The AI-M² Framework

A 26-dimension maturity model for industrial AI.

Three interconnected building blocks reflect the reality of AI transformation: it is not only about tools (architecture), but about embedding them in workflows (processes) and sustaining them through strategy, competency and governance.

3: Building Blocks
8: Pillars
26: Dimensions
0–5: Maturity Levels

Building Block 1

Cognitive Processes and Ecosystem

This first building block focuses not on the technology itself (i.e., the 'Architectural Foundations') in the abstract, but on how artificial intelligence is applied to the corporate value chain and how it transforms core business processes. This block highlights the critical difference between merely possessing an AI model and actively integrating that model into a production scheduling, predictive maintenance, or quality control process to generate tangible business value.

Building Block 2

Architectural Foundations and IT

This second building block defines the technical infrastructure and digital backbone required for cognitive processes to operate. It covers the vertical technical stack from physical assets (OT) to enterprise information systems (IT).

Building Block 3

Strategic Competency and Governance

This final block forms the organizational and human foundation. It focuses on who manages the AI transformation (strategy, culture), why (vision), and how it is secured to be safe, responsible, and ethical.

Assessment matrix

Select a dimension to see what it measures, its six maturity levels and sample assessment questions.

Building Block 1 · Cognitive Processes and Ecosystem

Pillar 1 · Operational Excellence

Pillar 2 · Product Lifecycle

Pillar 3 · Supply Chain and Ecosystem

Building Block 2 · Architectural Foundations and IT

Pillar 4 · Industrial Resources and Assets

Pillar 5 · Information Systems and Data Analytics

Building Block 3 · Strategic Competency and Governance

Pillar 6 · Strategy and Culture

Pillar 7 · AI Security and Governance

Pillar 8 · Ethics, Responsibility, and Trust

Cognitive Processes and Ecosystem › Operational Excellence

1. Cognitive Production Planning and Scheduling

measures the organization's ability to transform its production planning and scheduling processes from static, rule-based, and 'push' oriented approaches toward dynamic, data-driven, demand-responsive ('pull'), and autonomous systems supported by AI.

Maturity levels

0
Non-Existent — Production planning is not performed or is entirely informal, reactive, and based on verbal requests.
1
Reactive — Planning relies on static electronic spreadsheets. Plans are created manually at periodic intervals.
2
Connected — Basic ERP/MES system reduces reliance on paper but scheduling is still rule-based on static master data.
3
Cognitive — Planning is integrated with operational systems. Planners see real-time status. Active alerts for deviations.
4
Self-Adapting — AI/ML algorithms analyze data to predict bottlenecks or shortages. System presents optimized "what-if" scenarios.
5
Federated — AI system manages the schedule autonomously . It reacts instantly to events and re-optimizes the plan without human intervention.

Sample assessment questions

How do your production planning and scheduling processes work (e.g., manual, spreadsheets, integrated systems), and how frequently are these plans updated?
Does your planning system receive real-time data from the factory floor (e.g., machine downtime, current inventory)?
When an unexpected event occurs (e.g., machine breakdown), can your system automatically calculate the cascading impact on other orders and provide alternative scenarios?
Do you use AI models to forecast future production bottlenecks or potential material shortages based on current operational data?
Does your AI system have the authority to autonomously reorganize the production sequence without requiring human approval?

The 0–5 maturity scale

Each building block uses a level vocabulary suited to its nature, on a common 0–5 scale so that scores can be compared and aggregated.

	0	1	2	3	4	5
Cognitive Processes and Ecosystem	Non-Existent	Reactive	Connected	Cognitive	Self-Adapting	Federated
Architectural Foundations and IT	Non-Existent	Isolated	Connected	Standardized	Optimized	Service-Oriented
Strategic Competency and Governance	Non-Existent	Unaware	Defined	Managed	Integrated	Dynamic

Methodology

From scores to a roadmap

The prioritization algorithm is transparent and rule-based. It answers one question: where will the next unit of effort create the most strategic value?

1
Strategic impact (SIS)
SIS = Σ impactᵢ × weightᵢ
Each dimension carries an impact rating (1–5) on the four Compass drivers. Weighted by your Compass, this gives its strategic relevance.
2
Gross benefit (F)
F = SIS × (5 − current score)
The bigger the maturity gap on a strategically relevant dimension, the larger the potential benefit.
3
Dependency bonus
Final = F × (1 + Σ F of unlocked value dimensions)
Foundation and governance dimensions are boosted by the value they unlock — so prerequisites are sequenced before the use cases that need them.
4
Balanced selection
2 value + 1 foundation + 1 governance + 2 best of the rest
The roadmap always balances quick value with the foundations needed to scale it.

Benchmarking

Every report compares the company’s 26-dimension profile with its sector and with all AI-M² assessments, using anonymised, aggregated scores.

Know where you stand. Know what to do next.

Start with an AI-M² assessment and leave with a roadmap built on evidence.

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