Implementation playbook

Adopt AI cost control without disrupting production.

The safest path is progressive: observe first, simulate savings, select one control, validate integrity, then expand across workflows.

Collect request volume, tokens, model/provider, latency, retry count, RAG metadata and cost by workflow.

Find the top waste sources and rank them by safe savings potential and implementation effort.

Model savings from token reduction, RAG selection, routing, semantic cache, fallback and GPU optimization.

Apply one low-risk control to a selected workflow while measuring quality and integrity.

Expand policies across teams, providers, RAG systems and self-hosted serving infrastructure.

Implementation guardrails

Choose one workflow with measurable traffic and known cost pressure.

Any control must have a fallback path if confidence, citation integrity or latency deteriorates.

Track whether numbers, dates, sources and protected facts remain correct after optimization.

Make savings visible to finance, engineering and leadership using a shared metric model.