We want to train an AI model to predict job completion times and route dispatching based on our historical field service work orders, but years of technician notes are packed with typos, incomplete fields, and inconsistent terms. How do we clean this operational data before connecting it to an AI workflow?

Question

Tyler Smith · Accepted Answer

Feeding chaotic operational logs into a machine learning model or predictive AI engine guarantees expensive hallucinations and flawed scheduling logic. You cannot automate operational intelligence on top of garbage inputs.

Start by defining a narrow, highly structured standard for what a clean job record requires. For field dispatch, this usually means three verified fields: the precise problem code, parts utilized, and actual labor duration. Disregard long, unstructured narrative text until your core operational parameters are normalized.

Use a script or batch processing pipeline to run rule-based normalization first. Standardize job codes, strip out empty tickets, and map variant technician shorthand to a single controlled vocabulary. For example, if your technicians logged broken compressor in four different abbreviations, map all four to a single standardized category.

Next, run an LLM over the messy narrative text solely to extract and validate specific missing parameters, such as whether a delay was caused by weather, parts availability, or customer site access. If a historical record lacks basic integrity, quarantine it into an archive bucket rather than forcing it into the training set.

Clean the last twelve months of operational data rather than your entire ten-year history. Recent data reflects your current operational reality. Once the historical foundation is sanitized, lock down your field intake forms so technicians can only submit structured, validated data moving forward.