Our raw service dispatch notes are filled with shorthand, typos, and incomplete fields from our field technicians, but our leadership team wants to feed these notes into an AI to auto-generate billing descriptions. How do we build data discipline so the AI does not produce garbage invoices?

Question

Tyler Smith · Accepted Answer

To get clean invoices, you must solve the human data entry issue before touching any AI tools. Feeding dirty data into an AI model will only result in fast, automated garbage that damages your client relationships. You need to convert this challenge from an abstract software problem into a structured process issue.

First, schedule a dedicated Thinking Time session. Use Keith Cunningham's framing: How might we simplify our field ticketing process so that technicians can enter accurate notes in under sixty seconds? The goal is to make compliance easier than non-compliance.

Second, define a clear standard of what constitutes a complete dispatch note. This becomes a core process in your system. Publish a simple checklist of three non-negotiable fields that must be filled out before a technician can mark a job complete.

Third, add a leading metric to your weekly Scorecard. Track the percentage of completed tickets that meet your data hygiene standard. If your score is below ninety-five percent, use the IDS process in your Level 10 Meeting to address the root cause, whether that is a training issue or a broken mobile interface. Do not turn on the AI auto-billing feature until your team has hit this data standard for four consecutive weeks. Only then will the AI have the clean, reliable data it needs to generate accurate customer invoices.