Assess Your AI Project Success Rate Before Investing

The APOLLO™ Success Methodology

Developed by consultants certified by MIT Sloan Executive Program "Artificial Intelligence: Implications for Business Strategy":

Weighted 3P Model: People (40%) + Process (35%) + Product (25%)

Industry Benchmarks: Calibrated on European SME/Mid-Market data 2024-2025

Clear Decision Thresholds: GO (≥65), Adjustments (55-64), NO GO (<55)

Recommended Use Cases: Automatic matching with your profile and industry

Sector Use Cases

🏭 Industry & Manufacturing

Typical pilot budget: €50-150K

Recommended use cases:

• Predictive maintenance (success rate 65%)

• Vision-based quality control (success rate 68%)

• Production planning optimization

Identified risks: ERP/MES integration, IoT sensor data quality, shop floor resistance

🏦 Banking & Insurance

Typical pilot budget: €80-200K

Recommended use cases:

• Automated fraud detection

• Alternative credit scoring

• Compliance automation

Identified risks: Sector with enhanced AI Act obligations — compliance assessment recommended

🛒 Retail & Distribution

Typical pilot budget: €50-120K

Recommended use cases:

• Real-time customer personalization

• Demand and inventory forecasting

• Customer service automation

Quick win: Supplier invoice automation (2 months, success rate 78%)

💊 Healthcare & Pharma

Typical pilot budget: €100-250K

• Recommended use cases:

• Image-assisted diagnosis

• Patient journey optimization

• Clinical data analysis

Note: Sector with enhanced AI Act obligations — compliance assessment recommended

The Predictor in Action

Intuitive Interface

1. Enter your context: Industry, company size, project type

2. Assess People: Executive sponsorship, team skills, change resistance

3. Analyze Process: Governance, infrastructure, data quality, change management

4. Validate Product: Use case clarity, technical feasibility, estimated ROI

Instant results

• Global Score 0-100: With positioning vs industry benchmark

• 3P Analysis: Visual radar of your strengths and weaknesses

• Clear Verdict: GO / Adjustments / NO GO with explained thresholds

• Action Plan: Priority recommendations by dimension with timelines

• Recommended Use Cases: Match % with duration and expected success rate

Specialized FAQ

How does the scoring work?

The final score combines three dimensions according to their validated impact: People (40%) evaluates sponsorship, collaboration and skills. Process (35%) analyzes governance, infrastructure and change management. Product (25%) validates feasibility and potential ROI.

How does the scoring work??

A score of 50/100 without adjustments corresponds to a 60-70% failure probability. In other words, you're gambling your pilot budget (€50-150K) on a coin flip. The predictor helps you identify corrections before committing these amounts.

What do the decision thresholds mean?

≥65/100: Your project is ready, launch the pilot. 55-64/100: Targeted adjustments needed (2-4 weeks). <55/100: Risk too high, strategy rework required before investment.

How long to correct an insufficient score?

The typical action plan includes 4 phases of 2 weeks each: Leadership & sponsorship, Formalized governance, Technical PoC & data assessment, Feedback-based adjustments. That's 6-8 weeks to move from a moderate score to a solid GO.

"Initial score 48/100. After 6 weeks of adjustments on sponsorship and governance, we reached 71/100. The pilot is now in production"

Pierre M., CIO, Automotive Industry

★★★★★

"The use case matching steered us toward invoice automation rather than predictive maintenance. Result: first success in 2 months instead of 6. Confidence restored for subsequent projects."

Sophie L., Innovation Director, Mid-Market Manufacturing

★★★★★

A €100K AI project shouldn't be a coin flip.

Identify your strengths, risks and quick wins in 20 minutes.

Secured confidential data, Professional PDF report included, Immediate results