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Roadmap
This is a rough sketch of where the Open AI Transformation Maturity Model project might head. It’s not a fixed plan — more like “best guess right now.” As people start using the model, we’ll probably discover blind spots and change priorities.
Current status: Initial public release (v0.1.0). Useful, but definitely not finished.
Near-Term Priorities
1. Evidence Base Expansion
- Collect real case studies for Levels 3–5 (preferably not marketing fluff)
- See what patterns repeat across different industries
- Capture failure cases too — those are honestly more instructive
2. Assessment Framework Refinement
- Clarify pillar definitions where people keep asking “what does this mean?”
- Test whether different facilitators produce similar scores
- Add guidance so results aren’t purely subjective
- Reduce overlap between adjacent levels
3. Visual Artifacts
- Produce one clear diagram that everyone can point to
- Show possible transformation paths, not just a staircase
- Create materials that hold up in workshops
- Self-assessment worksheets
- Workshop scorecards
- Benchmark submission templates
- Simple visualization support
5. Domain Adaptations
Different sectors operate under very different constraints, so customization will likely be needed. Early candidates:
- telecom / network software
- finance
- safety-critical systems
- SaaS companies
- platform engineering orgs
Mid-Term Goals
If adoption grows:
- Community-maintained case study library
- Benchmark datasets (if enough data accumulates)
- Guidance on tracking transformation over time
- Alignment with other maturity frameworks where useful
Long-Term Vision
The aim is to build an open reference model for AI-native software engineering that people actually use in practice.
In a best-case scenario, it would become comparable in usefulness to frameworks like:
- CMMI (historically)
- DORA metrics
- Agile maturity models
- DevOps capability frameworks
Contribution Opportunities
Input is especially valuable from people with hands-on experience, including:
- empirical research
- large-scale engineering leadership
- regulated industries
- evaluation and testing methods
- architecture modernization work
- governance for autonomous systems
Practical lessons — especially the messy ones — are welcome.
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