Why AI Projects Fail and How to Fix Them: Avoiding the Proof-of-Concept Graveyard
Arpine Kozmanyan, Tamara Babaian, Chinar Movsisyan
Source abstract
A large share of AI/ML projects fail to progress from pilot to successful production deployment. We analyzed root causes of setbacks in reaching successful productionization across over 150 projects. We identify and discuss four systemic risks: overlooking key requirements and costs, missing the business objective in the problem definition, setting up model development misaligned with production conditions, and ignoring post-deployment monitoring. Recommended strategies for overcoming these risks are guided by a Risk Mitigation Checklist validated on end-to-end project trajectories. 
Evidence graph
No public relationships recorded yet.
Integrity note: This page is a factual metadata record created by deterministic ingestion. It is not a claim that the work moves a mathematical frontier or has been independently verified.