Data5 min read
Data readiness for AI automation
AI runs on context. If the data isn't accessible, structured and trustworthy, no model will save the project. Fix the feed first.
Most AI failures look like model problems and turn out to be data problems. Documents that aren't indexed. Records that aren't normalized. Sources that aren't connected. APIs that don't exist.
Before any model selection, the question is: can the system actually see what it needs to see, in a form it can use? Often the answer is no — and the highest-leverage early work is making it yes.
Data readiness is unglamorous. It's also the difference between a copilot that hallucinates and one that quietly does its job.