Your organization has 14 AI pilot projects. Three are in production, serving real users. Eight are stuck in "proof-of-concept limbo." Three have been quietly cancelled. The data science team is frustrated, business units are skeptical, and leadership is questioning the €2.8M AI investment.
When asked about AI maturity, the CTO says "we're pretty advanced"—meanwhile, the Chief Data Officer says "we're just getting started." The truth is somewhere in between, but without a clear understanding of where you actually are, you can't create a roadmap for where you need to go.
According to Gartner research, only 23% of organizations reach advanced AI maturity (Level 4-5), while 54% remain stuck at early stages despite significant investment. The difference isn't technology—it's organizational capability, governance, and systematic progression through maturity stages.
I've assessed AI maturity for organizations ranging from startups to Fortune 500 enterprises. The pattern is consistent: organizations that understand their maturity level and systematically address gaps achieve production AI 3-4x faster than those who randomly invest in tools and technology without assessing foundational readiness.
Here's the 5-level AI maturity model I've used to guide organizations from experimentation to systematic AI value creation—and the specific actions you can take to progress to the next level.
Most organizations dramatically overestimate their AI maturity. Here's why that's dangerous:
The Dunning-Kruger Effect in AI
Stage 1: "We're Not Doing AI Yet" (Accurate Assessment)
- Reality: No AI initiatives
- Self-assessment: "We're at the beginning"
- This group is honest—they know where they stand
Stage 2: "We've Got Some Pilot Projects, So We Must Be Mature" (Overconfidence)
- Reality: 5-10 pilot projects, 0-1 in production
- Self-assessment: "We're pretty advanced"
- This is where most organizations sit—confidence exceeds capability
Stage 3: "We Realize How Much We Don't Know" (Realistic)
- Reality: 10+ pilots, 3-5 in production, hitting organizational barriers
- Self-assessment: "We're struggling more than we expected"
- Organizations start understanding the complexity
Stage 4: "We're Systematically Scaling AI" (True Maturity)
- Reality: 20+ AI use cases in production, AI integrated into operations
- Self-assessment: "We've built repeatable capabilities"
- This is where the 23% of mature organizations operate
The problem with overestimating maturity: Organizations invest in advanced AI capabilities before building foundational ones—like hiring 20 data scientists before establishing data quality processes, or investing in MLOps before having consistent model development practices.
The Cost of Skipping Maturity Levels
I worked with a healthcare organization that jumped directly from Level 1 (no AI) to trying to implement Level 4 capabilities (production AI at scale). They hired a Chief AI Officer, built a 25-person data science team, invested €4.2M in AI infrastructure (GPUs, ML platforms, feature stores).
18 months later:
- 0 AI models in production serving patients
- Data quality issues prevented model training
- IT and security teams hadn't approved production deployment process
- Business units didn't understand how to integrate AI into workflows
- Half the data science team left out of frustration
The problem: They tried to build Level 4 capabilities without Level 2-3 foundations (data quality, governance, deployment processes, organizational change management).
The fix: Step back, assess true maturity, systematically address gaps. After 12-month reset:
- Established data governance (Level 2 foundation)
- Built deployment pipeline (Level 3 capability)
- Launched 4 AI models in production (Level 3 achievement)
- Created repeatable AI development process
- €6.8M investment total → €4.6M annual value (68% ROI)
Lesson: You can't skip maturity levels. Attempting to do so wastes time and money.
