Phase 3: Enterprise Scale (9-18 Months)
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:
- 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
- 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
- Design AI-first workflows — Redesign development workflows so that AI assistance is the default rather than the exception — see AI-First Development Workflows
- 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
- 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 3 months 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
| Month | Key Activities |
|---|---|
| Month 9 | Launch Phase 3; begin organization-wide policy drafting; identify remaining non-adopted teams |
| Month 10 | Draft advanced prompt engineering standards; begin AI-first workflow design; continue team onboarding |
| Month 11 | Organization-wide policy in review cycle; pilot AI-first workflows with 2-3 teams |
| Month 12 | Publish organization-wide policy; launch continuous improvement processes; mid-phase review |
| Month 13 | All engineering teams under governance; advanced prompt training rollout |
| Month 14 | AI-first workflows deployed to all teams; A/B testing of process variants begins |
| Month 15 | Maturity assessment methodology finalized; pilot assessment conducted |
| Month 16 | Full maturity assessment across all teams; continuous improvement processes validated |
| Month 17 | Certification results compiled; remediation for teams below target maturity |
| Month 18 | 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.