AI integration readiness checklist for business software

Choose a bounded task

“Add AI” is not a requirement. A useful starting point is a task with a clear input and reviewable output: extracting fields, drafting a reply, classifying a request or answering from approved documents.

Avoid giving a model irreversible authority simply because the demonstration looks fluent. Higher-impact actions need explicit user confirmation and conventional permission checks.

Prepare sources and evaluation examples

Collect representative examples, including difficult and incorrect cases. Define what a good answer contains, what the system should refuse and how a reviewer will identify unsupported output.

• Approved source documents and owners
• Examples of expected and unacceptable output
• A repeatable evaluation set
• Citations or traceability where appropriate

Set privacy and access boundaries

Document which information may reach the model provider, how long it is retained and who can see generated output. Remove unnecessary personal or sensitive fields before transmission.

Retrieval and model access must follow the same business permissions as the rest of the application. A user should not retrieve a document merely because the AI interface can search it.

Plan failure, cost and human review

Generated answers can be wrong, incomplete or inconsistent. Design a visible fallback, logging that avoids sensitive content and a route for human correction.

Estimate usage with realistic input and output sizes. Add limits, caching where suitable and monitoring so a successful feature does not create an uncontrolled operating bill.

• Human approval for consequential actions
• Timeout and provider-error handling
• Usage caps and cost monitoring
• A process for reviewing errors and improving evaluations