General Maturity Assessment

General — AI Maturity Assessment

Plot the organization on the AI maturity curve. Identify the binding constraint. Build the roadmap to the next stage.

Two complementary frameworks: MIT CISR's four-stage sequential model (where are we, and what is next?) + Accenture's Foundation × Differentiation 2×2 (what is the binding constraint that's keeping us here?). Use both — they answer different questions.

Output contract (stable): MIT CISR stage placement (Stage 1 / Stage 2 / Stage 3 / Stage 4) plus Accenture archetype, with named next-stage actions.

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Part 1: MIT CISR Stage Placement

Four stages, empirically associated with financial performance. Stages 1–2 = below-industry performance. Stages 3–4 = above-industry performance. The Stage 2 → Stage 3 transition is the most important threshold. (Consult mit-cisr-4-stages.md)

Stage diagnostics — answer each with evidence:

Stage 1 signals (Experiment and Prepare, 28% of firms):

• [ ] AI literacy program in place for board and senior leadership
• [ ] Acceptable-use policy for AI exists and is enforced
• [ ] Data accessibility initiative underway
• [ ] Individual AI experiments happening but no tracked, value-attributed pilots
• NOT yet: systematic pilots, APIs sharing data cross-silo, shared platform

Stage 2 signals (Build Pilots and Capabilities, 34% of firms):

• [ ] Tracked pilots with named business cases and measured value
• [ ] Data APIs connecting some silos
• [ ] LLMs in use to augment work in at least one function
• [ ] Internal storytelling about pilot learnings happening
• NOT yet: enterprise-wide platform; reusable models; test-and-learn as default; cross-function value

Stage 3 signals (Develop AI Ways of Working, 31% of firms):

• [ ] Shared AI platform with reusable services
• [ ] Business dashboards showing AI performance and value
• [ ] Test-and-learn is the default operating mode across functions
• [ ] Foundation models and SLMs in systematic use
• [ ] AI value visible and attributed across the organization

Stage 4 signals (Become AI Future Ready, 7% of firms):

• [ ] AI embedded in all default decision-making
• [ ] Selling AI capability as a service externally
• [ ] Combining analytical + generative + agentic + robotic AI
• [ ] CEO-level KPI on AI outcomes (e.g., DBS: 1,000 experiments/year)

STAGE PLACEMENT: [Stage 1 / Stage 2 / Stage 3 / Stage 4] Note: firms self-flatter. Use the NOT-YET criteria to triangulate. If the Stage 3 signals require "shared AI platform" and the org doesn't have one — it's not Stage 3, regardless of pilot count.

Output: STAGE PLACEMENT | KEY EVIDENCE | NOT-YET GAPS | NEXT-STAGE PREREQUISITES

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Part 2: Accenture Foundation × Differentiation Assessment

The Accenture 2×2 identifies the binding constraint — not just where you are, but WHY you're there. (Consult accenture-maturity-archetypes.md)

Foundation capabilities (x-axis): Score each LOW / MEDIUM / HIGH

• Cloud platform and AI infrastructure
• Data platform (unified, accessible, AI-ready)
• Model lifecycle management (MLOps)
• AI governance documentation and operation
• Technical documentation and reproducibility

Differentiation capabilities (y-axis): Score each LOW / MEDIUM / HIGH

• C-suite AI strategy (ratified, not just endorsed)
• CEO + senior sponsorship (champion posture, not cheerleader)
• AI talent strategy (hiring, training, retention)
• Innovation culture (test-and-learn as default, not exception)
• Responsible AI by design (embedded in process, not bolt-on)

ARCHETYPE PLACEMENT:

• Foundation HIGH + Differentiation HIGH → AI Achiever (12%) — 50% greater revenue growth vs. peers
• Foundation LOW + Differentiation HIGH → AI Innovator (13%) — vision-without-execution; binding constraint: platform
• Foundation HIGH + Differentiation LOW → AI Builder (12%) — substrate-without-strategy; binding constraint: strategy/culture
• Foundation LOW + Differentiation LOW → AI Experimenter (63%) — no anchor; binding constraint: Foundation first

BINDING CONSTRAINT: [Foundation / Differentiation / Both]

Output: FOUNDATION SCORE | DIFFERENTIATION SCORE | ARCHETYPE | BINDING CONSTRAINT

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Part 3: Performance Context

Frame the maturity placement against the performance data.

