Is Your Data Foundation Ready for Production AI?
A scored profile across the five conditions enterprise data has to meet before any AI workload can run safely on top of it.
- A scored profile across 6 dimensions — see exactly where you're strong and where the gaps are.
- Your biggest opportunities, mapped to specific next moves.
- A personalized video walkthrough from Shawn (optional) — a real read on your results.
Production AI runs on data. Not on the data your company has in theory, but on the data an AI workload can actually reach, trust, and respect. The gap between those two things is where most enterprise AI initiatives stall — and it's almost always invisible until the first deployment hits production.
This free assessment scores your data foundation across six dimensions in about six minutes. It looks at the conditions enterprise data has to meet before any AI workload can run safely on top of it: coverage, freshness, quality and labeling, lineage, privacy, and semantic clarity. It's built from 27 years of technology leadership across Fortune 500 and growth-stage companies — the same lens a fractional CTO or fractional CAIO would bring to your data architecture in the first conversation.
What the data foundation assessment measures
Data readiness for AI isn't a single number — it's a profile across six dimensions, each of which can become the constraint that determines what AI is possible. The assessment scores: Completeness & Coverage (whether the data your AI needs is actually present across on-prem, multi-cloud, and SaaS), Freshness & Pipeline Health (whether it's current enough for production decisions), Quality & Labeling (whether it's clean enough, and whether outcomes are labeled), Lineage & Trust (whether you can trace where each datum came from), Privacy & Permissions (whether access policy is clear enough for AI workloads to respect), and Semantic Clarity (whether field names, units, and meanings hold across systems).
Why data foundation matters before AI
The enterprise AI failure mode is not that the model didn't work — it's that the data underneath couldn't carry it. A model trained on incomplete, stale, or ambiguously-defined data produces output that's technically correct and operationally useless. Worse, foundation gaps amplify under production load: schema changes go undetected, lineage breaks, access policy gets bypassed, and the AI workload becomes a liability rather than an asset. Organizations that treat data foundation as the first deliverable — not an afterthought — capture disproportionate value from every AI investment that follows.
What you get at the end
You'll see an overall data foundation score, a band that describes where you stand (from Pre-Foundation through Production-Ready), a per-dimension breakdown showing exactly which conditions are met and which aren't, and a prioritized map of the foundation gaps most worth closing first. From there you can request a personalized video walkthrough — a short, recorded read on your specific results and what a fractional CTO/CAIO engagement would do for your situation. No generic sales deck.
Frequently asked questions
What is an enterprise data foundation for AI?
It's the set of conditions enterprise data has to meet before AI can run safely on top of it: completeness across source systems, freshness through reliable pipelines, quality and labeling at ingestion, end-to-end lineage, enforceable privacy and permissions, and consistent semantic definitions across systems. Without these, AI workloads inherit every gap and produce output that can't be trusted.
How long does the assessment take?
About six minutes. It's 18 scored questions across six dimensions, plus a few financial and prioritization questions that personalize the results. Your progress auto-saves, so you can leave and resume without losing answers.
Is the assessment free?
Yes. The assessment and your scored results are completely free. You can optionally request a personalized video walkthrough of your results, which is also free.
Who is this assessment for?
It's built for CTOs, CIOs, heads of data, and executives at mid-market and enterprise companies who are weighing an AI investment and want a clear-eyed read on whether their data foundation can carry one — and what to fix first if it can't.
What happens after I get my score?
You'll see a full foundation profile with per-dimension scores and the gaps most worth closing first. If you'd like, you can share a few details and receive a personalized video walkthrough explaining your results and what a fractional CTO/CAIO would prioritize for your specific data environment.