The verdict in three sentences
An AI-augmented analytics dashboard — one that doesn't just display numbers but explains variations and suggests actions — costs 25,000 to 55,000 EUR depending on the number of data sources and the depth of the AI layer. AI usage sits between 100 and 400 EUR/month and the lead time between 8 and 14 weeks. The value: faster decisions, proactive anomaly detection, and alerts that go straight to the right owner.
What makes up the budget
The core of the price is the data connection (ERP, CRM, accounting, business tools) and the AI layer that interprets the gaps. A "pretty but disconnected" dashboard costs little and serves little; value lies in reliable data and explanation.
| Line item | 2026 range (EUR) | Note |
|---|---|---|
| Scoping + KPI definition | 3,000 - 6,000 | Indicators, thresholds, owners |
| Data connection + cleaning | 6,000 - 16,000 | ETL, quality, historization |
| Visualizations + dashboard UX | 5,000 - 12,000 | Per-role views, mobile |
| AI layer (explanations, actions) | 8,000 - 18,000 | Gap analysis, recommendations |
| Smart alerts + anomalies | 3,000 - 7,000 | Dynamic thresholds, notifications |
| AI usage + hosting / month | 100 - 400 | Depends on analysis frequency |
Classic BI vs AI-augmented BI
Classic BI shows *what happened*. Augmented BI adds *why* and *what to do*. The price difference is real, but so is the analysis time saved.
| Criterion | Classic BI | AI-augmented BI |
|---|---|---|
| Project cost (EUR) | 12,000 - 30,000 | 25,000 - 55,000 |
| Explains variations | No (manual) | Yes (automatic) |
| Suggests actions | No | Yes |
| Anomaly detection | Fixed thresholds | Dynamic thresholds |
| Executive analysis time | High | Reduced (~-40 %) |
| Monthly cost | 30 - 150 EUR | 100 - 400 EUR |
Who the augmented version is for
The AI layer is justified when the leader already spends time *understanding why* an indicator moves: eroding margin, falling average basket, lengthening payment terms. If KPIs are simple and stable, classic BI is enough. As soon as interpretation is time-consuming or anomalies are costly to catch late, AI augmentation becomes profitable.
Mini case study
Élodie is CEO of a distribution SME in Toulouse (28M EUR revenue). She and her leadership team each spend 6 h/month analyzing reports to understand variances. Average loaded cost of the committee (5 people): 60 EUR/h.
Monthly time: 5 × 6 h = 30 h, i.e. 1,800 EUR/month. The augmented dashboard (40,000 EUR project) cuts this by 40 %, i.e. 12 h = 720 EUR/month recovered, and above all detects earlier a margin drift valued at ~15,000 EUR avoided over the year. Over 12 months: ~8,640 EUR of time + 15,000 EUR of anomalies = ~23,640 EUR, a payback of ~20 months excluding decision benefits.
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FAQ
Can the dashboard connect to several software systems?
Yes: ERP, CRM, accounting, e-commerce and files can feed the same dashboard. Cost depends mostly on the number of sources and their quality.
Does AI replace the analyst?
No, it saves them time: it pre-explains gaps and flags anomalies, the human decides. You always keep the ability to dig in manually.
How are smart alerts set?
Rather than fixed thresholds, the AI learns each indicator's normal and alerts on significant deviations. We calibrate with you to avoid noise.
Do we need large data volumes?
Not necessarily: the explanation layer works with a few months of clean history. Anomaly detection gets more precise over time.
How long before the first useful version?
A first version connected to key KPIs is deliverable in 6 to 8 weeks; the full AI layer follows over 8 to 14 weeks total.
Let's scope your project. List your data sources, your 8 to 12 priority KPIs and your alert cases, and we'll price the dashboard and AI layer. 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.
