AI ROI Calculator 🤖📊

Open-source calculator to estimate AI project ROI based on 200+ real B2B deployments (2022-2025)

AI ROI Calculator

Open-source AI ROI calculator based on 200 real B2B deployments (2022-2025)

📖 About

This calculator helps businesses estimate realistic ROI for AI projects using real-world data from 200+ deployments across French B2B companies (2022-2025).

Author: Denis Atlan, AI consultant | Lyon, France
Methodology: Full documentation

🎯 Why This Exists

73% of AI projects fail due to unrealistic expectations (Gartner 2024 + our field data).

This tool provides:

• ✅ Evidence-based ROI estimates (not hype)
• ✅ Transparent methodology (open dataset)
• ✅ Industry benchmarks (SaaS, B2B Services, Industry, Commerce, Healthcare)

📊 Key Findings (200 Projects Analyzed)

Metric	Median	Range
ROI Year 1	+347%	+180% to +520%
Breakeven	8 months	5-12 months
Productivity Gain	+25%	+18% to +40%
Lead Generation	+127%	+50% to +320%
Cost Reduction	-35%	-22% to -60%

Source: Internal data 2024-2025, 200 French B2B companies (anonymized)

🚀 Quick Start

Installation

git clone https://github.com/denis-atlan/ai-roi-calculator.git
cd ai-roi-calculator
pip install -r requirements.txt

Usage

from ai_roi_calculator import calculate_roi

# Example: SaaS B2B company
result = calculate_roi(
    industry="saas_b2b",
    employees=23,
    monthly_revenue=100000,
    ai_use_case="lead_scoring",
    budget=15000
)

print(f"Estimated ROI Year 1: {result['roi_year1']}%")
print(f"Breakeven: {result['breakeven_months']} months")
print(f"Confidence: {result['confidence']}%")

Web Interface

streamlit run app.py

Open http://localhost:8501

📁 Project Structure

ai-roi-calculator/
├── data/
│   ├── roi_dataset_v2.csv          # 200 projects (anonymized)
│   ├── methodology.md              # Full calculation methodology
│   └── data_dictionary.md          # Variable definitions
├── src/
│   ├── calculator.py               # Core ROI logic
│   ├── benchmarks.py               # Industry benchmarks
│   └── validators.py               # Data quality checks
├── tests/
│   └── test_calculator.py
├── app.py                          # Streamlit web app
├── requirements.txt
├── LICENSE (MIT)
└── README.md

📈 Dataset Details

Source: 200 AI projects deployed in French B2B companies (2022-2025)

Industries:

• 40% SaaS B2B
• 25% B2B Services
• 20% Industry/Manufacturing
• 10% E-commerce
• 5% Healthcare

Company sizes:

• 45% SME (10-50 employees)
• 35% Mid-market (50-250 employees)
• 20% Enterprise (250+ employees)

AI Technologies:

• 67% ChatGPT/GPT-4
• 18% Claude
• 8% Custom ML models
• 7% Other (Gemini, Mistral, open-source)

Data Quality:

• ✅ SHA256 checksums for versioning
• ✅ GDPR compliant (anonymized)
• ✅ Bias analysis included (see methodology.md)
• ✅ Outliers documented

🔬 Methodology

ROI Formula

ROI = (Total Gains - Total Costs) / Total Costs × 100

Total Gains:

• Time saved (hours × hourly rate)
• Additional revenue (new leads × conversion rate × average deal)
• Cost avoided (errors prevented, automation)

Total Costs:

• AI tools licenses (ChatGPT Enterprise, Claude Pro, etc.)
• Training (internal team upskilling)
• Consulting (external expert hours)
• Team time (implementation, maintenance)

Full methodology: methodology.md

Confidence Intervals

We provide 80% confidence intervals based on:

• Industry-specific variance
• Company size adjustments
• Use case maturity (proven vs experimental)

🎓 Use Cases Covered

1. Lead Scoring (B2B) — ROI: +320% median
2. Customer Support Chatbot — ROI: +180% median
3. Sales Proposal Generation — ROI: +240% median
4. Predictive Maintenance (Industry) — ROI: +450% median
5. Content Marketing Automation — ROI: +200% median

Full case studies: conferencier.ai/cas-usage

📚 Citations

If you use this dataset in research or business analysis, please cite:

@software{atlan2025_ai_roi,
  author = {Atlan, Denis},
  title = {AI ROI Calculator: Evidence-Based Estimation Tool},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/denis-atlan/ai-roi-calculator},
  version = {2.0}
}

🤝 Contributing

Contributions welcome! Please read CONTRIBUTING.md.

Especially needed:

• International data (US, UK, Germany benchmarks)
• Additional use cases (HR, Finance, Operations)
• Bias detection improvements

📞 Contact

Denis Atlan
AI Consultant | Lyon, France

• 🌐 Website: conferencier.ai
• 💼 LinkedIn: linkedin.com/in/denisat
• 🎤 Speaking: Available for conferences, workshops, advisory

📄 License

MIT License - see LICENSE file

🙏 Acknowledgments

• 200 companies who shared their data (anonymized)
• INSA Lyon, École Centrale Lyon (academic partnerships)
• French Tech Lyon ecosystem

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Disclaimer: ROI estimates are based on historical data and may not reflect future results. Always conduct due diligence for your specific context.

Last updated: November 2025 | Data version: 2.0