Before we train an internal AI assistant on our company SOPs and operational guidelines, what data hygiene checklist must our leadership team run through to eliminate conflicting historical rules?

Question

Tyler Smith · Accepted Answer

Connecting an AI model to outdated or contradictory standard operating procedures will only scale confusion. If your operations manuals contain five different versions of how to handle a client refund, the AI will confidently hallucinate an answer based on obsolete policies.

Before deploying any internal knowledge assistant, execute a disciplined data hygiene audit on your Core Processes:
- Consolidate single source of truth files. Strip out legacy Word documents and personal desktop drafts. Each documented process must live in one canonical document owned by a specific seat on your Accountability Chart.
- Delete conflicting policy exceptions. If a procedure was modified two years ago but the old rule remains in the introductory text, delete the historical record. AI models treat all supplied text as current unless explicitly told otherwise.
- Format for machine readability. Use clean, standardized headers, explicit bullet points, and clear condition-action statements such as If X occurs, execute Y. Ambiguous prose creates erratic AI responses.
- Add clear ownership and revision dates. Ensure every document header lists the seat responsible for GWC™ (Get it, Want it, Capacity to do it) over that process.

Do not launch a six-month documentation overhaul. Pick your top three most frequently referenced Core Processes, apply this data hygiene filter, and feed only those validated assets to the assistant. Expanding your AI knowledge base must occur incrementally as processes are verified.