# AI Readiness Roadmap - Technology Industry Supplement

**Version:** 1.0
**Date:** ___________________________
**Organization:** ___________________________
**Industry:** Technology (Software, SaaS, Platform, Services)

---

## How to Use This Supplement

This technology-specific supplement provides industry-tailored content for the base AI Readiness Roadmap. Use it to customize:
- Section 2-3: Executive Summary (ROI opportunities)
- Section 4-6: Where AI Creates ROI (technology use cases)
- Section 13-14: Vendor Recommendations (developer AI tools)
- Section 15: Risk Assessment (source code and IP risks)

---

## Technology AI ROI Opportunities

### Engineering & Development - ROI Scoring

| Opportunity | Current State | AI-Enabled State | ROI Range | Confidence | Priority |
|-------------|---------------|------------------|-----------|------------|----------|
| Code Generation/Completion | _____ lines/dev/day | ___% productivity gain | 150-300% | High / Medium / Low | ___ |
| Automated Testing | ___% test coverage | ___% improvement | 120-250% | High / Medium / Low | ___ |
| Code Review Automation | _____ hrs/PR | ___% reduction | 100-200% | High / Medium / Low | ___ |
| Bug Detection/Fix | _____ bugs/release | ___% reduction | 130-250% | High / Medium / Low | ___ |
| Documentation Generation | _____ hrs/feature | ___% reduction | 80-180% | High / Medium / Low | ___ |
| Security Vulnerability Detection | _____ vulns/quarter | ___% detection improvement | 150-280% | High / Medium / Low | ___ |
| Incident Response | _____ MTTR (hrs) | ___% reduction | 120-220% | High / Medium / Low | ___ |
| DevOps Optimization | _____ deployment/day | ___% improvement | 100-200% | High / Medium / Low | ___ |

### Product & Customer - ROI Scoring

| Opportunity | Current State | AI-Enabled State | ROI Range | Confidence | Priority |
|-------------|---------------|------------------|-----------|------------|----------|
| AI-Powered Product Features | ___% products w/ AI | ___% increase | 200-400% | High / Medium / Low | ___ |
| Customer Support AI | ___% ticket resolution | ___% improvement | 150-280% | High / Medium / Low | ___ |
| Usage Analytics/Insights | _____ insights/mo | ___x improvement | 100-200% | High / Medium / Low | ___ |
| Churn Prediction | ___% accuracy | ___% improvement | 150-280% | High / Medium / Low | ___ |
| Sales Intelligence | $_____ pipeline/rep | ___% increase | 120-220% | High / Medium / Low | ___ |

---

## Technology Cost Reduction

### Engineering Operations Automation

| Process Area | Current Cost | Automation Potential | Savings Range | Complexity | Priority |
|--------------|--------------|---------------------|---------------|------------|----------|
| Code Review | $_______ / year | ____% | 40-60% | Low | ___ |
| Testing/QA | $_______ / year | ____% | 50-70% | Medium | ___ |
| Documentation | $_______ / year | ____% | 60-80% | Low | ___ |
| Incident Response | $_______ / year | ____% | 30-50% | Medium | ___ |
| Security Scanning | $_______ / year | ____% | 40-60% | Medium | ___ |
| Infrastructure Management | $_______ / year | ____% | 35-55% | High | ___ |
| Customer Support (Tier 1) | $_______ / year | ____% | 50-70% | Medium | ___ |
| Technical Writing | $_______ / year | ____% | 45-65% | Low | ___ |

---

## Technology Risk Mitigation Value

### Security & IP Risk Reduction

| Risk Category | Current Exposure | AI Mitigation | Value Protected | Confidence |
|---------------|------------------|---------------|-----------------|------------|
| Security Vulnerabilities | $_______ / year | ___% reduction | $____________ | High / Medium / Low |
| Code Quality Issues | $_______ / year | ___% reduction | $____________ | High / Medium / Low |
| Production Incidents | $_______ / year | ___% reduction | $____________ | High / Medium / Low |
| IP/Source Code Exposure | $_______ / year | ___% reduction | $____________ | High / Medium / Low |
| Compliance Violations | $_______ / year | ___% reduction | $____________ | High / Medium / Low |
| Technical Debt | $_______ / year | ___% reduction | $____________ | High / Medium / Low |
| **Total Risk Mitigation Value** | **$_______** | | **$____________** | |

