AI & Machine Learning

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.

Artificial intelligence can give businesses a competitive advantage during (and despite) the COVID-19 pandemic.

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.

This infographic highlights research findings on technology governance issues key to implementing trusted AI.

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

The increasing adoption of AI and robots has implications for jobs, biases, and data privacy.

Assessments about China’s strengths in AI may be overblown.

New AI applications have immense potential to revolutionize communication and deepen human relationships.

Swarm systems draw input from individuals and use algorithms to optimize system performance in real time.

A webinar describes how to develop AI customer service chatbots that meet customer expectations.