Most teams do not fail at AI because of model choice. They fail because core foundations are unresolved: inconsistent data, unclear ownership, and no operational support model.
Use this checklist before committing to production scope.
1) Business objective is explicit and measurable
Define one primary outcome tied to business value (for example, reduce support handle time by 20%).
Avoid launching projects with broad goals like “improve productivity” and no measurable target.
2) Data readiness is proven, not assumed
Confirm:
- data coverage for real workflow scenarios
- freshness suitable for decision timing
- quality checks for nulls, outliers, and schema drift
If data quality is unstable, model tuning will not fix the problem.
3) Ownership model is clear
Assign explicit ownership for:
- prompts and retrieval logic
- model/version changes
- policy and escalation rules
- incident response
Shared ownership without accountability slows every critical decision.
4) Security and compliance controls exist upfront
Implement least privilege, logging, and output guardrails before launch.
For regulated workflows, include legal/compliance review in the release process.
5) Operating model supports production
You need:
- monitoring for latency and quality
- rollback strategy for prompts/models
- feedback loops for continuous improvement
Start with low-risk, high-signal workflows where human review remains easy. That creates confidence and evidence before scaling to mission-critical use cases.
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