AI & Machine Learning

Workforce ecosystems, machine discovery, human-machine collaboration, and the inventor of Wikipedia.

Companies that emphasize collaboration between AI and human workers are best positioned for success.

In this webinar, Jeffrey D. Camm and Thomas H. Davenport explain how the COVID-19 pandemic has impacted the practice of data analytics.

There are differences between what constitutes a successful early AI pilot and success in other types of  IT ventures.

Recommendation engines promise to revolutionize how customers buy and employees work.

Leaders need to examine their core beliefs if they want to prosper in a COVID-19 world.

Business — and society — should think of the governance of AI as an enabler rather than a constraint.

In this webinar, AI experts from Pure Storage and Nvidiadiscuss a unified plan for implementation.

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.

Using AI and simulations in health care can help doctors better serve patients.

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

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

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.