The 5-Level AI Maturity Model
Based on frameworks from MIT, Gartner, and practical implementation experience across industries:
Level 1: AI Aware (Foundation)
Description: Organization recognizes AI potential but has limited implementation
Characteristics:
- Strategy: No formal AI strategy, ad-hoc exploration
- Organization: No dedicated AI team, responsibility unclear
- Data: Data scattered, inconsistent quality, limited accessibility
- Technology: No AI infrastructure, using cloud notebooks or personal tools
- Governance: No AI governance, compliance, or risk management
- Skills: Few people with AI/ML expertise, mostly self-taught
- Process: No standardized AI development process
- Culture: Curiosity about AI, but skepticism from business
Typical Activities:
- Attending AI conferences and webinars
- Experimenting with AI tools (ChatGPT, image generators)
- Reading about AI use cases
- Maybe one "skunkworks" project
AI Capabilities:
- 0-2 pilot projects (often stalled)
- 0 AI models in production
- No measurable business value from AI
Key Challenges:
- No budget or leadership support for AI
- Data quality and accessibility issues
- Lack of skills and expertise
- Uncertainty about where to start
% of Organizations: ~25% (Gartner 2024)
Time to Progress: 6-12 months to Level 2 with focused effort
Level 2: AI Experimental (Exploration)
Description: Organization actively experimenting with AI through pilot projects
Characteristics:
- Strategy: Emerging AI vision, exploring use cases, no comprehensive roadmap
- Organization: Small AI team (2-5 people) or data science pod, reporting to CTO or CDO
- Data: Starting data consolidation, basic data governance, data lake or warehouse in progress
- Technology: Basic AI infrastructure (cloud ML platforms, Jupyter notebooks, experiment tracking)
- Governance: Basic AI ethics guidelines, discussing compliance
- Skills: 2-5 data scientists hired, some training for existing staff
- Process: Pilot project methodology, no standardized path to production
- Culture: Growing excitement about AI, but "pilot project paralysis"
Typical Activities:
- Running 5-10 AI pilot projects
- Building POCs (proof of concepts) to test feasibility
- Establishing data science tools and infrastructure
- Initial AI training for business stakeholders
AI Capabilities:
- 5-15 pilot projects in various stages
- 0-2 AI models in production (usually simple)
- Limited business value (~€100K-€500K annually)
- High pilot-to-production failure rate (80%+)
Key Challenges:
- "Pilot Project Purgatory": Pilots don't make it to production
- Disconnect between data science and engineering teams
- Business stakeholders unclear on AI integration
- No clear ROI or success criteria
- Data quality still a major blocker
% of Organizations: ~35% (Gartner 2024)
Time to Progress: 12-18 months to Level 3 with systematic approach
Level 3: AI Operational (Integration)
Description: Organization successfully deploying AI models to production with repeatable processes
Characteristics:
- Strategy: AI strategy aligned with business goals, prioritized use case portfolio
- Organization: Established AI/ML team (10-20 people), clear roles (data scientists, ML engineers, product managers)
- Data: Data platform in place, data quality management, governed data access
- Technology: Production ML infrastructure (MLOps pipeline, model registry, monitoring)
- Governance: AI governance framework, model risk management, compliance processes
- Skills: Mix of data scientists, ML engineers, and business translators
- Process: End-to-end ML lifecycle process (from ideation to production to monitoring)
- Culture: Business units actively seeking AI solutions, collaboration between teams
Typical Activities:
- Deploying 5-10 new AI models per year
- Operating 10-20 AI models in production
- Monitoring and retraining models
- Establishing MLOps practices
AI Capabilities:
- 10-25 AI models in production
- Repeatable deployment process (70-80% of pilots reach production)
- Measurable business value (€1M-€5M annually)
- Cross-functional collaboration working
- Starting to scale AI across multiple domains
Key Challenges:
- Scaling bottleneck: Can't deploy models fast enough
- Model monitoring and maintenance overhead
- Technical debt in ML systems
- Change management for AI-augmented processes
- Balancing innovation with operational stability
% of Organizations: ~30% (Gartner 2024)
Time to Progress: 18-24 months to Level 4 with strategic investment
Level 4: AI Integrated (Optimization)
Description: AI embedded across business operations with systematic value delivery
Characteristics:
- Strategy: AI as core competitive advantage, multi-year roadmap with clear ROI targets
- Organization: Enterprise AI function (30-50+ people), federated model (central + embedded data scientists)
- Data: Mature data platform, real-time data pipelines, data as a product
- Technology: Advanced MLOps (CI/CD for ML, automated retraining, A/B testing, feature stores)
- Governance: Comprehensive AI governance (ethics board, risk framework, regulatory compliance)
- Skills: Deep bench of AI talent, upskilling programs, AI literacy across organization
- Process: Industrialized ML factory (standardized, automated, monitored)
- Culture: AI-first mindset, data-driven decisions, continuous experimentation
Typical Activities:
- Deploying 20-30+ AI models per year
- Operating 50-100+ AI models in production
- Automated model retraining and monitoring
- AI embedded in core products and processes
AI Capabilities:
- 50-100+ AI models in production
- High deployment success rate (85-90% of projects reach production)
