Governance, Roadmap, and Operating Model

Section 20: Sample End-to-End Workflows

20.1 New Salesforce feature delivery

Review checkpoints

• BA validates stories
• Architect validates design
• Dev lead validates implementation
• QA validates coverage
• Release manager validates deployment

20.2 Custom Apex + LWC feature

1. Claude drafts capability and UX story.
2. Codex implements Apex service, LWC, and tests.
3. Claude creates user guide and support notes.
4. Human review validates security, UX, and performance.

20.3 Integration project delivery

1. Claude creates integration option matrix and sequence narrative.
2. Codex builds callout/service classes, payload mappings, and tests.
3. Claude documents support flow and operational ownership.

20.4 Production incident troubleshooting

1. Claude summarizes incident evidence and forms RCA hypotheses.
2. Codex inspects code, logs, and diffs to isolate likely defect path.
3. Claude drafts business and executive communication.
4. Human lead confirms containment and permanent fix plan.

20.5 Documentation and handover package creation

1. Claude turns implementation notes into runbooks/admin guides.
2. Codex extracts technical details from actual repo files.
3. Claude produces a final handover pack for support and operations.

Section 21: Governance, Security, and Compliance

21.1 Core enterprise risks

• sensitive data exposure
• PII or PCI pasted into prompts
• secrets or tokens exposed
• confidential code or architecture leakage
• hallucinated features or unsafe recommendations
• legal/IP issues from uncontrolled use
• non-compliant vendor usage

21.2 Prompt hygiene rules

• never paste secrets, auth tokens, certificates, private keys
• never paste raw customer PII unless policy explicitly allows sanitized handling
• minimize copied production data
• redact identifiers where not necessary
• prefer synthetic examples

21.3 Governance framework

21.4 Regulated industry considerations

For banking, insurance, healthcare, public sector, and similar environments:

• define approved data classes for AI use
• require security/legal review before scaling usage
• keep prompt templates sanitized by design
• add compliance signoff to high-risk artifacts
• avoid using AI as final authority on policy interpretation

21.5 Security review workflow

Use case proposed
  ->
Data classification check
  ->
Vendor/policy fit check
  ->
Approved prompt/data templates
  ->
Pilot with human review
  ->
Audit and expand

Section 22: Quality Control and Human Review Model

22.1 Why blind trust is dangerous

AI can be fluent and still be wrong. In Salesforce that can mean:

• incorrect sharing
• broken bulk behavior
• bad migration logic
• missing test coverage
• unsupported architectural assumptions

22.2 Code validation checklist

• compiles
• passes tests
• meets org coding standards
• bulk-safe
• sharing-safe
• no hard-coded secrets/IDs
• negative paths covered

22.3 Architecture validation checklist

• licensing fit confirmed
• standard-first assumption reviewed
• NFRs covered
• support model defined
• security impacts reviewed
• data ownership clear

22.4 Security validation checklist

• least privilege enforced
• sensitive fields identified
• logging does not leak secrets
• integration auth model approved
• external user access reviewed

22.5 Test validation checklist

• positive/negative/edge cases
• role-based behavior
• integration failure cases
• data quality cases
• traceability to requirements

22.6 Documentation review checklist

• technically accurate
• audience-appropriate
• includes assumptions and dependencies
• includes support/runbook content where needed
• reviewed by named owner

22.7 Signoff model

Section 23: Limitations and Failure Modes

23.1 Common failure modes

• hallucinated Salesforce features
• incorrect Apex syntax or unsupported methods
• poor bulkification
• incomplete edge case handling
• misunderstanding org-specific patterns
• outdated assumptions about platform capabilities
• weak security assumptions
• superficial test coverage
• over-engineering or under-engineering

23.2 Mitigation strategies

Section 24: KPI and ROI Framework

24.1 Suggested KPI areas

24.2 ROI framing

Measure AI value through:

• hours saved on repetitive artifacts
• reduced rework from better requirement clarity
• faster onboarding of new team members
• lower incident handling effort
• improved delivery predictability

