Modernizing legacy enterprise systems for real-time AI requires more than lifting workloads to the cloud; it demands architectural redesign. Key patterns include replacing batch jobs with event-driven flows, decoupling AI inference from application logic as an independent service, designing for real-time context (not just fast inference), selectively decomposing systems into microservices only where there's a clear boundary, using Kubernetes as an operational foundation rather than an architecture substitute, engineering for failure with retries and circuit breakers, embedding observability (including model drift and latency monitoring), building governance and traceability into runtime architecture, and modernizing incrementally through APIs and event exposure rather than a single disruptive migration.
