Research

As AI systems become more capable, companies will need to think beyond individual tools and begin managing AI as a digital colleague — part of how teams, workflows, and business processes operate.

Which AI risks could cause the most harm in the next five years? New MIT research shows that businesses should be aware of threats like competitive pressure and dangerous AI capabilities.

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.

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.

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.

Organizations are struggling to succeed with AI. Research from the MIT Center for Information Systems Research shows common mistakes and how to overcome them.

Learnright laws would give copyright holders the exclusive right to license their content for artificial intelligence model training.

When professionals try to validate outputs, generative AI often responds not with corrections or candor but with escalating persuasion tactics.

New research shows that AI delivers the most value when organizations redesign workflows, not just when they automate individual tasks.