Beyond Model Accuracy: An Engineering Framework for Production-Ready AI
About this session
A great model doesn’t guarantee a production-ready AI system. Real-world AI fails at the system level—through unreliable retrieval, unsafe actions, stale context, weak fallbacks, poor observability, and unpredictable behavior under failure.
This session introduces the Enterprise AI Readiness Framework (EARF), a practical framework for evaluating AI systems beyond model benchmarks. EARF examines readiness across dimensions such as reliability, safety and guardrails, retrieval quality, agent and tool behavior, observability, evaluation, and operational resilience.
Through real-world architecture patterns and failure scenarios, attendees will learn how to identify readiness gaps, evaluate AI systems systematically, and move from “the model works” to “the system is ready for production.
Speaker
Key takeaways
- Identify production-readiness gaps — Learn why strong model accuracy alone isn’t enough and how failures emerge across retrieval, tools, guardrails, reliability, and operations.
- Apply the EARF framework — Use a structured approach to evaluate AI systems across reliability, safety, observability, evaluation, and agent/tool behavior.
- Turn AI prototypes into production systems — Learn practical engineering patterns for fallbacks, failure handling, monitoring, and safe deployment of enterprise AI.