A practical standard for AI workflows that have to survive real operations.
Score one AI-enabled workflow across ownership, business value, operational controls, measurement and recovery. The goal is not more AI. The goal is a workflow that can be explained, checked and stopped when it fails.
Five things a production AI workflow should be able to prove.
SP-AOS converts broad governance ideas into a lightweight operating test for small and mid-sized teams. It is informed by the control logic seen in NIST AI RMF and ISO/IEC 42001, but it is an independent Stratum Praxis benchmark and does not claim equivalence with either framework.
Named accountability
A human owner, defined purpose, approved tools and clear decision rights exist.
Measured reason to exist
The workflow solves a real operational problem with a baseline and success metric.
Bounded permissions
Data access, actions, human review and prohibited behavior are intentionally constrained.
Observable performance
Outputs, exceptions, failures and cost are monitored rather than assumed.
Safe failure
There is a stop, rollback, escalation and review path when the workflow behaves badly.
Score one workflow in about five minutes.
Use evidence, not aspiration. Each answer scores 0 to 4. A high self-score is not a certification; it is a structured starting point for review.
The score only matters if the evidence behind it is real.
Turn a self-assessment into an operating system—or request independent review.
If your team wants to close governance gaps internally, the existing AI Workflow SOP & Governance Kit provides fixed-scope operating templates for repeatable AI workflows. If the workflow is material and you want an external evidence review, use the existing AI Workflow Opportunity Audit.
Built to complement serious governance, not impersonate it.
SP-AOS is intentionally narrow: operational readiness of a workflow. Organizations with material legal, safety, security or compliance exposure should use qualified specialists and the applicable formal standards.