- Significant business value (€10M-€50M+ annually)
- AI driving key business metrics (revenue, cost, customer experience)
- Self-service AI capabilities for business units
Key Challenges:
- Maintaining innovation while scaling
- Managing organizational change at scale
- Preventing AI technical debt accumulation
- Ensuring responsible AI as scale increases
- Talent retention and development
% of Organizations: ~9% (Gartner 2024)
Time to Progress: 24-36 months to Level 5 (continuous optimization)
Level 5: AI Native (Transformation)
Description: AI is fundamental to business model and competitive strategy
Characteristics:
- Strategy: AI central to business model, AI-driven innovation, proactive strategy
- Organization: AI pervasive across organization (100+ AI specialists), business leaders own AI outcomes
- Data: Data and AI platform as competitive advantage, real-time adaptive systems
- Technology: Cutting-edge ML infrastructure (AutoML, neural architecture search, continual learning)
- Governance: Mature, adaptive AI governance, proactive regulatory engagement
- Skills: AI literacy universal, organization attracts top AI talent
- Process: Continuous AI innovation, rapid experimentation to production, learning loops
- Culture: AI-native culture, AI-augmented workforce, innovation embedded
Typical Activities:
- Deploying 50+ AI models per year
- Operating 200+ AI models in production
- Continuous AI innovation and experimentation
- AI products and services generating significant revenue
AI Capabilities:
- 200+ AI models in production
- AI revenue contribution (10-30%+ of revenue)
- AI-driven products and experiences
- Competitive differentiation through AI
- Contributing to AI research and open source
Key Challenges:
- Staying ahead of competition
- Managing responsible AI at massive scale
- Regulatory and societal expectations
- Continuous innovation while maintaining operations
- Ethical AI leadership
% of Organizations: ~1% (Gartner 2024) - Examples: Google, Netflix, Amazon, Spotify
Time to Progress: Level 5 is continuous optimization, not a destination
Assessing Your Organization's AI Maturity
Use this assessment to determine your current level across 8 dimensions:
AI Maturity Assessment Framework
Score each dimension 1-5:
- 1 = Level 1 (AI Aware)
- 2 = Level 2 (AI Experimental)
- 3 = Level 3 (AI Operational)
- 4 = Level 4 (AI Integrated)
- 5 = Level 5 (AI Native)
Dimension 1: Strategy & Vision
Level 1: No AI strategy, ad-hoc exploration
Level 2: Emerging AI vision, exploring use cases
Level 3: AI strategy aligned with business, prioritized portfolio
Level 4: AI as core competitive advantage, multi-year roadmap
Level 5: AI central to business model, AI-driven innovation
Your Score: ____
Dimension 2: Organization & Governance
Level 1: No AI team, unclear responsibility
Level 2: Small AI team (2-5), basic guidelines
Level 3: Established team (10-20), governance framework
Level 4: Enterprise function (30-50+), comprehensive governance
Level 5: AI pervasive (100+), mature adaptive governance
Your Score: ____
Dimension 3: Data Foundation
Level 1: Data scattered, inconsistent quality
Level 2: Starting consolidation, basic governance
Level 3: Data platform in place, quality management
Level 4: Mature platform, real-time pipelines, data as product
Level 5: Data platform as competitive advantage, adaptive systems
Your Score: ____
Dimension 4: Technology & Infrastructure
Level 1: No AI infrastructure, personal tools
Level 2: Basic infrastructure (cloud ML, notebooks)
Level 3: Production infrastructure (MLOps pipeline, monitoring)
Level 4: Advanced MLOps (CI/CD for ML, A/B testing, feature stores)
Level 5: Cutting-edge (AutoML, continual learning)
Your Score: ____
Dimension 5: AI Capabilities (Models in Production)
Level 1: 0-2 pilots, 0 in production
Level 2: 5-15 pilots, 0-2 in production
Level 3: 10-25 in production, repeatable process
Level 4: 50-100+ in production, high success rate
Level 5: 200+ in production, continuous innovation
Your Score: ____
Dimension 6: Skills & Talent
Level 1: Few AI experts, mostly self-taught
Level 2: 2-5 data scientists, some training
Level 3: Mix of roles (10-20), formal training
Level 4: Deep bench (30-50+), org-wide AI literacy
Level 5: Universal AI literacy, attracts top talent
Your Score: ____
Dimension 7: Process & Operations
Level 1: No standardized process
Level 2: Pilot methodology, no production path
Level 3: End-to-end ML lifecycle, repeatable
Level 4: Industrialized ML factory, automated
Level 5: Continuous innovation, rapid experimentation
Your Score: ____
Dimension 8: Business Value
Level 1: No measurable value
Level 2: Limited value (€100K-€500K annually)
Level 3: Measurable value (€1M-€5M annually)
Level 4: Significant value (€10M-€50M+ annually)
Level 5: AI revenue contribution (10-30%+ of revenue)
Your Score: ____
Scoring Your AI Maturity
Calculate Average Score:
- Add scores across 8 dimensions
- Divide by 8
- Your AI Maturity Score: ____
Interpretation:
- 1.0-1.9: Level 1 (AI Aware) - Just starting the AI journey
- 2.0-2.9: Level 2 (AI Experimental) - Active exploration, pilot projects
- 3.0-3.9: Level 3 (AI Operational) - Production AI with repeatable processes
- 4.0-4.9: Level 4 (AI Integrated) - AI embedded across operations
- 5.0: Level 5 (AI Native) - AI fundamental to business model
Important Note: Your overall maturity is typically determined by your lowest score—weakest dimension becomes the bottleneck. An organization scoring 4 in Strategy but 2 in Data Foundation is functionally Level 2 (data blocks progress).
