The verdict in three sentences
A scoring or forecasting model is only as good as the data quality feeding it: preparation is about 60 % of the effort. In Dubai in 2026, a data/ML project costs 20,000 to 60,000 EUR and ships in 8 to 16 weeks. ROI comes from anticipated decisions: less churn, fewer unpaid invoices, better stock and cash forecasts.
Use cases, cost and data required
Price depends on the use case, volume and above all the state of the data. A clean, labelled history sharply reduces effort. 2026 ranges (order of magnitude).
| Use case | Project cost (EUR) | Data quality required | Expected gain |
|---|---|---|---|
| Customer churn scoring | 20,000 - 35,000 | 18-24 months history | -10 to -20 % churn |
| Unpaid-invoice prediction | 25,000 - 45,000 | 2-3 years payment history | -15 to -30 % bad debt |
| Demand / stock forecasting | 30,000 - 55,000 | sales + seasonality | -20 to -35 % stockouts |
| Lead scoring (prioritisation) | 20,000 - 40,000 | structured CRM | +10 to +25 % close rate |
| Cash-flow forecasting | 30,000 - 60,000 | historised financial flows | reliable 90-day forecasts |
Without enough history, it is better to start with a collection and cleaning phase before any model. A model trained on noisy data produces misleading scores.
Cost structure and timeline
| Line item | Share of project | Note |
|---|---|---|
| Data preparation | ~60 % | cleaning, features, labels |
| Modelling + evaluation | ~20 % | several algorithms tested |
| Production (API, batch) | ~15 % | integration with business tools |
| Monitoring + retraining | ~5 % + recurring | model drift monitored |
| Delivery time | 8 to 16 weeks | by state of the data |
A model degrades over time (drift): a periodic retraining and performance monitoring are essential to sustain value beyond the first months.
Mini case study
Karim is sales director at a B2B software vendor in Dubai: 4,500 customers, 14 % annual churn, average basket 2,400 EUR/year. He funds a churn scoring model at 32,000 EUR with 500 EUR/month monitoring. Targeting the 15 % highest-risk accounts with retention actions, he cuts churn from 14 % to 11.5 %, ~112 customers retained/year. At 2,400 EUR annual revenue each, that is ~269,000 EUR of preserved revenue. The project pays back in the first year, with wide margin.
FAQ
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How much does an AI scoring or forecasting project cost?
Between 20,000 and 60,000 EUR in 2026 depending on the use case and data state. Data preparation is about 60 % of the budget.
Why is data preparation so expensive?
Because model quality depends directly on it: cleaning, feature creation, labelling and handling missing history take most of the time. A good dataset is worth more than a sophisticated algorithm.
How long until a model is in production?
8 to 16 weeks depending on the data state. If history is incomplete, an upfront collection phase extends the timeline but guarantees reliability.
Does the model stay reliable over time?
Not without upkeep: models drift as behaviours change. Periodic retraining and monitoring (500-1,500 EUR/month) sustain performance.
What ROI can we concretely expect?
By case: -10 to -30 % churn or bad debt, -20 to -35 % stockouts. On high average baskets, preserved revenue quickly exceeds project cost.
Let's scope your project. Describe your use case (churn, bad debt, demand), available history and indicative budget (20,000-60,000 EUR): we assess feasibility and timeline. 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.
