We want to deploy language models to help our project managers estimate job costs based on past projects, but our historical project logs have messy notes and inconsistent formatting. What is the practical minimum data hygiene standard we need to enforce before running AI against our past records?

Question

Tyler Smith · Accepted Answer

Feeding dirty operational records into an AI model will generate inaccurate estimates that destroy your gross margins. However, launching an expensive, six-month data cleansing project across your entire historical archive is classic operational theater. You do not need pristine data across fifteen years of history; you need a defined, reliable sample size.

Begin by isolating a single slice of your business. Select your top twenty percent highest-margin, most repeatable project types completed over the past twelve to eighteen months. Have your operations seat pull those specific files and establish three mandatory data points: the original project scope, the actual labor and material costs from finalized accounting, and the post-job variance notes explaining why the job went over or under budget.

Delete duplicate drafts, unpriced change orders, and unverified estimates from this targeted folder. If a historical file is missing actual job costing or contains conflicting numbers, discard it entirely. You only need fifty to one hundred clean, verified reference jobs to create a reliable context baseline for an AI estimating assistant.

Simultaneously, enforce data hygiene on your forward-looking Core Process. Update your project closeout SOP so project managers cannot mark a job complete without entering standardized cost actuals and scope tags. If you do not fix the discipline of how your team logs data today, any AI automation you connect tomorrow will ingest garbage and produce financial liabilities.