13 Jun 2019 19:21 IST

AI vs Leadership Intelligence could become AI + LI

Eventually, humans and AI will work together, complementing each other’s strengths

Earlier this year, the Artificial Intelligence (AI) vs humans debate was in focus when IBM conceded that it’s AI-powered, Project Debater, lost a debate competition. In a debate before a live audience, Harish Natarajan, a seasoned debater, was able to persuade more people in the audience to switch sides to his point of view. In some sense, his AI-powered opponent had the easier motion to argue for — arguing for subsidising pre-schools. Harish argued against the motion and won. But this kind of story has been the exception.

In 2017, a Google designed algorithm AlphaGo narrowly beat the world’s best player in the ancient Chinese board game of Go. World number one Ke Jie anointed the program as the new ‘Go God’. The AI victory was considered a significant milestone in the evolution of AI because of the complexity of the game.

Since then, there have been continuous predictions of how AI will take humans to doomsday. Stephen Hawking warned that AI “could spell the end of the human race”. Bill Gates and Elon Musk expressed their concerns. A University of Oxford study predicted that 47 per cent of human jobs are at risk of being replaced by robots or AI. In this changing scenario where some jobs will be taken over, what should the leader focus on? What is the LI (leadership intelligence) she must deploy to avoid AI taking over? What skills must she make use of?

Creativity

One of the biggest ways humans can always be different from machines is in our creativity. If we keep honing this skill, we will find we have the edge. Especially in situations where the default may be to take the logical path or follow a past pattern that has worked. With the cricket World Cup on now, we can recall how New Zealand captain Martin Crowe opened his bowling with a spinner in a tight match. This had rarely or never been used as a strategy. In business perhaps, when logic dictates competing, the leader is willing to explore collaboration. The leader is willing to look at a solution or strategy from a completely different industry.

Alibaba’s founder Jack Ma, speaking at the World Economic Forum in Davos, put it succinctly: “Only by changing education can our children compete with machines. We cannot teach our kids to compete with machines. Teachers must stop teaching knowledge. We have to teach something unique, so a machine can never catch up with us… If we don’t change the way we teach, we will be in big trouble in 30 years from now. Because the way we teach, the things we teach our kids, are the things from the past 200 years – its knowledge based. We need to be teaching our children values, believing, independent thinking, teamwork, care for others...these are the soft parts. (The) knowledge will not teach you that.” He continued with his recommendation, “That’s why I think we should teach our kids sports, music, painting, art. Everything we teach should be different from machines.”

Data to insight

Jack Ma’s wise advice also gives us many clues on how we can prepare for this new human plus machine world. We need to go beyond knowledge. Machines will always be better at storing, retrieving, comparing and even analysing information. The leader has to focus on letting machines tread the path from data to information to analysis using their brute byte-power. But from there, the leader can take over to draw the right insights from that analysis. The leader also has to keep developing her connection quotient. Her ability to connect the dots — why did a competitor choose a particular strategy? What led to a shift in key talent? What has triggered a geographic mega-trend? — and throw up insights that would be more valuable than the hard logic that AI might throw up.

An analogy is Google maps telling us to turn left, but a gut instinct tells us the better route is right. So we roll down the shutter and ask and find we were right. The leader can develop the skill to look at the information and analysis and pause, suspend judgement and arrive at a nuanced conclusion that AI might not. George de Mestral came up with the idea for Velcro by observing the burrs that clung to his dog’s fur when he got back from hunting. That kind of insight is difficult for AI to ace. And that’s exactly what we should get better and better at.

Continuous learning of right skills

Curiosity is a valuable human trait. It could also prove distinctive when we compete with AI. The human leader trying to make airport traffic smoother can visit a supermarket for inspiration. The mobile company CEO could keep challenging his design team on whether their devices can get smaller and more intuitive. The ability to ask questions that a machine might not think to ask, to review and reflect on mistakes and to keep learning could all be valuable as we harness machines and AI. The trick will be to focus on the right skills — as Jack Ma put it — to focus on what makes us human.

Interpersonal and intrapersonal

The ability of the leader to reflect, be self-aware will be difficult for humans to replicate. The ability of humans to interact with each other — to persuade, to influence, to sell, to negotiate, to entertain may also be an ability that takes AI a while to catch up. These are skills that we must keep growing because they will always make us unique and give us a skill set which has a less chance of being out-dated. Doing a mega-deal is often about the chemistry between the people involved, not just the facts. Making an acquisition work depends more on the inter-personal skills of the leaders involved than the analysis of spreadsheets.

Leaders will also try to change the frame. AI vs humans frame may be the wrong frame to take. Eventually, humans and AI will work together, complementing each other’s strengths and the leader will need to be able to gauge those strengths and harness them in the right way. So, AI vs LI could become AI + LI — an opportunity rather than a crisis. AI is here to stay but LI could make all the difference in making the human-machine dynamic one that works for the good.

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