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Moving an AI proof of concept into production means proving more than that a model can work. You need to establish that the complete system is suitable for its intended use, assign responsibility for its risks, evaluate it with repeatable methods, and keep monitoring it after launch. Treat production as a lifecycle decision—not a one-time approval.
What changes between a proof of concept and production?
A proof of concept demonstrates that an approach can work under selected conditions. A production system operates in a defined context, where its outputs can affect users, workflows, and decisions. The question therefore shifts from “Can this model do the task?” to “Is this system appropriate here, and how will we know when it is not?”
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The OECD describes the transition as moving from research and development to deployment and operation, and says controlled experimentation can help systems be tested and scaled appropriately. Its AI Principle 2.3 states: “Governments should promote an agile policy environment that supports transitioning from the research and development stage to the deployment and operation stage for trustworthy AI systems.” For a team, that means scaling through deliberate testing and operational preparation rather than treating a successful demo as launch evidence.
Use governance to connect context, evidence, and action
NIST’s AI Risk Management Framework (AI RMF) 1.0, released January 26, 2023, is voluntary guidance for incorporating trustworthiness considerations into AI design, development, use, and evaluation. Its four functions—Govern, Map, Measure, and Manage—can help organize production work. They are related activities, not a substitute for a team’s own decisions about its system.
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Govern: name owners and decision rights
Establish who is accountable for the system and its risk-management practices. Make clear who can approve deployment, who receives escalations, and who can pause or change the system when evidence or operating conditions warrant it. Without named ownership, evaluation findings and user reports can accumulate without a decision-maker responsible for acting on them.
Map: define the use before judging the system
Describe the intended tasks, deployment context, and risks. A model’s performance in a demonstration does not establish its suitability for a different task or setting. The context gives the team a basis for deciding what to evaluate and which risks matter for the actual use.
Measure: make evaluation repeatable and documented
Assess risks through test, evaluation, verification, and validation (TEVV) processes that are objective, repeatable, or scalable. Record the methods and metrics so results can be understood and revisited. A result that cannot be reproduced or explained is weak evidence for a production decision.
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Use evaluation findings to inform risk responses, then monitor the system in operation. Management includes deciding what happens when a risk appears, who acts, and how the response is tracked—not simply recording that a test was completed.
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Turn the framework into a production decision
A practical way to apply those functions is to make the deployment decision traceable: connect the stated use to the risks considered, the evaluation evidence, the accountable approver, and the plan for operating the system after release. This is an implementation approach, not a prescribed NIST approval form.
- State the intended use. Describe the task and operating context the system is being considered for; identify the accountable owner.
- Identify relevant risks. Use that context to determine what could go wrong and what evidence would help assess those risks.
- Document evaluation. Record the TEVV methods and metrics, along with the results that inform the decision.
- Make and record the decision. Have the accountable decision-maker consider the context, risks, and evidence before deployment.
- Prepare for operation. Define how the system will be monitored, how concerns will be escalated, and how changes or incidents will be handled.
The point is not to produce paperwork for its own sake. The record should let the team explain why this system was considered suitable for this use and how it will respond if the conditions or evidence change.
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Keep governance active after launch
Launch approval is not the end of risk management. NIST’s core AI RMF material includes post-deployment monitoring and user input, as well as appeal and override processes, incident response, recovery, change management, and decommissioning. These are operational responsibilities: a team needs routes for feedback and escalation, a way to respond to incidents, and a process for managing system changes and eventual retirement.
Monitoring should therefore connect to action. If a report, incident, or change raises a concern, the team needs to know who reviews it and how the system or its use can be adjusted. A deployment plan that ends at release does not cover the full lifecycle described by the framework.
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Account for generative AI risks specifically
For generative AI systems, NIST’s AI 600-1 Generative AI Profile offers additional risk-management actions aligned with the AI RMF. NIST released the profile on July 26, 2024. It is intended to help organizations identify risks distinctive to generative AI and connect them to risk-management work; it complements the framework rather than changing the need to define the particular system’s intended context.
NIST also describes its AI RMF Playbook as voluntary companion guidance for navigating and applying framework outcomes in development, deployment, and use. Teams can use it to make the framework more practical, while retaining responsibility for deciding how the guidance fits their system.
Do not treat a framework as a legal determination
The AI RMF is voluntary guidance, not a statement that a deployment complies with law. Which obligations apply depends on jurisdiction, sector, and use case. A production decision should therefore keep framework-based risk management distinct from a separate analysis of the rules that govern the specific deployment. Because no geography, industry, or system classification is specified here, no particular legal requirement can be determined.
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