---

## Technology Build vs. Buy Considerations

### Technology-Specific BUILD Indicators

| Factor | Score (1-5) | Technology Considerations |
|--------|-------------|--------------------------|
| Core product differentiation | ___/5 | AI as product feature, competitive moat |
| Internal engineering talent | ___/5 | Strong ML/AI engineering team |
| Proprietary data/models | ___/5 | Customer usage data, domain expertise |
| Integration depth required | ___/5 | Deep IDE, CI/CD, platform integration |
| Privacy/security requirements | ___/5 | Cannot share code with external vendors |

### Technology-Specific BUY Indicators

| Factor | Score (1-5) | Technology Considerations |
|--------|-------------|--------------------------|
| Mature developer tools | ___/5 | GitHub Copilot, Tabnine, etc. |
| Focus on core product | ___/5 | AI tools vs. AI product features |
| Speed to developer adoption | ___/5 | Ready-to-use tools |
| Ecosystem integration | ___/5 | IDE, CI/CD native integrations |
| Proven productivity gains | ___/5 | Validated developer productivity |

---

## Technology Vendor Landscape

### Code Generation & Completion

| Vendor | Product | Languages | IDE Support | Deployment | Score |
|--------|---------|-----------|-------------|------------|-------|
| GitHub | Copilot | Multi-language | VS Code, JetBrains | Cloud/Enterprise | ___/5 |
| Anthropic | Claude for Enterprise | Multi-language | API, IDE plugins | Cloud | ___/5 |
| Amazon | CodeWhisperer | Multi-language | VS Code, JetBrains | Cloud/Self-hosted | ___/5 |
| Google | Gemini Code Assist | Multi-language | VS Code, Cloud IDE | Cloud | ___/5 |
| Tabnine | Tabnine Enterprise | Multi-language | All major IDEs | Self-hosted available | ___/5 |
| Sourcegraph | Cody | Multi-language | VS Code, JetBrains | Self-hosted available | ___/5 |

### Security & Code Analysis

| Vendor | Product | Focus Area | CI/CD Integration | Deployment | Score |
|--------|---------|------------|-------------------|------------|-------|
| Snyk | Snyk AI | Vulnerability detection | Native | Cloud | ___/5 |
| SonarQube | SonarQube | Code quality | Native | Self-hosted/Cloud | ___/5 |
| Checkmarx | Checkmarx AI | SAST/DAST | Native | Cloud/Self-hosted | ___/5 |
| GitHub | Advanced Security | Security scanning | Native | Cloud | ___/5 |
| Veracode | Veracode | Application security | Native | Cloud | ___/5 |

### Testing & QA

| Vendor | Product | Focus Area | Framework Support | Deployment | Score |
|--------|---------|------------|-------------------|------------|-------|
| Testim | Testim | Test automation | Multi-framework | Cloud | ___/5 |
| Mabl | Mabl | Intelligent testing | Web | Cloud | ___/5 |
| Functionize | Functionize | AI testing | Multi-platform | Cloud | ___/5 |
| Diffblue | Diffblue Cover | Unit test generation | Java | Cloud/Self-hosted | ___/5 |

### DevOps & Observability

| Vendor | Product | Focus Area | Integration | Deployment | Score |
|--------|---------|------------|-------------|------------|-------|
| Datadog | Watchdog | AIOps | Multi-platform | Cloud | ___/5 |
| Dynatrace | Davis AI | Observability | Multi-platform | Cloud | ___/5 |
| PagerDuty | AI Ops | Incident management | Multi-platform | Cloud | ___/5 |
| New Relic | AI Ops | Observability | Multi-platform | Cloud | ___/5 |

---

## Technology Compliance Requirements

### Regulatory Framework

| Regulation | Applicability | Requirements | AI Impact |
|------------|---------------|--------------|-----------|
| **SOC 2 Type II** | SaaS/Cloud | Security, availability, confidentiality | AI system security controls |
| **GDPR** | EU customers | Data protection, privacy | AI training data, customer data |
| **HIPAA** | Healthcare customers | PHI protection | AI in healthcare products |
| **PCI DSS** | Payment processing | Cardholder data security | AI with payment data |
| **FedRAMP** | US Government | Cloud security standards | AI system authorization |
| **ISO 27001** | Enterprise customers | Information security | AI system security |

