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AI-Native Maturity Models

Capability models for understanding how AI-nativity changes software delivery (SDLC), product management (PDLC), prioritization understood as asset allocation (Portfolio Prioritization), and the enterprise itself (Enterprise Architecture). These models do not ask how much AI an organization uses; they ask what AI-native capability the system actually has, and what realistically adjacent capability comes next.

How to read these models

Every dimension progresses through the same five operating states: A — Nascent, where good practice is not yet deliberately defined; B — Modeled, where an explicit method exists; C — Continuous, where the capability operates on an ongoing cadence; D — Integral, where it becomes load-bearing beyond its original owner or function; and E — Telemetric, where operation and observation form a continuous feedback loop. A is the first lettered state, not the floor: a dimension practiced with no AI-assisted method at all is Pre-AI, and a dimension an organization has deliberately excluded under a cited obligation is Exempt. The SDLC and EA models define both states; the PDLC and Prioritization models score A to E.

Every control these models describe is read by one rule: a guardrail contacted often is a broken process; one never contacted earns its place only by an obligation or an act that cannot be undone.

Each model stands alone — you don’t need the others to use one. Whole-Model Views for all four live models share the same interaction: select a cell to read it — click, or use the arrow keys — and the detail stays open beside the matrix.