Our team wants to purchase an expensive AI analytics tool to predict customer churn, but our CRM is full of duplicate records and outdated contact info. How do we establish proper data hygiene before we invest in AI?

Question

Tyler Smith · Accepted Answer

Buying an AI tool when your data is a mess is like putting a high-performance engine into a car with no oil. The AI will simply generate bad predictions faster and with more confidence. Before you write a check for any AI software, you must run a data audit. Start by assigning an owner to your data quality on your Accountability Chart. This is typically your Integrator or Operations Leader.

Next, define what clean data actually looks like for your business. Establish clear rules for entry. For example, every client record must have an industry code, a clear lead source, and an active account manager. Set a Rock for your operations team to purge duplicate files, archive inactive accounts, and standardize formatting across your entire CRM.

Once your data is clean, you must build a weekly discipline to keep it that way. Use your weekly EOS® Scorecard to track data completeness metrics. If your team is not maintaining a high accuracy rate on new entries, do not let them touch an AI tool. AI cannot fix human laziness or broken processes. It only amplifies what already exists. Clean up your database first, prove your team can maintain it for one full quarter, and only then should you introduce AI algorithms to analyze that clean data.