Measuring AI Success: Value Metrics and KPIs for Product Managers

As artificial intelligence becomes central to modern software, traditional product metrics are no longer enough to measure success. Product managers must bridge the gap between business value and complex technical performance to launch successful AI initiatives.\n\nThis text-only course teaches you how to establish, track, and optimize Key Performance Indicators (KPIs), Objectives and Key Results (OKRs), and value metrics specifically tailored for AI-driven products. You will gain the confidence to align engineering teams with business stakeholders using clear, measurable goals.\n\nWhat you'll learn:\n- Define foundational product metrics, KPIs, and OKRs for AI systems\n- Align technical model performance metrics with high-level business objectives\n- Track modern AI-specific metrics, including latency, cost per query, and model drift\n- Implement ethical and bias-tracking metrics to ensure responsible AI deployment\n- Design measurement frameworks that communicate value clearly to stakeholders\n- Analyze real-world case studies to practice selecting the right metrics for AI products\n\nThe course begins with essential terminology and the foundational principles of product measurement before guiding you through the specific nuances of AI products. You will progress from basic definitions to structured frameworks that you can apply immediately to your own projects.\n\nThis course is designed for aspiring and beginning product managers, data professionals, and business analysts looking to enter the AI space. No prior background in machine learning or statistics is required.\n\nStart learning how to measure and drive real business value with your AI products today.