Governing AI at Enterprise Scale: From Pilot to Production

Define intended use, evaluation criteria, human oversight and operating responsibilities before expanding an AI pilot into production.

An AI pilot is ready for production only when its intended use, evaluation criteria, failure handling and operating responsibilities are defined and tested in context. Assess representative data and workflows, establish human oversight, and plan monitoring and rollback. A successful demonstration or an isolated accuracy figure is not enough to establish operational suitability.

Understand the production workflow

A demonstration can use favorable examples, a narrow dataset or manual support that will not exist in production. A production workflow introduces changing inputs, access controls, integration dependencies and users who need to understand what the system can and cannot do.

NIST's AI Risk Management Framework is voluntary guidance for managing risks associated with AI. It organizes risk work around Govern, Map, Measure and Manage, rather than treating a model evaluation as the complete governance process. Using the framework does not establish regulatory approval or certify that an application is safe.

Evaluate the system in context

Define the intended task, affected users, decision boundaries and prohibited uses. Identify where an incorrect, missing or misleading output could affect people, operations or sensitive data. Decide which decisions require a human and how that person receives enough context to assess the result.

Evaluate against representative inputs, including difficult and out-of-scope cases. Record the source and suitability of the evaluation data, the criteria applied and known limitations. For a generative system, relevance, unsupported statements, information exposure and failure behavior may need separate assessment; a single score can hide important trade-offs.

Check the surrounding workflow. Authentication, data access, audit trails, dependency failures and recovery procedures matter even when the model behaves as expected. Confirm who can change prompts, retrieval sources or model configuration and how those changes are controlled.

Establish operating responsibilities

Assign an accountable risk owner and operational owner. Document the evaluation criteria before changing the system to meet them. Compare the proposed workflow with the current process without presenting a forecast as realized business value.

Run a bounded deployment with defined entry and exit criteria. Plan escalation, human override and rollback before expanding access. Monitoring should detect relevant data or behavior changes, not merely whether an API is available. Reassess when the task, model, data sources or user population changes.

Questions for your programme

Who can override a result? Which failure modes were tested? What changes trigger reassessment? Which responsibilities remain with the enterprise? These questions connect governance to practical system design. Application-specific legal, clinical and safety obligations require appropriate specialist advice.