Why Take the Data & AI Maturity
Assessment
You likely already have data platforms, analytics teams, and AI initiatives in motion. The real question is: are they truly working together to deliver measurable business impact?
- Pressure-test enterprise readiness for data-driven and AI-enabled decision-making beyond pilots and proofs of concept.
- Surface hidden maturity gaps across strategy, leadership, culture, operating models, and execution.
- Benchmark your organization’s current maturity against how leading enterprises are operationalizing data and AI.
- Reveal where trust, adoption, or ROI is breaking down and why initiatives stall before scale.
Guidelines for the Data & AI Maturity Assessment
Each question reflects a specific dimension of how data and AI are defined, governed, adopted, and operationalized across the enterprise.
For every question:
- Evaluate your current, real-world state - not your future plans and aspirations.
- Select the maturity level that best represents how consistently and effectively the capability is applied across the organization.
- Use the maturity definitions below as a reference to guide your selection.
Data & AI Maturity Levels
1 – Ad Hoc
2 – Emerging
3 – Defined
4 – Managed
5 – Optimized
Data & AI Maturity Levels
0 – Ad Hoc
- Data and AI efforts are fragmented, reactive, or largely informal.
- There is no shared enterprise vision, limited ownership, and minimal consistency in how data or AI is used to support decisions or outcomes.
1 – Emerging
- Initial data and AI initiatives exist, often driven by individual teams or leaders.
- Capabilities are uneven, success depends on local effort, and outcomes are difficult to repeat or scale.
2 – Defined
- Data and AI practices are documented and intentionally designed in parts of the organization.
- Governance, platforms, and processes exist, but adoption and impact vary across functions.
3 – Integrated
- Data and AI capabilities are aligned across strategy, people, process, and technology.
- Teams collaborate using shared standards, trusted data, and common platforms to support business decisions.
4 – Managed
- Data and AI initiatives are measured, governed, and actively managed against business outcomes.
- Quality, trust, and performance are monitored, and insights are reliably embedded into operations.
5 – Leading
- Data and AI are core to how the enterprise operates, innovates, and competes.
- Capabilities are continuously improved, ethically governed, and consistently deliver measurable business value at scale.