### Source Code Security Requirements

| Requirement | Status | Implementation | Owner |
|-------------|--------|----------------|-------|
| Code never sent to external AI (if required) | Complete / In Progress / Not Started | _____________________ | _____________________ |
| Self-hosted AI options evaluated | Complete / In Progress / Not Started | _____________________ | _____________________ |
| AI vendor security assessment | Complete / In Progress / Not Started | _____________________ | _____________________ |
| Code snippet filtering | Complete / In Progress / Not Started | _____________________ | _____________________ |
| AI training data controls | Complete / In Progress / Not Started | _____________________ | _____________________ |
| Audit logging for AI usage | Complete / In Progress / Not Started | _____________________ | _____________________ |

### Open Source Licensing Compliance

| Requirement | Status | Implementation | Owner |
|-------------|--------|----------------|-------|
| AI-generated code license review | Complete / In Progress / Not Started | _____________________ | _____________________ |
| Attribution requirements | Complete / In Progress / Not Started | _____________________ | _____________________ |
| Copyleft license detection | Complete / In Progress / Not Started | _____________________ | _____________________ |
| License compatibility checking | Complete / In Progress / Not Started | _____________________ | _____________________ |
| SBOM for AI components | Complete / In Progress / Not Started | _____________________ | _____________________ |

---

## Technology-Specific Risk Assessment

### Source Code & IP Risks

| Risk | Likelihood | Impact | Mitigation | Status |
|------|------------|--------|------------|--------|
| Source code exposure via AI | ___/5 | Critical | Self-hosted, filtering, policies | Not Started / In Progress / Mitigated |
| IP leakage to AI providers | ___/5 | Critical | Vendor contracts, self-hosted | Not Started / In Progress / Mitigated |
| Trade secrets in prompts | ___/5 | High | Training, monitoring, DLP | Not Started / In Progress / Mitigated |
| Competitor code in training | ___/5 | Medium | Vendor attestations, audits | Not Started / In Progress / Mitigated |
| Open source license violations | ___/5 | High | License scanning, review | Not Started / In Progress / Mitigated |

### Developer Productivity Risks

| Risk | Likelihood | Impact | Mitigation | Status |
|------|------------|--------|------------|--------|
| Over-reliance on AI | ___/5 | Medium | Training, code review requirements | Not Started / In Progress / Mitigated |
| AI-generated bugs | ___/5 | High | Testing requirements, review | Not Started / In Progress / Mitigated |
| Security vulnerabilities from AI | ___/5 | Critical | Security scanning, review | Not Started / In Progress / Mitigated |
| Code quality degradation | ___/5 | Medium | Quality gates, metrics | Not Started / In Progress / Mitigated |
| Skill atrophy | ___/5 | Low | Training, pair programming | Not Started / In Progress / Mitigated |

### Security & Compliance Risks

| Risk | Likelihood | Impact | Mitigation | Status |
|------|------------|--------|------------|--------|
| SOC 2 audit findings (AI) | ___/5 | High | Controls documentation | Not Started / In Progress / Mitigated |
| Customer data in AI training | ___/5 | Critical | Data segregation, policies | Not Started / In Progress / Mitigated |
| AI vendor data breach | ___/5 | High | Vendor assessment, contracts | Not Started / In Progress / Mitigated |
| Unauthorized AI tool usage | ___/5 | Medium | Policy, monitoring, approved list | Not Started / In Progress / Mitigated |

---

## Technology Governance Additions

### Engineering AI Governance Structure

| Role | Responsibilities | Recommended Assignment |
|------|------------------|----------------------|
| CTO/VP Engineering | AI strategy for engineering | CTO |
| Engineering Manager | AI tool adoption, productivity | Engineering leads |
| Security Lead | AI security, code protection | CISO/Security |
| Legal/IP Counsel | Open source, IP protection | Legal |
| Developer Experience | AI tool evaluation, rollout | DevEx lead |

### Developer AI Review Board

| Element | Requirement |
|---------|-------------|
| **Purpose** | Approve AI tools for development use, ensure security and IP protection |
| **Membership** | CTO, Security, Legal, Engineering leads, DevEx |
| **Frequency** | Monthly or as needed for new tool evaluations |
| **Authority** | Approve/reject AI tools, set policies |
| **Documentation** | Tool assessments, security reviews, policies |

