Data, AI, & Machine Learning
AI and Statistics: Perfect Together
Business leaders can identify and avoid flawed AI models by employing statistical methods and statistics experts.
Thomas C. Redman and Roger W. Hoerl
Latest Insights from MIT SMR
Customers
Fashioning the Perfect Fit With AI: Stitch Fix’s Jeff Cooper
Sam Ransbotham and Shervin Khodabandeh
Collaboration
Solving Real User Problems With Generative AI: Slack’s Jackie Rocca
Sam Ransbotham and Shervin Khodabandeh
More Insights from MIT SMR
Leadership Skills
How AI Changes Your Workforce
Watch this short video to learn more about how AI changes the rules of workforce management.
MIT Sloan Management Review
AI & Machine Learning
Mayo Clinic’s Healthy Model for AI Success
The nonprofit is delivering technology and services to help its staff build AI applications efficiently and safely.
Thomas H. Davenport and Randy Bean
AI & Machine Learning
In AI We Trust — Too Much?
To reduce the risk of accidentally using bad artificial intelligence, we need regulation — and skepticism.
Ayanna Howard
New Product Development
Driving Manufacturing Efficiency With AI: Pirelli’s Daniele Petecchi
On the Me, Myself, and AI podcast, Daniele Petecchi discusses how Pirelli uses AI to develop tires more efficiently.
Sam Ransbotham and Shervin Khodabandeh
Innovation Strategy
Our Guide to the Spring 2024 Issue
The spring 2024 issue of MIT SMR looks at opportunities in digital innovation, automation, generative AI, and more.
MIT Sloan Management Review
AI & Machine Learning
Who Profits the Most From Generative AI?
Analyzing factors behind generative AI’s value can help leaders determine who will benefit most from its growth.
Kartik Hosanagar and Ramayya Krishnan
AI & Machine Learning
How to Succeed With Predictive AI
To succeed with machine learning, manage projects as business initiatives, not technology projects.
Eric Siegel and Abbie Lundberg
AI & Machine Learning
What AI Means for Human Capital
Many organizations are experimenting with generative AI, and many questions remain about its impact on the workforce.
Lynda Gratton and Elizabeth Heichler
Automation
Will Large Language Models Really Change How Work Is Done?
LLMs have immense capabilities but present practical challenges that require human knowledge workers’ involvement.
Peter Cappelli et al.
Technology Implementation
Scaling Automation: Two Proven Paths to Success
Two automation initiatives succeeded by scoring potential processes and combining technical and process knowledge.
Ben Armstrong and Benjamin Berkowitz
Executive Guides
- Technology Ethics
- Leading Data & Analytics
- Data Strategy
IT Governance & Leadership
Building an Organizational Approach to Responsible AI
Kay Firth-Butterfield
Ethics
Designing Ethical Technology Requires Systems for Anticipation and Resilience
Kirsten Martin and Bidhan (Bobby) L. Parmar
Ethics
Why Building an Ethical Culture Must Start at the Top
Robert Chesnut, interviewed by Ally MacDonald
Executive Guides
- Technology Ethics
- Leading Data & Analytics
- Data Strategy
Data & Data Culture
Empowering a Data Culture From the Inside Out
Jonathan Tudor, interviewed by Ally MacDonald
Analytics & Business Intelligence
Leading With Decision-Driven Data Analytics
Bart de Langhe and Stefano Puntoni
Executive Guides
- Technology Ethics
- Leading Data & Analytics
- Data Strategy