Why a phased approach?
Most failed AI projects fail for the same reason: too much scope at once. Organisations try to build 'AI for everyone' without proving a concrete first use case. The result: an expensive, complex stack with no users and no demonstrable ROI.
Amaii's approach flips it: one department, one use case, one working pilot in weeks. From that win you scale — with governance and operations growing alongside. AI becomes demonstrably valuable before it becomes expensive.
Phase 1 — Discovery (week 1-2)
Discovery answers three questions: where is the most time lost in search, summarisation or answer formulation? Which data is available and accessible? Which compliance context applies? Output: one concrete pilot use case, a data inventory and a governance baseline.
- Kick-off workshop with product owner and stakeholders.
- AI Readiness Scan: data, processes, compliance context.
- Use case selection: one bounded problem with measurable time savings.
- Data inventory: which sources feed the pilot?
- Governance baseline: roles, retention, logging requirements.
Phase 2 — Pilot (week 3-8)
The pilot delivers a working private AI stack for one department. Model runs in the chosen EU environment, RAG pipeline surfaces agreed sources, users get SSO access and there is a dashboard for usage and logging. Focus on one use case — prove first, broaden later.
- Deployment of model + RAG in EU private cloud or on-premise.
- Integration with agreed data sources (SharePoint, DMS, CRM).
- SSO/SAML integration and role-based authorisation.
- Training of 10-30 pilot users.
- Measurable interim evaluation at 4 weeks: adoption, time saved, quality.
- Documented risk assessment per EU AI Act.
Phase 3 — Rollout (month 3-6)
After a successful pilot, rollout is phased. New departments, new use cases, new integrations. The governance baseline from discovery grows accordingly: additional roles, expanded logging, extra compliance controls where needed. Every expansion starts with brief scoping and ends with a measurable result.
- Adoption plan per department with its own product owner.
- New use cases added on the same stack.
- Integrations with line-of-business apps (CRM, ERP, HR, DMS).
- Skills transfer to internal key users.
- Quarterly governance reporting to DPO/board.
Phase 4 — Managed service
Private AI is not a project but a service. In the managed phase Amaii handles model updates, security patches, capacity management and compliance monitoring. New use cases are prioritised on business value. Your internal team focuses on content and direction — not on infrastructure.
- Managed service with SLA on availability and response.
- Quarterly reviews with use case backlog and business impact.
- Continuous model optimisation and RAG tuning.
- Compliance reporting aligned with GDPR, AI Act and NIS2.
- Proactive security monitoring and patching.
Roles and involvement
A successful implementation hinges on three roles on your side: a product owner prioritising use cases, an IT/security contact for access and integrations, and a DPO/legal contact for governance. Amaii provides project management, engineering and compliance expertise.
Private AI implementation — frequently asked questions
Bronnen en achtergrondinformatie
- Now Decides Next — GenAI - Deloitte
- The Adoption of AI in Firms - OECD
