Data, AI, & Machine Learning

JoAnn Stonier, chief data officer at Mastercard, discusses how design thinking enables better AI implementation.

This Strategy Guide offers expert advice on assessing your organization’s business readiness for AI.

To make data migration more effective, start with a minimum set of viable data, leave out nice-to-have data, and weigh speed vs. quality.

Amit Shah, president of 1-800-Flowers, explains why lifelong learners are the best technical talent.

Hiring with AI, creating learning organizations, and cultivating a high-purpose culture to support leadership at all levels.

Employers can vet people more ethically and accurately with explainable AI.

Will Grannis of Google Cloud explains the organization’s collaborative approach to AI and machine learning innovation.

This Strategy Guide covers identifying AI use cases — and how to know when AI isn’t the best answer.

Testing can guide decisions such as who needs to work in an office and what work hours are optimal.

Companies upskilling their workforce are less likely to be caught flat-footed by broad tech changes.

TCS and Halliburton Landmark executives discuss digital transformation in the oil and gas industry.

Preparing workers for a digital future, targeted learning, and the emotions of returning to the office.

Companies are taking different approaches to preparing their workers for the digital future.

Anticipating and withstanding cyberattacks — cyber resilience — must become a companywide concern.

Lyft’s Craig Martell talks education, eliminating bias, and cross-functional collaboration on machine learning projects.

Peer coaching plays a foundational role in developing human skills that technology cannot replace.

The skills challenge requires shifting initiatives and resources to where they are needed most.

Data science obstacles, concerns about data executive roles, and the virtual hiring process.

Only a third of data executives feel that their role is “successful and established.”

Organizations that struggle to gain payback from data science efforts can recognize and overcome five common obstacles.