Quick-Start
Launch controlled AI-assisted delivery this week.
Best for startups and small teams moving fast.
- Day-1 checklist by team size
- Copy-paste CI and policy starter configs
- Hands-on tutorial from first PR to release
Start Here
Choose Your Adoption Path
Whether you are standing up a team or scaling across the enterprise, pick the entry point that matches your operating reality.
Transformation Track
Adopt AI across teams with phased governance.
Best for organizations scaling AI usage for the first time.
- Foundation, expansion, and enterprise-scale phases
- Operating model lifecycle and gate design
- Org-level capability and maturity progression
Production Standards
Run AI-assisted engineering with enforceable controls.
Best for teams already shipping with AI in active products.
- Normative PRD-STD controls using RFC 2119 language
- Quality, testing, and security requirements
- Auditability, provenance, and policy-ready evidence
Most Used
Core Resources Teams Use Every Week
Jump directly to the assets that typically anchor implementation and governance.
PRD-STD Standards
Formal controls for prompt engineering, review, testing, and security.
CI/CD Starter
Reference pipeline patterns for quality gates in AI-assisted delivery.
KPI Framework
Measure risk, productivity, and financial outcomes with executive-ready metrics.
Maturity Model
Assess capability from uncontrolled adoption to AI-first operations.
Role Guides
Operational playbooks for developers, managers, security, QA, and executives.
Release Updates
Track framework evolution and standards change history.
Why AEEF
AI Adoption Is Not Optional. Governance Is.
The velocity gains are real, but unmanaged AI-assisted delivery compounds defects, vulnerabilities, and audit exposure.
74%
of developers worldwide adopted AI coding tools by 2026
95%
of developers use AI tools at least weekly
51%
of code commits are AI-assisted (GitHub 2026)
46%
developer preference for Claude Code (most loved)
Framework Core
Five Pillars, One Operating System for AI Engineering
AEEF is structured to cover the full delivery system: standards, controls, team behavior, and organization-level enablement.
Explore all pillarsBy Role
Guidance Tailored to Accountability
Align daily engineering decisions, management controls, and executive oversight on one framework.
By Capability
Maturity Progression You Can Operationalize
Move step-by-step from ad hoc AI usage to a controlled, measurable AI-first model.
Build Your AI Engineering System, Not Just AI Habits
Start with standards, enforce through workflow, and scale through governance that teams can actually run.