Playbook

AI-Assisted Delivery Implementation

A structured approach to adopting AI tools across your development team

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Overview

This playbook outlines a proven methodology for implementing AI-assisted development tools across engineering teams. The approach prioritizes measurable outcomes, gradual adoption, and sustainable change.

2-4 Weeks initial
3 Core phases
8+ Deliverables
Best for
Teams wanting structured AI adoption Chaotic or inconsistent tool usage No measurable productivity results Need for training and best practices

Typical Timeline

1
Assessment Days 1-4
Assessment Summary Tool Strategy Pilot Plan
2
Pilot Week 1-2
Best Practices Guide Configured Tooling
3
Results Week 3-4
Results Report Sustainability Guide Rollout Plan
+
Extended Rollout Ongoing
Organization-wide Rollout Internal Champion Program Quarterly ROI Reports

Engagement Model

Time Commitment

2-4 weeks for initial implementation. Can extend for organization-wide rollout if needed.

Working Style

Hands-on with your team. Pairing sessions, live troubleshooting, real code.

Communication

Daily check-ins during pilot. Weekly updates to leadership. Async support via Slack.

Phase 1

Assessment & Tool Selection

Days 1-4

Rapid assessment of your workflows and tool strategy based on budget, team size, and expected ROI.

Activities

  • Current workflow analysis
  • Developer interviews (key team members)
  • Codebase and tooling inventory
  • Bottleneck identification
  • Budget and ROI analysis
  • Security and compliance review

Deliverables

  • Assessment Summary — Current state, bottlenecks, team readiness
  • Tool Strategy — Recommended tools, tiered approach, ROI analysis
  • Pilot Plan — Use case, team selection, success metrics

Tools I Evaluate

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Codex
  • Gemini
Phase 2

Pilot Execution & Training

Week 1-2

Hands-on implementation with a pilot team. Setup, training, and daily iteration to establish effective practices.

Activities

  • Tool setup and configuration
  • Hands-on training sessions
  • Daily check-ins with pilot team
  • Prompt engineering workshops
  • Best practices documentation
  • Real-time troubleshooting

Deliverables

  • Best Practices Guide — When to use AI, how to review, workflow guidance
  • Configured Tooling — CLAUDE.md, guardrails, plugins via Labforge
Phase 3

Results & Handoff

Week 3-4

Measure results, refine practices, and ensure the team can sustain improvements independently.

Activities

  • Results measurement and analysis
  • Process refinement based on feedback
  • Internal champion identification
  • Knowledge transfer sessions
  • Rollout recommendations

Deliverables

  • Results Report — Metrics comparison, ROI analysis, recommendations
  • Sustainability Guide — Champions, onboarding new devs, troubleshooting
  • Rollout Plan — Next teams, timeline, budget (if continuing)

Metrics We Track

  • Cycle Time
  • PR Turnaround
  • Code Review Time
  • Developer Satisfaction
  • Tool Adoption
  • Output Quality
Optional

Extended Rollout

Ongoing

For teams that want continued support, I can help extend adoption across the organization with ongoing optimization.

Activities

  • Phased rollout to additional teams
  • Train-the-trainer program
  • Ongoing metric tracking
  • New tool evaluation as landscape evolves
  • Executive reporting

Deliverables

  • Organization-wide Rollout — Phased expansion across all teams
  • Internal Champion Program — Train-the-trainer, internal experts
  • Quarterly ROI Reports — Ongoing metrics, executive updates

Ready to implement?

Let's discuss how this playbook applies to your team.

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