CEO
Does this create visible operating leverage worth scaling across the company?
AI MVP and proof of concept development
Prove the AI workflow before scaling the transformation.
The first AI project should not be a vague transformation program. Ferre Torres B.V. helps companies select one high-value workflow, build an MVP or PoC around real constraints, and decide what deserves production investment.
- Business result
- Real users
- Data readiness
- Production path
MVP fit
A strong AI MVP tests value, not only technical possibility.
- Choose a workflow with a clear owner, repeated friction, and visible business impact.
- Use realistic data, permissions, user groups, and existing tools from the beginning.
- Measure whether the system improves speed, quality, cost, decision confidence, or reporting load.
- Design the MVP so successful components can become production architecture instead of throwaway code.
What each buyer needs to prove
The same MVP must answer different executive questions.
CFO
Does this reduce manual reporting, explain business movement, or improve decision timing?
CTO
Can this be operated with proper data access, evaluations, monitoring, and ownership?
Users
Does the workflow feel faster and more useful than the current manual process?
MVP examples
Typical first AI MVP surfaces.
Company Brain MVP
Connect documents, metrics, and decisions to a private knowledge core with source-grounded answers.
RAG assistant MVP
Validate retrieval quality, citations, permissions, confidence, and escalation behavior.
Finance intelligence MVP
Explain margin, cash, pricing, forecast movement, and anomalies with dashboard-plus-assistant workflows.
Agent workflow MVP
Test controlled tool use, task routing, approvals, exception handling, and audit trails.
MVP sequence
From first workflow to production decision.
The goal is to make the scale/no-scale decision concrete: either the MVP proves enough value to harden, or it reveals what must change before a larger AI transformation.
- Scope
Select the workflow, users, data sources, risk boundaries, and success metrics.
- Build
Ship a narrow system with realistic data, UI, AI components, and integration assumptions.
- Validate
Measure usefulness, quality, adoption, time saved, failure modes, and production constraints.
- Scale
Harden the system into governed infrastructure, or redirect based on the evidence.
AI MVP questions
Questions companies ask before starting an AI MVP or PoC.
Why start an AI project with an MVP or PoC?
An AI MVP or PoC lets the company test usefulness, accuracy, workflow fit, data readiness, and adoption before committing to a larger production build.
What can be validated in an AI MVP?
RAG quality, agent boundaries, finance dashboard explanations, Company Brain value, user adoption, permissions, and production constraints can all be validated in a focused MVP.
Who should sponsor the first AI MVP?
The first AI MVP should have a business sponsor who owns the operating result and a technical owner who can provide data access, integration context, and deployment constraints.
What happens after the AI PoC works?
If the PoC proves value, the system can be hardened into production infrastructure with permissions, monitoring, evaluations, workflow ownership, and integration with existing tools.
MVP next step
Bring one workflow that could prove AI value in weeks, not quarters.
Share the workflow, buyer role, data sources, current tools, expected users, and what the MVP must prove.