Why Most AI Projects Fail (And How CPMAI Helps)

I recently completed Cognitive Project Management for AI (CPMAI) training offered by the Project Management Institute. It confirmed something I’ve seen firsthand: most AI initiatives stumble when they’re managed like traditional projects.

CPMAI offers a practical approach tailored to the realities of AI and machine learning, tackling common pitfalls (such as unclear goals, poor data, and a lack of iteration) that cause over 80% of AI initiatives to fall short.

AI is driven by data, not just code. CPMAI is an iterative process that starts with understanding the business problem and ensuring you have the right data to support it. From there, teams refine the data, build models, test the results, and adapt as needed. Each iteration builds on the last, helping teams stay aligned and deliver real value.

 Some key takeaways:

💥 Not all problems need AI; start with a feasibility check 

💥 Bad data = bad models

💥 Deployment isn’t the end, it’s just the beginning

Framing a project around one or more AI patterns (such as conversational AI, predictive analytics, pattern and anomaly detection, etc.) can help project leaders set realistic expectations, clarify data needs, and guide delivery planning.

CPMAI gives project leaders the tools to bring alignment, accountability, and strategy to data science efforts.