Internal Team Presentation
Introducing AEEF
The AI-Accelerated Enterprise Engineering Framework
Enterprise Standards for AI-Assisted Software Development
aeef.ai
Why This Matters
AI Is Already Here. Governance Isn't.
92%
of US developers use AI coding tools daily
1.7x
more major issues in AI co-authored code
2.74x
higher security vulnerability rate
41%
of global code is now AI-generated
Sources tracked in Research Evidence Register
The Framework
What Is AEEF?
The AI-Accelerated Enterprise Engineering Framework provides
governance-embedded, measurable enterprise standards for AI-assisted software engineering.
What It Delivers
- Production-ready operating model for AI-assisted development
- Enforceable standards using RFC 2119 language (MUST, SHOULD, MAY)
- Role-based guidance for every team member
- Measurable maturity progression with KPIs
Design Principles
- Governance-embedded — built in, not bolted on
- Measurable — every standard has KPIs
- Transformation-ready — phased adoption roadmap
- Open-source — free to use and adapt
Framework Core
Five Pillars, One Operating System
AEEF covers the full delivery system: standards, controls, team behavior, and enablement.
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1
Engineering Discipline — Prompt engineering rigor, human-in-the-loop, AI output verification
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2
Governance & Risk — Code provenance, audit policy, IP protection, security frameworks
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3
Productivity Architecture — Workflow optimization, toolchain integration, metrics
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4
Operating Model — Sprint adaptation, estimation, team structure, change management
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5
Organizational Enablement — Training, culture, maturity assessment, center of excellence
Explore all pillars at aeef.ai/pillars
Start Here
Choose Your Adoption Path
Pick the entry point that matches your operating reality.
Quick-Start
Launch this week
- Day-1 checklist by team size
- Copy-paste CI & policy configs
- Hands-on first-PR tutorial
Quick-Start Guide
Transformation Track
6-month phased rollout
- Foundation, expansion, enterprise phases
- Operating model lifecycle
- Maturity progression
Transformation Track
Production Standards
Enforceable controls
- 16 PRD-STD standards
- Quality, testing, security gates
- Audit-ready evidence
PRD-STD Library
Path 1
Quick-Start: Launch in 1 Day
For startups and small teams who need AI governance without slowing down.
Day-1 Checklist
- Select and approve AI coding tools
- Set baseline security policy (acceptable use, data classification)
- Configure CI pipeline with AI quality gates
- Run your first AI-assisted PR with the review checklist
- Establish measurement baseline (velocity, defect rate)
What You Get Immediately
- Acceptable Use Policy template
- CI/CD pipeline starter config
- AI-specific code review checklist
- Self-assessment scorecard
- Step-by-step first PR tutorial
Start now at aeef.ai
Path 2
Transformation Track: 6-Month Roadmap
Structured, phased adoption for organizations scaling AI across engineering teams.
Phase 1: Foundation
Weeks 1-4
1-2 pilot teams
Tool assessment
Baseline policies
Phase 2: Expansion
Months 1-3
5-10 teams
Governance framework
CI/CD integration
Phase 3: Enterprise
Months 3-6
All teams
Org-wide policy
Maturity certification
Full roadmap at aeef.ai/transformation
Transformation Detail
Phase-by-Phase Roadmap
| Aspect |
Phase 1: Foundation |
Phase 2: Expansion |
Phase 3: Enterprise |
| Timeline | Weeks 1-4 | Months 1-3 | Months 3-6 |
| Scope | 1-2 pilot teams | 5-10 teams | All engineering teams |
| Governance | Baseline policies | Formal framework | Organization-wide policy |
| Tooling | Tool assessment | CI/CD integration | AI-first automation |
| People | Training cohort | Communities of practice | Enterprise prompt eng. |
| Metrics | Baselines | KPI dashboards | Maturity certification |
| Milestone | Pilots operational | Gates automated | Certification awarded |
Continuous Process
Operating Model Lifecycle
Six stages for every AI-assisted development initiative, regardless of phase.
1
Business Intent
Capture need & success criteria
2
AI Exploration
Time-boxed prototyping in sandbox
3
Human Hardening
Expert review & security analysis
4
Governance Gate
Compliance & quality checkpoint
5
Controlled Deploy
Canary releases with monitoring
6
Post-Impl. Review
Outcomes & lessons learned
Full lifecycle at aeef.ai/transformation/operating-model
Path 3
16 Production Standards (PRD-STD)
Enforceable controls for AI-assisted engineering using RFC 2119 language.
