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"The Productivity Cost of AI"

The Productivity Cost of AI

Generative AI adoption is skyrocketing. Yet for many organizations the promised productivity gains remain elusive. A recent MIT Media Lab study found that while AI usage in workplaces has doubled since 2023, 95% of companies still see no measurable return on their investment.

Why? New research from BetterUp Labs and Stanford suggests the problem lies not in the AI tools but in how employees use them. Too often, workers are producing polished but shallow outputs that lack the depth and context to move projects forward.

The result: employees must spend time deciphering, correcting, or redoing AI-generated work—producing a “hidden tax” that can cost large organizations millions of dollars each year.

In this live, interactive HBR webinar,  Jeffrey T. Hancock of Stanford University and Gabriella Rosen Kellerman of Boston Consulting Group shared research-backed insights on what they call “workslop” and provided a framework to help leaders:

Define and quantify the hidden costs of AI adoption
Recognize, address, and avoid this hidden-tax phenomenon—before it undermines trust and efficiency
Provide a framework for avoiding this hidden AI tax
Employ best practices for realizing significant value from AI
Based on survey data from 1,100+ employees, Hancock, and Kellerman exposed the costs of ineffective AI use and shared a proven framework and examples for delivering real value from AI adoption.

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