Progressing to the Next Maturity Level
Here's what to focus on to move from your current level to the next:
From Level 1 → Level 2: Start Experimenting
Goal: Establish AI exploration with 3-5 pilot projects and basic foundations
Priority Actions:
1. Secure Executive Sponsorship (Critical)
- Identify executive champion (CTO, CDO, COO, CEO)
- Present AI opportunity with industry use cases
- Secure initial budget (€200K-€500K for Year 1)
- Get commitment for 6-12 month exploration phase
2. Form Small AI Team (2-5 People)
- Hire or designate 1-2 data scientists
- Identify 1-2 engineers with Python/ML interest
- Appoint AI program manager or product owner
- Team reports to executive sponsor
3. Establish Basic Data Foundation
- Inventory data assets (what data do we have?)
- Prioritize 2-3 critical data sources for first pilots
- Establish data access for AI team
- Address immediate data quality issues for pilot data
4. Start 3-5 Pilot Projects
- Select high-value, achievable use cases
- Focus on supervised learning (classification, regression)
- Use cloud ML platforms (AWS SageMaker, Azure ML, Google Vertex AI)
- Set 90-day timeboxes for pilots
5. Build AI Awareness
- AI 101 training for leadership team (4-hour workshop)
- Lunch-and-learn series on AI use cases
- Share pilot project results broadly
- Start building coalition of AI advocates
Timeline: 6-12 months
Investment: €250K-€600K (mostly people)
Success Criteria: 3+ pilot projects completed, 1+ model demonstrating value
From Level 2 → Level 3: Production AI
Goal: Deploy 5-10 AI models to production with repeatable deployment process
Priority Actions:
1. Develop AI Strategy & Roadmap
- Workshop with business leaders (identify 20-30 use cases)
- Prioritize based on value + feasibility
- Create 18-month AI roadmap with milestones
- Align with business OKRs
2. Build Production ML Infrastructure (MLOps)
- Establish ML pipeline: Train → Test → Deploy → Monitor
- Implement model versioning and registry
- Set up production monitoring (model performance, data drift)
- Automate deployment process (CI/CD for ML)
3. Establish ML Development Process
- Define end-to-end ML lifecycle stages
- Create templates (project kickoff, model cards, documentation)
- Establish model governance (review, approval, decommissioning)
- Set standards (code quality, testing, documentation)
4. Build Data Platform
- Centralized data warehouse or data lake
- Data quality monitoring and validation
- Governed data access (catalogs, permissions)
- Data pipeline automation
5. Expand AI Team & Skills
- Hire ML engineers (focus on deployment, not just modeling)
- Cross-train engineers on ML basics
- Train business analysts to identify AI opportunities
- Establish partnerships with universities or consultants
6. Pilot-to-Production Framework
- Define production readiness criteria
- Establish production support model
- Create runbooks for model operations
- Set SLAs for model performance
Timeline: 12-18 months
Investment: €800K-€2M (infrastructure + people)
Success Criteria: 5-10 models in production, 70%+ pilot success rate, €1M+ annual value
From Level 3 → Level 4: Scale AI
Goal: Deploy 20-30+ models per year, embed AI across business operations
Priority Actions:
1. Industrialize ML Operations
- Automated model retraining pipelines
- Feature store (centralized feature management)
- A/B testing infrastructure for models
- Self-service ML platform for approved users
2. Federate AI Capabilities
- Embed data scientists in business units
- Central AI team provides platforms, standards, consulting
- Business units own AI use cases and outcomes
- Create AI centers of excellence by domain
3. Advanced MLOps
- Continuous training (models automatically retrain)
- Shadow mode deployment (new models tested in parallel)
- Automated model performance alerts
- Model explainability tools (for compliance)
4. Enterprise AI Governance
- AI ethics board (review high-risk models)
- Model risk management framework
- Regulatory compliance processes (GDPR, AI Act, etc.)
