Is your AI project using the right data, and is it set up to succeed? Ask the right questions to head off failure.
Data science project failure can often be attributed to poor problem definition, but early intervention can prevent it.
Roger Hoerl, Diego Kuonen, and Thomas C. Redman
Kay Firth-Butterfield (the World Economic Forum), Ya Xu (LinkedIn), and Charlotte Degot (BCG GAMMA) join MIT SMR senior project editor Allison Ryder for a discussion on innovating with artificial intelligence.
Kay Firth-Butterfield, Ya Xu, Charlotte Degot, and Allison Ryder
Using AI and simulations in health care can help doctors better serve patients.
Sam Ransbotham and Shervin Khodabandeh
A successful AI-enabled workforce requires key hiring, training, and risk management considerations.
There has been a huge demand for data scientists in the past decade. Is that about to change?
Jeffrey D. Camm, Melissa R. Bowers, and Thomas H. Davenport
It’s early in the age of experimentation — and the right time to start building expertise.
Michael Luca and Max H. Bazerman
Five essential management practices illustrated through the lens of sports analytics.