Data, AI, & Machine Learning

This free webinar offers expert advice on adopting AI today and preparing for tomorrow’s challenges.

Learn about technical readiness for AI in this free webinar with experts from IDC, Grupo Financiero Banorte, and SAS.

In this webinar, experts from Kaiser Permanente, Accenture, and SAS discuss business readiness for AI.

This webinar describes identifying AI use cases – and recognizing when AI isn’t the best answer.

Companies that manage employee data responsibly are better able to grow trust across the company while gaining insights.

Randstad and TCS executives offer insights on ethical use of AI for recruiting and hiring.

DBS Bank’s CEO exemplifies how a willingness to experiment and even fail can help advance new technologies like AI.

This Strategy Guide shares insights on AI use for strategic advantage and positive societal impact.

Seven steps to addressing burnout, an intersectional approach to mitigating bias in AI, and running mixed-mode meetings.

In artificial intelligence, race and gender too often generate a bias double whammy.

Retailers should analyze customers’ preferences before deciding how to optimize their delivery networks.

In-store technology promises a better customer experience — and fuel for improved retail analytics.

Developments in enabling technology are opening up more use cases for virtual models of real-world objects.

Making your organization fit for data, enhancing value with nontraditional stakeholders, and supporting working parents.

New research highlights nine key factors impeding organizations’ ability to advance their data science progress.

New value creation with strategic data assets, in-store shopping to build customer loyalty, and habits to enable enduring culture change.

To monetize data, companies must first transform it so it can be reused and recombined to create new value.

Barry Libert, CEO of AIMatters, describes how subscription models and a platform-first mindset can help organizations define valuable new offerings for customers.

Data science success strategies, developing simple “no-code” apps, and independent contractors’ outsider advantage.

Six strategies can help guide data science teams toward greater success in cross-unit projects.