AI Adoption Maturity Model (SDLC-Oriented)
Overview
This model defines AI adoption across four dimensions:
- Coverage (SDLC stages touched)
- Autonomy (AI vs human execution)
- Trust (quality and predictability)
- Governance (traceability and control)
Stage 0 — Baseline (Pre-AI)
Purpose
Establish a control group for comparison.
Metrics
- Lead Time = Time from ticket creation → production
- Cycle Time = Time from work start → completion
- Defect Rate = Defects / tickets
- MTTR = Mean time to resolve incidents
Example
- Lead Time: 24 days
- Cycle Time: 10 days
- Defect Rate: 0.3 per ticket
Stage 1 — Non-Degrading Augmentation
Goal
Increase AI usage without degrading delivery quality.
Metrics
AI Code Contribution %
AI_Code_% = (AI-generated LOC / Total LOC) * 100
Quality Parity Index
QPI = (Defect rate AI code) / (Defect rate human code)
Target: ~1.0 or lower
Review Rejection Rate
Rejection Rate = Rejected PRs / Total PRs (AI vs Human)
Example
- AI Code: 45%
- QPI: 0.95 (AI slightly better)
- Rejection Rate: 12% AI vs 10% human
Stage 2 — Assisted Execution
Goal
AI contributes to execution tasks with human oversight.
Metrics
AI-Assisted Ticket %
AI_Ticket_% = (Tickets with AI assistance / Total tickets) * 100
Human Touchpoints per Ticket
Touches = Number of manual interventions per ticket
Time-to-Merge Reduction
TTM Reduction = (Baseline TTM - Current TTM) / Baseline TTM
Example
- 60% tickets AI-assisted
- Avg touches: 3 per ticket
- TTM reduced by 20%
Stage 3 — Multi-Step Agentic Workflows
Goal
AI executes chained workflows across SDLC steps.
Metrics
Workflow Automation Depth
Depth = Number of SDLC steps executed by AI per ticket
Failure Rate
Failure Rate = Failed workflows / Total workflows
Override Frequency
Overrides = Human interventions / workflows
Example
- Avg depth: 4 steps (code + test + PR + deploy draft)
- Failure rate: 8%
- Override: 25%
Stage 4 — Full SDLC Coverage
Goal
AI is present across all SDLC stages.
Coverage Areas
- Business Analysis
- Requirements
- Architecture
- Development
- Testing
- Release
- Support artifacts
Metrics
Artifact Coverage %
Coverage = (AI-assisted artifacts / Total artifacts) * 100
Consistency Score
Consistency = Alignment between requirements, code, and tests (manual or automated scoring)
Example
- Coverage: 70%
- Consistency: High (traceable links between artifacts)
Stage 5 — Autonomous Delivery
Goal
AI executes end-to-end delivery with human oversight.
Metrics
Fully AI-Executed Tickets %
AI_E2E_% = (Tickets completed end-to-end by AI / Total tickets) * 100
Exception Rate
Exception Rate = Tickets requiring human intervention / AI tickets
Rollback Rate
Rollback Rate = Rollbacks / deployments
Example
- 30% fully AI-executed
- Exception rate: 15%
- Rollback rate: 3%
Stage 6 — Governed Intelligence (Provenance-Aware)
Goal
All decisions are traceable and explainable.
Metrics
Decision Coverage %
Decision Coverage = Decisions with full metadata / Total decisions
Decision Reuse Rate
Reuse Rate = Reused decisions / Total decisions
Conflict Detection Rate
Conflict Rate = Conflicting decisions detected / Total decisions
Example
- 80% decisions tracked
- 25% reused
- Conflicts detected early in 10% cases
Stage 7 — Self-Optimizing System
Goal
System improves itself using feedback loops.
Metrics
Decision Success Rate
Success Rate = Successful decisions / Total decisions
Skill Efficiency Score
Efficiency = Outcome quality / Cost (tokens + time)
Drift Detection
Measure deviation in model outputs over time
Example
- Success rate improves from 70% → 85%
- Efficiency increases 30% over quarter
Summary
Stages: 0. Baseline
- Safe Augmentation
- Assisted Execution
- Agentic Workflows
- Full SDLC Coverage
- Autonomous Delivery
- Governed Intelligence
- Self-Optimizing System