Ready to Automate Your Headcount? Not So Fast . . .

Think Task Automation, Not Job Automation

James Kotecki
Nov 27, 2019 · 3 min read
Not the employee you’re looking for. Photo by Rock’n Roll Monkey on Unsplash.

In a 2019 report called “Automation and Artificial Intelligence: How machines are affecting people and places,” Brookings researchers wrote that in general:

Machines substitute for tasks, not jobs

A job is a collection of tasks, and even under the most aggressive scenarios, it is unlikely that machines will substitute for all tasks in any one occupation.

Automation also complements labor

Workplace activity that isn’t taken over by automation is complemented by it — making the remaining human tasks more valuable.

While the prospect of automation might make people fear that their entire job is on the line, the truth is that automation is often best suited to specific tasks — the very tasks, in fact, that are the least “human” and most “robotic” parts of a given job. If you’ve ever found yourself grumbling that “a robot could do this,” the truth is that one day a robot (or software tool) will do it — and probably should.

For many roles, automation simply means that humans get to focus on other parts of their jobs that make them feel more, well, human. Software, powered at times by machine learning, can take on the tasks that feel tedious, monotonous and brainless, reducing the waste of human time and potential.

Seeing automation as a one-to-one replacement for people (“we’ll bring in robots, let all of our people go, and do exactly the same work we were doing before”) is a short-sighted approach that could leave tremendous value on the table. Instead, think about how machines can help humans do more of what they do best.


After working with the natural language generation company Automated Insights to automate relatively rote financial stories, The Associated Press reported that “the reaction has been positive from staff, largely because automation has freed up valuable reporting time and reduced the amount of data-processing type work they had been doing.”

Not only did automation not take jobs, it actually made jobs better.

Unlocking employees’ full potential should be a mission for every company. Think about automation not as something you impose on people, but something you do for them: freeing them from tedious tasks to pursue more fulfilling work.

It’s worth noting that Automated Insights did not even attempt to automate the entire job of a human journalist —it just focused on one tedious part of it. There’s no technology that can currently replace a journalist’s judgement, instinct, or ability to write prosaically and factually about non-standardized topics. Attempting to automate an entire person in this case would be a heavy lift and, frankly, a waste of time given the gains available when focusing on a specific task.

This is a perspective all business leaders should consider. Rather than beginning a machine learning/AI/automation project dead-set on reducing jobs from your organization, focus instead on reducing less desirable tasks. Not only can this approach be more technically feasible, it also helps align your incentives with those of the affected workers — the people on the ground who are often critical to making the adoption of new automation actually work.

After all, implementing machine learning in a business setting is hard. There’s no need to make it harder by attempting to automate the full complexity of a human being. Focusing on tasks can be enough.


James Kotecki is the Director of Marketing & Communications at 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.

James Kotecki

Written by

Director of Marketing & Communications for Infinia ML, a machine learning company. Speaker from North Carolina to South Korea.

Machine Learning in Practice

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

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