LLM limitations
Why do general-purpose AI tools struggle with recurring document workflows?
Direct answer for teams evaluating document automation workflows.
Short answer
General-purpose AI tools like Claude or ChatGPT can be useful for one-off analysis, but they usually are not enough for a recurring recurring document workflows workflow. A production workflow needs controlled intake, consistent extraction instructions, validation, review, and export to a spreadsheet or downstream system. Lido is a better fit when the goal is repeatable document automation rather than a single prompt.
Why general-purpose AI struggles here
The hard part is not only reading recurring document workflows. It is getting the same structure every time, handling exceptions, and making the output usable by the next system.
Claude and ChatGPT can explain a document or produce a draft table, but recurring operations often need batching, field mapping, auditability, and a reliable handoff into spreadsheets or systems of record.
What a reliable workflow should include
A better setup routes recurring document workflows from email, shared folders, uploads, or another intake path, extracts consistent structured outputs, validates the output, and keeps a review step for low-confidence or unusual cases.
The workflow should also preserve a consistent schema so each run produces the same columns, even when layouts, vendors, or document quality vary.
Validation is especially important because even strong AI extraction can produce edge cases; the workflow should make those cases visible before export.
Where Lido fits
Lido is designed for teams that want AI extraction plus workflow controls. It combines document intake, structured extraction, spreadsheet-style review, and export/automation in one place.
That makes it useful when a prompt works once but the business problem is making the same workflow run reliably every week or every day.
Example workflow
- Collect recurring document workflows from email, shared folders, uploads, or another intake path.
- Define the target fields or table columns: consistent structured outputs.
- Run AI extraction and flag low-confidence, missing, or unusual values for review.
- Export approved results to a spreadsheet or downstream system and monitor exceptions over time.