AI Readiness for Enterprise Scale

In this partner-led, recorded webinar, Matt and a panel of Xidian machine learning engineering leads (Reinhardt, Savas, and Nishay) discuss enterprise AI readiness and why many AI POCs fail to reach production. They emphasize that productionizing AI goes far beyond model accuracy, requiring machine learning engineering, production architecture, data and model pipelines, APIs, testing, security, observability, versioning, monitoring, retraining, governance, and auditability. The panel highlights key enterprise blockers such as fragmented systems, siloed ownership, data readiness, legacy integration, hidden dependencies, executive and workforce readiness concerns, and restrictions on accessing production data. They also cover build-vs-buy considerations for foundation models and cloud/platform choices, and note that specialist engineering remains necessary despite managed services. The session concludes with guidance to prioritize use cases by highest business value and lowest complexity, including ensuring fallbacks. 00:50 Why AI Must Scale 03:18 Meet the Panel 04:12 Enterprise Challenges Today 07:44 Why POCs Stall 12:23 From Accuracy to MLOps 15:28 Integration and Legacy Systems 19:26 Automation Testing and Versioning 21:16 Data Pipelines and Drift 23:15 Build Buy and Ownership 25:49 Cloud Platforms and Portability 29:24 Audience Q&A Governance 35:53 Java Integration and Observability 39:40 Choosing What to Productionize