AI Maturity & Prioritization Model
From isolated AI pilots to a cognitive, federated factory.
AI-M² measures where your organisation stands across 26 dimensions of industrial AI — and turns that baseline into a prioritized roadmap aligned with your strategy.
- 3
- Building Blocks
- 8
- Pillars
- 26
- Dimensions
- 0–5
- Maturity Levels
The challenge
Most manufacturers are stuck in the pilot trap.
Awareness of AI is high. Turning it into a scalable, secure and sustainable capability is where most organisations stall.
Isolated pilots
Predictive maintenance on one machine, a vision check on one line — successes that never connect into an enterprise capability.
No common baseline
Without an objective, comparable measure of maturity, investment decisions rely on vendor pitches and intuition.
Wrong sequencing
Value use cases get funded before the data, architecture and governance foundations they depend on.
Two objectives, one methodology
Evaluate
Quantitatively measure current (as-is) AI maturity on a 0–5 scale across 26 dimensions — processes, technology, competencies and governance.
Prioritize
Combine maturity gaps, strategic weights and dependency logic in a rule-based formula to identify the initiatives with the highest impact.
The framework
Three building blocks. Eight pillars. Twenty-six dimensions.
A holistic, 360° view: how AI is applied to the value chain, the architecture that carries it, and the organisation that governs it.
Cognitive Processes and Ecosystem
- 1. Cognitive Production Planning and Scheduling
- 2. AI-Assisted Quality Management
- 3. Predictive Maintenance Processes
- 4. Process and Intra-Factory Logistics Optimization
- 5. AI-Oriented Design and Simulation
- 6. Smart Products and After-Sales Services
- 7. Process Integration and Orchestration
- 8. Cognitive Control Tower Capability
- 9. Data Ecosystem Participation and Sovereignty
Architectural Foundations and IT
- 10. Asset Digitalization and Digital Representation
- 11. Operational Interoperability (OT/IT)
- 12. Edge AI Capability
- 13. Data Infrastructure and AI Pipelines
- 14. Model Development Lifecycle (MLOps)
- 15. Model Output Quality and Performance
- 16. Analytics Capability and Sourcing Strategy
- 17. AI Platform Integration (Open/Hybrid)
Strategic Competency and Governance
- 18. AI Strategy and Vision
- 19. Organizational Agility and Capability
- 20. AI-Focused Human Resource Development
- 21. AI Supply Chain Security
- 22. AI Model Transparency and Provenance (AI BOM)
- 23. AI Data Governance and Compliance
- 24. Responsible AI and Ethical Principles
- 25. Explainability (XAI) and Interpretability
- 26. Environmental Footprint and Efficiency
Strategic Compass
Your strategy decides what comes first.
Leadership distributes 100 points across four strategic drivers. Every dimension’s impact on those drivers is weighted accordingly — so two companies with identical scores can receive different roadmaps.
Operational Efficiency
Cost, throughput, OEE, waste.
Growth & New Value
New products, services and business models.
Resilience & Risk
Supply chain disruption, agility, continuity.
Compliance & Sustainability
Regulation, AI Act, ESG, traceability.
How it works
A structured engagement, from kickoff to roadmap.
- 1
Scope & Kickoff
Align on sites, functions and stakeholders. Collect documents and system landscape.
- 2
On-site Assessment
A certified assessor scores all 26 dimensions through interviews, walkthroughs and evidence review.
- 3
Compass & Prioritization
Leadership sets strategic weights; the algorithm ranks every dimension by expected impact.
- 4
Roadmap & Report
The top six dimensions are translated into concrete projects with timelines and expected returns.
What you receive
Decision-grade outputs, not a slide of buzzwords.
Maturity Scorecard
Scores and evaluator findings for each of the 26 dimensions, grouped by building block.
Sector & Global Benchmark
Your profile compared with your sector and the full AI-M² assessment base.
Priority Ranking
Every dimension ranked by strategic impact, maturity gap and dependency value.
Strategic Roadmap
Six priority dimensions balanced across value, foundation and governance.
Project Portfolio
Defined projects with purpose, implementation stages, timeline and expected return.
Executive Report
A board-ready report and an online portal to revisit results at any time.
Built on open standards
Grounded in the reference models industry already trusts.
AI-M² is anchored in established standards for interoperability, data sovereignty and AI trust — so recommendations translate directly into implementable architecture.
Who it is for
Manufacturers
Get an objective baseline, a benchmark against peers and a roadmap your board can fund.
Request an assessmentConsultants
Become a certified AI-M² assessor and deliver structured, comparable assessments to your clients.
Become an assessorEcosystem partners
Technology providers, clusters and public programmes can use AI-M² to target support where it matters.
Talk to usRead the AI-M² whitepaper
Methodology, standards groundwork, all 26 dimensions with maturity levels and sample questions, and the prioritization algorithm. Available to certified assessors and AI-M² clients.
Frequently asked questions
How long does an assessment take?
Typically two to four weeks end-to-end: a short preparation phase, one to three days on site depending on scope, and a debrief with the roadmap.
Is AI-M² only for large enterprises?
No. The framework scales from single-site SMEs to multi-plant groups. The scope of the on-site phase is adjusted accordingly.
Who performs the assessment?
Certified AI-M² assessors, trained and calibrated on the methodology so that scores are comparable across companies and sectors.
Can we see our results online?
Yes. Each client receives portal access to view the scorecard, benchmark, roadmap and report.
How is our data protected?
Assessment data is confidential. Benchmarks only use anonymised, aggregated scores — never company names or comments.
Know where you stand. Know what to do next.
Start with an AI-M² assessment and leave with a roadmap built on evidence.