Episode 34: How to Govern & Monetize Agentic AI | DataRobot Agent Workforce Platform
Romi Datta, VP and Head of Product Management at DataRobot, explains why enterprise AI is moving past chatbots and copilots toward full agent workforces, and why 95% of agentic AI pilots still fail to deliver measurable ROI. He walks through production use cases from insurance claims processing to industrial safety at Chevron (with NVIDIA edge AI), then breaks down the governance, observability, secure context management, and agent memory an enterprise platform needs to run agents in cloud, hybrid, on-prem, and air-gapped environments.
Special Guests
Romi Datta
VP and Head of Product Management at DataRobot.
Timestamps
00:00Introduction: Building the Business Behind Open Source AI00:04Meet Romi Datta and the DataRobot Agent Workforce Platform00:37What Is DataRobot?00:55Agents as the Modern Workforce01:18DataRobot's Evolution from AutoML to Agentic AI01:58Enterprise Customers Using DataRobot02:29How Agentic AI Has Evolved Over the Last 18 to 24 Months02:55From Chatbots and Copilots to Autonomous Agents03:43What Makes an AI Agent Different?04:12Where Enterprise Agents Create Value04:32Reimagining Business Processes with Agentic AI05:02DataRobot's Agent Marketplace05:22Production-Ready AI Agents for Business Outcomes05:49Claims Processing Agent Example06:22How AI Agents Support Claims Adjudication07:08Reimagining Insurance Claims Processing07:51Industrial AI Use Case: Chevron and Field Safety08:43NVIDIA, DataRobot, and Chevron Partnership09:30Edge AI for Safer Industrial Operations10:30Continuous Safety Validation with Agents11:11Plume Prediction and Drone Route Planning12:09Agent Workflow with NVIDIA NeMo and PhysicsNeMo13:09Guardrails, Monitoring, and Safety Standards14:09Open-Source AI Components Behind the Chevron Use Case14:43Why Enterprises Need Purpose-Built AI Platforms15:17Lessons from Industrialization: Cars, Software, and AI16:57Why 95% of Enterprise Agentic AI Pilots Fail17:23What It Takes to Move Agents to Production17:47Data, Architecture, RBAC, Observability, and Governance18:16Why Agents Can Take 12 to 16 Months to Reach Production18:32Operating and Governing Agents in Any Environment18:56Runtime, Inference, Governance, and Monitoring19:33Secure Context Management, RAG, Identity, and Agent Memory20:02Building Agents Anywhere, Deploying with DataRobot20:28Tracing, Monitoring, and Evaluating Agent Execution20:52DataRobot and NVIDIA Integration21:23Deploying, Scaling, and Governing Enterprise Agents21:52The Future of Agentic AI and Drug Discovery22:42Accelerating Drug Discovery with AI23:44Closing ThoughtsRelated Episodes
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