The pattern shows up over and over: an organisation runs an AI proof of concept, the demo looks impressive, leadership signs off on production rollout — and twelve months later the project is quietly shelved.
The reasons are predictable, and they are almost never about the model.
What kills AI projects in production
Three problems do most of the damage:
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No integration with real data. A POC runs on a curated sample. Real data is messy, has access controls, lives in five different systems, and changes shape every quarter. If your AI workflow does not have a serious plan for ingesting and refreshing real data, it will not survive contact with operations.
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No evaluation framework. A model that "looks right" in a demo is indistinguishable from one that is silently wrong 30% of the time. Without a regression suite, prompt evaluations, and a way to measure drift, you have no way to tell whether the system is improving or degrading. That gap is fatal.
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No cost ceiling. LLM calls cost money on every request. A pilot with a handful of users is cheap; the same workflow at organisational scale can produce alarming bills with no visible upper bound. If you do not have token budgets, caching, and a fallback strategy from day one, the finance conversation kills the project before the technical conversation gets a chance.
What working AI projects do differently
The teams that ship AI to production treat it as a systems problem, not a model problem. That means:
- Observability built in. Every inference logged, every prompt versioned, every output traceable to its inputs.
- Evaluation as a first-class engineering practice. Not a one-off benchmark — a regression suite that runs on every change.
- Human-in-the-loop where it matters. Not because AI is unreliable, but because the boundary between automation and judgement is a design decision, not a default.
- Cost discipline. Token budgets, caching layers, fallback paths to cheaper models, and clear policies on which workloads can or cannot run on which models.
The boring conclusion
Most AI failures are not AI failures. They are systems failures with an AI component. Treat AI like any other production system — observability, evaluation, cost control, integration discipline — and the survival rate goes up dramatically.
Expanded version with concrete code examples and case study material is in development.