Data, AI, & Machine Learning

Today’s organizations need tech leaders who can also take on the cultural and change management challenges of AI.

Straightforward strategies can keep in check the mistakes, misunderstandings, and data privacy concerns that go hand in hand with AI use.

The retailer’s AI ethics approach starts with concrete business examples and teaches employees to keep asking questions.

Leaders detail Agoda’s development of a KPI that accounts for external factors and aids strategic goal alignment.

Causal ML helps managers improve decision-making by enabling them to explore different options’ potential outcomes.

Should a chief data officer lead data and AI efforts? Three potent forces will determine the future of the CDO role.

The goal isn’t eliminating technical debt but managing it, focusing on the highest-value fixes, and supporting innovation.

This collection of articles provides a broad range of expert insights on using GenAI for significant, measurable business outcomes. It also includes real-world examples from organizations in many industries that are already seeing impressive business value from their AI investments, along with practical takeaways and steps for turning insights into action.

Companies are getting real value from generative AI today and building for future transformation by managing risk.

AI’s ability to create value rests on the philosophy determining how and what it learns.