Data, AI, & Machine Learning

Companies adopting the best practices of responsible AI extract benefits while mitigating AI’s risks.

Kay Firth-Butterfield (the World Economic Forum), Ya Xu (LinkedIn), and Charlotte Degot (BCG GAMMA) join MIT SMR senior project editor Allison Ryder for a discussion on innovating with artificial intelligence.

The pandemic’s impact on business strategy, digital superpowers to thrive through disruption, and “explicit uncertainty” to avoid algorithmic harm.

To address algorithms’ potential harm, companies must be willing to focus on users and rethink their business models.

Fostering tech-mediated collaboration, dignity in employee data use, and in-house social intrapreneurship.

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.