The Case for Standards
AI-assisted engineering is accelerating. Without governance, speed becomes liability.
Two Tracks, One Framework
Choose your entry point based on where your organization is today.
Transformation
For organizations adopting AI-assisted engineering for the first time. A phased journey from foundation to enterprise scale.
- 3-phase adoption roadmap (0-18 months)
- Operating model lifecycle integration
- Governance gate implementation
- Maturity assessment framework
Production Efficiency
For teams already using AI tools who need day-to-day standards, best practices, and governance guardrails.
- 8 formal production standards (PRD-STD)
- Best practices for AI pair programming
- Tool integration guides
- Quality gates and security scanning
The Five Pillars
AEEF is built on five structural pillars that together form a complete enterprise framework for AI-accelerated engineering.
Role-Based Guides
Every role has a different relationship with AI-assisted engineering. Find the guidance tailored to your responsibilities.
Developer
Daily workflows, prompt engineering, code review, security awareness
Development Manager
Team enablement, quality oversight, metrics, tooling decisions
Scrum Master
Sprint adaptation, estimation, ceremony adjustments, team health
Product Manager
Roadmap planning, stakeholder expectations, velocity trade-offs
CEO / Executive
Strategic imperative, risk governance, investment ROI, board metrics
CTO / VP Engineering
Technology strategy, architecture, build vs buy, org design
QA / Test Lead
Testing strategy, AI test coverage, defect analysis, automation
Maturity Model
AEEF defines a five-level maturity progression for AI-assisted engineering adoption.