CEO
Which AI capabilities change how the company operates over the next two quarters?
AI integration roadmap
Plan how AI becomes part of the company operating system.
AI integration is not a list of isolated pilots. Ferre Torres B.V. helps companies define the sequence of workflows, data connections, AI systems, dashboards, agents, and governance needed to move from first MVP to production-scale AI capability.
- Workflow sequence
- Data readiness
- Architecture path
- Governed scale
Why it matters
AI starts creating value when it connects to real operating flows.
- Define which workflows should be redesigned, automated, assisted, or monitored first.
- Clarify which data sources, documents, metrics, permissions, and tools must connect.
- Sequence MVPs so successful components become reusable AI infrastructure, not throwaway pilots.
- Set decision points for funding, adoption, evaluation, governance, and production hardening.
Buyer alignment
The roadmap makes AI integration concrete for every stakeholder.
CFO
Which use cases have enough measurable value to fund, and how will ROI be tracked?
CTO
Which architecture, data, security, evaluation, and ownership choices prevent future rebuilds?
Teams
Which daily workflows become faster, more searchable, more automated, or easier to govern?
Roadmap components
What the integration plan normally covers.
AI workflow portfolio
Priority workflows, expected users, current friction, business owner, value metric, and MVP candidate.
Company Brain layer
Documents, systems, metrics, decisions, permissions, knowledge retrieval, and reusable context infrastructure.
RAG and agent plan
Retrieval quality, tool boundaries, approvals, escalation paths, evaluations, audit trails, and monitoring.
Scale gates
Evidence required before moving from discovery to MVP, from MVP to production, and from one workflow to many.
Roadmap sequence
A practical path from first integration to AI-first operations.
The roadmap should create momentum without pretending the whole company can transform at once. Start where value and feasibility overlap, then compound the architecture as each workflow proves useful.
- Map
List workflows, systems, data sources, owners, risks, users, and current reporting loops.
- Rank
Prioritize by operating value, feasibility, data readiness, risk, and implementation speed.
- Build
Launch one MVP or PoC around the strongest workflow and measure practical adoption.
- Scale
Turn proven components into production architecture, governance, and reusable AI capability.
AI roadmap questions
Questions companies ask before planning AI integration.
What is an AI integration roadmap?
An AI integration roadmap defines how AI should connect to company workflows, data sources, tools, dashboards, permissions, governance, and production delivery through a practical sequence of MVPs and scale steps.
How is an AI roadmap different from AI strategy?
AI strategy explains direction. An AI integration roadmap translates that direction into buildable workflows, data requirements, architecture decisions, ownership, metrics, and delivery sequence.
What systems are usually included?
Typical systems include RAG assistants, Company Brain knowledge layers, agentic workflows, finance intelligence dashboards, workflow automation, internal AI tools, evaluation pipelines, and monitoring.
Who should own the AI integration roadmap?
The roadmap should be jointly owned by a business sponsor and a technical owner, with input from users, finance, data, security, compliance, and operations depending on the workflow.
Roadmap next step
Use the roadmap to decide what AI should connect to first.
Share the business area, current tools, data sources, expected users, executive sponsor, and what needs to improve if AI integration works.