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No. 1: Taking responsibility for analytics and artificial intelligence.

Businesses want to achieve growth, equip front-line workers with solutions, and process unwieldy data.

Relevance-based prediction can be used in finance, politics, and sports for more accurate forecasting.

A startup showcase featured AI-enabled apps for small-business owners, wearable devices, and financial trading at quantum speed.

A responsible AI framework mitigates the risks from artificial intelligence systems developed outside the company.

MIT Sloan’s Hal Gregersen explains question bursts, question audits, and why it’s important for leaders to not just ask questions but listen to the answers.

Decontextualize data and prepare data assets that can be reused for many purposes. A new book from MIT CISR researchers explains how.

New research reveals that cash-constrained companies suffer the most when supply chain disruptions delay the production and delivery of new equipment.

Companies that intend to use generative artificial intelligence should first consider how people regard work created by AI, humans, or some combination of the two.

Most companies comprehend the impact data has on business strategy. Fewer understand how to convert data efforts into monetary value.