### AI Tool Approved List

| Category | Approved Tools | Restrictions | Owner |
|----------|----------------|--------------|-------|
| Code completion | _____________________ | _____________________ | _____________________ |
| Code review | _____________________ | _____________________ | _____________________ |
| Testing | _____________________ | _____________________ | _____________________ |
| Documentation | _____________________ | _____________________ | _____________________ |
| Security scanning | _____________________ | _____________________ | _____________________ |

---

## Technology 90-Day Roadmap Additions

### Week 1-2: Engineering Assessment

| Activity | Owner | Deliverable |
|----------|-------|-------------|
| Current AI tool usage audit | _____________________ | Usage inventory |
| Developer productivity baseline | _____________________ | Productivity metrics |
| Security requirements for AI | _____________________ | Security requirements |
| IP/legal requirements review | _____________________ | Legal guidance |

### Week 3-4: Tool Evaluation

| Activity | Owner | Deliverable |
|----------|-------|-------------|
| AI tool vendor assessment | _____________________ | Vendor scorecard |
| Self-hosted vs. cloud analysis | _____________________ | Deployment recommendation |
| Security architecture review | _____________________ | Security design |
| Integration requirements | _____________________ | Integration spec |

### Week 5-8: Pilot & Rollout

| Activity | Owner | Deliverable |
|----------|-------|-------------|
| Pilot team selection | _____________________ | Pilot team roster |
| Tool deployment (pilot) | _____________________ | Deployed tools |
| Developer training | _____________________ | Training completion |
| Productivity measurement | _____________________ | Pilot metrics |

---

## Technology Success Metrics

### Developer Productivity Metrics

| Metric | Baseline | Target | Current | Source |
|--------|----------|--------|---------|--------|
| Code velocity (commits/dev/week) | ____ | ____ | ____ | Git analytics |
| PR cycle time | ____ hrs | ____ hrs | ____ hrs | GitHub/GitLab |
| Code review time | ____ hrs/PR | ____ hrs/PR | ____ hrs/PR | Code review tool |
| Bug escape rate | ____/release | ____/release | ____/release | Issue tracker |
| Developer satisfaction | ___/5 | ___/5 | ___/5 | Survey |

### Quality & Security Metrics

| Metric | Baseline | Target | Current | Source |
|--------|----------|--------|---------|--------|
| Test coverage | ____% | ____% | ____% | CI/CD |
| Security vulnerabilities | ____/quarter | ____/quarter | ____/quarter | Security scanner |
| MTTR (incidents) | ____ hrs | ____ hrs | ____ hrs | Incident management |
| Code quality score | ___/100 | ___/100 | ___/100 | SonarQube |
| Technical debt ratio | ____% | ____% | ____% | Code analysis |

---

## AI Product Integration

### AI as Product Feature

| Feature Category | Current Status | AI Enhancement | Priority |
|------------------|----------------|----------------|----------|
| Search/Discovery | _____________________ | _____________________ | High / Medium / Low |
| Recommendations | _____________________ | _____________________ | High / Medium / Low |
| Automation/Workflows | _____________________ | _____________________ | High / Medium / Low |
| Analytics/Insights | _____________________ | _____________________ | High / Medium / Low |
| Content Generation | _____________________ | _____________________ | High / Medium / Low |
| Customer Support | _____________________ | _____________________ | High / Medium / Low |

### AI Product Differentiation Strategy

| Question | Assessment |
|----------|------------|
| How does AI create competitive moat? | _____________________ |
| What proprietary data enables AI features? | _____________________ |
| How does AI improve customer outcomes? | _____________________ |
| What AI capabilities should we build vs. buy? | _____________________ |
| How do we communicate AI value to customers? | _____________________ |

---

## Document Information

**GenAI Maturity Portal:** https://genaimaturity.net
**Assessment Tools:** https://genaimaturity.net/assessment
**Implementation Resources:** https://genaimaturity.net/implementation

---

_This technology supplement provides industry-specific customization for the AI Readiness Roadmap. Use in conjunction with the base strategic document._

**Document Version:** 1.0
**Last Updated:** ___________________________