ML Models in Production: Why Enterprise AI Projects Stall

Six months in, fourteen ML models were live—but none had automated retraining. Three still used hardcoded paths from a laptop. When compliance flagging hit 40% false positives, an eleven-day investigation traced it back to an undocumented schema change. This is where enterprise AI projects actually break. Full article: https://davidohnstad.net/ml-models-production-enterprise-ai-stall/

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-02
DOI
https://doi.org/10.5281/zenodo.23892946
Primary Topic
Software Engineering Techniques and Practices
Type
article
Field-Weighted Citation Impact
0.00
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article

ML Models in Production: Why Enterprise AI Projects Stall

David Ohnstad
1 links
Zenodo (CERN European Organization for Nuclear Research)
Software Engineering Techniques and Practices
article

ML Models in Production: Why Enterprise AI Projects Stall

David Ohnstad
article en

Abstract

Six months in, fourteen ML models were live—but none had automated retraining. Three still used hardcoded paths from a laptop. When compliance flagging hit 40% false positives, an eleven-day investigation traced it back to an undocumented schema change. This is where enterprise AI projects actually break. Full article: https://davidohnstad.net/ml-models-production-enterprise-ai-stall/

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 10%
Software Engineering Techniques and Practices
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ML Models in Production: Why Enterprise AI Projects Stall — David Ohnstad · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS