Most AI initiatives don't stall because the model was wrong. They stall because the data underneath it was never ready. Score yourself against the checklist below before you spend on anything else.
Source: Drexel LeBow / Precisely, 2026 survey of data and AI leaders on what blocks AI initiatives from reaching production.
Duplicate, stale, or inconsistent records feed the model bad ground truth before it ever runs.
Sensitive fields aren't classified or masked, so no one can say what the model was actually allowed to see.
No owner, no policy, no answer to 'who approved this data being used this way.'
Data lives in disconnected systems that were never designed to be joined, so every AI use case starts with a custom pipeline.
Answer honestly. Most mid-market data platforms score 2–3 out of 7 the first time.
Every value is traceable to its source and every transformation it passed through.
A written, enforced policy for how long data is kept and why — not a default nobody chose.
A named, provable answer to who and what can read or write any given dataset.
The ability to reconstruct what happened to a record after the fact, on demand.
Profile the dataset, quantify duplicate/null/staleness rates, and fix the pipelines that produce them — not just the symptom.
Classify sensitive fields, apply masking/access controls at the platform layer, and document what's safe to expose to a model.
Assign data ownership, write the retention and access policy, and wire it into the platform so it's enforced, not aspirational.
Build the pipelines that join your source systems into one governed platform, so each new AI use case doesn't start from zero.
Fixed scope, fixed price, fixed timeline — agreed before we start. See published price bands for current rates.
