Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
GenAI unit economics for PMs is not merely an expense math problem; it is a design discipline that ties product value to engineering cost, latency budgets, and governance in production AI features.
GenAI's impact on sprint planning is best described as an augmentation, not a replacement. When embedded in a disciplined planning fabric, GenAI accelerates.
AI agents can orchestrate end-to-end A/B test variant generation in production environments, dramatically reducing cycle time while preserving statistical rigor.
AI agents have moved beyond isolated model experiments. In production environments, they can synthesize data-driven user personas by combining structured CRM data, product telemetry, and qualitative inputs into a living, graph-enabled representation.
AI agents can accelerate the production of convincing, interactive UI mockups when embedded in a disciplined design pipeline.
In production-grade AI programs, user personas drive feature relevance, risk controls, and decision workflows. Generating personas from real data reduces bias and accelerates alignment between product and users.
Modern sales enablement requires artifacts that match current product realities, buyer contexts, and channel constraints.
AI agents are increasingly capable of drafting structured user stories and measurable acceptance criteria from product goals, user journeys, and data signals.
Generative agents accelerate rapid prototyping by turning design goals into autonomous planning, tool orchestration, and verifiable artifacts.