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!
23:05
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Quick takeaways
- AI projects often fail due to mismanagement and a lack of understanding of business value, highlighting the need for tailored data methodologies.
- Successful AI project management requires continuous iteration and adaptation of strategies, focusing on clear metrics and a culture of learning.
Deep dives
Understanding AI Project Failures
A significant percentage of AI projects struggle to deliver expected results, with over 80% facing challenges that lead to failure. Common reasons include the mismanagement of AI as software development projects, resulting in poor data quality and insufficient understanding of the business value provided. Project managers often overlook the importance of appropriate data methodologies, which are crucial for the success of any AI initiative. Many organizations jump into AI without evaluating the return on investment in terms of time, money, and resources, which leads to disillusionment with their AI endeavors.
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