Research

Whether a task is worth automating depends not only on whether machines are cheaper than human labor but also on whether the task is performed at scale to spread fixed costs.

To avoid redundant investments, applications that don’t scale, and unexpected costs and risks associated with AI, organizations should determine where oversight is likely to have the greatest impact.

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

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.

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.

A new research briefing explains how semantic layers make enterprise data more accessible and understandable by humans and machines.

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.