24.3 Sample KPI table

Section 25: Implementation Roadmap

Phase 1: individual productivity

• Objectives: prove value safely
• Actions: pilot with architects, senior devs, BAs
• Owners: CoE lead, delivery lead
• Success measures: faster artifact drafting, positive feedback
• Risks: ungoverned usage, inflated expectations

Phase 2: team standardization

• Objectives: repeatable patterns
• Actions: standard prompts, review checklists, role guidance
• Owners: tech lead, BA lead, QA lead
• Success measures: consistent artifact quality
• Risks: inconsistent adoption

Phase 3: governance and templates

• Objectives: secure and auditable usage
• Actions: acceptable use policy, redaction standards, prompt libraries
• Owners: security, enterprise architecture, CoE
• Success measures: approved usage model
• Risks: over-restriction or under-control

Phase 4: scaled enterprise adoption

• Objectives: multi-team rollout
• Actions: training, approved use cases, KPI tracking, documentation repository
• Owners: platform leadership
• Success measures: measurable productivity and quality improvement
• Risks: uneven maturity across teams

Phase 5: continuous optimization

• Objectives: improve prompts, controls, workflows
• Actions: review KPIs, refine templates, expand safe use cases
• Owners: CoE and delivery leadership
• Success measures: sustained value and lower defects
• Risks: stagnation, tool sprawl

Section 26: Recommended Operating Model

26.1 Where each tool fits

26.2 Standard operating model

1. Use approved prompt templates.
2. Classify input data before using AI.
3. Keep implementation work traceable.
4. Require review for any production-impacting artifact.
5. Store validated outputs in controlled repositories.

26.3 Center of excellence model

The Salesforce AI enablement CoE should own:

• approved use cases
• prompt library
• review checklists
• training
• KPI reporting
• vendor/policy coordination
• maturity roadmap

26.4 Repository and documentation practices

• keep AI-generated artifacts in version control
• record human reviewer
• tag templates and patterns as approved
• maintain reference examples by domain

26.5 Training model

• intro training for all delivery roles
• advanced pattern training for architects/devs
• security and prompt hygiene training for everyone
• periodic refreshers based on incidents and lessons learned

Section 27: Final Recommendations

27.1 When to start

Start now, but start narrowly. The right first move is not "AI for everything." The right first move is a controlled pilot in a Salesforce workstream with visible repetitive effort.

27.2 What to pilot first

• user story and acceptance criteria drafting
• architecture option summaries
• Apex/LWC test generation
• release notes and runbooks
• incident RCA drafts

27.3 What not to do

• do not paste secrets or raw customer-sensitive data
• do not let AI define security or compliance unreviewed
• do not treat AI output as production-ready by default
• do not measure success only by token usage or number of prompts

27.4 How to scale safely

• define approved use cases
• standardize prompts and review checklists
• train teams
• track KPIs
• review incidents and near misses
• evolve the operating model deliberately

27.5 Final balanced recommendation

Codex and Claude should be adopted as complementary enterprise delivery tools for Salesforce, not as replacements for architecture judgment, development skill, or governance discipline. Codex is the stronger implementation engine when the work is code- and repo-centered. Claude is the stronger strategy and synthesis engine when the work is document-heavy, cross-functional, and conceptually broad. Used together under clear governance, they can improve delivery speed, raise quality, reduce documentation drag, strengthen support readiness, and create a more mature Salesforce engineering system.

The real opportunity is not just writing code faster. It is running the full Salesforce lifecycle with better clarity, stronger artifacts, better review discipline, and more reusable team knowledge.

Appendix A: Enterprise Maturity Model

Appendix B: Human Review Required Notes

• Human review required for all code, architecture, security, migration, release, and support RCA outputs.
• Human review required before any externally shared stakeholder or customer-facing communication.
• Human review required whenever assumptions touch regulatory, contractual, or legal interpretation.

Appendix C: Practical Anti-Patterns

• Asking AI for "best practice" without org or business context
• Generating code without test and review prompts
• Using AI to justify decisions already made politically
• Copying production data into prompts because it is "faster"
• Publishing AI-generated documentation without technical owner signoff