Data, AI, & Machine Learning

This webinar outlines how human-AI collaboration leads to measurable results for organizations.

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.

New technologies can help solve critical global problems in energy, medicine, and urban planning.

To gain business agility, leaders must deconstruct jobs into tasks and deploy workers based on their skills.

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.

Revolutionary recommendation engines, data access as a leadership priority, and the essentials of successful corporate social justice efforts.

Transforming a company into a truly data-driven business involves fundamental organizational changes.

Data accessibility must be managed from the start of AI projects in order to be implemented in production.

Data-driven decision-making anchors on available data, which can lead decision makers to focus on the wrong question.

Collecting and analyzing the right employee data can help leaders build more equitable workplaces.

Leaders must focus on quality, build organizational capabilities, and put data to work in new ways.

Top chief data officers share insights on how leaders and organizations can ensure data success.

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.

Encouraging employees to speak up, growing data and analytics talent, and nimbler supply chains.

A Q&A with AWS’s Ishit Vachhrajani on how leaders can generate excitement about and support for AI organization-wide.

Companies need to identify the type of talent they need in order to become data-driven.

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