This module develops your ability to plan and oversee AI sprints where work includes both experimentation and delivery. You'll learn how to define sprint goals that align with learning objectives and delivery commitments, structure experimentation spikes with clear validation criteria, and interpret burndown trends when progress doesn't follow traditional patterns. By the end of this module, you'll be able to plan sprints that account for research uncertainty, recognize meaningful signals in delivery data, and make informed adjustments without overreacting to normal variability.

This module builds your ability to plan and communicate AI project resource requirements with confidence. You'll learn why AI projects require different cost and staffing approaches than traditional software, including how experimentation phases create cost uncertainty and how resource needs shift significantly between exploration and production. By the end of this module, you'll be able to interpret Azure Pricing Calculator outputs, create staffing plans aligned to project phases, build RACI matrices that clarify accountability, and communicate resource plans to stakeholders in terms they can evaluate and approve.

This module develops your ability to identify, assess, and manage the distinctive risks that AI projects carry. You'll learn how to use RAID logs to maintain visibility into risks, assumptions, issues, and dependencies, how to prioritize risks using probability-impact reasoning, and how to assign ownership and track mitigation through to resolution. By the end of this module, you'll be able to conduct effective risk reviews, make informed decisions about escalation and mitigation, and communicate risk status to stakeholders in terms that support confident decision-making.