How do you integrate AI models into existing enterprise applications?

AI integration into enterprise applications follows a layered approach: abstraction layer (standardized interface between applications and AI models), model serving layer (manages model deployment, scaling, and versioning), and integration layer (connects to enterprise systems via APIs, events, or direct database access). Implementation patterns: sidecar pattern (AI model runs alongside the application as a microservice), embedded pattern (lightweight model runs within the application process), and gateway pattern (centralized AI gateway serves multiple applications). Key considerations: latency requirements determine deployment pattern (embedded for sub-10ms, sidecar for sub-100ms, gateway for sub-1s), data privacy determines where models run (on-premise for sensitive data, cloud for scalability), and model size determines infrastructure needs. Integration with common enterprise systems: ERP integration via REST APIs or middleware (MuleSoft, Dell Boomi), CRM integration through platform-specific APIs (Salesforce Einstein, HubSpot), and data warehouse integration using batch prediction pipelines. Change management: implement A/B testing for gradual rollout, maintain fallback to non-AI logic, and monitor business metrics alongside model metrics. Feature flagging allows instant rollback if AI predictions degrade business outcomes.