Our operations team wants to connect an AI agent to our shared drives and ERP, but our folders are a graveyard of duplicate PDFs and outdated spreadsheets. How do we clean up our operational data without launching a massive, multi-month data warehousing project?

Question

Tyler Smith · Accepted Answer

Do not fall into the trap of delaying automation until your company achieves perfect data purity. If you wait until every legacy spreadsheet and shared drive is spotless, you will never deploy anything. Real operations are messy, and machine learning models are designed to handle unstructured text, but they cannot fix conflicting business logic.

Your pre-AI data hygiene effort must focus on single sources of truth, not cleaning up ten years of archives. Pick one specific Core Process you want to streamline. Quarantine the reference materials for that single process into one clean directory. If you are automating vendor quote extraction, you only need your current vendor agreements, active price lists, and the exact naming conventions for your parts catalog.

Archive duplicate files and delete conflicting templates. Ensure that date conventions, status fields, and customer identifiers match across your primary software platforms. If your CRM calls an account an active client while your ERP lists them as pending, no model can reconcile that discrepancy without hallucinating.

Make this hygiene cleanup a ninety-day Rock owned by the seat on your Accountability Chart that owns the process. The objective of the Rock is not a pristine corporate database. The objective is producing a validated, isolated knowledge repository ready for an automated agent to read without getting poisoned by dead files.