Analytics trends: The blurring of the man-machine dichotomy

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JESUS LAVA lll

JESUS LAVA lll

A few weeks ago, a close friend and former flatmate of mine in Singapore posted a photo on Facebook showing the latest trend among fast-food chains on the island state. The traditional way of ordering food and the cash register counters have mostly been replaced by the self-order and payment kiosks. Posted along with the photo was a simple comment: “automation = lesser jobs.”

This post got me thinking: Are machines really coming for us?

The newsstand rhetoric agrees with my friend: smart machines will soon take over our jobs. To be frank, there is some truth to this.

I read an article about a deep-learning system that examines patients’ X-rays and CT scans and dispenses expert radiology advice that is more accurate than a doctor’s advice.


Similarly in North America, a cognitive computing system is being developed that would give veterinarians access to extensive, up-to-date knowledge on animal diseases and help them personalize patient care. The system will allow veterinarians to pose free-form questions about animal diseases, their specific treatment, and insights to inform individual patient care plans. In addition, the system will help veterinarians factor in a pet’s own hereditary susceptibilities, lifestyle characteristics, and geography-specific risk factors when developing treatment plans.

Already, there is a system built using the IBM Watson cognitive computing platform that has been dubbed the “world’s first artificially intelligent lawyer.” ROSS is a legal research tool that allows law firms to slash the time spent on research—and thereby drive down price points for legal fees—by sifting through over a billion text documents a second. More than that, the tool learns from feedback so that the more lawyers use it, the smarter it gets.

Where before, low-skilled manual work was the most vulnerable to automation, these examples I highlighted show that high-skill, white-collar work is now at risk as well. With ever-smarter machines being developed, what determines vulnerability to automation now is not whether the work is low-skill or white-collar, but whether the work is routine or not.

Complementing one another
Going back to my friend’s post, I told him to fear not, there is still a place for humans. People have always managed to add value to machines as processes become automated, and this is likely to continue. As automation takes over certain routine work in companies, other areas of work, which may provide more value to the company and its customers, may arise.

There are likely to be a variety of ways in which smart people and smart machines will work alongside each other. Some humans will have to build and implement cognitive technologies. Others will ensure that those technologies fit into a work process and will then monitor their performance. And some humans will complement computers in roles that machines can’t perform well, such as those involving high levels of creativity, caring, or empathy.

However, there is no denying that the information and cognitive age is upon us, as indicated by more than $1 billion in venture capital funding for cognitive technologies in 2014 and 2015. Analysts from the International Data Corporation project that overall market revenue for deep-learning and cognitive solutions will exceed $60 billion by 2025.

That being said, deep learning and cognitive technology may become just another tool in the toolbox—very useful for the right application but not enough to replace traditional analytics capabilities that also complement the human thought process. We can definitely say that the man-machine dichotomy is not an “either-or,” but an unequivocal “both-and.”

Paving the way to a collaborative future
Of course, this combination of smart technology and people won’t happen seamlessly or instantly. Organizations will need to examine knowledge-intensive processes and determine which tasks can be best performed by machines and which should remain with humans.

Some degree of retraining of the workforce may be necessary to develop and realign new skills required for the re-engineered processes and operations. Smart companies should think about these issues early in the game and help employees prepare for a collaborative future with smart machines.

In our next article, we will touch on the current analytical talent gap companies are facing.

The author is a director with the Risk Advisory group of Navarro Amper& Co., the local member firm of Deloitte Southeast Asia Ltd., a member firm of Deloitte Touche Tohmatsu Limited – comprising Deloitte practices operating in Brunei, Cambodia, Guam, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam.

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