An AI prototype can look convincing with a few carefully selected examples. A production system must handle inconsistent records, missing context, changing processes, permissions and real users. Many AI projects become difficult not because the model is incapable, but because the surrounding data and operating process were never prepared.
Start with the workflow and decision owner
Document the user, the task, the current process and the decision that the AI feature is expected to support. Identify who owns the process, who can approve changes and who handles exceptions. Avoid vague targets such as “use AI to improve productivity”. Define a measurable outcome such as reducing manual classification effort while preserving an agreed accuracy and escalation rate.
Inventory and assess the data
List the systems that contain relevant information, who owns them, how often they change and whether the organisation has permission to use them for the proposed purpose. Check duplicates, missing fields, inconsistent naming, outdated documents and conflicting versions. For knowledge assistants, identify the authoritative source and owner for each document collection.
Design access and privacy boundaries
Do not send all available data to a model simply because it is technically possible. Apply least-privilege access, remove unnecessary personal information, define retention, and decide which content can be sent to an external model provider. User identity and permissions should be enforced in the application and retrieval layer, not left to a prompt instruction.
Build an evaluation set before the feature
Create realistic examples representing common tasks, edge cases, ambiguous requests and situations where the correct behaviour is to ask a question or escalate. Agree on what counts as a correct result with the process owner. Include unacceptable outcomes such as fabricated facts, cross-user data exposure or an action taken without approval.
Plan for operations and change
Assign owners for source updates, model or prompt changes, monitoring, incidents and user feedback. Track task completion, correction rate, escalation, latency, cost and failure categories. Re-run evaluation after changing the model, retrieval index, instructions or connected tools. The AI feature becomes an operational product, not a one-time experiment.
Further reading and resources
The article is original GrapeCrux editorial content. The following official documentation and technical resources provide deeper reading on the concepts discussed:
- NIST — AI Risk Management Framework
- NIST — Generative AI Profile
- Google Cloud — Responsible AI overview
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