Milo Jones and Philippe Silberzahn predict that high-potential recruits of the future will be human-machine ‘centaurs’.
When IBM’s computer Deep Blue defeated chess champion Garry Kasparov in 1997, he dismissed its calculating brute force as a US$10 million alarm clock. Today, brute-force computing has given way to deep machine learning and artificial intelligence (AI) that will disrupt every sector, and even the future of work itself. This need not be a ‘good versus evil’ contest of wills between man and machine, but a means to reinforce and enhance human excellence by marrying it with machine intelligence.
Kasparov called human-machine chess teams ‘centaurs’, the mythological horse with a human upper body. Such human-machine hybrids already operate in financial markets, the law, medicine and a rapidly expanding number of other fields. Only ten years ago, we assumed that machines would not substitute for humans in such complex tasks as … driving a car. AI’s exponential growth suggests that machines will touch your sector rather sooner and more deeply than previously assumed.
AI does not need to be a ‘good versus evil’ contest of wills but a means to reinforce and enhance human excellence by marrying it with machine intelligence
For example, Rentokil, a British company involved in pest control, is now working on wifi-connected rodent traps with algorithms linking catches, weather and mapping data. Amazon’s Alexa, an intelligent personal assistant, assists with kitchen tasks, reporting sport scores and gathering customised news. Even judicial decisions can be improved. Algorithms applied to the question of whether US defendants awaiting trial should be kept in jail or allowed to go home, determined that the jail populations could be reduced by 42% without any increase in crime. AI’s disruptive potential has not gone unnoticed by investors: last year, more than 550 AI-based startups in the US raised some $5bn.
Talking points for business leaders
Companies can thrive in this new centaurian world if these hybrid relationships are carefully managed. Here are four issues for leaders to consider:
No place for complacency or fear. First and foremost, business leaders must ditch the all-too-common misconceptions that ‘no machine could ever do my job’, or worse: that ‘my industry must resist the threat of automation.’
Identify and improve areas of human superiority. Humans’ comparative advantages tend to be in non-analytical tasks such as questioning, hypothesising and synthesising. We are also good at discerning causes in the big-data correlations that machines spot so well. To maintain this qualitative advantage, managers must access a wide variety of information sources and create genuinely diverse teams that can help us make informed choices, especially regarding what tasks to delegate to machines.
To govern is to choose. Algorithms will not necessarily work together: marketing centaurs may demand more resources to increase sales, while operations centaurs will make the case for investing in better machinery. Each competing argument will be supported by data, models and intelligent systems. But it will be left to generalist business leaders to see the big (often unquantifiable) picture and decide which algorithm to believe, and then to explain and execute the decisions with sufficient empathy for those affected.
Reinvent business processes. An effective division of labour—between man and machine, as well as between centaurs—is only part of the challenge. It is also about how we all work together. You cannot simply cram AI tools into existing processes. Kasparov himself observed that a weak human with a machine and a good process is superior to a strong computer alone, and even to a strong human with a machine but an inferior process. The key is to think how best to incorporate human strengths systematically into your processes, and constantly seek ways to improve them. HR will play a vital role in this, especially by identifying and hiring those who embrace rather than resist digital change. In short, finding and developing centaurs should be part of your strategic automation plan.
Milo Jones is a Visiting Professor at IE Business School. Philippe Silberzahn is associate professor at EMLYON Business School and research fellow at Ecole Polytechnique.
This article was written for FT|IE Corporate Learning Alliance
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