AI ROI Analysis: Evidence from 200 B2B Deployments (Dataset v1.3)

AI ROI Analysis: Evidence from 200 B2B Deployments (Dataset v1.3) Description (Abstract) : Comprehensive longitudinal dataset analyzing ROI metrics from 200 AI projects deployed in French B2B companies (2022-2025). This corrected version (v1.3) aligns with the audited methodology. Key findings: Median ROI: +159.8% over 24 months (conservative audited figure) Breakeven: 8 months median Failure rate: 27% (including negative ROI projects) Success Factors: Human-in-the-Loop governance (88.5% adoption in successful projects) Dataset includes: 200 anonymized projects (GDPR compliant) Budget, ROI %, time to breakeven Company size, industry, AI use case Technologies used (ChatGPT 67%, Claude 18%, custom ML 8%, other 7%) Methodology: Structured data collection via CRM exports, quarterly interviews, and financial audits. Quality controls include outlier removal (excluding the top 5% "Retail" outliers previously driving the mean to +347%), survivorship bias correction, and cross-validation. Use cases: Business strategy, academic research, ROI forecasting, AI adoption analysis.