Don’t Let The Algorithm Become Your Rockstar
Source: Harper
In business, the rise of the algorithm has led to a new management trend: the algorithmic corporation. No matter which publication you open these days, “the algorithmic CEO (link is external),” “algorithmic marketing (link is external),” or “algorithmic HR (link is external)” are all the rage. The adage “You can only manage what you measure”―the lowest common denominator of all modern management―seems no longer good enough; now we are eager to not just measure but predict everything, or more precisely, have it predicted. Increasingly, we delegate not only efficient task execution but also complex decision-making (link is external) to smart machines. For many, the algorithm has emerged as the new rock star―the best performer on the team.
However, the track record of algorithmic performance is somewhat mixed at best. In marketing, consider the “inadvertent algorithmic cruelty (link is external)” that occurred when a Facebook algorithm curated a user’s “annual highlights” and included the death of his child, or when Uber’s pricing system tactlessly introduced surge fares during the Sydney hostage crisis (link is external), due to suddenly heightened demand. Algorithms always seek the optimal solution―which is not always the most humane. They can correlate but not relate: in fact, they are incapable of suffering and thus incapable of such fundamental human qualities as empathy and compassion.
This limitation applies to the employee experience as well. What happens when data becomes the only driving force is evident at Amazon, the epitome of the quantified corporation. After the New York Times (link is external) recently exposed the ruthless data-darwinism of the retailer’s workplace culture, even some hardcore measurement apostles realized that the paradigm of radical efficiency comes on the backs of worker’s wellbeing and at the expense of basic decency. The ensuing public backlash was humbling for Amazon, not the least because it learned that the power of human stories, in other words, the power of subjectivity, still trumps the idea of an objective, data-based truth, after all.
The “mathematization of subjectivity (link is external)” (Leon Wieseltier) is ultimately bad for business: it undervalues relationships; shrinks our imagination to mere anticipation; and eliminates the ambiguity, curiosity, and exuberance, yes, even messiness, that are at the heart of innovation and collaboration.
But there’s more at stake here than just business success: our very humanity. An algorithmic society poses the real risk that we will soon no longer be able to think outside the cloud, outside our recorded and projected intentions, our augmented, quantified super-selves. What will it do to our sense of identity when others know us better than we know ourselves, as Charles Handy (link is external) warned? Ultimately, we are human because we are unpredictable. We are human because we can’t be trusted.
So instead of crunching numbers and calculating results, I urge you to un-optimize and un-quantify in your business. Data is a powerful tool, and we should definitely take advantage of it to better understand our problems and inform more effective solutions. It might be the new oil but should not be the new religion. We are only truly free as long as we can act against data, as long as we can remain knowledge workers who will never know it all.
As Frederic Laloux (link is external) writes in his book, Reinventing Organizations, the most important innovation in the 21st century won’t be a technological one, but a humanistic one: the design principles that cherish the best of our humanity and enable us to live, work, and play together in peace and prosperity. To find them, we must create soulful organizations, not just efficient ones. We must create thick cultures instead of lean operations. We must maintain the ability to discover without exploiting. We must venture to new places instead of just optimizing the ones we already know.
What we therefore need in our organizations are not smarter, more powerful algorithms, we need more true rock stars―visionaries and “misfits” who defy the confines of strict rationality, and rhyme more than they reason. They should be what actual rock stars are supposed to be: charismatic, erratic, and hard-to-read, with “secrets poetic enough to be believable” (Mick Jagger). In other words, the exact opposite of algorithms.
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