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The Digital Project Manager
Delivering AI Projects
Nov 23, 2022
Ronald Schmelzer and Kathleen Walch from Cognilytica discuss the importance of responsible project management in the success of AI projects. They talk about the origins of Cognilytica and the need for AI education. They also explore patterns in AI, the challenges of delivering AI projects, and the importance of aligning expectations. Collaboration and learning are highlighted as crucial factors in AI projects.
53:42
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Quick takeaways
- Understanding why AI projects fail is crucial for project managers, with common reasons including issues with data quality, overpromising on what AI can do, and failure to follow best practices.
- Successful AI project management requires a data-centric perspective, focusing on utilizing data-specific methodologies and practices, addressing data-related challenges early on, and leveraging best practices.
Deep dives
Understanding the Impact of AI Projects
Artificial intelligence (AI) projects have become a hot topic, but they have been around for decades. AI is all around us, from suggesting email phrasing to recommending videos and optimizing routes. However, delivering successful AI projects is a big responsibility. Not every project succeeds, and understanding why AI projects fail is crucial for project managers. Common reasons include issues with data quality, overpromising on what AI can do, and failure to follow best practices. Setting realistic expectations, scoping projects correctly, and focusing on fundamental education can increase the chances of AI success.
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Discover
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Listen to the best highlights from the podcasts you love and dive into the full episode