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.

  • 1 Engineering Discipline — Prompt engineering rigor, human-in-the-loop, AI output verification
  • 2 Governance & Risk — Code provenance, audit policy, IP protection, security frameworks
  • 3 Productivity Architecture — Workflow optimization, toolchain integration, metrics
  • 4 Operating Model — Sprint adaptation, estimation, team structure, change management
  • 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
TimelineWeeks 1-4Months 1-3Months 3-6
Scope1-2 pilot teams5-10 teamsAll engineering teams
GovernanceBaseline policiesFormal frameworkOrganization-wide policy
ToolingTool assessmentCI/CD integrationAI-first automation
PeopleTraining cohortCommunities of practiceEnterprise prompt eng.
MetricsBaselinesKPI dashboardsMaturity certification
MilestonePilots operationalGates automatedCertification 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

Key for Adoption

When every role understands their part, adoption is not a top-down mandate — it is a shared operating system.

Browse all role guides at aeef.ai/roles

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)

Week 2: Launch

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.

Start Quick-Start Plan Transformation View Standards

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