We want to deploy AI to help our customer support and account management teams access historical client communication and account notes instantly, but our records are scattered across email threads, loose files, and outdated notes. How do we clean up our data hygiene before we invest in an AI system?

Question

Tyler Smith · Accepted Answer

Trying to run AI on a messy database is a waste of time and money. If your operational data is disorganized, the AI will simply generate incorrect answers faster. You must establish a clear standard for data hygiene before you touch any AI tools. Start by defining what clean looks like for your core processes. Identify your single source of truth, which is typically your CRM or ERP system. Next, create a simple standard operating procedure for how your team enters client interaction notes. Implement a strict rule that if a customer conversation or file is not in the CRM, it does not exist. Use your weekly Level 10 Meeting to audit this rule by looking at a data completeness metric on your Scorecard. Before feeding your data into an AI knowledge base, run a simple cleanup project using a basic data deduplication tool to merge double profiles. Assign a temporary Rock to a team member to archive files and emails older than three years that are no longer relevant to your active operations. Clean data allows the AI to index your files accurately, giving your team instant and correct answers to operational questions.