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Home · Getting Started · Model · Assessment Hub · References

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

4. Templates and Tooling

  • 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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