- Responsible AI scorecard
5. AI Literacy at Scale
- AI training for all employees (awareness)
- Advanced training for business analysts (AI translators)
- Technical training for engineers (ML fundamentals)
- Executive AI briefings (quarterly)
6. Optimize for Speed
- Reduce time from idea to production (target: < 90 days)
- Templates and accelerators for common use cases
- Reusable components and models
- Streamlined approval processes
Timeline: 18-24 months
Investment: €2M-€5M (platforms + scaled team)
Success Criteria: 50-100 models in production, 85%+ success rate, €10M+ annual value
From Level 4 → Level 5: AI Native
Goal: AI fundamental to business model, 200+ models, continuous innovation
Priority Actions:
1. AI-Driven Business Strategy
- AI central to competitive strategy
- Board-level AI governance
- AI KPIs tied to executive compensation
- Proactive engagement with regulators
2. AI Product Innovation
- AI features in all products
- AI-powered customer experiences
- New revenue streams from AI
- Open-source contributions to AI community
3. Continuous AI Innovation
- Research partnerships (universities, AI labs)
- Internal AI research team
- Contribution to cutting-edge AI (papers, conferences)
- Experimentation culture (fail fast, learn)
4. Responsible AI Leadership
- Industry leadership in ethical AI
- Transparency in AI practices
- Proactive bias auditing and mitigation
- Societal impact assessment
5. AI Talent Ecosystem
- Organization as destination for AI talent
- AI training programs (internal academy)
- Partnerships with universities for talent pipeline
- Thought leadership (speaking, publishing)
Timeline: 24-36+ months (continuous)
Investment: €5M-€20M+ annually (sustained)
Success Criteria: 200+ models, 10-30% revenue from AI, industry leadership
Real-World Maturity Progression
Case Study: Regional Healthcare System (8 Hospitals, 12,000 Employees)
Starting State (Level 1.8):
- Strategy: Vague "explore AI" mandate from CEO
- Organization: 2 data scientists hired, no clear charter
- Data: EHR data locked in vendor system, inconsistent data quality
- Technology: Personal Jupyter notebooks, no production infrastructure
- Capabilities: 4 pilot projects, 0 in production
- Value: €0 from AI
Goal: Progress to Level 3 (AI Operational) within 24 months
12-Month Program (Level 1 → Level 2):
Actions Taken:
- Secured CMO as executive sponsor + €1.2M budget
- Formed 8-person AI team (4 data scientists, 2 ML engineers, 1 product manager, 1 program manager)
- Established data platform (Snowflake + DBT for transformations)
- Implemented basic MLOps (SageMaker for training + deployment)
- Launched 8 pilot projects focused on clinical operations
- AI 101 training for 200+ clinical and operational leaders
Results After 12 Months (Level 2.4):
- 8 pilots completed, 2 in production:
- No-show prediction: Predict patient no-shows, enable proactive outreach (14% reduction in no-shows = €840K annual value)
- ED wait time forecasting: Predict emergency department volumes (12% reduction in wait times, improved patient satisfaction)
- 6 pilots in production pipeline (scheduled for next 6 months)
- Data platform serving 40+ users
- €840K annual value (first year)
Next 12 Months (Level 2 → Level 3):
Actions Taken:
- Developed AI strategy: 25 use cases prioritized over 3 years
- Built production MLOps pipeline (model registry, monitoring, retraining)
- Established AI governance (ethics review for patient-facing models)
- Grew team to 15 people (added ML engineers + business analysts)
- Deployed 8 additional models to production
Results After 24 Months Total (Level 3.2):
- 10 AI models in production:
- No-show prediction (€840K/year)
- ED volume forecasting (€620K/year)
- Readmission risk prediction (€1.2M/year)
- Length of stay prediction (€780K/year)
- Sepsis early warning (patient safety + €450K/year)
- 5 more operational models (€1.1M/year combined)
- Repeatable deployment process: 75% pilot success rate
- €5M annual value from AI (Year 2)
- AI embedded in clinical workflows
Total Investment: €3.2M over 24 months (team + infrastructure + training)
Total Value: €5.8M cumulative (€0.8M Year 1 + €5M Year 2)
ROI: 81% by end of Year 2 (continuing to compound)
Key Success Factors:
- Executive sponsorship: CMO personally championed AI, removed barriers
- Clinical focus: Prioritized use cases clinicians cared about (no-shows, readmissions, sepsis)
- Quick wins: First production model (no-show prediction) in 8 months built credibility
- Change management: 200+ clinical leaders trained on AI, involved early
- Systematic progression: Didn't skip maturity levels, built foundations first
Current State (30 Months):
- Progressing toward Level 4 (AI Integrated)
- Planning to deploy 15 additional models over next 12 months
- Expanding to revenue cycle and supply chain use cases
- Target: €12M annual value by Month 36
Action Plan: Assess and Progress Your AI Maturity
Quick Wins (This Week):
Step 1: Complete AI Maturity Assessment (1 hour)
- Score your organization across 8 dimensions (1-5)
- Calculate average maturity score
- Identify weakest dimension (the bottleneck)
- Document assessment results
Step 2: Benchmark Against Industry (30 minutes)
- Research AI maturity in your industry
- Identify 2-3 competitors' AI capabilities (public information)
- Assess competitive gap (are you ahead, behind, or on par?)
