Two 40-year old robots returned to the big screen this year: handy mechanic droid R2-D2 of the Star Wars Rebel Alliance and the terrifying Terminator. The latter has been used by many headline writers as a metaphor for the perceived risk that automation will destroy human jobs.
But in 2020, we will see that the real future of work lies with R2-D2, whose strength is his ability to work alongside humans and enhance their performance. Next year will be the year of augmentation technology.
Our fears about automation come down to three factors: machines will execute tasks more efficiently; machine learning will enable artificial intelligence (AI) to make complex decisions more effectively; and technology companies will sell software and algorithms to replace slow and distractible people with fast and focused machines.
Such fears aren’t without basis, but the biggest technology opportunities have always augmented the work of humans, rather than replaced it altogether. In 2020, new technological breakthroughs will amplify this trend. Augmentation technology will combine machine efficiency with human empathy to make people’s interactions more efficient and effective. AI and machine learning will enable teams of people to learn much faster than before. Individuals’ skills gaps will be augmented by technology, enabling them to succeed in more valuable, reconfigured jobs that meet real needs.
We have already automated many routine tasks in the workplace and, today, most of what workers get paid for are effective interactions such as communicating, problem-solving in teams, learning what users value and finding the “sweet spots” that make companies and their customers better off. And those tasks will be enhanced, not replaced, by technology. A good example of this already in action is Superhuman, which streamlines workers’ experience of email.
Superhuman is a combination of ingenious design (by humans), software extensions and hacks to Gmail, and a 30-minute onboarding by a real person who “befriends” new customers and personalises Superhuman’s interface to suit a customer’s email needs.
In medicine, startups such as PathAI use experienced pathologists to train their algorithms that then use machine-learning methods to improve the accuracy of clinical diagnoses and inform innovation in personalised medicine.
And software-as-a-service company Medallia, which is entering 2020 on a strong trajectory, makes use of the knowledge and resourcefulness of frontline customer-support employees to enhance its AI, connecting the dots between frontline choices and customer satisfaction and allowing it to make smart, practical suggestions to workers in real-time.
Technology is not like gravity. It does what the humans who design and deploy it are rewarded to have it do. We have an opportunity to reconfigure the human values that we want technology to embody. The challenge, for the next decade at least, won’t be the ability of technology to work like R2-D2 – we have that already – but distorted market rules of the game that pressure it to act like the Terminator.
Our tax codes, accounting standards, executive-compensation systems, dysfunctional training systems, exclusionary hiring practices and divided societies do create institutional biases for automation over augmentation.
As companies such as Superhuman, PathAI and Medallia achieve success, a new Rebel Alliance will rise, and in 2020 “augtech” will blossom into a commonly understood category like fintech or biotech, boosting venture-capital inflows, multiplying startups and encouraging more established technology companies to emphasise their augtech offerings.
If we come to grips with our institutional blockers to progress, we’ll create the conditions for augtech to flourish and deliver more overall productivity. Adopters of augtech will be rewarded with rapid reskilling, more inclusive innovation, creativity at every level, greater openness to change, better earnings and more fulfilling experiences.
In 2020, it will be R2-D2’s time to shine.
Byron Auguste is CEO of the Opportunity@Work nonprofit
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