Many AI programs do not generate business gains. New research finds the key to success is scientific, application, and stakeholder consistency.
AI is a tool to get things done. To use it properly and generate value, organizations need the right capabilities — including a good understanding of data.
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.
Machine learning is a powerful form of artificial intelligence that is affecting every industry. Here’s what you need to know about its potential and limitations and how it’s being used.
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.
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.
