Data, AI, & Machine Learning

Would NBA teams make fewer draft mistakes if they measured basketball IQ?

Done right, automation can be a win for everyone — even workers.

Banks need a clear AI strategy to get digital transformation right.

Recommendation algorithms don’t just reflect consumer preferences — they also shape them.

Businesses that make and sell products that replicate human connection are serving a deep need, but they may also be changing social norms in ways that can’t be reversed.

Makers of AI applications should stop overpromising, be transparent, and consider certification.

One key strategy for AI success: retraining employees to have the skills your company will need.

Retail companies that neglect machine learning do so at their peril.

An industry executive and a scholar offer perspectives on new competition in the staffing business.

Prediction is appealing, but detection may be equally valuable for businesses.

Data-driven decision-making comes with challenges and opportunities, says GoDaddy’s chief revenue officer.

Data best supports marketing when researchers fully understand what they want to measure and how.

Executives in the automotive sector believe that machine learning can help them achieve their marketing goals, but that doesn’t necessarily mean they invest in that ambition.

Five key principles explain how to use platforms to create value in the digital economy.

Five steps to make sure your data and analytics efforts pay off in the long term.

Consulting is vulnerable to technology and market forces that are disrupting services everywhere.

In the growth of artificial intelligence, technology is changing faster than society can keep up.

Viewing technology as a set of solutions misses opportunities to innovate in bigger, bolder ways.

Automation will affect jobs in four ways. The path jobs take depends on what kind of value they provide — and how.

Capgemini’s Didier Bonnet explores the complexity and necessity of digital transformation in 2018.