replaced gradually and almost without notice, it’s no issue for skilled workers to forge new positions for themselves. But if a huge swath of jobs become obsolete simultaneously, this could be problematic.
- Skill acquisition. Data analysis and algorithm assessment demand an entirely different skillset even from skilled workers. They might have to return to college, or invest in new training and development, which not everyone would be willing to do.
- Job availability. Finally, a single machine or algorithm may only require one data analyst to monitor it, but it might replace multiple jobs. Only some jobs would be salvageable, while others truly would become obsolete.
It’s Evolution, Not Obsolescence
The bottom line is that it’s unlikely our jobs will suddenly vanish due to the onset of hyper-sophisticated machines and algorithms. In fact, the term “luddite,” which has long been used to describe someone who’s resistant to technological advancements, arose as a term to describe English textile workers who were afraid of their jobs disappearing during the Industrial Revolution .
Their fear was rooted in uncertainty, and wasn’t necessarily warranted. We’re facing a similar situation today.
It’s inevitable that robots will replace some of our jobs, but this will (mostly) improve our productivity and economy. Most jobs aren’t going to disappear; they’re going to evolve, and they’ll be available to anyone willing to evolve along with them.