SG - AI Engineering Transformation Playbook - Template

1. Strategy for AI Engineering Transformation

Concise vision, pillars, and measurable outcomes to align leadership and engineering teams.

2. Standards for Secure, Responsible AI Use

A standards checklist to embed in engineering contracts, vendor evaluations, and onboarding.

• Data Security — PII/data filters, encryption in transit & at rest, minimal retention policies.
• Transparency — Record prompts, model versions, and decision rationale when used for ops or customer-facing flows.
• Bias & Fairness — Scheduled bias & safety tests, remediation plans for critical modules.
• Compliance — Map usage to GDPR, SOC2, HIPAA, and local regulations.
• Auditability — Version-controlled prompts, model artifacts, and reproducible outputs.
• Human Oversight — Define checkpoints where human approval is mandatory for medium/high risk outputs.

3. How to Lead Adoption of AI-Enabled SDLC — (AI-DLC Step by Step)

A practical stepwise playbook for rollout and measurement.

1. Assessment & Discovery — Map friction points across requirements, coding, testing, CI/CD, and docs. Prioritize by impact & feasibility.
2. Pilot & Experiment — Run 2–3 focused pilots (IDE assistant; AI test generator; documentation copilot). Collect productivity and quality metrics.
3. Standards & Guardrails — Publish a playbook: safe prompting, data handling, required approvals, and blocking rules.
4. Golden Path Definition — Create opinionated, repeatable flows (repo templates, CI snippets, SDKs).
5. Scale & Integrate — Embed into CI/CD, developer portal, cloud console, and platform APIs.
6. Measure & Optimize — Track adoption, cycle time, defect rate, and developer satisfaction. Iterate quarterly.

4. AI-Enabled SDLC Golden Paths

Opinionated golden paths reduce cognitive load and ensure consistency across value streams.

5. AI Roadmap for Developer Tooling

A phased roadmap you can adapt to your organization's cadence.

6. Standards, Governance & Implementation

How to structure teams, approvals, and continuous oversight.

7. Available / Trending Tools (Coding, Testing, Documentation)

Cards below show quick pros/cons. Tables further down provide a slightly more exhaustive vendor list for procurement evaluation.

GitHub Copilot

Code completion, refactor, PR suggestions

Pros: Seamless IDE integration, large model tuning for code.

Cons: Subscription cost; caution with proprietary snippets.

ChatGPT / GPT-4

Versatile assistant for coding, testing, and documentation tasks.

Pros: Strong natural language, large-context reasoning (for some models).

Cons: Context window limits; must filter sensitive inputs.

Tabnine

Autocompletion with on-premise options.

Pros: Private/self-host options; customizable models.

Cons: Smaller ecosystem vs. Copilot.

JetBrains AI Assistant

Context-aware assistant across JetBrains IDEs.

Pros: Deep context, IDE synergy.

Cons: Evolving feature set.

CodiumAI

Focused test-generation and coverage analysis.

Pros: Improves test coverage quickly.

Cons: Narrow scope focused on testing.

SonarQube + AI

AI-augmented static analysis & quality checks.

Pros: Proven quality platform, strong scanning rules.

Cons: Requires maintenance & tuning.

Microsoft Copilot

Documentation & collaboration copilots inside MS 365.

Pros: Deep Office integration & enterprise connectors.

Cons: Licensing cost; MS ecosystem lock-in.

Swimm

AI-assisted continuous documentation for dev teams.

Pros: Keeps docs in sync with code changes.

Cons: Requires structured repo practices.

Amazon KIRO

AI-powered, agentic IDE that helps you go from prototype to production with spec-driven development.

KIRO

Pros: Strong retail data intelligence & seamless AWS integration.

Cons: Limited availability; optimized mainly for Amazon ecosystem.

Vendor table & procurement checklist

How to evaluate

• Procurement criteria: security posture, model provenance, offline/self-host ability, enterprise SLAs, pricing model.
• Run a 4–6 week evaluation with representative repos and measure: suggestion accuracy, security false-positives, dev satisfaction, and cost.

Appendix: Playbook Artifacts & Templates

Copy-ready artifacts you should store in your developer portal and link to from onboarding flows.

• AI Usage Playbook (safe prompts, blocking rules, risk tiers)
• Golden Path Templates (repo templates, CI snippets, SDKs)
• Model Approval & Onboarding workflow diagrams
• Measurement dashboards: adoption, cycle time, defect rate, security exceptions

CI Snippet Example (copy into pipelines)

<!-- Example: CI snippet to run AI test-generator & fail on coverage drop -->
steps:
  - run: ai-test-generator --repo . --output tests/ai
  - run: pytest --maxfail=1 --disable-warnings

8. Moving from Tool to an Intelligent Collaborator across multiple BUs

Cards below show quick Overview of collaborative AI Agent AI-DLC, Infrastructure etc .

Appendix Cards & Reports & White Papers

How to evaluate

• Procurement criteria: security posture, model provenance, offline/self-host ability, enterprise SLAs, pricing model.
• Run a 4–6 week evaluation with representative repos and measure: suggestion accuracy, security false-positives, dev satisfaction, and cost.