Bridging the gap between 99% benchmark accuracy and harsh production realities. Through peer-reviewed literature, think-tank findings, and real-world MLOps post-mortems, we provide the blueprint for sustainable AI deployment.
85%+
Enterprise AI Failure Rate
10x
Serving vs. PoC Cost Multiple
250M+
OpenAlex Corpus Analyzed
4 Pillars
Tech, Org, PM & Governance
A deep technical dissection of the landmark 2024 RAND Corporation research report. Unpacking the 5 structural failure modes and the fundamental communication gap between engineering and the C-suite.
정현 (Jeonghyun) 
Dissecting the exact technical mechanisms behind silent model failure in high-throughput enterprise serving, and architecting resilient real-time drift mitigation pipelines.
Research Fellow: MLOps Lab 
Why traditional deterministic Agile roadmaps fail when applied to probabilistic machine learning systems, and how AI Product Leaders navigate PoC purgatory.
AI Product Strategy Practice From Laboratory Illusion to Production Reality
Why AI Projects Fail Research Team
Forthcoming Worldwide
While global corporations allocate hundreds of millions to AI initiatives, fewer than 15% deliver sustainable economic ROI in production. This book examines the four fundamental failure mechanisms: data drift, serving bottlenecks, the PoC trap, and organizational misalignment, providing actionable engineering frameworks for C-suite leaders.
Bespoke briefings and workshops for enterprise digital taskforces, engineering leads, and C-level executives.
Diagnostic frameworks based on global think-tank research and post-mortems of enterprise AI stalls.
Deconstructing serving skew, feature pipeline debt, and infrastructure cost runaway in production.
Quantifiable KPI modeling, executive stakeholder alignment, and preemptive risk defense systems.
Weekly analytical syntheses of academic literature, think-tank reports, and real-world MLOps post-mortems delivered directly to your inbox.