AI
July 31, 2026
·
AI
<p>AI-ready data is marketing data an AI tool can query and interpret correctly without a person translating it first, which is a higher bar than putting it somewhere a model can reach. It needs a collection layer that is not quietly dropping conversions, a written definition for every metric and dimension so one question has one answer, and no personal data sitting in fields nobody has inspected. Availability is a storage problem, readiness is a definitions problem.</p>
Why it matters for agencies
The risk is not that the AI refuses to answer, it is that it always answers. Ask which channel drove revenue last month, and a tool pointed at a raw GA4 export in BigQuery has three traffic-source records to choose between, which Google's schema documents as three different things: traffic_source is the source that first acquired the user and does not change when that user meets a later campaign, collected_traffic_source is what was present on the events themselves, and session_traffic_source_last_click is last-click session attribution. Each produces a defensible number, none is labelled as the channel in the client's report, and the answer arrives with no sign a choice was made.
What teams get wrong
The most common mistake is running AI-readiness as an access project. The export lands in BigQuery, the connection works, the dataset is declared ready, and nobody notices that the parameters carrying the metrics arrive as a repeated key and value record, not as columns. So every question needs a query plus a decision about what its words mean, and the definition of revenue, of a conversion, of a channel lives in the head of whoever built the reports.
The second is starting at the warehouse at all. When 15-30% of conversions never arrive, the model reasons over a dataset that under-reports and reads the shortfall as weak performance, because a missing conversion and one that never happened are indistinguishable from inside the data. The third is handing over a dataset nobody has read: Google's own policy is that no data be passed to it that it could recognise as personally identifiable, and it ships redaction for email addresses and query-string values precisely because they do end up in event parameters. At Archon Labs the order is collection first, then definitions, then a read of what is in the fields, because a model never volunteers that the answer it just gave was built on the wrong column.
Related terms