Emotional intelligence, the empathy no robot can (yet) emulate

SUMMARY:

While much attention is paid to the onslaught of machines as job replacement across any sector you care to name there are plenty of examples where the need for emotional intelligence trumps any machine.

human-and-robotsWe live in an age of rapid and wrenching change in our personal and professional lives as technology evolution continues to nibble away at what were once considered secure jobs. So far the discussion has centered primarily on the automation aspects of blue-collar work and how machines are able to replace human labor.

But much the same is happening, albeit at a slower pace, to the professions and what professionals do about the encroachment will have a lot to do with their ultimate career success and job satisfaction. Megan Beck a digital consultant at OpenMatters and researcher at the SEI Center at Wharton, and Barry Libert, CEO of OpenMatters, and Senior Fellow at the SEI Center at Wharton, co-authored a book, “The Network Imperative: How to Survive and Grow in the Age of Digital Business Models” from which this HBR article is drawn.

Off the bat, Beck and Libert did a smart thing by abstracting the common denominators in most, or at least many, professions into five basic functions. They say that regardless of profession we all,

  1. Gather data
  2. Analyze the data
  3. Interpret the results
  4. Determine a recommended course of action
  5. Implement the course of action

and that’s hard to argue with.

Whether you’re a doctor, a financial advisor or a consultant that’s what you spend you working life doing. The challenge we all face is that automation is increasingly good, and even better than we are, at the first 3 or 4 activities.

Even in CRM where I spend my life, it’s easy to see that machine recommendations for next action or stack ranking the deals most likely to close are increasingly valuable. Or machine recommendations for which piece of content or presentation to use in a sales call will work best.

The next deduction to draw from this is that machines are way better than people at gathering huge amounts of data but we can. As the authors have it,

Human beings can’t just plug in more servers when we reach our limits processing new information.  Instead, we must rely on our own, often biased, preferences, habits, and rules of thumb.

Humans call it experience and we have less of it upon which to make decisions and recommendations.

Pay close attention to that last sentence because it encapsulates the research lives of Daniel Kahneman and Amos Tversky. After Tversky’s death Kahneman, a Ph.D psychologist, won a Nobel in Economics for research into human decision-making. Their work debunked the economics profession’s long held view that people in markets are rational and make rational decisions. All of that laid the foundation for behavioral economics.

It’s the rules of thumb mentioned in the article that Kahneman and Tversky would have identified as heuristics and yes, they are often biased but that’s not necessarily a bad thing. I’d argue the biases are only bad when they are subconscious and unrealized. Understanding the biases is the basis for tailoring humane solutions for human beings.

With all that in mind it’s easy to formulate a game plan for one’s career whatever phase you think you are in. Some things go without saying like don’t fight technology’s progress also recommended by McAfee and Brynjolfsson in The Second Machine Age but even more important than what you shouldn’t do is what you ought to do. As Beck and Libert suggest,

  • Examine your own capabilities interacting with, motivating, and assessing people. Recognize your strengths and weaknesses when it comes to emotional intelligence.
  • Invest in developing your emotional intelligence. The simplest way is to change your mental model about what is important in your role, and begin focusing on how you can better manage, influence, and relate to others.  Or, take it a step further by seeking out training and stretch opportunities.

Of course changing one’s mental model is difficult because the model was hard won yet got us as far as we have. Abandoning the model is challenging though from time to time necessary.

My take

This whole discussion is appropriately found in a business journal. Over my career I have often interacted with people in the social/teaching/healing arts and for them these things are second nature. The depth of their insights often humbles me when all I can see is the problem. That’s especially true when I just can’t fathom why the logical answer doesn’t simply leap to mind and cause everyone to do the right thing. But as the article puts it,

A smart machine might be able to diagnose an illness and even recommend treatment better than a doctor.  It takes a person, however, to sit with a patient, understand their life situation (finances, family, quality of life, etc.), and help determine what treatment plan is optimal.

Social workers, teachers, psychologists, doctors and others do this kind of thing instinctively, requiring more than a modicum of emotional intelligence. In part, the future of business and of being better business people lies in understanding what those people already know. Perhaps that’s one reason there’s such a difference between the public or political sector and the private or business world and why so often business leaders fail to translate their successes into the political domain. I’m not making any comparisons here, but you can.

There’s plenty to digest on this thorny topic and you might find some of the recommended stories listed below of interest.

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