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At your service: 5 Automations that help workers, not replace them

When artificial intelligence meets human intelligence, it can be a beautiful thing

TIllustration by Christina Ung

Automated Teller Machines (ATMs) were first introduced in the late 1960s, saving customers from waiting for human assistance to conduct simple transactions. But the innovation didn’t replace bank teller jobs — in fact, economist James Bessen writes that the number of full-time employed bank tellers has since risen by at least 2% every year. Same goes for cashiers’ jobs after the invention of barcode scanners. Bessen dubs this “The Automation Paradox”.

History is teeming with examples of automations — whether in the form of physical machines or technologies like machine learning algorithms — designed to support human workers by taking over the most time-consuming, manual aspects of their jobs, freeing them up to use higher value skills like selling new products and building relationships with customers.

Here’s a look at a few other automations in aviation and aeronautics, automotive manufacturing, law, finance, and healthcare that are making workers’ jobs safer, more accurate, and more efficient.



Dorothy Vaughn was the head of the National Advisory Committee for Aeronautics’ (NACA’s) West Area Computing Unit from 1949 until 1958 (which was segregated at the time), an expert mathematician, and NASA’s very first African-American manager.

If her name rings a bell, it’s because she was recently portrayed by Octavia Spencer in the 2017 biopic Hidden Figures. In real life, Dorothy Vaughn supervised a group of female mathematicians (literally “human computers”) who performed complex computations to determine flight paths and the trajectory of objects in space.

When a brand new machine was introduced to crunch these same numbers at a rate dramatically higher than its human counterparts, Vaughn smartly asserted that someone would have to manage the machine and others would have to program and analyze it — so she made herself an expert in the FORTRAN programming language required to run it and trained other “human computers” to analyze the machine’s data.


Less time with noses in case files means legal staff can turn their focus towards important face-to-face proceedings, like court hearings, negotiations, and advising clients. Maybe with room for a little shut eye somewhere in between.


2. Supercharging lawyers with AI

Toronto’s ROSS Intelligence promises to “supercharge lawyers with artificial intelligence.” The startup produces a software program that uses natural language processing to scan thousands of legal documents, helping lawyers find relevant documents to help build stronger cases. Information that formerly took paralegals hours to put together, even days or weeks, now has the potential to be found in minutes.

Less time with noses in case files means legal staff can turn their focus towards important face-to-face proceedings, like court hearings, negotiations, and advising clients. Maybe with room for a little shut eye somewhere in between.

3. Collaborative robots take on hazardous manual labor at GM

It’s no surprise that industrial sectors, where efficiency is everything, are quick to take up new automation technologies. At GM manufacturing plants, a safety green-hued collaborative robot takes up post on the factory floor alleviating its human teammates of stacking heavy tires, a task New York Times reporter Kim Tingley observes employees “relinquished gladly.”

It’s not just a question of efficiency. Tingley also recognizes the role of collaborative robots in improving overall worker safety and reducing critical injuries resulting from fatigue brought on by monotonous work. Even more interestingly is her perspective on how access to collaborative robots could have a “democratizing effect, giving people of various ages, sexes, dexterities and sizes equal shot at excelling at all sorts of physically demanding careers.”

4. Software that helps accountants do more than sign checks

In-house accountants used to spend their valuable mental resources manually tabulating payroll, managing invoices and accounts payable, and doing as much as they could to keep the business financially afloat.

Enter software like Intuit’s Quickbooks (2002), and Vancouver, B.C.-based Beanworks (2012) that automated a host of lengthy payroll processes which, apart from helping accountants do their jobs faster and with fewer errors, allow them more time and energy to mine their existing financial data for opportunities to conserve or increase a business’s funds.

5. Computer-assisted diagnoses: A path to better pathology?

Machine learning algorithms have been an ally to healthcare providers in better predicting, and preparing for, patient events.

Take the work of a pathologist, who closely examines images of a patient’s biological tissues for signs of tumors or abnormalities. As Google’s research blog explains, “there can be many slides per patient, each of which is 10+ gigapixels when digitized at 40X magnification. Imagine having to go through a thousand 10 megapixel (MP) photos, and having to be responsible for every pixel. Needless to say, this is a lot of data to cover, and often time is limited.”

Now there are programs that scan myriads of slides and assist pathologists in generating computer-assisted diagnoses, reported to have found tumors too small to be detected in mammograms. For many healthcare professionals, prevention is the name of the game and some are turning to machine learning to predict heart disease and diabetes with almost 82% accuracy. With this data, doctors can focus on supporting patients in managing their symptoms, reducing costly hospitalizations.

New automations require investment, not just financially, but in shoring up the current labor force with the new skills required to build and maintain these machines and algorithms. But is it worth it? As Bessen’s research shows, using technology to handle rote tasks allows workers to focus on valuable skills machines don’t have. The idea here isn’t that machines ought to replace all aspects of human work, but that they serve or work alongside humans so that, together, they have the capacity to create greater opportunities in the long run.


Lima Al-Azzeh is cautiously optimistic about the future of automation.

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