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.

60 questions 5 dimensions 5 levels Results in minutes

The problem it 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.

What it assesses

What the assessment evaluates

Each dimension is assessed in depth — no critical area is left out.

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).

The scale

The 5 maturity levels

Each dimension and the organization as a whole are placed at a clear level — color, number, and name.

1
Exploratory

AI absent or in isolated experiments, with no strategy, ready data, or governance; ad hoc initiatives by enthusiasts.

2
Experimental

Deliberate pilots emerge with some coordination, but most don't reach production; first investments in data.

3
Structured

AI as an organizational capability: formal strategy, designated leadership, managed portfolio, and cases in production.

4
Scalable

AI integrated into critical processes, generating consistent value, with monitored models and formalized governance.

5
Transformative

AI as a central competitive advantage, with responsible, scalable use and systematized continuous learning.

Calibrated, not generic

Provenance and calibration

The analysis carries the reasoning of the reference frameworks — that’s what separates a calibrated assessment from generic advice.

MIT CISRGartnerISO/IEC 42001NIST AI RMF

Want to go deeper? Understand the methodology, dimensions, and levels in detail in the technical guide.

Understand the model in depth

Secure your founder access

Join by August 1, 2026 and get 6 months of Pro free — plus 6 more for active founders. No card.

Get founder access