PrimisPro Insights

Before an AI pilot, draw the data and decision boundaries.

Frame AI adoption around the task, information permissions, human decisions and evaluation evidence.

Start with the work, not the model

Describe the task, users and decision the output will support. Identify where an error could affect a person, a customer commitment or an operational process. A useful pilot has a clear purpose and an owner who can decide whether its results are acceptable.

Separate assistance from authority. Drafting a response, retrieving a document and executing a privileged action have different consequences. Record which decisions remain with an accountable person.

Draw the information path

Map input sources, retrieval stores, model providers, application logs and output destinations. Decide what each component is allowed to receive. Use synthetic or appropriately approved data during early evaluation; convenience does not establish permission.

Permissions must survive retrieval. A user who cannot open a document should not receive its contents through an AI answer. Review tenant boundaries, access checks, retention and provider arrangements before connecting business information.

Treat tools as privileged interfaces

An AI application that can call a tool needs explicit authorization around that tool. Identify which actions require confirmation, which parameters are allowed and which server-side checks enforce the user’s scope. Model instructions are not a substitute for access control.

Agree what is logged, who can inspect it and how long records are retained. A demonstration should never suggest that a model performed a real assessment when no such assessment occurred.

Evaluate useful behavior and failure cases

Agree an evaluation set before the pilot. Include ordinary tasks, ambiguous requests, unavailable information and attempts to exceed permissions. Test whether the application can explain uncertainty and return a decision to a person.

Measure behavior relevant to the task. A decorative confidence score is not evidence of correctness. Capture reproducible observations, the system configuration and limitations.

Plan for operation

Identify the owner, escalation route, change process and conditions for disabling the workflow. Model, retrieval and integration changes may alter behavior, so evaluation belongs in the operating process.

NIST’s AI RMF is a voluntary risk-management reference. Use official materials with qualified review and the organization’s actual context. Adopting a reference does not establish compliance with every law or standard.

References

NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework