AI implementation
Discovery → demo → pilot → scale. No mystery, no surprises.
If you don't clearly see what you're delivering at the end of each week, it's not a project — it's an experiment with a bill.
In short
A typical project has 4 phases: discovery (1 week) for process and data mapping; demo (1 week) on real input; in-production pilot (2–3 weeks) with human-in-the-loop (human involved in the validation loop of automated decisions) and measurement; scaling based on evidence (2–4 week sprints). Each phase has a clear deliverable and a go/no-go criterion.
- Discovery with output: process map + technical plan
- Demo on real input — go/no-go for pilot
- In-production pilot with human-in-the-loop
- Scale only on proven ROI, not enthusiasm
Phase 1 — Discovery (1 week)
Workshop with the operational team, current process mapping (as-is), identification of data sources, evaluation of necessary integrations, identification of risks (GDPR — General Data Protection Regulation, the European regulation on the protection of personal data; EU AI Act — European Regulation 2024/1689 on artificial intelligence; data quality). Deliverable: documentation + technical plan + pilot scope estimate.
Phase 2 — Demo (1 week)
We build a functional demo on real input (not fake data). We run it with the team that will use the solution. Output: validation/invalidation of the approach before investing in full integrations.
Phase 3 — Pilot (2–3 weeks)
Full integration with your systems, audit log, human-in-the-loop, team training, go-live in shadow mode (AI proposes, the team decides). We measure: hours saved, accuracy, escalations, satisfaction.
Phase 4 — Scale (2–4 week sprints)
Based on pilot data, we decide on scaling: increased autonomy, other adjacent processes, other teams, other verticals. No new sprint without proven ROI on the previous one.