AI Maturity Diagnostic Tool
Assess your organization's AI maturity across six key dimensions
Based on the AI Capability Maturity Model (CMM) framework developed by the Kellogg School of Management, Northwestern University
Interactive AI Maturity Diagnostic
This tool helps you assess your organization's current AI maturity level using the structured framework from Kellogg School of Management. Evaluate your capabilities across six critical dimensions to identify strengths, weaknesses, and opportunities for growth.
Your data is private. All information is processed locally in your browser and is not uploaded to any server.
About the AI Maturity Diagnostic
The AI maturity diagnostic refers primarily to the AI Capability Maturity Model (CMM), a structured framework used by organizations to evaluate their current AI readiness and capabilities. It acts as a diagnostic tool that offers a granular picture of an organization's strengths and weaknesses across critical dimensions related to AI adoption.
Prof Keith Carter emphasizes that using the maturity model to decide what type of AI solution to provide is critical—organizations with the right maturity in these areas have a much higher chance of AI project success.
The CMM assesses maturity along five essential pillars:
Strategy & Leadership
Clarity of AI vision, strategic alignment, leadership support
Data & Infrastructure
Data availability, quality, integration, privacy, security
Solution Development & Deployment
Design, implementation, testing, deployment processes
Talent & Expertise
AI skills, talent acquisition, development, collaboration
Governance & Ethics
AI governance, compliance, responsible AI, accountability, explainability
These pillars are mapped against four stages of increasing AI maturity: Crawl (experimentation), Walk (moderate fine-tuning), Run (embedded operations), and Fly (full productization).
Related Resources
Maturity Assessment
| Criterion | Crawl | Walk | Run | Fly |
|---|---|---|---|---|
| Customization level | Low-OOTB (Out-Of-The-Box) solutions, minimal setup | Moderate fine-tuning with proprietary data | High custom or specialized LLMs (Large Language Models) | Very high full productization and industry transformation |
| AI expertise | Basic AI knowledge, primarily operational | Developing an in-house AI team and a COE (Center of Excellence) | An advanced AI team and a hub-and-spoke COE | Expert-level AI infrastructure and federated capabilities |
| Use cases | Internal use cases (e.g., automation, productivity) | Tailored use cases (e.g., personalization, analytics) | Externalized AI products; monetization | Monetized GenAI (Generative AI) products; transformational use cases |
| Data needs | Public or minimal internal data | Proprietary data for fine-tuning | Large proprietary datasets and high computational power | Advanced real-time data integrations |
| ROI focus | Efficiency and cost savings | Business value through personalization and decision-making | New revenue streams through AI products | Industry disruption; significant revenue through AI |
| AI infrastructure | No unified data, a minimal production environment and few use cases | A basic unified data repository, a production environment and some use case factory capabilities | Advanced data infrastructure, a production-grade AI environment and an established use case factory | Fully automated AI infrastructure, real-time data flow, a production environment and a scalable use case factory |