Enterprise AI Maturity Model

Strategic Center Evaluation

Adjacent Publisher Context

The following materials are published by MIT CISR and address topics relevant to the Five Forces, but are not explicitly positioned as components of the MIT CISR Enterprise AI Maturity Model. They are not included in the /25 coverage score above.

No current adjacent MIT CISR materials were identified for Global & Geopolitical Dynamics or Human Needs & Aspirations.

Narrative Strengths & Gaps

Force 1 — Internal Pressures & Capabilities. The model’s centre of gravity is internal capability building. Each stage carries concrete characteristics: workforce AI education and acceptable use policies in stage 1; process simplification, use cases, API-based data sharing and a coach-and-communicate management style in stage 2; architecting for reuse, a test-and-learn way of working and expanding automation in stage 3; and AI embedded in decision-making and processes in stage 4 (Building Enterprise AI Maturity). The August 2025 update adds a clear account of the hardest transition, naming strategy, systems, synchronization and stewardship as the four challenges in moving from pilots to scale, and illustrating each through Guardian (including its three-phase value-tracking framework) and Italgas (including more than 30,000 training hours on AI and data in 2024) (Grow Enterprise AI Maturity for Bottom-Line Impact). Total AI Effectiveness gives leaders a measurable basis for placing their enterprise on the model, and the briefing’s team exercise asks executives to agree their stage today (Map: Met). The stage characteristics and the four scaling challenges show what moving up involves, but the update observes that “there is no proven playbook” and encourages executive teams to develop one (Course: Partly met). Its destination is explicit: stage 4, “Become AI Future Ready”, with teams setting their own aspirations and time frames along the way. The Talking Points that accompany each briefing are reserved for member organisations.

Force 2 — Global & Geopolitical Dynamics. The model draws its exemplars from several regions — Kaiser Permanente and Guardian in the US, DBS in Singapore, Ping An in China and Italgas across Italy and Greece — so its stage logic is presented as applying across markets. National AI strategies, cross-border data, export controls and sovereignty sit outside its scope, which concentrates on how an enterprise converts AI into financial performance.

Force 3 — Societal & Regulatory Pressures. Responsible use is built into the maturity path from the first stage, where enterprises discuss “what is acceptable and ethical use of AI technology” and where humans need to be in the loop, illustrated by Kaiser Permanente’s seven principles for responsible use (Building Enterprise AI Maturity). Stewardship is one of the four scaling challenges, defined as “embedding and monitoring compliant, human-centered, and transparent AI practices by design”; Guardian’s codified risk, legal and compliance barriers and fast-track review board, and Italgas’s group AI office, show how this works in regulated settings (Grow Enterprise AI Maturity for Bottom-Line Impact). Responsible use is treated as a condition of maturing rather than one of the model’s measured dimensions, and the guidance is directional; MIT CISR’s adjacent risk and guardrail briefings (Section 2.75) go further.

Force 4 — Industry & Competitive Pressures. The model frames every stage against the industry: growth and profit are reported in percentage points above or below industry average, from −12.6 pp growth in stage 1 to +17.1 pp in stage 4, giving leaders a competitive reference point for maturity (Building Enterprise AI Maturity). Ecosystem development is one of the three components of Total AI Effectiveness, and stage 4 is characterised by “creating and selling AI-augmented business services”, illustrated by Ping An’s AI banking service for thirty other banks and by Italgas’s Bludigit. Competitor behaviour, vendor dynamics and industry structure are not analysed directly; MIT CISR’s business-model and value-creation briefings (Section 2.75) address that territory.

Force 5 — Human Needs & Aspirations. Customer experience is one of the three equally weighted measures in Total AI Effectiveness, so external customer outcomes are part of how maturity is defined. Kaiser Permanente’s principles — including customer prioritization, equity and trust — and Guardian’s aim to deepen customer engagement show customer-facing intent (Grow Enterprise AI Maturity for Bottom-Line Impact). Customer, talent-market and public expectations of AI are not analysed as external pressures in their own right, a scope choice consistent with the model’s focus on enterprise performance.

What this framework does not give you. A full route from one stage to the next; the update notes there is no proven playbook. Without a route, even a well-chosen destination stays an aspiration. A way to choose your own destination beyond setting aspirations and time frames; it supports progress towards stage 4, “Become AI Future Ready”. An enterprise that has not stated its own ambition cannot check whether this destination is the right one for it. Global & Geopolitical Dynamics. Sovereignty, cross-border data rules and export controls can change which AI an enterprise can use, and where.

Synthesis. The MIT CISR Enterprise AI Maturity Model serves executive teams and boards that want an evidence-based view of where their enterprise sits on its AI journey and what it takes to move from pilots to scaled value — particularly organisations in stages 1 and 2 preparing for the step to stage 3. Its destination fits enterprises whose ambition is to turn AI into continuous innovation and new revenue. Its strengths are a clear four-stage structure, a measurable placement metric, explicit links to financial performance, and a well-illustrated account of the four scaling challenges. The Five Forces lens adds a fuller route, a way to set the destination itself, the geopolitical dimension, a fuller treatment of external human expectations, an explicit choice between automation and augmentation, and the shift in value from execution to ideas that the model’s innovation and test-and-learn language implies.

Sources

Tier 1 (scored), recency-gate pass unless noted:

1. Building Enterprise AI Maturity – 19 Dec 2024; MIT CISR Research Briefing XXIV-12; canonical anchor
2. Building Enterprise AI Maturity — Audio edition – 19 Dec 2024; linked from the anchor
3. Enterprise AI Maturity — Talking Points – 19 Dec 2024; linked from the anchor; member-only, content not used as evidence
4. Grow Enterprise AI Maturity for Bottom-Line Impact – 21 Aug 2025; MIT CISR Research Briefing XXV-8; the model’s update, building on the anchor
5. Grow Enterprise AI Maturity for Bottom-Line Impact — Audio transcript – Aug 2025; public full text of the update, used for Force evidence
6. Challenges in Maturing Enterprise AI — Talking Points – 21 Aug 2025; linked from the update; member-only, content not used as evidence

Tier 2 (reported in Section 2.75, not scored):

1. Business Models in the AI Era – 16 Oct 2025
2. Data Monetization: Generating Financial Returns from Data – 20 Nov 2025; Working Paper 468
3. Mapping the Generative AI Risk Space – 15 Jan 2026
4. Leveraging Digital Colleagues for Enterprise Value – 16 Apr 2026
5. Designing Decision Rights for AI – 18 Jun 2026
6. AI Value Creation: Five Provocative Propositions – 20 Aug 2026
7. Establishing Guardrails on the AI Roller Coaster – 17 Sep 2026

Tier 3 (third-party corroboration, not scored):

1. None used.

Excluded on recency or quality (not scored):

1. None cited.