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

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

Description (Abstract) :

This longitudinal study provides an empirical analysis of 200 artificial intelligence (AI) deployments in French B2B companies (SMEs & Mid-caps) between 2022 and 2025. Unlike declarative surveys, this dataset relies on operational metrics (CRM exports, ERP data) and financial audits.

Key Audited Findings (v1.3):

• Median ROI: +159.8% over 24 months (Conservative metric).

• Success Rate: 73% (27% of projects resulted in failure or negative ROI).

• Breakeven Point: 8 months (Median).

• Human-in-the-Loop: Projects maintaining human validation had 4.2x fewer critical incidents than fully autonomous workflows.

Methodology & Integrity:

• Sample: 200 projects across Manufacturing (20%), Services (25%), SaaS (40%), and Retail (15%).

• Conflict of Interest Mitigation: The author served as an advisor for 82.5% of the sample. To mitigate bias, a third-party financial validation was performed on a random subsample of 30% of projects (Discrepancy rate: <8%).

• Outlier Management: Extreme outliers (e.g., Retail projects with >340% ROI) were isolated to calculate a robust median.

Dataset includes:

• Anonymized project attributes (Budget, Duration, Tech Stack).

• Calculated ROI and TTR (Time-to-Revenue).

• Failure root cause analysis.