CXOTalk: Leadership, AI, and the Digital Economy cover image

CXOTalk: Leadership, AI, and the Digital Economy

Why AI Projects Fail (and How to Succeed)

Feb 20, 2023
Join us as we explore AI project failures with guests Iavor Bojinov, Assistant Professor at Harvard Business School, and QuHarrison Terry, Head of Growth Marketing at Mark Cuban Companies. They discuss the causes of AI failures, differences from traditional technology projects, and offer practical advice on how to avoid failures. They also touch on ethical and privacy considerations. A must-watch for business leaders building AI groups.
44:30

Podcast summary created with Snipd AI

Quick takeaways

  • AI projects can fail in different ways, either in achieving operational efficiencies or delivering revenue growth.
  • AI projects require careful consideration of when to follow recommendations and when to overrule them, due to their probabilistic nature.

Deep dives

Defining AI Failure

AI failure refers to the failure of AI systems to deliver on their promised outcomes. There are two types of AI applications: internal and external. Internal applications are meant to improve operations within an organization, while external applications are customer-facing algorithms. Failure in internal applications is related to failure in achieving operational efficiencies, while failure in external applications is failing to deliver revenue growth or cost-cutting.

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