Does AI-Flavored Feedback Require a Human Touch?

This article originally published by MIT Sloan

Michael Schrage

Companies must choose whether humans or machines should get the last word on employee performance.

Digital tools and technologies are now relentlessly and remorselessly transforming how performance management works. Customized and continuous data-driven feedback is becoming a new normal for enterprises worldwide. This feedback appears both qualitatively and quantitatively superior to its performance review precursors and should lead to better outcomes. But does AI-flavored feedback require a human touch to measurably improve its impact?

Organizations committed to state-of-the-art talent management are revisiting the role managers should play in delivering, facilitating, and/or curating employee feedback. Are managers mainly conduits for criticism? Or do they add meaningful value and insight? “We know that putting the manager back in performance management is one of the keys to making it work,” states McKinsey & Co. partner Bryan Hancock during a recent webinar on performance management. “You can create the best system in the world with the best amount of employee involvement,” he continues, “but if at key junctures, the managers aren’t taking responsibility, it’s a problem” — especially since Netflix, Google, Amazon, and other digital innovators have successfully personalized sophisticated analytic assessments for their users.

Whether average managers represent an organization’s best option for constructively critiquing employees is now an open and important question. Preliminary findings from our recent research suggest that ongoing investment and innovation in AI capabilities will provoke conflicting answers. The implications for legacy HR and people management are enormous.

This research highlights how direct managerial involvement both complements and competes with data-determined performance reviews. Increasingly, organizations are discovering they must explicitly choose whether humans or machines should get the last word on people’s performance. This process is as much about cultural transformation as organizational transition. However, productively balancing analytic insight with managerial interaction is challenging. Who owns the feedback?

IBM’s digital journey offers a superb case study in confronting these performance management challenges. The company’s HR leadership, for example, explicitly tracks managerial impact on employee engagement and outcomes.

“The role of the manager is incredibly important still, even in an agile culture,” acknowledges Diane Gherson, IBM’s chief human resources officer and senior vice president of human resources. “If there’s a manager who’s not ‘bought in’ or not engaged, the chances of their people not being engaged is something like three times higher. Making sure that managers fully understand the strategy and are fully engaged really can’t be forgotten.”

But Gherson emphasizes that her group’s intensifying commitment to AI has dramatically changed IBM’s human capital management. “We’ve got a lot of AI in our HR,” she notes. That investment profoundly alters how managers and employees engage with one another. Smarter software has fundamentally restructured IBM’s performance management economics and expectations. Increased digitalization often disintermediates direct managerial engagement.

AI’s most significant influence is on productivity, says Gherson. HR has replaced many human resources with chatbots, for example, that learn to advise employees while generating analytics for monitoring how helpful the advice proves to be. In other words, IBM gets feedback on feedback.

Comparable AI systems offer decision support with actionable insights into possible attrition and suggestions about appropriate pay levels for employees with highly competitive skill sets. “Enabling better employee experiences” is another AI focus, Gherson says. These systems embrace career development advice, personalized learning programs, and Blue Matching (IBM’s proprietary system that intelligently matches candidates to desirable job openings within the company).

As important as managers may be, IBM HR’s clarion message is that digitalization must deliver smarter, better, faster, and more engaging talent management services for less. Personalized and productive feedback can’t come at a premium price. At the same time, most organizations authentically want their higher-cost human managers engaged and involved.

This conflicted sensibility and expectation is not unique. ADP vice president and chief behavioral economist Jordan Birnbaum observes that empowering managers and employees has become an important part of performance management systems design. “When performance management is designed well,” he says, “managers have a toolbox that helps them improve by several orders of magnitude, leaving employees feeling empowered to succeed moving forward.”

The catch, he acknowledges, is that being objectively data-driven often forces people to incorporate uncomfortable algorithmic advice. “If you’re going to use evaluative data properly,” says Birnbaum, “then the job is to frame that data properly, particularly if it is being used to drive future performance. That also includes incorporating feedback not easily measurable or captured by data, like ‘teamwork’ or ‘supportiveness.’ But as long as relevant data is not easily captured, there’s a place for the human manager in the process. Whether or not the human manager feels that to be the case, though, is another story.”

The tension becomes obvious: Is being asked — or told — to follow a prescription that makes one measurably better a source of managerial empowerment or disempowerment? For managers and employees alike, does it bring about more confusion? For example, would managers have the discretion to ignore or significantly alter their data-driven advisories? How do conflicts between intuition and evidence get resolved? Most managers are grateful for contextually relevant analytic advice. But advice that must be followed is no longer advice — it’s compulsion.

As AI and machine learning technologies improve, says Birnbaum, managerial prescriptions become even more specific and explicit. This leads to a natural question: At what point does it make sense — and save money — to simply bypass the manager as a feedback delivery system and directly advise the employee?

Posted by Zak Kennedy