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Far too often, organizations move forward with AI projects without having a clear understanding of how their AI and ML models are going to be used in the real world. This lack of real world understanding is a major reasons why AI projects fail. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer dig deeper into this topic, and share ways companies can overcome this issue.
Show Notes:
- CPMAI Methodology
- AI Today Podcast: AI Failure Series – Data Quantity & Data Understanding Issues
- AI Today Podcast: AI Failure Series – ROI Misalignment
- AI Today podcast: AI Failure Series- AI projects are NOT like traditional software development projects
- AI Today Podcast: AI Failure Series – Iteration Time & Proof of Concept vs. Pilots
