• Nikhil Vaish

If You Want Magic, Hire People with the Wrong Experience

Updated: Jun 7, 2021

“You have an amazing amount of experience but they are looking for someone with exactly the right experience” said the recruiter. If I had a penny for every time I have heard those words, I might have retired by now.

Over the last few years job descriptions in most industries, particularly in marketing and advertising seem to have gotten increasingly narrow and specific. It is like companies want to eliminate all risk and only hire people who have done the exact same job before, rather than look for competent, experienced and intelligent candidates who would also be able to do the job, maybe with a small learning curve, but would bring a fresh and new perspective to it.

Companies have become obsessed with matching exact skill requirements and started using tools that sift through resumes to eliminate anyone whose resume does not contain specific keywords. Now many recruiting firms and HR departments have graduated to using artificial intelligence (AI) to help find the “perfect” and narrow fit for open positions.

The allure behind AI was driven by streamlining the hiring process, compared to when humans do it, and a belief that algorithms would eliminate human bias. Sure algorithms can search through troves of resumes faster than humans can, but what we gain in speed, we lose by missing out on the intangible elements that used to result in making brilliant hires. Experienced HR executives were trained to identify candidates who did not fit the mold, in that they often lacked the “right” experience on paper, but had qualities that helped them excel.

Aside from the sexism, racism and ageism biases that researchers have found in AI algorithms, relying on technology, built on narrow job descriptions that attempt to fit people into check boxes, all but eliminates the art and the serendipitous nature of finding outstanding candidates. People will argue that this eliminates risk in hiring, but that is exactly what hiring great candidates is about – there is inherent risk in hiring someone who does not check every box but the rewards are also far greater for the company.

Imagine if AI or keywords had been used in the search to find IBM’s new CEO in the early 1990’s when the company was faltering and on the verge of extinction. Louis Gerstner would have been automatically eliminated before a human had a chance to view his resume. Even if a human had reviewed his resume, based on today’s narrow criteria and obsession with the “right” experience, they would have eliminated him based on the fact that he lacked any technical experience and knew nothing about computers and servers.

At the time Gerstner was Chairman and CEO of RJR Nabisco, a food products and tobacco conglomerate, which could not have been more opposite from the type of experience one imagined was needed to save a dying technology behemoth. At the time the stakes to save IBM had never been higher and few people would have been willing to take a risk on a person who knew nothing about technology.

Yet this is exactly what Jim Burke, former CEO of Johnson & Johnson and head of the IBM search committee did. Initially, it was Gerstner who was not interested, but Burke persisted and courted him until he agreed to take the helm in April, 1993. Equally brilliant and risky was the move to bring Steve Jobs back to Apple, after he has been unceremoniously fired, years earlier. We know how both these risky and highly creative hiring moves turned out.

The same was true when Brad Bird was hired by Steve Jobs to build a new team at Pixar studios. Bird did not look for people who fit the usual criteria but sought those who would be the first eliminated by most AI tools and hiring managers. He calls them the Black Sheep and malcontents: people who are frustrated, do not follow orders, or do things in the rote ways. Pixar is the most awarded and box office dominating animation studio in history.

By no means am I suggesting that we go back to the pre-digital age and sift through paper resumes again, but I am saying that the problem with relying solely on a narrow set of "experience" criteria and on machines that cannot think out of the box or act creatively will never allow you to deliver on the kind of hiring kismet that I am referring to. It will not give you the lateral thinkers and imaginative misfits who bring fresh and wildly different perspectives to a role and provide not good, but outstanding business results.

There is no question that hiring like this requires courage and taking some calculated risks, but the rewards are also far greater. So, the next time you want to hire a Rockstar, remember to spend time looking at the wrong candidates, who lack the right experience for the role.

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