Many AI programs do not generate business gains. New research finds the key to success is scientific, application, and stakeholder consistency.

Four reasons stakeholders don’t trust AI systems, and how companies can overcome them.

Businesses have identified two types of generative AI: broadly applicable tools that boost personal productivity, and tailored solutions for specific purposes.

Researchers launched an in-house Data Science and Artificial Intelligence (DSAI) challenge to beat MIT’s machine-learning models for predicting clinical trial outcomes. The results are now available.

The MIT Laboratory for Financial Engineering (LFE) and Informa Pharma Intelligence announced a new initiative, Project ALPHA (Analytics for Life-sciences Professionals and Healthcare Advocates).

Launching artificial intelligence initiatives can be a daunting task. Start with these five data monetization capabilities.

To train employees on digital skills, companies need precise insight into current workforce skills. Artificial intelligence can help.

MIT Sloan and CSAIL researchers apply artificial intelligence techniques to one of the largest datasets of clinical trial outcomes to handicap the drug and device approval process

Are you experimenting with artificial intelligence, or are you “AI future-ready”? A new model maps four stages of enterprise AI maturity.

Companies need a plan for when employees use unapproved, publicly accessible generative artificial intelligence tools for work-related tasks.