Ai Maturity Map: Discover where You Are and how to Scale Your Competitive Advantage

The Five Key Dimensions of AI Maturity

- **Strategy and Vision****

**Defines the priority that senior management gives to AI: from isolated pilots to explicit integration into the strategic plan and the allocation of multi-year budgets.

- **Data and Governance****

**Covers data quality, availability, lineage, security, and regulatory compliance. Without a solid and reliable foundation, any model will end up eroding its credibility.

- **Technology and Infrastructure****

**Includes data lakes, feature stores, development environments, computing capacity, deployment automation, and MLOps platforms. Their maturity determines the speed at which you can iterate and scale models.

- **Processes and Automation****

**Establishes repeatable flows to develop, validate, deploy, and monitor models with clear business metrics. Having robust processes allows for sustained efficiency and traceability.

- **Talent and Culture****

**Refers to the level of technical and business skills around AI, as well as the organizational willingness to experiment, learn, and adopt data-driven decisions.

Maturity Levels

- ** Explorer**. Initial interest in AI, without a budget or well-defined use cases. **Next step:** choose a concrete and feasible business problem for a low-risk pilot.

- ** Experimenter**. Isolated pilots are executed without consistent metrics or clear data governance. **Next step:** set success criteria, assign responsibilities, and create a three-month *roadmap*.

- ** Implementer**. Some models are in production and generate value, with basic MLOps practices. **Next step:** allocate an annual budget and create a committee to prioritize use cases.

- ** Scaler**. There is a common platform and several areas use AI; *retraining* and monitoring processes are already active. **Next step:** automate *pipelines* and appoint adoption *champions* in each unit.

- ** Transformer**. AI is integrated into the business model and generates new data-based revenue streams. **Next step:** deepen strategic alliances and shield the competitive advantage.

- How many models generate measurable and recurring value?

- Are there automated and audited data pipelines?

- Is the AI budget listed as a strategic investment?

- Is the ROI reviewed as frequently as other critical KPIs?

- How many models generate measurable and recurring value?

- Are there automated and audited data pipelines?

- Is the AI budget listed as a strategic investment?

- Is the ROI reviewed as frequently as other critical KPIs?

- **Form an AI committee** with representatives from business, technology, and compliance.

- **Centralize data** in a governed environment.

- **Implement basic MLOps**: code and data versioning, automated testing, and *drift* monitoring.

- **Develop internal talent** through training programs and communities of practice.