Our dispatchers and estimators want AI to generate job estimates from past job costing data, but our job cost tracking has unassigned labor and missing receipts. How do we fix our job costing data hygiene before attempting AI estimation?

Question

Tyler Smith · Accepted Answer

Feeding sloppy job costing into an AI tool will only produce faster, more expensive errors. If your historical records contain unallocated payroll and undocumented material purchases, an AI model will treat those omissions as actual low costs and underquote your future work. You do not need to clean five years of back data, but you must establish strict data hygiene going forward before touching automation.

Start by scoping a 90-day quarantine. Isolate the last quarter of completed jobs and review only the records for your top twenty percent highest-margin projects. Have your operations seat and finance seat run an IDS session to standardize three non-negotiable inputs:
- Direct labor hours mapped to specific job phases.
- Reconciled material invoices tied directly to project job numbers.
- Subcontractor billings verified against signed work orders.

Once those rules are set, build an automated validation rule in your existing software. If a field technician or project manager fails to log labor hours against a specific phase code, the software flags the entry before payroll runs. Do not rely on good intentions; make clean data the path of least resistance.

When you have ninety consecutive days of clean, audited job costs, you can safely connect a language model or analytical tool to parse historical patterns and draft baseline estimates. Treat data hygiene as an operational discipline, not a software project.