Amazon Quick Automate builds RFI workflow from one prompt

AWS's post is aimed at organizations that handle hundreds of RFI questionnaires a year, each arriving as a multi-tab workbook with hierarchical question sets, category metadata, and varied response types. Manually extracting, structuring, and resolving those questions requires repeated coordination, delays responses, and can introduce errors whenever the questionnaire format changes — the specific pain the walkthrough is built around.

The demonstrated approach chains a few concrete pieces: an Amazon S3 action connector, an automation group, and an automation project where the user describes the processing logic in plain language. Quick Automate's AI assistant then generates a multi-step workflow covering reading the workbook, extracting main questions and subquestions by numbering and indentation, merging parent context into subquestions, and writing the output as a CSV back to S3. AWS notes that because the assistant relies on generative AI, the exact steps and wording can vary between runs, which is why the post stresses incremental validation and explicit handling of edge cases such as missing, duplicated, or inconsistently formatted data.

The stakes here likely hinge on how reliably the generated workflow holds up against real, messy RFI workbooks rather than the tidy example file — that is presumably the test before teams trust it in production, and it is why AWS routes validated versions through Import/Export into a production account or Region. For procurement and bid-response teams, the promise is fewer formatting inconsistencies and faster responses; for the IT staff supporting them, the work shifts to connector setup, IAM permissions, and promotion between environments.