This article offers a guide on how to effectively measure the return on investment (ROI) for AI initiatives. It covers key metrics and methods to evaluate the financial impact of AI tools and strategies in business settings.

This article explains how evaluation frameworks, or "evals," help businesses measure AI performance against defined goals. It outlines a process for creating contextual evals tailored to specific workflows, emphasizing the importance of clear objectives and continuous improvement.

This article outlines five essential recommendations for platform engineering teams to succeed in 2026. It emphasizes the importance of dedicated leadership, measuring success, fostering a culture of voluntary adoption, and continuous upskilling to overcome common challenges and drive ROI.

This article discusses how AI is changing consumer search behavior, particularly affecting link clicks and site visits. While traditional search remains important, marketers need to adapt their measurement strategies to focus on comprehensive search signals and incrementality modeling to better understand consumer intent.

AI teams often focus too much on tools and frameworks rather than understanding their product's performance through effective measurement and error analysis. By prioritizing error analysis and empowering domain experts to directly contribute to prompt development with customized data viewers, teams can significantly enhance their AI products and achieve better outcomes.