We want to train an internal AI assistant to help our customer service team answer client questions quickly, but our company documentation is scattered across Google Drive, Slack, and various spreadsheets. How do we prepare our messy operational data so the AI actually provides accurate answers?

Question

Tyler Smith · Accepted Answer

Training an AI assistant on messy, unorganized data is a recipe for operational disaster. If you feed the tool outdated spreadsheets and conflicting Slack conversations, it will output inaccurate, confusing answers to your customer service team. You must clean your digital house before you turn on the automation.

Start by identifying your core operational processes. Under the EOS® model, every business has a handful of Core Processes that define their unique way of doing business. Focus only on the documentation that supports these Core Processes.

To prepare your data, execute these three steps:

- Designate one central repository as your single source of truth, such as a secure wiki or document hub.

- Audit and delete. Have your customer service manager review all existing training documents, delete outdated versions, and archive obsolete information.

- Structure the information in a simple, question-and-answer format. AI tools excel at reading structured text that directly addresses common client inquiries.

Once your documents are clean and centralized, you can securely connect your AI tool to this repository. This structured approach ensures that the AI only references verified, up-to-date information, giving your customer service reps the confidence to use the tool daily without fearing inaccurate results.