Between technical MLOps and general business operations lies a missing discipline that determines whether AI creates lasting value or becomes expensive technical debt. Here is the complete ai operations framework that applies proven manufacturing excellence principles like continuous monitoring, quality assurance, and systematic improvement to AI systems in production.
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. Eighty-eight percent 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. Quality degradation is silent, and traditional metrics miss it completely. Here is how to monitor what actually matters for LLM applications in production.
Your prompts are code. Treat them like it. Most teams hardcode prompts and wonder why their AI apps break. Here is why version control, testing, and deployment matter more than perfect prompts.
The scrappy approaches that make AI pilots successful become liabilities at enterprise scale. Here is how to build AI capabilities that work for 50 people, not just 5.
Consultants get you started with Claude. Operations managers keep it running. With 95% of AI pilots failing after launch, the real work begins after implementation - monitoring usage, optimizing costs, preventing drift, and maintaining the systems that actually deliver ROI.