Phase 3: Enterprise Scale (Months 3-6)

Phase 3 achieves enterprise-wide AI-assisted engineering with organization-wide policies, advanced prompt engineering standards, AI-first development workflows, continuous improvement loops, and maturity certification. Where Phase 1 proved the concept and Phase 2 built scalable governance, Phase 3 makes AI-assisted engineering the default way of working across the entire organization. By the end of this phase, AI assistance is not a special initiative — it is an embedded, governed, continuously improving organizational capability.

Goals

Phase 3 has five primary goals:

1. Establish organization-wide AI policy — Codify AI-assisted development standards into formal organizational policy that applies to all engineering teams — see Organization-Wide AI Policy
2. Advance prompt engineering maturity — Move beyond basic prompting to advanced techniques including complex prompt architectures, chain-of-thought patterns, and domain-specific prompt libraries — see Advanced Prompt Engineering Standards
3. Design AI-first workflows — Redesign development workflows so that AI assistance is the default rather than the exception — see AI-First Development Workflows
4. Implement continuous improvement — Establish feedback loops, retrospective analysis, A/B testing, and iterative refinement processes that ensure practices evolve with the technology — see Continuous Improvement & Feedback
5. Certify organizational maturity — Formally assess and certify the organization's AI-assisted engineering maturity — see Maturity Assessment & Certification

Prerequisites from Phase 2

Phase 3 MUST NOT begin until the following Phase 2 prerequisites are verified:

Mandatory Prerequisites

• Phase 2 go/no-go review completed with a "Go" decision from the Steering Committee
• Governance framework operational and enforced for all expansion teams (minimum 5 teams)
• CI/CD pipelines include automated AI governance checks for all active teams
• Community of Practice meeting regularly with documented participation from all teams
• Organizational KPI dashboard operational with at least 4 weeks of trend data
• Risk assessment process operational with automated scoring
• No unresolved Critical severity security incidents
• Aggregate defect density not increased more than 5% relative to baselines

Recommended Prerequisites

• At least 30% of engineering teams actively using AI-assisted development under governance
• Developer satisfaction with AI tools >= 3.5/5.0 across all active teams
• Prompt library contains at least 50 verified prompts
• At least 3 internal showcases completed with documented case studies
• All Team Champions have received advanced training

Deliverables

By the end of Phase 3, the following artifacts MUST be produced:

Deliverable	Owner	Approval Required
Organization-Wide AI Policy (formal document)	Governance Lead	CTO + CISO + Legal + Steering Committee
Advanced Prompt Engineering Standards	Knowledge Sharing Lead + Expert practitioners	Engineering Director
AI-First Workflow Designs	Platform Engineering + Team Champions	Steering Committee
Continuous Improvement Process documentation	Phase Lead	Steering Committee
Maturity Assessment Methodology	Governance Lead	Steering Committee + External assessor (if applicable)
Initial Maturity Certification results	Assessment team	Steering Committee
Transformation Completion Report	Phase Lead	Executive sponsor + Steering Committee

Team Composition

Phase 3 evolves the team structure to reflect institutionalization:

Core Team

• Phase Lead / Transformation Director (1 person, 100% allocation) — Accountable for enterprise-wide rollout and maturity certification. This role MAY transition to a permanent "AI Engineering Excellence" role post-transformation.
• Governance Lead (1 person, 75% allocation) — Owns organization-wide policy development and enforcement.
• Platform Engineering Lead (1 person, 50-75% allocation) — Owns AI-first workflow tooling and automation.
• Knowledge Sharing Lead (1 person, 50% allocation) — Coordinates advanced training, prompt engineering standards, and continuous improvement.

Distributed Team

• Team Champions (1 per team, ongoing) — Now embedded in every engineering team, serving as the local AI engineering excellence resource.
• Prompt Engineering Specialists (2-3 people, 50% allocation) — New role in Phase 3; developers with demonstrated expertise in advanced prompt engineering who develop standards and train others.
• Assessment Assessors (2-3 people, for certification periods) — Trained assessors who conduct maturity assessments across the organization.

Timeline

Period	Key Activities
Week 14	Launch Phase 3; begin organization-wide policy drafting; identify remaining non-adopted teams; draft advanced prompt engineering standards
Weeks 15-16	AI-first workflow design; organization-wide policy in review cycle; pilot AI-first workflows with 2-3 teams; continue team onboarding
Weeks 17-18	Publish organization-wide policy; all engineering teams under governance; advanced prompt training rollout; launch continuous improvement processes; mid-phase review
Weeks 19-20	AI-first workflows deployed to all teams; A/B testing of process variants; maturity assessment methodology finalized; pilot assessment conducted
Weeks 21-22	Full maturity assessment across all teams; continuous improvement processes validated; certification results compiled; remediation for teams below target
Weeks 23-24	Re-assessment for remediated teams; Transformation Completion Report; transition to steady-state operations; certification awarded

Success Criteria

Phase 3 is considered successful when ALL mandatory criteria are met:

• Organization-wide AI policy published and acknowledged by all engineering staff
• 90%+ of engineering teams actively using AI-assisted development under governance
• AI-first workflows operational across the organization
• Continuous improvement process producing measurable refinements quarterly
• Maturity assessment completed for all teams with average score meeting target threshold
• Aggregate velocity improvement of 20%+ compared to Phase 1 baselines
• No increase in aggregate defect density or vulnerability density compared to baselines
• Knowledge sharing processes self-sustaining without core team intervention

Transition to Steady State

Phase 3 concludes the formal transformation. The following structures MUST be established for ongoing steady-state operations:

• AI Engineering Excellence team (permanent, 3-5 people) — Owns ongoing policy maintenance, tool evaluation, training, and continuous improvement
• Community of Practice (permanent) — Continues with distributed leadership
• Maturity certification renewal (annual) — Ensures standards are maintained over time
• KPI dashboard and reporting (permanent) — Integrated into standard engineering operations reporting

The transformation does not end with Phase 3 — it transitions from a program into a permanent organizational capability. The structures built across all three phases ensure that AI-assisted engineering continues to improve, adapt to new tools and techniques, and deliver value while managing risk.