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 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 Intelligence module: €2,999/year.
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'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.
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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