Audience
Teams worried their data is too messy to start.
Workflow
Data readiness
Diagnosis
Important fields are missing, duplicated, or entered inconsistently.
The team cannot produce five normal cases and five edge cases.
The automation would need to infer too much from unclear records.
Practical Move
Collect real examples, label what good output looks like, and decide which missing fields must be fixed before a pilot.
LinkedIn Post Seed
Your data does not need to be perfect before AI can help. It needs to be clean enough for the specific workflow decision you want to improve.
Outreach Question
Could your team pull five good examples and five messy examples of the workflow you want to automate?