Just about everyone agrees that the traditional, annual-review-based way of evaluating employees simply doesn’t work anymore, if it ever did. In fact, it’s hard to think of anything businesses routinely do that’s more universally unpopular. A recent global Mercer survey of HR leaders worldwide found that just 2% believe their current performance-management systems are very effective. No wonder McKinsey reported, in another recent study, that two-thirds of employers say they’re making big changes, or trying to.
“Managers and employees alike see the old annual-review approach as too subjective, too bureaucratic, and too backward-looking,” notes Bryan Hancock, a McKinsey partner in Washington D.C. who has worked closely with companies where artificial intelligence is being applied to evaluating humans’ performance. Those employers are working on doing away with annual reviews, replacing them with ongoing feedback in real time.
The new systems also provide managers with a wide range of up-to-the-minute information, from how long someone has been in their current job, to what skills they have that might be transferable elsewhere in-house. Using the data, Hancock says, managers can concentrate on “coaching people rather than rating them. It’s much more objective than the old way, and much more focused on future results.”
The most advanced A.I.-powered systems also do something else: Recommend specific actions—when to consider promoting someone, for instance—based on patterns in vast seas of companywide data points.
That, of course, raises some questions. If human managers begin to feel that their job is ultimately just to rubber-stamp an algorithm’s decisions, how engaged and enthused will they be at leading their teams? How can companies design performance management systems that don’t shut managers out? And what is a manager’s role exactly, anyway?
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