Some Jobs Are Not Meant to be Performed by People

Robbie Allen
Dec 31, 2019 · 3 min read
Clearly, we shouldn’t have people doing this kind of work.

There are roles within each enterprise that consist largely of boring repetitive mental tasks better suited to software. In a sense, these jobs were never meant for humans — and it’s frankly embarrassing that executives are allowing these roles to continue in their current state.

Are you inspired yet? Photo by Nathan Dumlao on Unsplash

There are repetitive jobs where the person does the same thing every day, eight hours a day, five days a week, 12 months a year. That’s not an inspiring job. It certainly isn’t a career. There are some jobs that humans were just not meant to do — but until now, there’s been no alternative.

It’s not a bad thing once we start automating these kinds of jobs. It’s a good thing. It will allow us to put people on higher-order function tasks that make better use of human talents, versus being a bad alternative to a robot.

A good rule of thumb is that machine learning can replace anything a human can do with less than one second of thinking. Imagine a clerical worker toggling between spreadsheets, or a security professional staring at an x-ray screen. These jobs are digital equivalents the chocolate assembly line staffed by Lucille Ball and Ethel Mertz: prone to human foibles and ripe for automation. They are clearly roles better suited to a machine.

These type of repetitive jobs lead to two problems for a company:

In every job, people get tired, get sick, have bad days, express conscious and subconscious biases, and don’t always understand what they’re supposed to be doing. The extreme tedium of certain tasks can accentuate these issues.

Going into the same system, pulling the same piece of data out of the same field and then putting it onto the same spreadsheet every day is a workflow guaranteed to make anyone’s mind wander. It leaves people prone to making mistakes which potentially have a negative impact on your business.

Even though machines can break down, they don’t get tired, have a bad day, or let the tedium of their work accentuate their flaws.

It’s pretty hard to have pride in the business if you come in every day and do a mind-numbing task. There’s no career in that kind of a role. It’s a job. You’re punching a clock. There’s nothing inspiring about it.

The worker never excels because it’s not inspiring. As soon as people can get out of that job and into something else, they do. As a result, the company doesn’t get good performance out of their people because they’re continually losing experienced team members and having to train new ones.

Even though some jobs aren’t ideal for people, people have been the only ones who could do the work. Software changes this dynamic. In terms of speed, accuracy, and cost, there’s clearly a business case for replacing some jobs with software.

There’s an interesting parallel in the history of computation. “Computers” used to be people who did math. Replacing such “human computers” with more modern technology is an obvious win — no one argues that humans should reclaim these computational tasks just for the sake of providing employment.

A company that clung to old-school human computers would find its benevolent gesture misguided, as it would soon be put out of business by modern competitors. Once technology is able to improve a task with automation, then doing so becomes a business imperative. Companies in competitive markets have no other choice.

But despite the obvious business benefits of automating certain jobs away, the uncomfortable question remains: what happens to those people? Fortunately, there’s some good news there, too, which we’ll explore in an upcoming article.

Robbie Allen is a Senior Advisor to Infinia ML, a team of data scientists, engineers, and business experts putting machine learning to work.

Machine Learning in Practice

Practical insights for executives, managers, and project managers eager to deploy machine learning inside their company.

Robbie Allen

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Machine Learning in Practice

Practical insights for executives, managers, and project managers eager to deploy machine learning inside their company.

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