Artificial Intelligence

Bridge-building for business and data teams, responsible AI practices, and smart time management.

Organizations need to develop more-robust processes to ensure responsible use of AI.

Companies that change processes to facilitate organizational learning with AI realize the biggest business value.

Boards will need increased technology fluency to provide adequate oversight of AI risk management.

Rooting out AI bias, assessing new tech investments, and customers’ pandemic-affected preferences.

Protocols that are used to root out bias in AI tools can— and must — be turned on the industry itself.

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

MIT Sloan’s Sinan Aral discusses social media as a marketing tool that can have a positive impact — if used ethically.

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.