What can go wrong in an AI application, and how should you plan for it?

What can go wrong in an AI application, and how should you plan for it?

An AI application can fail in two different ways: the system may not complete the request, or it may complete the request with an unsuitable result. A reliable project plan accounts for both.

Separate processing failures from content problems

A generation service might time out, return an error, or produce an unusable file. These are processing failures. A fluent script with incorrect information or narration with poor pronunciation is a content problem, even if every service reports success.

Give users a response appropriate to the problem. A failed transfer may need another attempt. Incorrect text may need an edit and review before regeneration. Retrying the same input is not a universal fix.

Define honest job states

Track whether work is pending, processing, complete, or failed. Only mark a result complete after verifying that the expected output is available. If a stage fails, preserve enough information to explain what happened and determine which work remains valid.

Think through duplicate clicks and repeated requests. A user who presses “Generate” twice should not unknowingly start two expensive jobs. Associate the user's intended operation with a stable identity and define how repeat submissions behave.

Choose a recovery path for each failure

For a simplified video project, list scenarios such as an empty brief, an unavailable narration service, a failed file upload, or a video that does not match the reviewed script. Decide what the user sees and whether they should edit, retry, or contact support.

Keep retries bounded, particularly when external generation can incur costs. Preserve useful completed work when it is safe to reuse, and avoid reporting success merely because a request was accepted. Exercise at least one failure path early so recovery is part of the design.