Projectified How to Avoid AI Project Failure
Mar 26, 2025
Kathleen Walch and Ron Schmelzer, experts in AI project management at PMI Cognilytica, explore the pitfalls of AI projects. They discuss common failure reasons like poor data quality and the unique challenges of managing AI. The duo emphasizes the need for structured approaches and setting smart metrics to ensure real ROI. They also share strategies on balancing urgency with quality and the importance of continuous learning in the rapidly evolving AI landscape. Tune in for insights on navigating the complexities of successful AI initiatives!
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AI Project Failures
- Many AI projects fail due to misaligned expectations and a learning curve in effective delivery.
- Common reasons include treating AI projects like application development, neglecting data methodologies, and overlooking ROI.
Data-Centric Approach
- Treat AI projects as data projects, prioritizing data methodologies over software development practices.
- Ensure sufficient data quantity and quality to avoid the "garbage in, garbage out" scenario.
AI's Unpredictability
- AI involves machines performing tasks previously done by humans, leading to unpredictable outcomes.
- Traditional project management approaches may not suit AI's probabilistic nature and data dependency.