Between technical MLOps and general business operations lies a missing discipline that determines whether AI creates lasting value or becomes expensive technical debt. With roughly 80 percent of AI projects failing in production, this ai operations framework applies Lean Six Sigma principles like continuous monitoring, quality assurance, and systematic improvement to AI systems at scale.
Pilots work because they are protected environments with dedicated resources. Production fails because it is the real world with real constraints. The gap is not technical - it is operational. IDC research shows 88% of AI pilots never reach production, not because the technology fails but because companies underestimate the operational readiness required.
Traditional monitoring tells you if your LLM is running. It does not tell you if it is delivering garbage to users. LangChain found 89% of agent teams now implement observability, but evaluation adoption lags at 52%. Here is how to build LLM monitoring that catches quality failures in production.
Your prompts are code. Treat them like it. LaunchDarkly found that teams lose hours figuring out which prompt version runs in production. Here is why version control, testing, and deployment pipelines matter more than writing perfect prompts.
Only 7 percent of organizations fully scale AI past the pilot stage, per MIT Sloan research. The approaches that work for 5 people become liabilities at enterprise scale for 50.