From Laboratory Illusion to Production Reality
《Why AI Projects Fail》 combines academic rigor with frontline MLOps engineering experience to systematically analyze why enterprise AI initiatives fail and build repeatable blueprints for production success.
Evidence-Based Inquiry
Rejecting vendor hype in favor of 250M+ peer-reviewed scholarly publications and authentic post-mortem logs.
Production Pragmatism
Prioritizing sustainable serving infrastructure, pipeline integrity, and unit economics over isolated benchmark records.
Actionable Prescription
Moving beyond diagnostic critique to deliver concrete execution frameworks applicable by C-suite executives and engineers.
Research Faculty & Authors
Jeonghyun
Lead Author & Enterprise Advisory DirectorAuthor of 《Why AI Projects Fail》 and Advisory Director specializing in enterprise AI risk mitigation and MLOps post-mortems. Bridges the critical divide between academic AI research and sustainable enterprise production.
Research Fellow: MLOps Lab
Senior MLOps Research ArchitectSpecializes in high-throughput model serving failures, silent data drift detection, and post-mortem engineering of enterprise feature pipelines.
AI Product Strategy Practice
Enterprise AI Strategy LeadAdvises enterprise leaders on escaping PoC purgatory, resolving C-suite expectation mismatches, and establishing probabilistic AI governance frameworks.