Data

Organizations have options when it comes to using or adapting off-the-shelf large language models to handle tasks or business use cases.

While generative AI is widely accessible and useful, businesses need to know when to use other AI tools, like traditional machine learning.

Effective data leadership starts with modernizing data technology — and calls for taking action, no matter how daunting.

Sarah Biller builds tech organizations ready for a “permissionless, frictionless, contactless” financial services sector.

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

Using the wrong datasets to train AI models can result in legal risks, bias, or lower-quality models. The Data Provenance Initiative’s tool can help.

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

Artificial intelligence can be useful in the workplace, but humans have to first define what success looks like, according to MIT Sloan’s Danielle Li.

Machine learning can drive climate action initiatives, but its widespread use could have negative implications, according to Climate Change AI’s Priya Donti.

Experts from Salesforce, S&P Global, and Corning share six key strategies to unlock generative AI’s potential without falling for the hype.