85% of AI projects fail because of data quality, not model quality. AI Readiness score — measured before the first sprint, not 18 months and €480K later.
Download the white paperEach project stalled — not because of the model, but because of the data. Below is what APOLLO Data Auditor would have surfaced before the budget was committed.
Credit scoring AI abandoned after 14 months. 3 databases, 680,000 records. No data governance documentation for training data.
18% of records: deceased patients, no deletion process. 12% duplicates from migrations. No training data documentation. AI Act Art. 10: 0/100.
CRM, 1.8M records. Purchase history skewed by 2020–2022 lockdown data. Age proxy and postal code in training features — no bias review.
8 pages. No filler. Scored cases, methodology, and a pricing comparison.
RAND found AI projects fail at twice the rate of traditional IT. MIT confirmed 95% of GenAI pilots produce no ROI. This paper explains what the AI market doesn't want to admit.
EU financial €480K overrun, US healthcare $2.1M compliance exposure, French retail €340K bias incident. AI Readiness scored before the project started.
Three factors, one grade. The score tells you whether your data is ready for AI deployment — before you spend a euro on models or consultants.
August 2, 2026: enforcement begins for high-risk AI. Art. 10 requires formal data governance for training datasets. Art. 15 requires cybersecurity documentation. No questionnaire produces these metrics.
20% of breaches now involve shadow AI (IBM 2025). Average shadow AI breach cost: $4.63M vs. $3.96M standard. 63% of organizations have no AI governance policy.
AI readiness frameworks (free–$50K consulting), AI Act tools (€100K), enterprise DSPM with AI module ($250K+). APOLLO Data Auditor: all four modules and every connector included in every plan — see pricing.
The European financial firm hired data scientists, selected a model, and launched a credit scoring pilot. Eighteen months later, the project was abandoned — three times over budget, no usable output.
APOLLO Data Auditor's AI Readiness score would have been 27/100 on day one: 34% duplicates, 22% missing values in key fields, Art. 10 documentation at 15/100. The estimated cost overrun was €480,000. Time to fix the data before starting: 8 weeks.
“The model was not the problem. The data was. And in most cases, no one had checked before the budget was committed.”
— TechShift Enterprise AI Readiness Report 2026Four modules. Four papers. One scan that covers them all.
PII mapping, financial exposure in € and $, toxic combinations, risk zones.
Read the paperArt. 5, 9, 30, 32 — scored per article. CCPA, NIS2, SOC2, DORA, AI Act.
Read the paper93% of ransomware attacks target backups first. Backup resilience, encryption, access control.
Read the paperSee your actual exposure — not a sample score. 5 sources, 60 scans, no commitment.
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