Pain point
EA teams are being asked to support AI strategy, but many still operate with traditional application review, project gating, and static reference architecture methods.
Free Showcase Library
Preview the methods before buying a package.
These showcase modules give reviewers practical concepts, questions, sample artifacts, and package mapping. They are designed to prove that StrategicaLabs Academy is more than a PDF store.
Showcase Map
Six preview modules designed for architecture reviewers.
Use these as a public preview of the Academy’s method, not as a hidden sales page.
Enterprise Architecture in the AI Age
AI changes the enterprise architect’s role from standards reviewer to business capability translator, integration control designer, governance advisor, and modernization strategist.
Core method
Map business capabilities to AI opportunities, data sources, platforms, integration points, risk levels, and modernization dependencies before approving solution direction.
Preview asset
AI-age architect skills map and executive architecture questions.
| Traditional EA focus | AI-age EA focus | Reviewer question |
|---|---|---|
| Application standards | AI use-case architecture controls | Which AI use cases touch customer, employee, or regulated data? |
| Project design review | Continuous governance of AI-enabled products | Who owns review when the model, vendor, or data source changes? |
| Technology roadmap | Capability, data, integration, and modernization roadmap | What must be modernized before AI can scale safely? |
Connects to: AI-Age Architect Starter Pack / Full BundleReview package fit
AI Governance for Architects
This is the strongest public preview. It shows how architects can review AI systems using intake, risk classification, architecture evidence, and approval paths.
Pain point
AI tools enter through SaaS, productivity suites, analytics, automation, and custom development faster than ARBs can govern them.
Core method
Classify AI use cases by data exposure, business criticality, human oversight, vendor dependency, and regulatory risk before approving implementation.
Preview asset
AI intake fields, mini risk scoring table, and architecture review questions.
| Intake field | Example question | Why it matters |
|---|---|---|
| AI function | Is the system summarizing, recommending, deciding, generating, or automating? | Different functions require different review depth. |
| Data touched | Will the system access customer, employee, financial, health, or regulated data? | Data exposure drives security, privacy, and compliance controls. |
| Human oversight | Can a person review, override, or approve the AI output? | Oversight affects risk tier and accountability. |
| Vendor dependency | Is AI embedded in a SaaS vendor, cloud platform, or third-party model? | Vendor AI needs contractual and architecture review. |
Mini risk scoring preview:Low: internal productivity / no sensitive dataMedium: business workflow support / controlled dataHigh: regulated process, customer impact, automated decision, or sensitive data
Connects to: AI Governance for Architects Professional PackageReview flagship package
Secure AI Integration with Enterprise Data
GenAI integration is not only API connectivity. It requires clear boundaries for identity, retrieval, data access, logging, human review, and auditability.
Pain point
Teams want to connect models to documents, CRM, claims, payments, knowledge bases, or ticketing systems without exposing sensitive data.
Core method
Review the integration pattern from user request to model call, retrieval source, API gateway, data filtering, output review, and audit log.
Preview asset
Enterprise AI data access checklist and RAG architecture review questions.
| Control area | Architecture question | Expected artifact |
|---|---|---|
| Identity | Does the AI action inherit the user’s role and data entitlement? | Access control model |
| Retrieval | Which sources can be retrieved and what is excluded? | Retrieval boundary map |
| Logging | Can prompts, sources, outputs, and exceptions be audited? | Audit trail checklist |
| Security | Where are secrets, tokens, and vendor API calls controlled? | Integration security review |
Connects to: AI Architecture & Integration Practitioner KitReview integration kit
Cloud Transformation for AI-Enabled Enterprises
AI adoption increases pressure on cloud operating models, landing zones, observability, identity, resilience, data platforms, and cost governance.
| Cloud readiness area | Question | Signal of maturity |
|---|---|---|
| Landing zone | Are AI workloads isolated by environment, risk, and data sensitivity? | Standard workload patterns exist. |
| FinOps | Who owns AI usage cost and how is it reported? | Showback / chargeback model exists. |
| Observability | Can operations monitor AI dependency, latency, failures, and cost spikes? | Dashboards and alerts exist. |
| Resilience | What happens when model APIs, data sources, or retrieval layers fail? | Fallback and exception paths exist. |
Connects to: Cloud Transformation & Multi-Cloud Governance ToolkitReview cloud toolkit
Technical Debt in the AI Age
Technical debt becomes more visible when AI initiatives require clean data, stable APIs, secure integrations, scalable platforms, and reliable operating controls.
| Debt signal | AI impact | Modernization response |
|---|---|---|
| Manual data extraction | AI cannot reliably access governed data. | Prioritize API or data service modernization. |
| Legacy identity model | Entitlements cannot follow AI requests. | Modernize identity and access controls. |
| Fragile point integrations | AI workflows break across systems. | Consolidate integration through governed patterns. |
| Poor observability | Failures, drift, and cost spikes are hidden. | Improve monitoring and operational telemetry. |
Connects to: Starter Pack / Full BundleReview full bundle
Architecture Review Board for AI Systems
The ARB needs AI-specific evidence, questions, review tiers, and escalation triggers so it can govern beyond diagrams and technology standards.
| ARB review question | Evidence expected | Escalation trigger |
|---|---|---|
| What data will the AI system access? | Data classification and access map | Sensitive or regulated data |
| Who is accountable for outputs? | Owner, reviewer, and approval path | Automated or customer-impacting output |
| What vendor or model dependency exists? | Vendor AI risk questionnaire | Third-party model or SaaS AI dependency |
| How is evidence retained? | Decision record and audit checklist | High-risk or regulated workflow |
Connects to: AI Governance for Architects Professional PackageReview ARB toolkit