Artificial Intelligence

Enterprises looking to make the most of agentic AI will have to rethink how work gets done and how teams are organized, without forgetting the human workers who set their companies apart.

Here’s what businesses need to know as AI agents move from centralized systems toward a decentralized network of trillions of personal and organizational agents.

Transformative technologies like artificial intelligence succeed when societies make parallel social investments to ensure gains are distributed equitably, MIT Sloan researchers find.

Debiased machine learning, the currency of invoicing, and training good models with bad data: Meet the new experts bringing their knowledge and skill sets to the MIT Sloan School of Management.

Three levers — data architecture, data preparation, and data permissions — determine whether data becomes a reusable strategic asset or stays trapped in silos.

Artificial intelligence can make workers more capable and productive, but only if leaders design and deploy it to augment human judgment.

Researchers at the MIT Initiative on the Digital Economy share the latest insights about getting the most from working with AI, such as personality pairing and reorganizing job tasks.

As organizations scale generative AI, traditional governance models prove to be too rigid or too loose. Minimum viable governance calibrates oversight to risk, enabling responsible innovation.

In MIT’s international AI Negotiation Competition, “warmer” agents achieved better outcomes in negotiations with other AI agents.

A new paper explores how seeing economic value from artificial intelligence hinges on closing the gap between what AI can do and how humans can verify its outputs.