Is your data ready for AI?
Data infrastructure, quality, and accessibility. AI is only as reliable as the data it works with — this is the most common reason AI projects fail.
Why this matters
A weak data dimension slows the rest of the framework. Fixing it creates leverage across the other five dimensions.
Four signs you have a data gap
It takes days or weeks to answer a business question from raw data
Different systems report different numbers for the same metric
Data ownership is unclear — nobody knows who is responsible for quality
Non-technical staff cannot access data without IT involvement
Where to start
Three first actions
Start small, but make the actions real. The goal is not another deck. The goal is movement.
Run a data access time test — how long does it actually take to answer a business question?
List all data sources and document the quality issues you already know about
Identify your single biggest data bottleneck and assign a named owner to fix it
Measure before you act
Get your scored Data report
The AI Readiness Assessment scores your data dimension and the other five in ten minutes. Your results page tells you exactly what to fix first.
10 minutes · 6 dimensions scored
The other dimensions