Artificial Intelligence

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

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

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

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

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

The fashion retailer’s chief data and analytics officer uses agile pilots to assess and scale technology initiatives.

Porsche is accelerating innovation by emphasizing the need for collaboration between humans and AI technology.

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

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.