7.4 Maturity Model & Roadmap
Adopting the AI Innovation model is a journey, not a destination. Organizations progress through maturity levels as they build capabilities, refine processes, and embed new ways of working. This maturity model provides a framework for assessing current state, setting realistic goals, and planning the progression toward AI Innovation excellence.
No organization achieves Level 5 maturity immediately—or perhaps ever. The goal is continuous improvement, not perfection. Each level builds on the previous one. Attempting to skip levels typically results in superficial adoption that collapses under pressure.
AI Innovation Maturity Levels
Level 1: Initial
AI development is ad-hoc, project-based, with no consistent structure.
Characteristics
- AI work organized as projects, not products
- No clear ownership model
- Handoffs between teams common
- Governance is afterthought or absent
- Success depends on heroic individuals
Typical Symptoms
- Models rarely make it to production
- Models degrade quickly after deployment
- No one knows who owns what
- Compliance issues discovered late
- Difficulty retaining AI talent
Level 2: Developing
Beginning to establish pod structures and ownership, but inconsistently applied.
| Dimension | Level 2 State |
|---|---|
| Structure | Some teams organized as pods; others still project-based |
| Ownership | STOs exist but authority unclear; still rely on committees |
| Governance | Model Cards exist but quality varies; governance still mostly external |
| Ways of Working | Agile ceremonies adopted; ML experiments still separate from engineering |
| Tooling | Basic MLOps in place; manual processes still common |
Level 3: Defined
AI Innovation model is standardized and consistently applied across the organization.
- All AI products have designated STOs with clear authority
- Pods follow consistent structure with core roles filled
- Ethics Liaisons embedded in all pods
- Model Cards required for all production deployments
- Risk-tiered governance consistently applied
- Standard MLOps stack deployed across pods
- AI Council actively governing portfolio
Level 4: Managed
Quantitative management with data-driven improvement of AI Innovation operations.
| Dimension | Level 4 State |
|---|---|
| Metrics | Comprehensive pod health metrics tracked; baselines established |
| Prediction | Can predict pod outcomes based on leading indicators |
| Automation | Governance guardrails highly automated; self-service infrastructure |
| Scaling | Mitosis strategy actively used; platform teams mature |
| Learning | Systematic knowledge sharing across pods; failure analysis routine |
Level 5: Optimizing
Continuous improvement embedded; organization adapts and evolves the model itself.
Characteristics
- Innovation in operating model itself
- Pods experiment with new practices
- External recognition as industry leader
- Attracts top AI talent based on reputation
- Influences industry standards
Indicators
- Pod velocity continuously improving
- Zero governance incidents despite speed
- Time to value for new AI products industry-leading
- Organization contributes back to AI community
- Model evolves based on emerging best practices
Self-Assessment Framework
Assessment Dimensions
Evaluate your organization across these key dimensions:
| Dimension | Level 1 | Level 3 | Level 5 |
|---|---|---|---|
| Ownership Model | No clear owners | STOs for all products | Ownership culture pervasive |
| Pod Structure | Project teams | Stable pods, standard roles | Self-organizing, adaptive pods |
| Governance | Ad-hoc or absent | Embedded, risk-tiered | Automated, predictive |
| Lifecycle Management | Build & forget | Cradle-to-grave practiced | Proactive lifecycle optimization |
| MLOps Maturity | Manual, inconsistent | Standardized, automated | Self-service, self-healing |
| Metrics & Learning | Anecdotal | Systematic tracking | Predictive, continuous improvement |
| Scaling Capability | Linear, constrained | Mitosis-enabled | Organic, effortless growth |
Assessment Process
Gather Evidence
Collect data: pod health metrics, governance compliance rates, velocity measures, stakeholder feedback, incident history.
Rate Each Dimension
Have multiple stakeholders independently rate each dimension. Calibrate through discussion.
Identify Gaps
Compare current state to target state. Prioritize gaps based on impact and feasibility.
Create Improvement Plan
Define specific actions to address priority gaps. Assign owners and timelines.
Implementation Roadmap
Phase 1: Foundation
Establish the basic AI Innovation structure and governance.
- Pilot 2-3 AI Innovations with willing teams
- Train and appoint initial STOs
- Establish Model Card template and process
- Create risk tier classification framework
- Deploy basic MLOps infrastructure
- Form initial AI Council
- Develop training materials and onboarding
Phase 2: Expansion
Expand the model across the organization while refining based on learnings.
| Workstream | Key Activities | Success Criteria |
|---|---|---|
| Pod Formation | Transition remaining AI teams to pod model | All AI products have designated pods |
| Governance | Embed Ethics Liaisons in all pods; automate guardrails | 100% Model Card compliance; automated checks in CI/CD |
| Tooling | Standardize MLOps stack; deploy governance tools | All pods on standard platform |
| Training | Certify all Ethics Liaisons; STO development program | All key roles trained and certified |
| Metrics | Establish pod health dashboards; baseline metrics | Metrics tracked for all pods |
Phase 3: Optimization
Refine and optimize the model based on quantitative data.
Process Optimization
- Analyze velocity bottlenecks
- Streamline governance processes
- Reduce toil through automation
- Optimize pod composition patterns
Scaling Enablement
- Mature platform team capabilities
- Refine mitosis playbook
- Develop shared services catalog
- Create self-service capabilities
Knowledge Management
- Cross-pod learning programs
- Failure analysis and sharing
- Best practice documentation
- Internal community of practice
Phase 4: Excellence
Achieve and maintain industry-leading AI Innovation operations.
- Innovation Focus: Pods experiment with new practices; organization evolves the model
- Predictive Operations: Leading indicators predict issues before they occur
- External Recognition: Share learnings externally; attract talent based on reputation
- Continuous Evolution: Model adapts to new AI technologies and business needs
Critical Success Factors
Executive Sponsorship
Sustained executive support is non-negotiable:
- Middle management will resist power shifts
- Resource allocation will revert to old patterns
- Competing priorities will override transformation
- Accountability will remain diffused
Early Wins
Demonstrate value quickly to build momentum:
- Choose pilots wisely: Select teams likely to succeed; avoid the hardest cases first
- Measure and communicate: Track and share velocity improvements, quality gains
- Celebrate publicly: Recognize early adopters and their successes
- Learn and adapt: Use pilot learnings to refine approach before broader rollout
Persistence Through the Dip
Expect and prepare for the "implementation dip":
Enthusiasm
New model generates excitement. Early adopters eager to try new approaches.
The Dip
Reality sets in. New skills required. Old habits persist. Temporary productivity loss as people learn. Resistance increases.
Recovery
New habits form. Skills develop. Benefits become visible. Productivity recovers and exceeds previous levels.
Excellence
Model becomes "how we work." Continuous improvement kicks in. Sustained performance gains.
Measurement Discipline
What gets measured gets managed:
Adaptability
The AI Innovation model must evolve with your organization:
- Context matters: Adapt the model to your organizational culture, not vice versa
- Learn continuously: Gather feedback and adjust based on what works
- Stay current: As AI technology evolves, the operating model must evolve too
- Avoid rigidity: Principles matter more than specific practices
The Journey Is the Destination
Organizations that thrive with AI don't reach a final state—they build the capability to continuously adapt. The AI Innovation model provides a framework for this adaptation: clear ownership enables fast decisions, embedded governance enables safe experimentation, and the cradle-to-grave mindset ensures learning accumulates. The goal is not to implement the perfect operating model, but to build an organization that can continuously improve how it develops, deploys, and operates AI. That capability—the ability to get better at getting better—is the ultimate competitive advantage in the age of AI.