In short
Why do AI projects stall before they go live?
Most AI projects stall because what is missing is not model quality but three things: a measurable criterion that says what counts as success, a per-transaction cost calculation, and a business unit that owns the system. A model nobody measures cannot prove it improved, a system with unknown cost cannot get budget approval, and a service with no owner is switched off at the first outage.
Most enterprise AI pilots we reviewed over the past two years were technically successful. The models worked, the demos impressed, leadership was excited. They still never reached production.
What three things are missing in failed pilots?
Failed pilots were almost always missing the same three things: a measurable acceptance criterion, a per-transaction cost model, and an operational owner.
- A model without an evaluation set cannot prove it improved.
- A system without a known cost cannot get budget approval.
- A service without an owner is switched off at the first outage.
Why is the evaluation set the project contract?
The first artefact we write on an AI project is not code — it is the evaluation set. Two to five hundred examples drawn from real usage, with expected outputs. It becomes both the compass during development and the regression suite in production.
“You cannot improve a model you do not measure, and you cannot ship a model whose improvement you cannot prove.”
How do you model AI cost on day one?
Cost per token, cache hit rate, retry policy and context length compound. The same product can differ tenfold in unit cost depending on those four decisions. Doing that arithmetic before production is far cheaper than doing it after.
Why is the rollout done in stages?
Do not compress the transition into a single moment. Weeks spent in shadow mode (the system decides but nothing is applied), then semi-autonomous mode (a human approves), then autonomy, accumulate both trust and training data.
| Artefact | In a pilot | In production |
|---|---|---|
| Evaluation set | A handful of sample prompts | 200–500 real examples with expected outputs |
| Cost | Not measured | Per transaction, including cache and retries |
| Ownership | The project team | A named operational owner |
| On failure | The demo is repeated | A deterministic fallback path |
| Monitoring | None | Quality, latency, cost and drift alerts |
Sources
- 01Regulation (EU) 2024/1689 — Artificial Intelligence ActAvrupa Birliği Resmî Gazetesi · 2024
- 02AI Risk Management Framework (AI RMF 1.0)NIST · 2023
- 03ISO/IEC 42001:2023 — Yapay zekâ yönetim sistemiISO/IEC · 2023
- 04GEO: Generative Engine OptimizationPrinceton University · ACM SIGKDD · 2024