Representative capability
Evaluate common, edge, missing-data, adversarial, and high-impact cases with defined metrics, samples, thresholds, versions, and failure analysis.
Enterprise AI go-live control
What must be true before an enterprise AI system goes live?
A successful demonstration establishes possibility, not production readiness. Release only when the complete workflow has an owner, mandatory controls pass, failure and recovery are tested, and operators can run the system within an agreed cost and service envelope.
By Onyx Devs Lab · Published · Updated
Owned workflowBusiness, technical, data, and risk owners
Mandatory gatesQuality, privacy, authority, and safety
Operable systemMonitoring, recovery, cost, and support
Evidence required for the release decision
Every gate should point to a reproducible record, not a confidence statement.
Data and authority
Verify provenance, consent and purpose where relevant, retention, tenant isolation, least privilege, confirmation, audit, and prohibited actions.
End-to-end workflow
Test integration, queues, retries, idempotency, latency, availability, cost, human review, escalation, and resulting state in systems of record.
Operational conditions after go-live
Production readiness includes the people and procedures that respond when reality differs from the test set.
Observability and response
Monitor quality signals, system health, tool failures, cost, data drift, security events, and user exceptions with named responders.
Fallback and recovery
Provide safe degradation, rollback, kill and revoke paths, backups where applicable, incident records, and verified recovery objectives.
Adoption and change
Train operators, publish limitations, define support and feedback, control model and prompt changes, rerun regression tests, and review authority regularly.
How to run the go-live gate
Freeze the release candidate
Record model, prompt, retrieval, tools, policies, data, infrastructure, and evaluation-set versions.
Run mandatory gates
Execute workflow, control, failure, recovery, performance, cost, and operator tests; preserve results and unresolved exceptions.
Sign the release decision
Business, technical, data, and relevant risk owners approve the exact authority tier and accepted residual risk.
Release progressively
Limit users, volume, data, and actions first; watch leading indicators and expand only after the evidence window passes.
Frequently asked questions
Is model accuracy the main go-live criterion?
It is one criterion. A useful model can still be unsafe or uneconomic when permissions, integration, reliability, recovery, review effort, or operating cost fail.
Must every issue be closed before launch?
No. Non-mandatory residual risks may be accepted by authorised owners when impact, mitigation, monitoring, expiry, and review date are recorded.
How often should readiness be reviewed?
After material changes to models, prompts, data, tools, permissions, workflow, infrastructure, regulation, or observed failure patterns—and on a scheduled basis.
Is this a security certification?
No. It is a provider-authored delivery checklist, not an audit, certification, or replacement for organisational governance.
Start with one concrete operating problem.
We will first clarify the workflow, evidence, data boundary, and outcome standard before recommending the next step.
Contact Onyx