Quality & Risk Oversight
When AI tools accelerate code production by 30-50%, quality assurance must scale proportionally or defects will accumulate. As a development manager, you are responsible for ensuring that the speed benefits of AI-assisted development are not purchased with quality debt. This section provides the review processes, risk indicators, escalation procedures, and dashboard designs you need to maintain quality at velocity. It implements Pillar 2: Quality Assurance and Pillar 3: Governance & Oversight at the team level.
The Quality Challenge
AI-assisted development creates a specific quality dynamic that differs from traditional development:
| Traditional Development | AI-Assisted Development |
|---|---|
| Code volume limited by typing speed and thinking time | Code volume limited only by review capacity |
| Defects correlate with developer fatigue and complexity | Defects correlate with review thoroughness and prompt quality |
| Quality bottleneck: writing correct code | Quality bottleneck: reviewing generated code |
| Technical debt accumulates slowly | Technical debt can accumulate rapidly if reviews are superficial |
The fundamental insight is this: in AI-assisted development, the quality bottleneck shifts from code generation to code review. Your processes must reflect this shift.
Review Process Design
Enhanced Code Review Requirements
All code containing AI-generated content must undergo enhanced review per PRD-STD-002. As a development manager, you are responsible for ensuring these processes are followed.
Mandatory review elements for AI-assisted PRs:
- Author self-review attestation. The PR author confirms they have reviewed and understand all AI-generated code. This should be a checkbox in your PR template.
- AI disclosure. PRs should indicate which portions involved AI assistance. This is not about blame -- it is about directing reviewer attention.
- Security scan results. Automated security scanning must pass before human review begins.
- Test coverage verification. New code must meet minimum coverage thresholds. AI-generated tests must themselves be reviewed for quality.
- Peer review. At least one human reviewer must approve, with enhanced scrutiny per the Code Review Responsibilities checklist.
Review Capacity Planning
As code velocity increases, plan review capacity explicitly:
| Team Size | Recommended Review Time | Review Load Per Developer |
|---|---|---|
| 4-6 developers | 1-1.5 hours/day | 2-3 PRs/day |
| 7-10 developers | 1.5-2 hours/day | 3-4 PRs/day |
| 11+ developers | 2+ hours/day or dedicated reviewers | 4+ PRs/day |
If review queues consistently exceed 24 hours, your team's code generation pace has outrun its review capacity. This is a leading indicator of quality problems. Either slow down generation, add review capacity, or invest in better automated checks.
Risk Indicators
Monitor these indicators to detect quality degradation early.
Leading Indicators (Predict Future Problems)
| Indicator | Healthy Range | Warning Threshold | Action |
|---|---|---|---|
| PR review queue age | < 12 hours | > 24 hours | Add review capacity or slow generation |
| AI code acceptance rate (team avg) | 40-70% | > 80% | Investigate insufficient review rigor |
| Test coverage on new code | > 80% | < 70% | Enforce coverage gates in CI |
| Security scan findings per sprint | Trending down | Trending up | Security review session, training refresh |
| Prompt quality score (peer-rated) | > 3.5/5 | < 3/5 | Prompt engineering training |
Lagging Indicators (Confirm Existing Problems)
| Indicator | Healthy Range | Warning Threshold | Action |
|---|---|---|---|
| Defects per sprint (AI-assisted code) | At or below team baseline | > 1.5x baseline | Root cause analysis, process review |
| Production incidents from AI code | Zero or trending down | Any increase | Immediate investigation, escalation |
| Rework rate (changes after review) | < 15% | > 25% | Improve prompting, strengthen first review |
| Customer-reported bugs in new features | Stable or declining | Increasing | Comprehensive quality audit |
| Security vulnerabilities in AI code | Zero critical/high | Any critical/high | Immediate remediation, process review |
Quality Dashboard
Build a team-level quality dashboard that provides at-a-glance visibility into these metrics. Update it weekly and review it in your team's retrospective.
Dashboard sections:
- Quality Trend. Line chart showing defect density over the past 6 sprints, with a separate series for AI-assisted code vs. manually-written code.
- Review Health. Bar chart showing PR review queue age distribution and average review time.
- Security Posture. Count of open security findings by severity, trend over time.
- Test Coverage. Coverage percentage for AI-assisted code vs. team target.
- Risk Heatmap. Color-coded view of leading indicators: green (healthy), yellow (warning), red (action needed).
Share this dashboard with your CTO and use it to feed the Board-Ready Metrics reporting chain.
Escalation Procedures
Define clear escalation paths so that quality issues are addressed at the appropriate level.
Level 1: Team-Level Resolution
Trigger: Individual PR with quality issues, isolated defect in AI-generated code, single security finding.
Action: Address through normal code review feedback, pair with the developer on better prompting, update team checklist if the issue reveals a gap.
Owner: Developer + reviewer.
Level 2: Manager Intervention
Trigger: Pattern of quality issues across multiple PRs, review process not being followed, developer consistently producing low-quality AI-assisted code.
Action: One-on-one coaching, additional training, temporary pairing requirement, process adjustment. Document the pattern and the intervention.
Owner: Development manager.
Level 3: Cross-Team Escalation
Trigger: Quality issue affects other teams or services, systematic tool-level problem, security vulnerability with broad exposure.
Action: Engage QA Lead for defect analysis, engage security team for vulnerability assessment, engage CTO for tool-level decisions. Coordinate with Scrum Master on sprint impact.
Owner: Development manager + relevant stakeholders.
Level 4: Organizational Escalation
Trigger: Production incident caused by AI-generated code, compliance or regulatory impact, customer data exposure.
Action: Invoke incident response process, engage Executive leadership per Risk & Governance Executive Summary. Consider temporary suspension of AI tool usage for affected code areas.
Owner: Development manager + CTO + security team.
Risk Mitigation Strategies
Automated Quality Gates
Invest in automated checks that run before human review to catch the most common AI code issues:
- Static analysis configured with rules targeting common AI patterns (hardcoded values, missing error handling)
- Security scanning (SAST/DAST) with rulesets tuned for AI vulnerability patterns
- Test coverage enforcement with minimum thresholds per PRD-STD-003
- Dependency scanning for outdated or vulnerable packages suggested by AI
- Code complexity limits to prevent AI from generating overly complex solutions
Process Safeguards
- Mandatory PR templates with AI-specific checklists
- Branch protection rules requiring passing checks and human approval
- Weekly quality review where the team discusses AI-related quality findings
- Monthly security audit sampling AI-generated code for vulnerability patterns
For testing strategy specifics, see the QA Lead Guide: Testing Strategy. For defect pattern analysis, see Defect Pattern Analysis. This page focuses on management oversight; those pages focus on technical execution.