AI & Machine Learning

Returning “boomerang” CEOs, job crafting, and working with robots.

Despite advances in automation, good people and good techniques remain essential to manual work.

The resilient, knowledge-based economy; a COVID-19 data disaster; smart buildings; and democratized AI.

By better integrating human and device intelligence, we can foster collective intelligence.

Developing AI-enabled business models, managing corporate social responsibility, and growing digital ecosystems.

Leaders must focus on managing the gaps in AI skills and processes within the organization.

Companies and leaders must strive to build business models using three key components for growth.

CFOs need to lead AI technology decision-making — and they should start now.

Your AI strategy needs to be approached differently than regular technology strategy.

A successful AI-enabled workforce requires key hiring, training, and risk management considerations.

Employers could use surveillance tools — with constraints — to keep workers safe and healthy.

Preparing for AI’s next phase means prioritizing your talent pipeline and technology infrastructure.

A Q&A with AWS’s Michelle K. Lee on the challenges and advantages of adopting machine learning.

Insights for developing and executing AI strategy at the leadership, organization, and talent levels.

Companies are beginning to reboot their machine learning and analytics, disrupted by the global pandemic.

Ask these four questions to determine whether your AI system is really an AI solution.

Cognitive automation could help managers make better decisions more quickly in the wake of COVID-19.

Data-wrapping techniques, building diverse AI teams, and communicating with empathy and authority.

Computer scientists typically lead AI development, but teams with diverse expertise can build better systems.

How to boost innovation capacity and institutional pride, and addressing tech inequity and the ethics of automation.