001Prompt Engineering
002Code Review
003Testing Requirements
004Security Scanning
005Documentation
006Technical Debt
007Quality Gates
008Dependency Compliance
009Multi-Agent Gov.
010AI Product Safety
011Model & Data Gov.
012Inference Reliability
013Multi-Tenant AI
014AI Privacy & Rights
015Multilingual AI
016Channel Governance
Full standards library at aeef.ai/production/standards
By Role
Guidance Tailored to Every Role
Each role has a dedicated playbook with specific responsibilities, actions, and KPIs.
Developer
Development Manager
Scrum Master
Product Manager
Executive
CTO / VP Engineering
Solution Architect
QA / Test Lead
Security Engineer
Platform Engineer
Compliance Officer
Each Playbook Includes
- Role-specific responsibilities in AI-assisted delivery
- Week-by-week actions aligned to transformation phases
- Standards to enforce and KPIs to track
- Common pitfalls and how to avoid them
By Capability
Five-Level Maturity Model
Assess where you are today and chart a clear path to AI-first operations.
L1
Uncontrolled — No governance, shadow IT, individual tool choices
L2
Exploratory — Informal guidelines, pilot teams, initial tool evaluation
L3
Defined — Formal standards, approved toolchains, mandatory training
L4
Managed — Fully integrated governance, KPI dashboards, automated scanning
L5
AI-First — AI-native workflows, predictive analytics, competitive advantage
Assessment checklists at aeef.ai/pillars/maturity |
Self-assessment tool
Measurement
KPI Framework
Measure what matters across three dimensions with executive-ready metrics.
Risk Metrics
- Vulnerability density in AI code
- Policy compliance rate
- Incident frequency
- Audit finding closure time
Productivity Metrics
- Developer velocity change
- AI-assisted code acceptance rate
- Cycle time improvement
- Review turnaround time
Financial Metrics
- Cost per feature delivery
- AI tool ROI
- Defect remediation cost
- Time-to-value acceleration
Full KPI framework at aeef.ai/pillars/kpi
Getting Started
How to Introduce AEEF to Your Team
Week 1: Assess & Align
- Run the Self-Assessment to baseline your maturity
- Identify an executive sponsor and name a Phase Lead
- Select 1-2 pilot teams with willing developers
- Choose your adoption path (Quick-Start or Transformation)
Ready to Use
Resources Available Today
Policy Templates
Acceptable Use, Data Classification, Incident Response, Tool Evaluation Scorecard
Download templates
CI/CD Starter Configs
Reference pipeline patterns with AI quality gates baked in
Get configs
Prompt Library
14+ role-based prompts, 4 languages, 6 frameworks, 7+ use-case templates
Browse prompts
Scenario Tutorials
Python, Next.js, Django, Spring Boot, Go — end-to-end AI-assisted scenarios
View tutorials
Code Review Checklist
AI-specific review checklist covering provenance, quality, and security
Get checklist
Integration Guides
Claude Code, Cursor, and other AI tool integration configurations
View guides
Results
Expected Outcomes
Organizations that complete the full transformation should expect:
▲
30-50% improvement in developer velocity on AI-amenable tasks
■
No increase in vulnerability density vs. pre-adoption baselines
●
Standardized, auditable AI usage with full traceability
★
Maturity certification evidenced by formal assessment
↻
Self-sustaining improvement loops that refine practices continuously
⚙
Reduced compliance risk with governance built into daily workflows
Before You Start
Prerequisites for Success
Organizational Requirements
- Executive sponsorship — Named C-level or VP sponsor with budget authority
- Baseline SDLC maturity — Existing version control and CI/CD
- Security foundations — Established AppSec program
- Developer willingness — Teams receptive to AI workflows
- Regulatory awareness — Understanding of SOC 2, HIPAA, PCI-DSS, GDPR constraints
What AEEF Does Not Require
- Specific AI tool vendor lock-in
- Dedicated AI/ML engineering team
- Custom model training or fine-tuning
- Large upfront investment — start with pilot teams
Read the FAQ at aeef.ai/pillars/faq
Build an AI Engineering System,
Not Just AI Habits
Start with standards, enforce through workflow, and scale through governance
that your teams can actually run.
aeef.ai
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Open-source framework
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info@codemeld.io