As a CFO who started his career coding, I have a simple rule in my boardroom: It is forbidden to say “F1-Score” without saying “EBITDA” in the same sentence.
I see many Data Science teams celebrating victories that, in practice, are financial defeats. They spend $100k on cloud computing to increase a model’s accuracy by 0.5%, but this gain brings no new customers, nor does it reduce any operational costs.
This is what I call “Computational Vanity”. And vanity doesn’t pay the bills.
The Reality Check
For the Company Board, AI is not magic; it is CAPEX (Investment) and OPEX (Operational Cost). If the money going out is greater than the money coming in (or ceasing to go out), the project is a failure, no matter how “state-of-the-art” the algorithm is.
- Data Scientist: “Our churn model has an AUC of 0.95!”
- CFO: “Great. How many more customers did we retain? What is the LTV (Lifetime Value) saved? What is the retention cost?”
If you can’t answer the second line, you don’t have an AI project; you have an expensive hobby.
The Translation Framework: Tech -> Business
To survive the next round of budget cuts, you need to learn to translate. Here is the dictionary:
| Technical Metric (What you measure) | Business Metric (What I want to hear) |
|---|---|
| Accuracy / Precision | Operational Error Reduction (Fewer fines, less rework) |
| Latency (ms) | Sales Conversion (Customer doesn’t wait, customer buys) |
| Throughput (tokens/s) | FTE Efficiency (How many man-hours saved?) |
| Uptime / Availability | Revenue Continuity (The system doesn’t stop selling) |
The Centrato Value Formula
At Centrato, we use a simple formula to approve or kill AI projects. If the result is not positive and clear, the project does not pass the pilot phase.
$$ \text{ROI} = (\text{Problem Frequency} \times \text{Financial Impact}) - \text{Total Solution Cost} $$
- Frequency: How many times does this happen per month? (Ex: 10,000 support tickets).
- Impact: How much does each occurrence cost? (Ex: $5.00 per human ticket).
- Solution Cost: Include everything. Cloud, licenses, data team salary, and maintenance.
Practical Example:
- Problem: Classifying support emails.
- Current Scenario: 10,000 emails/month x $1.00 (human time) = $10,000/month.
- AI Solution: Automates 80% (8,000 emails). Potential savings = $8,000/month.
- AI Cost: $1,000/month (API + Server).
- Result: Net profit of $7,000/month.
This is music to my ears. “99% accuracy” is just noise if it doesn’t come with this calculation.
Conclusion
Don’t fall in love with the technology. Fall in love with the problem and the result. The best AI model is not the most complex; it is the one that makes the bottom line of the P&L bluer.
