Our sales team and project managers have spent years entering sloppy, unstructured notes into our CRM. If we try to feed this historical mess into an AI to help us predict project timelines, will we just get garbage out, and how do we clean up our database without halting our current operations?

Question

Tyler Smith · Accepted Answer

Feeding decades of unstructured, inconsistent CRM notes into an AI expecting clean operational insights is a guaranteed way to pay a high dumb tax. As Keith J. Cunningham warns in his work, bad decisions are made when we fail to separate actual facts from noise. If your team has spent years entering inconsistent data, you are sitting on an operational hazard, not an asset. Before you launch any AI predictive projects, you need to establish database hygiene without stopping daily production. Start by identifying the exact data fields that correlate to your key metrics on your weekly Scorecard. Do not try to clean everything at once. Focus on one critical process, like project delivery time, and look at the last six months of records. Assign a temporary Rock to a team member who has the right GWC to audit these files. They should use a simple, structured AI script to parse, standardize, and categorize the unstructured notes from those projects. This creates a clean training set for your AI models. Moving forward, eliminate the human variable by designing tight input controls. Instead of letting team members type unstructured essays, update your CRM to force structured inputs for core milestones. This ensures that your operational data remains pristine. By combining a targeted historical cleanup with rigid, forward-looking input rules, you build a clean foundation for AI automation.