MIT Sloan Management Review Article on What Leaders Should Know About Measuring AI Project Value
- 8m
- Eric Siegel
- MIT Sloan Management Review
- 2024
Most AI/machine learning projects report only on technical metrics that don’t tell leaders how much business value could be delivered. To prevent project failures, press for business metrics instead.
“AI” can mean many things, but for organizations using artificial intelligence to improve existing, large-scale operations, the applicable technology is machine learning (ML), which is a central basis for — and what many people mean by — AI. ML has the potential to improve all kinds of business processes: It generates predictive models that improve targeted marketing, fraud mitigation, financial risk management, logistics, and much more. To differentiate from generative AI, initiatives like these are also sometimes called predictive AI or predictive analytics. You might expect that the performance of these predictive ML models — how good they are, and how much value they deliver — would be front and center. After all, generating business value is the whole point.About the Author
Eric Siegel is a consultant and a former Columbia University and University of Virginia Darden School of Business professor. He is the founder of Machine Learning Week and author of The AI Playbook: Mastering the Rare Art of Machine Learning Deployment (MIT Press, 2024) and Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Wiley, 2013).
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MIT Sloan Management Review Article on What Leaders Should Know About Measuring AI Project Value
