Where does your organization stand on the path to AI-native engineering?

The eight dimensions

Strategy · Organizational Strategy

Our organization has a clear, leadership-backed AI strategy that is linked to our business goals.

Workforce · Workforce & Culture

Our people are trained to work with AI, and a culture of openness toward AI is established.

Workflow · Workflow Re-Engineering

We have deliberately re-engineered existing workflows to meaningfully integrate AI.

Governance · Risk & Governance

Clear guidelines and accountabilities exist for responsible AI use (risk, ethics, compliance).

Data · Data

Our data is relevant, high-quality, and secure, ready for production AI use.

Engineering · Engineering

Our engineering practices (AI architecture, testing, integration, explainability) are built to take AI features into production reliably.

Operations · Operations

We monitor and operate AI solutions in production with defined processes and metrics.

Ecosystem · Technology Ecosystem

Our technology landscape (tools, platforms, integrations) supports AI use seamlessly and at scale.

The five maturity levels

Level 1 · Exploratory AI

Early, often isolated steps: individual teams experiment with AI, with no organization-wide alignment or structure. Lots of experimentation, little repeatability.

Level 2 · Implemented AI

Individual AI solutions are live in production, typically limited to single teams or functions, with baseline risk practices in place but no organization-wide AI strategy yet.

Level 3 · Aligned AI

AI initiatives are aligned with business goals. Early organization-wide standards, clear accountabilities, and repeatable processes are in place.

Level 4 · Scaled AI

AI is scaled across multiple areas, backed by established processes, resilient data, and clear success metrics.

Level 5 · Future Ready AI

AI is deeply embedded in culture, operations, and the technology ecosystem. The organization continuously adapts to new possibilities instead of chasing them.