HBR Guide to AI Basics for Managers by Harvard Business Review

Author:Harvard Business Review
Language: eng
Format: epub
Publisher: Harvard Business Review Press
Published: 2023-10-02T00:00:00+00:00

Phase 3: The Coach

In a recent PwC survey nearly 60% of respondents said that they would like to get performance feedback on a daily or a weekly basis. It’s not hard to see why. As Peter Drucker asserted in his famous Harvard Business Review article “Managing Oneself,” people generally don’t know what they are good at. And when they think they do know, they are usually wrong.

The trouble is that the only way to discover strengths and opportunities for improvement is through a careful analysis of key decisions and actions. That requires documenting expectations about outcomes and then, nine months to a year later, comparing those expectations with what actually happened. Thus the feedback employees get usually comes from hierarchical superiors during a review—not at a time or in a format of the recipient’s choosing. That is unfortunate, because, as Tessa West of New York University found in a recent neuroscience study, the more people feel that their autonomy is protected and that they are in control of the conversation—able to choose, for example, when feedback is given—the better they respond to it.

AI could address this problem. The capabilities we’ve already mentioned could easily generate feedback for employees, enabling them to look at their own performance and reflect on variations and errors. A monthly summary analyzing data drawn from their past behavior might help them better understand their decision patterns and practices. A few companies, notably in the financial sector, are taking this approach. Portfolio managers at MBAM, for example, receive feedback from a data analytics system that captures investment decisions at the individual level.

The data can reveal interesting and varying biases among PMs. Some may be more loss-averse than others, holding on to underperforming investments longer than they should. Others may be overconfident, possibly taking on too large a position in a given investment. The analysis identifies these behaviors and—like a coach—provides personalized feedback that highlights behavioral changes over time, suggesting how to improve decisions. But it is up to the PMs to decide how to incorporate the feedback. MBAM’s leadership believes this “trading enhancement” is becoming a core differentiator that both helps develop portfolio managers and makes the organization more attractive.

What’s more, just as a good mentor learns from the insights of the people who are being mentored, a machine learning “coachbot” learns from the decisions of an empowered human employee. In the relationship we’ve described, a human can disagree with the coachbot—and that creates new data that will change the AI’s implicit model. For example, if a portfolio manager decides not to trade a highlighted stock because of recent company events, they can provide an explanation to the system. With feedback, the system continually captures data that can be analyzed to provide insights.

If employees can relate to and control exchanges with artificial intelligence, they are more likely to see it as a safe channel for feedback that aims to help rather than to assess performance. Choosing the right interface is useful to this end. At MBAM, for

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