“With artificial intelligence, we are summoning the demon. In all those stories where there’s the guy with the pentagram and the holy water—yeah, he’s sure he can control the demon. Doesn’t work out.”
Those words weren’t uttered by some neo-luddite about the scary pace of computation in the digital age. They were said recently by Elon Musk, founder of SpaceX and Tesla, investor in Deepmind (an AI company) and, in some circles, proclaimed as the next Steve Jobs. When Musk and other high-thinkers, like Bill Gates and renowned Cambridge physicist Stephen Hawking, propose similar doomsday warnings about artificial intelligence, it creates a big, global debate.
In a recent Harvard Business Review article, Walter Frick writes about the rise of the machine and its impact on business and our jobs. He offers an alternate perspective, rejecting the “obsession of job-eliminating technology in favor of a focus on complementarity.”
If it is about complementarity, then what skills do we need to build to thrive alongside computers in the age of algorithms?
For starters, you can’t just go and “turn on” algorithms at a company; deploying algorithms at scale requires a strong technical foundation, including the ability to integrate, maintain and identify what can be done with mounds and mounds of data. Put simply, automated decision-making through algorithms and machine learning is not an easy task and will require many more years of work.
But let’s assume for a second that the data foundation has been laid, as it likely will for most companies in the coming decade. Then how do we, as humans, avoid the journey on a long road to machine-driven oblivion?
1. Move from assumption-based decisions to data-driven decisions.
Too many decisions in enterprises are based on assumptions that are grounded in experience. However, past experience may not be an accurate predictor of the present or the future when industries and markets are being disrupted. Assumptions are often based on an outdated view of how the world works.
The age of algorithms allows patterns to be surfaced based on what we know is happening, instead of relying on what we “feel” or we “think” may be happening. Analytical decision-making is no longer the preserve of a few data geeks, especially with algorithms willing and able to do a lot of that dirty work. As a result, we must move beyond intuition-, emotion- and anecdote-based decisions. Intuition is great for ideas, but data is actual proof.
2. Ask the right questions of data.
Data will give you the answers to whatever questions you have. But data and algorithms cannot tell you how good your questions are. We have to learn how to ask the right questions.
This requires us to know how to work with data, how to relate data to our work and how to tell stories with data. We have to understand what metrics matter for the business, what decisions need to be driven by the data and how to harness algorithms for the most strategic decisions.
To take an analogy from self-driving cars…. While the cars may be great at driving themselves, they cannot decide where you should be going.
3. Add context to algorithms.
Machines can’t think outside the data like our brains can. We can quickly see correlations in completely unrelated data sets that machines often miss because we understand the business context within which the correlations occur and the process that gives rise to the data.
We need to be skilled at pattern recognition and contextual interpretation of data. This in turn requires a combination of domain knowledge, an understanding of how our role or department fits within the broader context of the business, the ability to introduce insights not found in the data and to accept the most relevant insights, and reject the others.
4. Combine facts with feelings.
Machines are also really poor at truly understanding individual human behavior and the nuances of motivation, emotion and interaction. So we will continue to need skilled sociologists, psychologists, communicators, economists and leaders that understand how to elicit a response from fellow humans. Every boardroom conversation will start with algorithms and facts, but they will end with a handshake.
By focusing on the critical job functions where machines free up humans to do more strategic, complex and creative work, we can remain in the driver’s seat even, and especially, in an age of algorithms. Which, by the way, isn’t as demonic as Musk makes it seem or as benign as some would argue. It is how we adapt to that nuance that will define our role in the age of algorithms.