The Independent AI Scale Gate
A six- to eight-week, fixed-scope engagement for one consequential business process that already has an AI pilot, proposed vendor purchase, early deployment, or stalled implementation. The result is an explicit go, conditional go, remediate, rebid, replace, defer, or stop decision.
Five integrated workstreams
Process and economics
- Current-state map
- Volume, time, labor, error, rework, delay, and quality baseline
- Value hypothesis and non-AI alternatives
Technical comparison
- Vendor, model, and architecture scorecard
- Controlled test set
- Quality, latency, robustness, security, cost, integration, and portability
Implementation verification
- Architecture review
- Production-readiness assessment
- Workflow and data controls
- Human escalation and operational ownership
Governance and evidence
- AI system record
- Risk classification
- Evaluation evidence
- Monitoring, incident, and change requirements
Scale decision
- Decision memorandum
- Investment case
- Acceptance gates
- Implementation or recovery roadmap
Best entry conditions
- A pilot works in a demo but executives do not trust the scale case.
- Vendor proposals are difficult to compare across price, architecture, quality, and lock-in.
- A deployment is live but the realized business value is uncertain.
- Implementation cost is rising and incumbent explanations are no longer sufficient.
- Governance approval is blocked by missing evaluation or control evidence.
Decision standard
A polished prototype is not enough. Scale requires verified process value, critical quality thresholds, acceptable residual risk, production ownership, human fallback, observability, and a defensible cost per successful business outcome.
