Frequently Asked Questions
Everything you need to know about the AI Data Maturity Score and assessment.
How is the AI Data Maturity Score different from organizational AI maturity models?
Most AI maturity frameworks — from Gartner, MITRE, and McKinsey — assess whether your organization is ready to adopt AI. They measure strategy, culture, talent, and governance at the organizational level. The AI Data Maturity Score asks a different question entirely: is your data ready for AI? These are not the same question. You can have a mature AI strategy and still connect your tools to data that AI cannot reliably interpret. The AI Data Maturity Score operates at the dataset level — column by column — not the organizational level.
What is an AI Data Maturity Score?
A numeric score from 0–100 that measures how ready a specific dataset is for AI analytics tools. It evaluates every column across four dimensions — data quality, naming clarity, PII risk, and ambiguity — and produces a single score that tells you where your data stands before you connect it to AI.
How is it calculated?
Each column is assessed individually across data quality, naming clarity, and privacy risk. Column scores are weighted and aggregated into an overall dataset score. The result is mapped to five maturity levels: Not AI Ready, Low AI Readiness, Moderate AI Readiness, High AI Readiness, and AI Ready.
What does a score of 60 mean?
A score of 60 falls in the High AI Readiness range. Your dataset is mostly ready — AI tools will perform reasonably well — but specific columns have issues worth addressing before connecting to production AI workflows.
How long does an assessment take?
Most assessments complete in under two minutes regardless of dataset size.
Is my data safe?
We analyze a statistically representative sample of your dataset — not your full file. Only column statistics and sample values are sent to OpenAI for analysis. Your file is never stored.
How does ADM sample my data?
ADM analyzes a statistically representative sample of your dataset rather than scanning every row. Sample size is calculated dynamically using the Cochran formula (95% confidence level, ±5% margin of error), scaling with your dataset size. Rows are selected using stratified random sampling to ensure proportional representation across data categories, with minimum and maximum outlier values always included to capture edge cases. This methodology was developed in consultation with Bruce Ratner, PhD, Predictive Analytics Consultant.
Is there an enterprise version?
Yes. AI Data Maturity is designed to run inside your own environment. Your IT team points it at your internal AI gateway and your own database and it runs entirely behind your firewall. No data leaves your walls. Contact us at hello@aidatamaturity.com to discuss a pilot.
Why is it free?
We believe every data team should be able to assess their data before connecting it to AI — without a procurement cycle or a sales call. The free tool is the starting point. Enterprise deployments and premium features are coming.
Which AI does it use?
The assessment is powered by OpenAI's GPT-4o mini — a fast, efficient model well suited for structured data analysis. Enterprise deployments can be configured to use your own internal AI gateway, including Azure OpenAI or compatible model endpoints.
How does it run so quickly?
The assessment runs column analysis in parallel — every column is evaluated simultaneously rather than sequentially. A 20-column dataset triggers roughly 100 API calls, processed in parallel, which is why results arrive in under two minutes regardless of dataset size.