The verdict in three sentences
A sales director who wants to sharpen forecasts pays in 2026 a predictive model at 18,000-50,000 EUR, depending on granularity (product, region, channel) and integration into reporting. The number-one success factor is not the algorithm but the quality and history of available data. The return is measured in optimized stock, avoided stockouts and freed-up cash, not in headline accuracy points.
What the budget covers
A predictive AI project breaks down into data preparation, modelling, integration and monitoring. Data preparation is often the heaviest line item.
| Phase | 2026 order of magnitude | Share of project |
|---|---|---|
| Data audit & preparation | 4,000 - 12,000 EUR | 25 - 35 % |
| Modelling & training | 6,000 - 18,000 EUR | 30 - 40 % |
| Integration into reporting / ERP | 4,000 - 12,000 EUR | 20 - 25 % |
| Interface & rendering | 2,000 - 6,000 EUR | 10 - 15 % |
| Retraining / MLOps | 300 - 900 EUR/month | Recurring |
Without 18-24 months of clean history and enough granularity, better to fix the data first before investing in the model.
Accuracy gain and ROI
Value does not come from the model alone but from its effect on stock and cash.
| Indicator | Before AI (estimate) | After AI (estimate) |
|---|---|---|
| Forecast error (MAPE) | 25 - 40 % | 12 - 20 % |
| Tied-up safety stock | Baseline | -15 to -30 % |
| Stockouts on best-sellers | Frequent | -20 to -40 % |
| Depreciated overstock | Baseline | -10 to -25 % |
| Forecast cadence | Manual, monthly | Automatic, weekly |
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Mini case study
Karim, sales director of a consumer-goods SME in Lyon, ties up ~600,000 EUR of stock with an inflated safety buffer to avoid stockouts. He invests 34,000 EUR + 600 EUR/month in a predictive model fed by 24 months of history and his ERP. The model cuts his safety stock by 20 %, i.e. ~120,000 EUR freed; at a 12 %/year carrying cost, that is ~14,400 EUR/year, plus avoided stockouts on best-sellers. The project is paid back in under 24 months, and the freed cash funds other investments.
FAQ
Do we need lots of data to start? Ideally 18-24 months of clean history per product or category. Below that the model is still possible but reliability drops; a 4,000-12,000 EUR data-prep phase then becomes unavoidable.
What accuracy gain can we expect? As an order of magnitude, moving from 25-40 % to 12-20 % error is realistic on stable series. On very erratic or new products, gains are more modest.
Where is the real ROI? In reduced safety stock and avoided stockouts. Freeing 15-30 % of tied-up stock often has more cash impact than any marketing margin.
Does the model degrade? Yes, without retraining. Plan 300-900 EUR/month of MLOps to watch drift and retrain; a frozen model loses its value in a few months.
Can we start with a pilot? Yes: a pilot on a key category at 12,000-18,000 EUR validates the data and the gain before scaling. It is the least risky path.
Let's scope your project. Tell us your available history, your granularity (product/region/channel) and your tied-up stock; we price a pilot then the rollout and its cash ROI. Detailed quote within 48 h. WhatsApp +221 77 596 93 33.
Mohamed Bah
Fondateur, Kolonell
Passionate about digital and entrepreneurship in Africa, Mohamed has been helping Sénégalese businesses with their digital transformation since 2020. Founder of Kolonell, he believes every SME deserves a professional and accessible online présence.
