Enterprises don’t fail at GenAI for lack of models – they fail at productization. Over the last two years leading Enterprise AI at a Fortune 200, my team shipped GenAI and AgenticAI apps across finance, inventory, intelligent document processing, semantic search/RAG, and agentic workflows. This talk shares the scars and the playbook.

First, ruthless use-case intake: tie every idea to a KPI and an operator persona; most ideas die at the gate, and that’s healthy. Second, build on a data and governance spine: policy-aware retrieval, PII handling, evaluation for hallucination, bias, and safety, plus human-in-the-loop for low-confidence outputs. Third, engineer for enterprise realities: latency budgets, cost observability, prompt and memory versioning, golden datasets, and rollbacks like any other software service. Fourth, portability beats purity: we run models across Databricks, Azure AI and Vertex AI, and keep prompts/tools abstracted to avoid lock-in. Finally, change management wins the day: embedded champions, sandbox→pilot→scale stages, and value dashboards that CFOs trust.

I’ll share patterns, anti-patterns, and before/after results. Attendees will leave with a pragmatic blueprint, reusable checklists, and evaluation templates to move from promising POCs to secure, governed, ROI-positive production.