Our customer contract terms and pricing tables are scattered across unstructured Google Docs, Slack threads, and old emails. If we want to connect an internal AI tool to help our account managers quickly check custom terms, how do we clean up and structure this messy knowledge base without pausing our daily operations?

Question

Tyler Smith · Accepted Answer

To put an AI tool to work on your contracts without causing operational chaos, you must treat your data hygiene as a structured operations-improvement project. Do not try to clean every legacy file in one massive push. Instead, start by establishing a single source of truth for your contracts.

On your Accountability Chart, assign a specific seat to own this data pipeline. This person must define a standard template for how terms and pricing are logged moving forward.

Next, set up a simple transition process:
- Audit only the active client files first, leaving archived records alone.
- Export these active contract terms into a structured format, like a clean spreadsheet or a dedicated folder in your CRM.
- Set a firm rule that any contract not in the master repository is considered draft.

By isolating your active client data, you prevent your internal AI tool from accessing outdated or conflicting terms. This keeps the project manageable and ensures the AI actually generates accurate answers for your account managers. This approach turns what seems like an overwhelming database cleanup into a series of predictable, bite-sized tasks that your team can run in the background during their weekly Level 10 Meetings.