MIT Sloan — How to boost your organization’s AI maturity level
TL;DR
A summary of an MIT CISR research briefing by Stephanie Woerner, Peter Weill, Ina Sebastian, and Evgeny Káganer. Introduces a four-stage enterprise AI maturity framework and four organizational challenges (“Four S”: Strategy, Systems, Synchronization, Stewardship) for moving from Stage 2 (pilots) to Stage 3 (scaled AI ways of working) — the financial inflection point. Backed by MIT CISR’s 2022 Future Ready Survey (N=721) plus 2024 interviews with 16 executives at 9 enterprises.
Key claims
The four stages of enterprise AI maturity
| Stage | Name | % of firms | Attributes | Focus |
|---|---|---|---|---|
| 1 | Experiment and prepare | 28% | Educating workforce; setting acceptable-use policies; making data accessible; ensuring decision-making uses data; identifying where humans need to be in the loop | Exploration and education |
| 2 | Build pilots and capabilities | 34% | Beginning to simplify and automate processes; creating use cases; sharing data via APIs; coach-and-communicate management style; using LLMs (out-of-box traditional and GenAI) to augment work | Business cases and pilots |
| 3 | Develop AI ways of working | 31% | Expanding process automation; test-and-learn; architecting for reuse; incorporating pretrained models; investigating proprietary models; exploring autonomous agents | Scaling AI platforms and dashboards |
| 4 | Become AI future-ready | 7% | Embedding AI into decision-making and processes; creating and selling AI-augmented business services; combining traditional, generative, agentic, and robotic AI | Continuous innovation and new revenue streams |
The financial inflection is at Stage 2 → Stage 3
- Organizations see the greatest financial impact moving from Stage 2 to Stage 3.
- Enterprises in Stages 1–2 had financial performance below industry average.
- Enterprises in Stages 3–4 had financial performance well above industry average.
- Maturity score is constructed as the equally-weighted combination of three measures: effectiveness of AI to (i) improve operations, (ii) improve customer experience, (iii) support and develop the ecosystem. On a 0–100% scale: Stage 1 = 0%–49%; Stage 2 = 50%–74%; Stage 3 = 75%–99%; Stage 4 = 100%.
Four challenges to move Stage 2 → Stage 3 (the “Four S”)
- Strategy — Align AI investments with strategic goals, and offer measurable, scalable value.
- Systems — Architect modular, interoperable platforms and data ecosystems to enable enterprise-wide intelligence.
- Synchronization — Create AI-ready people, roles, and teams while redesigning work around AI capabilities.
- Stewardship — Embed and monitor compliant, human-centered, and transparent AI practices by design.
Case study 1: Guardian Life Insurance
Three areas of AI use: customer experience, operating efficiency, employee productivity.
- Strategy — Data and AI team owns AI strategy and prioritization; value-tracking framework runs initiatives from hypothesis → pilot → scale, kept tied to measurable impact. Concrete outcome: automated RFP and quoting process — turnaround cut from ~1 week to 24 hours. Plans to scale in 2026.
- Systems — CTO reorganized around products and platforms with small cross-functional teams, microservices, and APIs enabling reuse and faster delivery. Modernized legacy systems and data architecture.
- Synchronization — Reskilling the workforce by reorganizing employees into AI-focused roles and emphasizing solving end-to-end business problems. Longer-term plans include rotations and training programs to build hybrid business-technical skills.
- Stewardship — Given its regulated environment, embedded governance with risk, legal, and compliance teams. Architecture reviews via both formal and fast-track boards, ensuring privacy, security, and regulatory requirements built into new solutions.
Case study 2: Italgas
Europe’s largest natural gas distributor. Three areas of AI use: managing infrastructure, boosting efficiency, improving safety. Digital Factory is the innovation hub anchoring this work, supported by executive sponsorship and cross-functional teams.
