CEO
Which AI workflow creates visible operating leverage and can become infrastructure?
AI consulting process
Start narrow, prove value, then scale the AI system.
Ferre Torres B.V. uses a practical AI consulting process for companies: choose one workflow, validate the business and technical assumptions, build a useful MVP or PoC, then scale only what proves value.
- Workflow first
- MVP proof
- Production architecture
- Governed scale
Process map
A clean path from first conversation to scale decision.
The process is designed for CEOs, CFOs, CTOs, and operating leaders who need a serious implementation path without committing to a vague company-wide AI transformation before evidence exists.
- Brief
Capture the workflow, buyer role, data sources, target users, timeline, and result that should improve.
- Assess
Rank feasibility, value, risk, data readiness, and stakeholder ownership before choosing the first build.
- Roadmap
Define the RAG, agent, dashboard, Company Brain, governance, and production sequence.
- MVP
Ship one narrow working system with realistic data, users, constraints, and measurement.
- Scale
Harden the validated workflow into reusable AI infrastructure or internal AI-native software.
First call inputs
The first discussion should be concrete enough to build from.
- Business area and owner: CEO, CFO, CTO, operations, finance, product, legal, compliance, or delivery.
- Workflow: the repeated manual work, reporting loop, knowledge bottleneck, approval path, or decision process.
- Data and tools: documents, databases, ERP, CRM, SharePoint, Google Drive, PowerBI, Tableau, tickets, email, or custom systems.
- Proof target: time saved, reporting latency, accuracy, adoption, margin insight, response time, quality lift, or production readiness.
Decision-maker questions
The process answers the questions each buyer needs before funding scale.
CFO
Which result justifies investment, and how will value be measured?
CTO
Can the MVP be built with secure data access, evaluations, monitoring, and ownership?
Users
Does the workflow actually become faster, clearer, or more reliable than the current process?
Useful outputs
Each phase should leave a decision artifact, not only discussion notes.
Opportunity map
Prioritized AI workflows with value, feasibility, data readiness, risk, and ownership.
Buildable roadmap
Sequence for Company Brain, RAG, agents, dashboards, governance, and production rollout.
MVP scope
Users, data sources, interface, AI components, success metric, timeline, and scale gate.
Architecture view
Permissions, retrieval, evaluations, monitoring, audit trails, hosting, and integration constraints.
Proof measurement
Adoption, quality, workflow speed, failure modes, manual work reduction, and production readiness.
Scale decision
Clear recommendation to harden, expand, pause, or redirect the AI implementation.
Choose the starting route
Start at the level of clarity the company already has.
Need prioritization
Use an AI opportunity assessment to rank workflows before building.
Assessment routeNeed architecture
Use an AI integration roadmap when the workflow is clear but the system path is not.
Roadmap routeNeed proof
Use an MVP or PoC when the first workflow, owner, and data access are ready.
MVP routeNeed confidence
Use a guided demo walkthrough to align business and technical stakeholders around the shape of the system.
Demo routeNeed qualification
Use the AI readiness checklist when the company needs to know whether the first build is concrete enough.
Readiness routeAI consulting process questions
Questions companies ask before starting an AI consulting engagement.
How does an AI consulting engagement start?
A practical engagement starts with one business workflow, the owner of that workflow, available data sources, current bottlenecks, and a measurable result that would justify an MVP or proof of concept.
What happens before building an AI MVP?
Before building an AI MVP, the team defines users, data access, workflow scope, success metrics, risk boundaries, security constraints, and the production decision criteria.
How fast can a company validate an AI workflow?
The timeline depends on data access and scope, but the process is designed to reach a useful MVP or PoC signal quickly instead of starting with a broad transformation program.
Next step
Use one email to decide the right starting route.
Share the workflow, buyer role, data sources, constraints, target users, and what the first MVP should prove.