- 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
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 will share research-backed insights on what they call “workslop” and will provide a framework to help leaders:
Based on survey data from 1,100+ employees, Hancock and Kellerman will expose the costs of ineffective AI use and share a proven framework and examples for delivering real value from AI adoption.
AI can be a transformative force—but only if organizations set clear norms and expectations. Join Hancock, Kellerman, and HBR on February 18 to learn more.
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