4 Things Every Business Leader Should Know About Artificial Intelligence and Automation
In 2011, MIT economists Erik Brynjolfsson and Andrew McAfee self-published an unassuming e-book titled Race Against The Machine. It quickly became a runaway hit. Before long, the two signed a contract with W. W. Norton & Company to publish a full-length version, The Second Machine Age that was an immediate bestseller.
The subject of both books was how “digital technologies are rapidly encroaching on skills that used to belong to humans alone.” Although the authors were careful to point out that automation is nothing new, they argued, essentially, that at some point a difference in scale becomes a difference in kind and forecasted we were close to hitting a tipping point.
In recent years, their vision has come to be seen as deterministic and apocalyptic, with humans struggling to stay relevant in the face of a future ruled by robot overlords. There’s no evidence that’s true. The future, in fact, will be driven by humans collaborating with other humans to design work for machines to create value for other humans.
1. Automation Doesn’t Replace Jobs, It Replaces Tasks
When a new technology appears, we always seem to assume that its primary value will be to replace human workers and reduce costs, but that’s rarely true. For example, when automatic teller machines first appeared in the early 1970s, most people thought it would lead to less branches and tellers, but actually just the opposite happened.
What really happens is that as a task is automated, it becomes commoditized and value shifts somewhere else. That’s why today, as artificial intelligence is ramping up, we increasingly find ourselves in a labor shortage. Most tellingly, the shortage is especially acute in manufacturing, where automation is most pervasive.
That’s why the objective of any viable cognitive strategy is not to cut costs, but to extend capabilities. For example, when simple consumer service tasks are automated, that can free up time for human agents to help with more thorny issues. In much the same way, when algorithms can do much of the analytical grunt work, human executives can focus on long-term strategy, which computers tend to not do so well.