- Document competitive landscape
Step 3: Identify Next-Level Actions (1 hour)
- Based on current level, identify top 3-5 actions for next level
- Estimate investment required (time, budget, people)
- Identify executive sponsor candidate
- Draft 30-60-90 day roadmap
Near-Term (Next 30-60 Days):
Step 4: Present Assessment to Leadership (Week 1-2)
- Create executive summary of maturity assessment
- Present current state, competitive gap, and path forward
- Request budget and resources for progression
- Secure executive sponsor commitment
Step 5: Address Weakest Dimension (Week 3-8)
- Focus on bottleneck (lowest scoring dimension)
- If Data: Launch data quality initiative
- If Organization: Form AI team or hire key roles
- If Technology: Establish basic infrastructure
- If Strategy: Develop AI roadmap
Step 6: Launch Quick Win Project (Week 4-12)
- Select high-value, achievable AI use case
- Form small team (2-4 people)
- Set 90-day goal (pilot or production model)
- Track progress weekly, report monthly
- Celebrate success and share learnings
Strategic (3-12 Months):
Step 7: Execute Maturity Progression Plan (Months 3-12)
- Follow roadmap for progressing to next level (see framework above)
- Track progress against maturity assessment monthly
- Adjust plan based on learnings and results
- Build momentum through visible successes
- Expand AI awareness and engagement across organization
Step 8: Reassess Maturity (Month 12)
- Repeat AI maturity assessment after 12 months
- Compare scores: Current vs. 12 months ago
- Measure business value achieved
- Identify next level priorities
- Celebrate progress and reset goals for next 12 months
The Journey to AI Maturity
AI maturity isn't achieved overnight—it's a multi-year journey requiring systematic investment in strategy, organization, data, technology, governance, skills, processes, and culture.
The good news: Organizations at any maturity level can create business value. Level 2 organizations generate €100K-€500K annually. Level 3 organizations generate €1M-€5M. Level 4 organizations generate €10M-€50M+.
The key is knowing where you are, understanding what's required to reach the next level, and systematically addressing gaps. Organizations that try to skip levels waste time and money. Organizations that progress systematically achieve production AI 3-4x faster.
Most importantly, AI maturity is a team sport. It requires collaboration between data scientists, engineers, business leaders, and executives. No single function can do it alone.
If you're unsure about your organization's AI maturity or struggling to progress to the next level, you're not alone. Most organizations overestimate their maturity and underestimate what's required to scale AI.
I help organizations assess AI maturity and create systematic progression plans. The typical engagement involves:
- AI Maturity Assessment (1-2 days): Evaluate current state across 8 dimensions with your leadership team
- Gap Analysis & Roadmap (2-4 weeks): Identify bottlenecks, prioritize actions, create 12-18 month roadmap
- Execution Support (3-12 months): Coach team through progression to next level, remove barriers, track progress
→ Book a 30-minute AI maturity consultation to discuss your current state and create a roadmap for progression.
Download the AI Maturity Assessment Template (Excel) with scoring rubric and progression roadmap: [Contact for the assessment]
Further Reading:
- "AI Maturity Framework" by Gartner
- "Building AI Capability" by MIT Sloan
- "Competing in the Age of AI" by Marco Iansiti and Karim Lakhani