The article argues that as AI automates data queries, pipelines, and models, the real value shifts to “measurement engineers” who decide if we’re measuring the right things and interpret ambiguous results. It breaks down why judgment—construct validity, reliable metrics, and decision theory—is a teachable skill that organizations must build into hiring, training, and structure.

The ninth AI Index report from Stanford HAI compiles global metrics on AI research, performance, adoption, economics, policy, and public opinion through 2025. It highlights rapid generative AI uptake, gaps in governance and evaluation, new economic and labor estimates, and standalone chapters on AI in science and medicine.

Rill’s Metrics SQL lets you define business metrics once and query them using plain SQL across dashboards, notebooks, and AI agents. It compiles metric views into optimized OLAP queries, handling grouping, filters, time functions, and security automatically.

This article examines the high rate of unused and broken dashboards in organizations, highlighting how they often fail to provide lasting value. It discusses the disconnect between dashboard creation and actual usage, driven by shifting priorities and limited attention spans within teams. The piece also touches on the implications of this phenomenon for organizational behavior and project management.

The article discusses the shifting landscape for data scientists and machine learning engineers in the age of large language models (LLMs). It emphasizes the importance of data science fundamentals in evaluating AI systems, addressing common pitfalls in metrics, experimental design, and data quality. The author argues that the core work of data scientists remains vital, even as their roles evolve.