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AI projects aren’t dying because of big problems. Rather it’s the small things that are causing projects to fail. In this episode of AI Today hosts Kathleen Walch and Ron Schmelzer discuss what iteration really means for AI projects.
Continuous Model Iteration
We always say that AI projects are never set it and forget it. AI project lifecycles are continuous. They require regular evaluation and retraining. Treating AI projects as one-time investments without considering continuous iteration leads to misaligned ROI and eventual project failure. Successful AI projects require ongoing financial and resource allocation to adapt to real-world changes and evolving data. In this episode we discuss why this is so critial.
CPMAI: Best Practices for AI Projects
Certainly, AI projects need to be treated like data projects. So, following a best practices step-by-step approach is critical. Adopting iterative and agile methodologies, such as CPMAI, ensures key steps aren’t missed. AI projects often fail not because of big issues but due to small, overlooked details. Initial excitement fades as the realities of AI project implementation set in. If you don’t plan iteration properly, it will lead to AI project failures. Mainly because organizations neglect long-term costs and maintenance.
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