# 📊 AI Platform Maturity Assessment Framework

## Purpose
This framework enables structured, honest assessment of an organization's AI platform capabilities across five dimensions. Use it for baseline audits, roadmap planning, and executive reporting.

## Assessment Dimensions

### 1. Technical Platform
- Self-service deployment & inference
- Evaluation & red-teaming harnesses
- Feature & vector platforms
- Observability, lineage, and audit trails
- Cost attribution and optimization tooling

### 2. Governance & Risk
- Risk tiering methodology
- Automated policy enforcement
- Model & system cards in production
- Continuous monitoring & drift detection
- Incident response playbooks

### 3. Developer Experience & Adoption
- Time-to-first-value for new practitioners
- Documentation & internal education
- Internal community health
- Platform NPS and support ticket volume

### 4. Economics & Value
- Cost per unit of capability (tokens, queries, agents)
- Clear chargeback or value attribution
- Portfolio-level ROI visibility
- Optimization programs with measured results

### 5. Team & Operating Model
- Platform team size, skills, and product mindset
- Clear ownership boundaries with product teams
- OKRs and success metrics for the platform itself
- Relationship with central risk, legal, and security functions

## Scoring Guidance

For each dimension, score 1-5 using the Aether Maturity Model levels. Provide specific evidence (artifacts, metrics, or observed behaviors) rather than aspirations. The gap between current and target state becomes the strategic roadmap.

## Recommended Cadence
- Full platform audit: annually or after major strategic shifts
- Lightweight health check: quarterly
- Real-time dashboards for leading indicators (adoption, cost, incident rate, developer friction signals)