AI & automationJune 20266 min read
Is your data ready for AI?
We all dream about the model. But an AI project rarely fails because of the model. It fails because the data was not ready. Here is how to check before you spend a single euro on code.
Why step 0 is your data
We all dream about the model, but an AI project rarely fails because of the model. It fails because the data was not ready: incomplete, scattered, unreadable by a machine. That is step 0, the one people skip because it is less exciting, and it is exactly where success or failure is decided. Before writing a line of code, I look at your data.
The four questions I ask your data
Four questions are enough to know whether your data holds up.
- Is it complete, or full of holes?
- Is it up to date, or three years old?
- Is it gathered in one place, or scattered across ten tools?
- Can a machine use it, or is it buried in scanned PDFs?
What knowing it beforehand changes
Answering these questions before starting changes everything: you rule out the cases doomed to fail from the start, you put a real number on the project without nasty surprises, and you begin with the minimal useful cleanup, not a full overhaul. You invest in what is ready, not in a dream that collapses at the first real piece of data.
You do not build AI on data that is not ready. You would be building on sand.
You do not need to tidy everything before you start. You need to know what is usable today, and where to begin. That is exactly what a data audit tells you, before you commit a single euro.