1. Identify operational intensive processes
Start by mapping out repetitive, manual, error-prone, or time-consuming workflows. These are often:
- Customer relationship management (CRM)
- Accounting and invoicing
- Technical support (tickets, FAQs)
- Inventory or logistics management
Useful tool : Quick audit of internal processes via an ERP like Odoo or a BPM (Business Process Management) tool.
2. Analyze the current cost of inefficiency
Assess :
- Time spent per task
- The human error rate
- The cost of labor
- Opportunity losses
Concrete example : An e-commerce SME that spends 30 hours/week manually entering orders could save €20K/year by automating this process with an OCR + RPA model.
3. Determine value potential via AI
Use an AI use case evaluation grid:
- Technical feasibility (available data, quality)
- Business impact (cost reduction, increase in turnover)
- Time-to-value (fast implementation = less than 3 months)
Simple method : Impact x Feasibility Matrix to prioritize.
4. Create concrete and ROI-compatible use cases
Structure of an AI use case:
- Business problem
- Proposed AI Solution
- Expected benefits
- Estimated cost vs. projected gains
- Deployment duration
Example use case :
- Problem : Customer service overwhelmed by the same questions
- Solution : NLP Chatbot trained on FAQ base
- Benefits : 50% reduction in repeat requests
- ROI : Payback in 2 months, +15K€/year savings
5. Test in lean mode and measure
Start with a PoC (Proof of Concept) on a simple task. Measure:
- The reliability rate of the model
- Adoption by teams
- Real productivity gains
Tip: Set a clear ROI metric from the start.
Conclusion: The 5-step method
- Identify internal friction points
- Quantifying inefficiencies
- Assess AI potential through feasibility and impact
- Formalize a structured use case
- Test, measure, scale
Motto : Prioritize useful AI , not trendy AI. The goal remains measurable added value .