AI4EAv1.0

Section 14

Enterprise AI Reference Architecture

An illustrative layered mapping from consumption to infrastructure, with cross-cutting concerns.

AI4EA is technology-neutral, but a reference architecture can help practitioners map the framework to common enterprise technology layers. The reference architecture below is illustrative rather than prescriptive.

Data Sources

  • Enterprise Systems (ERP, CRM, Core Banking)
  • Documents & Content (Policies, Manuals, Knowledge Bases)
  • Streaming Data (Events, IoT, Logs)
  • External Data (Partners, Open Data, Web)

1. Consumption Layer

Web PortalMobile AppBusiness ApplicationsConversational Interfaces

2. Application Layer

AI AssistantsIntelligent AutomationAnalytics & InsightsDomain Use Cases

3. Orchestration Layer

AI AgentsWorkflow & OrchestrationAPIs & IntegrationEvent Processing

4. Intelligence Layer

Foundation Models (LLMs / SLMs)Knowledge Layer (Vector DB, Knowledge Graph)Model Services (Inference, Fine-tuning)Evaluation & Monitoring

5. Data Layer

Data Ingestion (Batch & Streaming)Data Lake / LakehouseData Processing (ETL / ELT)Metadata & Lineage

6. Infrastructure Layer

Cloud / On-PremCompute (CPU / GPU)StorageContainer Platform

Cross-Cutting Concerns

  • Security (Identity, Access, Data Protection)
  • Governance (Policies, Standards, Responsible AI)
  • Compliance (Regulatory, Audit, Risk Management)
  • Monitoring (Performance, Cost, Model & Data Drift)
  • People & Skills (Organization, Training, Change)
  • FinOps (Cost Management, Resource Optimization)
Figure 10 — AI4EA Enterprise AI Reference Architecture

The layered model separates consumption, application, orchestration, intelligence, data and infrastructure concerns while treating security, governance, compliance, monitoring, people and FinOps as cross-cutting capabilities. Organizations may adapt, combine or replace layers according to their existing enterprise architecture.

The reference architecture should be interpreted together with the Core Engine: sourcing decisions shape the intelligence layer; data and knowledge decisions shape the knowledge and data layers; integration decisions shape orchestration and application interaction; and governance, security and economics span the complete stack.