AI automation works great in demos.
Production is where it breaks.
In enterprise systems, AI increasingly acts without human confirmation. When those decisions are wrong — or just contextually incomplete — failures propagate faster than teams can respond.
The real issue isn’t model accuracy.
It’s the absence of decision boundaries, rollback logic, and human override.
AI systems need resilience the same way distributed systems do — otherwise automation just creates faster failure.
I found this breakdown useful for thinking about AI beyond models and pipelines:
🔗 https://mjbtech.com/blog_pages/Why-Enterprise-Resilience-Not-Automation-Will-Define-AI-Success-on-ServiceNow.html
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