Artificial Intelligence

The AI Decision Matrix uses ambiguity and risk to determine the optimal decision-making structure between humans and AI automation.

In today’s AI-driven environment, robust data monetization capabilities are both a competitive advantage and a precondition for success, research from the MIT Sloan School of Management finds.

The 2026 MIT delta v Demo Day showcased startup innovations in biotechnology, healthcare, AI, cybersecurity, robotics, finance, and manufacturing.

New faculty members are thinking about how AI can help decision-making, the value of adversarial AI agents, and the skills workers need in the AI era.

Market research is essentially a prediction problem. MIT Sloan’s John Horton says organizations should consider using generative AI to simulate the effects of their decisions before they make them.

A report from the MIT Working Group on Generative AI & the Work of the Future identifies three principles and seven outcomes that ensure that jobs are better, not just faster, with AI.

A new study found that developers using AI tools can write much more code than those working without AI, but they don’t release as much new software.

Professional service advisers are seen as more capable than AI — but that advantage fades when clients feel ashamed. An MIT Sloan study shows when people prefer AI’s nonjudgmental assistance.

In his new book, MIT Sloan School of Management’s Paul Cheek argues that companies lose competitive ground when they keep humans in the loop on decisions that AI can make more efficiently.

MIT Sloan’s Bill Aulet and Jenny Larios Berlin explore how founders can combine artificial intelligence with the principles of Disciplined Entrepreneurship to move from concept to market more quickly.