Enterprise AI project playbook
Is your AI project ready for production? Use this seven-gate readiness checklist
Use this enterprise AI production-readiness checklist to decide whether to deploy, remediate or stop an AI project based on accountable ownership, tested risks,
Executive answer
An AI project is ready for controlled production only when its executive owner can approve seven evidence gates: a defined business outcome, bounded use case, validated performance, security and access controls, human operating controls, production monitoring, and a recoverable incident plan. If a gate lacks evidence, approve remediation rather than treating a successful demonstration as production readiness.
What decision should this production-readiness gate support?
The accountable executive owner should make one observable decision: approve a controlled production release, require remediation before release, or stop the project. The owner is commonly the business executive responsible for the workflow outcome, not only the technology delivery lead.
NIST frames AI risk management as a lifecycle activity. Its AI RMF calls for systems to be tested before deployment and regularly while operating, with documented measurements used to inform risk monitoring and response. A production decision should therefore rely on evidence from the real intended workflow, not a vendor demonstration alone.
- Approval: all non-negotiable gates pass and residual risks are accepted by the accountable owner.
- Remediate: the business case remains valid, but a named evidence gap has an owner and due date.
- Stop: intended value, safe operating boundaries or workable controls cannot be demonstrated at an acceptable cost.
Gate 1: Is the production use case bounded and owned?
Start with a production-use statement that identifies the users, affected people, decisions influenced, data categories, allowed actions, prohibited actions and fallback process. This prevents a pilot from expanding through informal requests after deployment.
The business owner must define the measurable outcome and the harm threshold that would suspend the system. The product or process owner must confirm that the workflow can absorb review, escalation and exception handling.
- Named business owner accountable for value and residual business risk.
- Named process owner accountable for adoption, exceptions and fallback work.
- Written allowed and prohibited tasks, including actions the system cannot take.
- Defined affected groups and material impact scenarios.
- Baseline metric and production target, such as handling time, quality, conversion or error reduction.
Gate 2: Has the system been evaluated against representative work?
Require a versioned evaluation set drawn from representative, permitted enterprise work. Test the complete system, including retrieval, prompts, model settings, integrations, policy controls and human review steps. Record the system version, test method, results and known limitations.
NIST states that measurement should use rigorous testing and performance assessment, benchmarks where appropriate, and formal reporting and documentation. For generative AI, measure useful task outcomes as well as unsafe, incorrect, irrelevant or unsupported outputs.
- Evaluation set covers routine, edge, ambiguous and high-impact cases.
- Pass thresholds are defined before the final test run.
- Results are segmented by task, user group, language, data source and integration path where relevant.
- Known failure modes have documented user guidance, control or exclusion.
- Independent reviewer validates the method or challenges material assumptions.
Gate 3: Are data, identity and tool permissions constrained?
Production access should follow the minimum data, functionality and privilege needed for the approved use case. Confirm which identities the AI service, application, user and downstream tools use. Test that downstream systems enforce authorization independently of model output.
OWASP identifies excessive functionality, permissions and autonomy as common causes of excessive agency in LLM systems. For any system that can call tools or modify records, require explicit action boundaries, approval points for consequential actions and auditable downstream authorization.
- Data inventory identifies inputs, outputs, retention locations and approved data sources.
- Service accounts and tool permissions are limited to the approved task.
- High-impact actions require a deterministic policy check or authorized human approval.
- Secrets are not exposed to prompts, logs or end users.
- Prompt-injection and unauthorized-action tests cover each connected tool.
Gate 4: Can people supervise, challenge and override the AI system?
Define who reviews outputs, what requires review, when work is escalated and who can disable the system. Oversight must be part of the operational workflow rather than a statement that a human remains responsible.
Choose review intensity from the consequence of an error. Low-impact drafting may use sampling and user correction. Decisions affecting customers, employees, finances, safety or legal commitments should use stronger review, restricted actions and clear escalation paths.
- Human reviewer has the authority, information and time to intervene.
- Users can identify AI-generated recommendations and report failures.
- Review queues have service targets and a named escalation owner.
- A manual fallback process is tested with real operational users.
- Override and exception decisions are logged for later analysis.
Gate 5: Is monitoring sufficient to detect degraded performance and misuse?
Create a production monitoring plan before release. NIST AI RMF identifies production monitoring, identification of emergent risks and periodic review as governance outcomes. Monitoring should connect model and system behavior with business effects, not only infrastructure uptime.
CISA describes logging and monitoring as a way to establish normal behavior, detect suspicious activity and support faster response. Centralize relevant records, protect access to them and define alerts for high-risk events.
- Operational metrics: availability, latency, failures, tool calls and queue backlog.
- Quality metrics: task accuracy, groundedness, correction rate, escalation rate and reviewer disagreement.
- Risk metrics: policy violations, sensitive-data events, prompt-injection attempts and unauthorized tool requests.
- Business metrics: the agreed outcome compared with the pre-production baseline.
- Review cadence, alert thresholds, evidence retention and on-call ownership are documented.
Practical questions
Who signs off an enterprise AI production release?
The business executive accountable for the workflow should approve value and residual business risk. Technology, security, privacy, legal and operations owners should approve their respective gates. No single technical lead should accept all risks alone.
Can a successful AI POC move directly into production?
Usually not. A POC can show feasibility, while production requires evidence for real workflow performance, access control, operational ownership, monitoring and recovery.
What is the minimum evidence for a controlled AI production release?
At minimum, require a bounded use case, accountable owner, representative evaluation results, approved data and access design, human operating controls, monitoring plan, incident and fallback plan, and documented residual-risk decision.
How should an enterprise release an AI system when uncertainty remains?
Use a limited production release with restricted users, data, permissions and actions. Set a review date, measurable thresholds and pre-agreed conditions for expansion, rollback or stop.
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