Enterprise Data Readiness Diagnostic

Could two teams feed the same model and disagree on what a core metric means?

No, every metric the model uses has one certified definition

Are the metrics your AI consumes defined once and owned by someone?

Does your data have a single resolved record per customer, patient, or entity the model reasons about?

Could your model be training or predicting on duplicate or unmerged entities?

Can you trace where each feature value came from at the moment the model used it?

If a regulator or auditor asked how a model decision was made, could you show the data trail?

Are the features your models use versioned and monitored for drift?

Would you get alerted if a feature silently changed or degraded in production?

Is your AI running on a data layer built for it, or bolted onto your BI warehouse?

Can your data layer serve features fast and reproducibly to production AI?

You are in the 1 in 4. The foundation is largely in place; the work now is keeping it governed as you scale.

Use a short audit to confirm the remaining gaps and sequence the first or next production use case.

You have real exposure in at least one failure mode. Fix it now and most pilots ship; ignore it and they stall.

Target the two lowest-scoring failure modes below with focused remediation before adding more AI scope.

Multiple failure modes are exposed. This is exactly the profile that burns the 4.2 million dollar pilot. The model is not your problem yet, the data underneath is.

Start with a 30-day Analytics Truth Audit to scope the data-layer remediation before any further AI spend.

A 10-question diagnostic that scores your exposure to the five data-layer failures that most often stall enterprise AI before production. It takes about 5 minutes and returns your most likely failure plus the full report.

It is drawn from 47 enterprise engagements: the five recurring data-layer failures behind stalled AI, what the 1 in 4 organizations that succeed do differently, and how to sequence the fix. You get it as a downloadable PDF.

Almost never the model. Only about 7% of enterprises say their data is fully AI-ready, and Gartner expects 60% of AI projects to be abandoned for lack of AI-ready data. The failure is in the foundation underneath.

About 5 minutes for the diagnostic. The report is yours immediately after, on screen and by email.

No. Your answers are used only to generate your result and are stored in our own systems so we can follow up if you ask. We never sell or share them.

Your most likely data-layer failure, the full 22-page report as a downloadable PDF, and a copy emailed to you.

The full 2026 Enterprise Data Readiness Report has the complete failure-mode taxonomy and fixes. A 30-day Analytics Truth Audit turns this diagnostic into a costed remediation plan for your environment.

A 10-question diagnostic that scores your exposure to the five data-layer failures that stall enterprise AI, and returns a personalized PDF plus the full 22-page report.

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Across 47 enterprise engagements, about 75 percent of AI projects never reached production. The same five data-layer failures showed up again and again. Answer 10 questions to see which ones are most likely stalling yours.

10 questions. Work email at the end to unlock your personalized result and the full 22-page report.

Your result, a personalized PDF, and the full 22-page report unlock on the next screen. We follow up from [email protected] only if your situation suggests we should talk.