Move beyond feature roadmaps to agentic products that learn, earn trust, and compound value. Master the Agentic Product Stack, measure Return on Cognition (ROC), and build defensible learning moats. Evolve from product manager to cognitive economy architect shaping scalable AI portfolios. This 3-hour hands-on course is based on AEBOP T5.1.
AEI members save 20% with code MEM_C21_20.
Traditional product management metrics break when applied to autonomous AI systems. This module deconstructs why shipped features aren't successes and establishes Learning Velocity as the new core metric. You'll contrast traditional vs. agentic PM roles and success criteria through concrete enterprise failure and recovery cases.
You will adopt the Agentic Product Diamond framework to diagnose product health across four axes: Value, Trust, Economics, and Cognition. Learn to identify "cognitive debt" and redesign planning rituals around closing learning loops instead of delivering outputs, setting the foundation for a compounding product strategy.
Agentic products require managing cognition as infrastructure, not just features. This module details the five-layer Agentic Product Stack (User Value, Cognition, Governance, Operations, Business) and its interlocking dependencies. You'll learn to assign clear cross-functional ownership using a Stack Charter, ensuring accountability for learning outcomes.
Translate architecture into action with Cross-Layer Reviews, replacing feature demos with evidence-based audits of value, trust, and economics. Establish operational rhythms that detect drift and reinforce alignment, turning the stack from a model into a live operating system for your product's intelligence.
Strategic advantage comes from building what competitors can't copy. Learn to use the Human-Agency Map to prioritize automation that amplifies human capability, not replaces it, based on AI readiness and genuine user desire. Avoid adoption collapse by aligning automation with human agency through proven enterprise case studies.
Transform your product into a defensible asset by executing the Moat Playbook. Discover how to bind cognition to proprietary context, capture feedback as compounding capital, and surface governance as a product feature. Conduct quarterly Moat Sprints to pressure-test copyability and deepen your product's learning architecture.
Prove and improve your system's intelligence with the Minimum Metric Stack. Implement five key metrics—from Time to Decision to Cost per Cognition—instrumented via existing telemetry. Build a Trust-in-Motion Dashboard with actionable thresholds, enabling data-driven decisions on when to scale, investigate, or pause autonomous capabilities.
Master the economics of learning by calculating and optimizing Return on Cognition (ROC), the north-star metric that unites product, engineering, and finance. Execute the Five-Step Quarterly Play to prune unproductive loops and reinvest in compounding learning. Graduate from managing products to architecting a portfolio of self-improving, economically sustainable systems.