- Strategy — Prioritized AI projects:
- WorkOnSite — accelerated construction projects by 40% and reduced inspections by 80%
- DANA — a generative AI-based network control system
- Each MVP sprint backed by a C-level sponsor for strategic alignment
- Systems — Italgas has digitized assets and processes since 2017: cloud-based platform, IoT infrastructure, 300-terabyte data platform, 23 AI models. Business translators embedded in units to drive adoption and modular component application.
- Synchronization — Engaged >1,000 employees in innovation initiatives; delivered 30,000 hours of AI/data training in 2024. Italgas Academy supports a new digital leadership model — building an agile, AI-ready workforce while maintaining continuity.
- Stewardship — Governance via a chief people, innovation, and transformation officer + an AI officer + a group AI office overseeing integration and monitoring. Initiatives balance efficiency gains with new business opportunities — e.g., commercializing WorkOnSite generated €3M revenue in 2024.
AI maturity = major organizational change
The researchers emphasize that transitioning through stages of AI maturity represents a major organizational change, requiring overcoming both human resistance and technological complexity. Driving change requires a united front among the CEO, CIO, chief strategy officer, and the head of human resources.
Notable quotes
“Now is the time for executive teams to align, commit, and lead the charge toward enterprise-scale AI by developing a playbook for strategy, systems, synchronization, and stewardship.” — Woerner, Weill, Sebastian, Káganer
“Organizations that are AI mature outperform early-stage AI implementers — but many companies struggle to advance from AI pilots to AI at scale.”
My take
This is a clean conceptual companion to the AI Index 2025. Where AI Index gives the macro-empirical landscape (what % of orgs use AI, by how much, with what financial impact), this MIT CISR briefing gives the micro-organizational mechanism — the stage transitions and the four kinds of change required to move between stages.
Two specific things worth flagging:
-
The 7% Stage 4 number rhymes with the AI Index’s “1% mature” finding (different instrument, different sample, different definition — see ai-maturity-measurement-comparison). Multiple measurements are converging on a thesis: most organizations using AI are not yet capturing meaningful value at scale.
-
The Stewardship pillar is doing more work than it gets credit for. The Guardian case study shows architecture review boards as the operationalization of responsible-ai — RAI as a procurement-and-design discipline, not a separate ethics function. This is a more practical framing than the typical “RAI principles” framing in policy literature.
The case studies are valuable as concrete numbers I can cite from this point forward:
- Guardian: RFP turnaround 1 week → 24 hours (28× speedup)
- Italgas: WorkOnSite +40% construction speed, -80% inspections; 300TB data platform, 23 AI models; 30,000 training hours in 2024; €3M revenue from commercializing WorkOnSite
The methodological caveat: the MIT CISR Future Ready Survey is from 2022 (N=721), supplemented by 2024 interviews at only 9 enterprises. The 2022 survey predates the GenAI explosion. The 28%/34%/31%/7% stage breakdown should be treated as the 2022 baseline — current distributions may have shifted, especially given how rapidly enterprise GenAI use grew in 2024 (AI Index 2025 §4.4: GenAI use 33% → 71% in one year).
Linked entities and concepts
Entities (this wiki): MIT CISR, Stephanie Woerner, Peter Weill. Dangling (not yet promoted): Ina Sebastian, Evgeny Káganer, Kristin Burnham (journalist), Guardian Life Insurance, Italgas, IESE Business School, MIT Sloan.
Concepts: enterprise-ai-adoption (substantially enriched by this source), responsible-ai (stewardship operationalized), generative-ai (agentic AI in Stage 4).
Threads: ai-maturity-measurement-comparison (opened by this ingest).
Source
- Raw PDF: article file
- Public URL: mitsloan.mit.edu/ideas-made-to-matter/how-to-boost-your-organizations-ai-maturity-level
- Underlying research: MIT CISR 2022 Future Ready Survey (N=721) + 2024 interviews at 9 enterprises