If we want to make talent identification more effective — and more meritocratic — it’s important to continue to look beyond existing methods, particularly if technological innovations enable us to predict, understand, and match people at scale.
Few things seem creepier than algorithms mining our voices or photos to determine whether we should be considered for a job, and yet we’re not that far from this scenario at all. What’s more, it may not be as creepy as you think.
For starters, all organizations struggle with talent identification, which is why many complain that they are unable to find the right person for key positions, and why most people end up in jobs that are far from inspiring. Consider that even in the biggest economy in the world, where talent management practices are far more science-driven and sophisticated than anywhere else, the labor market is quite inefficient. Today in the U.S., there are around six million job seekers for seven million job openings. Even if we look at the global knowledge economy, comprised of the most qualified and skilled cognitive elite (roughly the 500 million people who are on LinkedIn), job satisfaction is the exception rather than the norm: it is estimated that as many as 70% of these top talented individuals are open to other, hopefully more meaningful or interesting, jobs or careers. Elsewhere, the norm characterizing recruitment and hiring processes is considerably more backward, with hiring managers over-emphasizing hard skills at the expense of the more important and critical soft skills, or using intuitive and biased hiring methods, such as the unstructured job interview, to determine who gets the job. All the while, predictive assessments and data-driven tools are largely under-utilized, and the prevalence of prejudice, bias, and discrimination are everywhere.
Read the full article on HBR here.