Robots make excellent colleagues

Earlier this year, a New York Times quiz presented readers with a set of eight quotes and posed the following question: “Did a human or computer write this?”

I’ll re-post two:

  • “Apple’s holiday earnings for 2014 were record shattering. The company earned an $18 billion profit on $74.6 billion in revenue. That profit was more than any company had ever earned in history.”
  • “Kitty couldn’t fall asleep for a long time. Her nerves were strained as two tight strings, and even a glass of hot wine, that Vronsky made her drink, did not help her. Lying in bed she kept going over and over that monstrous scene at the meadow.”

If you guessed that the first was written by a computer and the second by a human, you’d be wrong. The first is written by Jay Yarow, a real live human working for Business Insider, and the second is from the Russian novel True Love, written by a computer in St Petersburg in 72 hours.

If you guessed correctly, chances are you were second-guessing me, which would have been the obvious thing for a human to do (presuming you are human), but probably quite advanced for an algorithm.

I got 50%. Let’s say you did just as badly. You know what that means. Time to worry. What about our jobs?

South Africa’ unemployment stands at around 25%, the eighth-highest in the world, and the issue couldn’t be more sensitive. And yet more than two thirds of SA executives are seeing a future in which smart machines share the workplace with human colleagues.

Just as many think there will be a need to ‘train’ machines (with intelligent software, algorithms and machine learning) within three short years. And that has been coming for a long time. Back in 2010 a Financial Times article profiled arobot training instructor, whose job entails training students to program robots to perform advanced tasks that will eat into human jobs.

But is it truly such a big scare? You can be negative about robots taking your jobs, or you can embrace the opportunity to rise above it by learning a higher skill — in this case, programming.

But seriously, which jobs are safe anymore? Robots were once deaf, dumb, blind and stupid, and only did what you told them to do.

Currently, the answer is this: People are thought (by people, admittedly) to be better suited for creativity, contextual understanding and complex communications, while machines provide superior precision, scale and consistency. For that reason, humans are much in demand in a collaborative capacity. As a team, humans and machines can accomplish more than either human or machine could on their own, providing enterprises with increased intelligence, performance and productivity.

For example, intelligent software by Quakebot is Los Angeles Times’ secret weapon to being first with the earthquake news. Yet, no matter how clever the program’s automated data analysis and report writing is, the end product is a collaborative effort with a warm body. The bot extracts data from US Geological Survey reports and plugs it into a pre-written template, and the human verifies the article, edits and publishes it.

It is utterly reassuring as a model for a blended workforce. Teaming with a robot colleague not only makes reporters’ lives easier, it improves the speed at which an article can be posted. Recently one went up within three minutes!

Many more examples exist, proving that it is not necessarily a dystopian vision of humans versus machines, but one of humans and machines.

Another issue that removes this from being a political hot potato is the level of skills being eroded. As companies become more digital in their external touch points and inner workings, in search of efficiencies and new ways to grow, they’re encountering global talent shortages — in critical skill areas such as IT, cybersecurity and analysis of huge datasets. Forward-looking organisations are building blended workforces to fill those skill gaps.

The trends and technologies to watch here are advances in more natural human interfaces such as natural language processing (NLP), which makes it more acceptable to turn to machines for assistance. NLP is expected to grow rapidly to a $10 billion market by 2018.

But it isn’t just robots that augment our workforce efficiency. By bringing the digital into the physical world, even wearables are transforming people into “better versions” of themselves. Heads-up displays in particular are making employees smarter and more efficient. According to Gartner, that can mean adding more than $1 billion per year to field service company profits by 2017.

Looking at it more broadly, Gartner also predicts that by 2018, the total cost of ownership for business operations will be 30% lower than today because of the wider use of smart machines and industrialised services such as robotic assembly.

The push to employ a blended workforce is becoming increasingly prevalent, and it couldn’t be more opportune. On balance, it ought not to impact manual labour forces in particular too negatively. It both creates demand for new skills and have a higher impact on knowledge workers (the industrial revolution was in fact the chief destroyer of manual jobs, while also creating new types of jobs).

But it raises many new issues as well. A few among them: which jobs should be assigned to humans and which to humans working with machines? What governance systems are in place to help us decide? And how do we strategically decentralise decision-making so that machines can carry more of the load? How to shift the entire value chain of business operations from a labour-driven andtechnology-enabled paradigm to a digital-driven and human-enabled model?

Big questions, and big changes, all. But companies can’t afford to shy away from them. Human and machine — each on their own — won’t be enough to drive businesses in the coming decades. Tomorrow’s leading enterprises will be those that know how to meld the two effectively.