How Contour Asset Management, a $6B fundamental long/short technology, media and telecom fund, measures the return on AI spend through live A/B tests of analyst research, a cost per useful insight metric, timing agents that resurface old ideas, and AI workflows across trading, operations and compliance.
This episode explores the critical gap between AI investment and return, featuring insights from industry leaders. It covers why most organizations fail to see ROI from AI, the crucial role of mathematical optimization in agentic AI to ensure constraint adherence, practical strategies for structuring AI skills in tools like Claude for quality output, and a 10-20-70 budget framework emphasizing upskilling. Additionally, it delves into the growing importance of the semantic layer for analytics agents to ensure consistent data interpretation and efficiency in large enterprises.
Pete Johnson, Field CTO of AI at MongoDB, discusses effective AI strategies, why most organizations struggle with AI ROI, and how to build reliable AI systems. He covers the importance of choosing the right embedding models for RAG pipelines, introduces Matryoshka embeddings, and explains the evolution of agentic memory to combat token maxing and ensure consistency in production AI.
John Roese, Dell's Global CTO, discusses the "agent-washing" phenomenon and how Dell achieved a $10 billion revenue boost while cutting costs through disciplined AI adoption. He introduces the concept of the "knowledge layer" as a crucial new component in AI architecture and details his 2026 predictions. These include a focus on governance, a four-part technical definition for agentic AI systems, the need for modern resiliency in AI factories, and the maturation of sovereign AI strategies.
Russ Fradin, founder of Larridin, draws parallels between the early days of ad tech and the current AI boom, arguing that a robust measurement infrastructure is the missing piece to unlock AI's true enterprise value. He discusses the challenges of measuring AI ROI, the gap between AI spending and actual usage, and how to overcome employee anxiety to foster productive adoption. The key is to move beyond simple surveys and marry behavioral data with real-world outcomes to understand if AI tools are actually making companies more productive.