FINANCIAL PERFORMANCE IMPLICATION (from MIT CISR):

• Stage 1: −12.6 pp vs. industry average on revenue; −9.6 pp on profit
• Stage 2: −3.5 pp vs. industry average on revenue; −2.2 pp on profit
• Stage 3: +11.3 pp vs. industry average; +8.7 pp on profit
• Stage 4: +17.1 pp vs. industry average; +10.4 pp on profit

The gap between Stage 2 and Stage 3 is +14.8 pp on revenue. This is not a technology decision — it is a platform and culture investment decision.

AI PORTFOLIO OBJECTIVE MIX (consult mckinsey-3-objective-mix.md):

• What percentage of the current AI portfolio is efficiency vs. growth vs. innovation?
• 100% efficiency = structural cap on value; not a technology issue
• High performers add growth/innovation objectives

PwC REINFORCEMENT:

• Efficiency-only = 1.6× leader-laggard productivity gap
• Reinvention = 2.6× leader-laggard gap
• Documented Responsible AI strategy = 1.7× more likely to be an AI leader

Output: FINANCIAL PERFORMANCE IMPLICATION | OBJECTIVE MIX | VALUE CEILING

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Part 4: Roadmap to the Next Stage

Produce a stage-advancement roadmap based on Parts 1–3.

CURRENT STATE SUMMARY:

• MIT CISR Stage: [X]
• Accenture Archetype: [Y]
• Binding constraint: [Foundation / Differentiation / Both]

TARGET STATE (next stage):

• What does the target stage look like for this org specifically?
• What is the time horizon? (CISR recommends defining this explicitly)

CAPABILITY GAPS (ordered by priority):

1. [Gap 1 — specific; binding constraint first]

• Required action: [specific]
• Owner: [function or role]
• Timeline: [weeks/months]

1. [Gap 2]
2. [Gap 3]

BOLD GOAL (required for Stage 4 aspiration):

• DBS Bank example: 1,000 experiments/year; S$370M AI economic impact
• What is the equivalent bold, tangible goal for this organization?
• Without a named goal, the Stage 4 conversation is aspirational, not operational

NEXT REVIEW CADENCE: [Quarterly / Bi-annual — stage does not change in 30 days]

Output: CURRENT STATE SUMMARY | TARGET STATE | CAPABILITY GAPS (ordered) | BOLD GOAL | REVIEW CADENCE

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References

All files below live in references/ at the plugin root (${CLAUDE_PLUGIN_ROOT}/references/ when installed as a plugin).

• mit-cisr-4-stages.md — four-stage model; performance data; Stage 3 inflection; DBS/Ping An exemplars
• accenture-maturity-archetypes.md — Foundation × Differentiation 2×2; archetype definitions; Achiever performance premium
• mckinsey-3-objective-mix.md — efficiency/growth/innovation objective mix
• pwc-roi-2026-governance.md — 1.6× vs. 2.6× productivity gap; 1.7× RAI advantage
• bcg-future-built.md — Future-Built archetype; multi-dimensional investment
• deloitte-cheerleader-to-champion.md — 74/21 governance gap; HR five jobs
• hiten-skill-library.md — skill library as the Stage 3–4 knowledge architecture

Reference files are bundled with this skill — Claude resolves them by filename regardless of install layout (single-skill or plugin).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: geledek
• Source: geledek/enterprise-ai-transformation-skills
• License: MIT

Install and usage instructions live in the source repository linked above.