Technical guide · Artificial Intelligence
AI Maturity Index
How ready your organization is to adopt AI methodically — assessed across 5 dimensions in light of MIT CISR, Gartner, ISO/IEC 42001, and NIST AI RMF.
01 · The problemWhat this assessment solves
Budget, a technical team, and running pilots aren't enough: projects don't reach production — or they do and no one uses them. The C-level wants results, the team wants data, and the business doesn't know what to ask for.
The problem is almost never technology. According to MIT CISR, organizations in the early stages of AI maturity perform financially below the sector average; the leap is in turning experiment into capability.
02 · What it isWhat the model is
It assesses AI readiness across 5 dimensions and 60 questions — strategy, data, people, use cases, and governance —, with a score by dimension and an overall level.
The model is calibrated by MIT CISR research and by NIST AI RMF, Gartner, and ISO/IEC 42001 — it is not a generic opinion about AI.
03 · The scaleThe 5 maturity levels
Each dimension — and the organization as a whole — is placed at one of these levels, always with a color, number, and name.
AI absent or in isolated experiments, with no strategy, no ready data, and no governance. Initiatives are ad hoc, led by individual enthusiasts, with no dedicated budget or formal leadership.
Deliberate pilots emerge with some coordination, but most don't reach production. There are first investments in data and infrastructure; the transition from experiment to real results is still the main bottleneck.
AI treated as an organizational capability: formal strategy, designated leadership, a managed portfolio, and first use cases in production with measurable results. It is the frontier where financial performance begins to exceed the sector average.
AI integrated into critical business processes, generating measurable, consistent value. Models are monitored and updated, governance and ethics are formalized, and financial performance is above the sector average.
AI is a central competitive advantage and the organization is a benchmark in responsible, scalable AI use, with systematized continuous learning and influence on the ecosystem. Fewer than 5% of organizations reach here.
04 · The structureWhat the assessment evaluates
No critical area is left out. Each dimension brings together the themes evaluated by the assessment.
AI Strategy & Leadership
Vision, investment, and executive leadership — roadmap, ROI, and a formal owner for AI.
AI Data & Infrastructure
Data quality, platforms, architecture, and MLOps ready for AI at scale.
People, Culture & Skills
The right people, defined roles, and a culture for AI to thrive beyond the technical.
Use Cases & Operationalization
Turning opportunities into real value — from the idea portfolio to production.
AI Governance, Ethics & Risk
Structures, policies, and controls for responsible and compliant AI (LGPD, AI Act).
05 · HighlightsWhy apply this assessment
06 · AudienceWho it's for
07 · How to applyFrom questionnaire to plan
There are 60 questions organized into 5 dimensions and 15 themes, all mandatory — answer based on your current reality.
In minutes you receive an overall score, by dimension and by theme, the maturity level, and an analysis with prioritized gaps and an initial action plan.
08 · ReferencesBased on international standards
In practiceWhat the assessment reveals
A mid-sized financial company (800 people) invested R$2.5 million in AI over 18 months; of eight pilots, only one reached production — and with no systematic monitoring.
We didn't have an AI problem: it was a data and process problem.