Leading Cross-Functional AI Delivery

Microsoft via Coursera

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Overview

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Leading Cross-Functional AI Delivery is an intermediate course aimed at mid-management roles. It focuses on managing AI initiatives through structured execution and disciplined coordination. AI projects require close collaboration between data scientists, engineers, architects, legal teams, and business stakeholders. This course equips you to guide that collaboration using practical project management frameworks adapted for AI development. You’ll explore agile methodologies for AI initiatives, resource planning techniques, and structured risk management practices. The course also introduces Azure DevOps as a tool for organizing workstreams and maintaining visibility across teams. You will examine how generative AI tools can support project planning, documentation, and stakeholder communication, improving clarity and efficiency without replacing human oversight. By the end of this course, you’ll be able to manage AI project execution from idea to delivery while maintaining alignment, mitigating risk, and supporting cross-functional coordination in enterprise environments.

Syllabus

Setting up and managing AI projects in Azure DevOps

This module builds your ability to understand and evaluate how AI projects are structured within Azure DevOps for effective delivery oversight. You'll learn why AI projects require different organizational approaches than traditional software, particularly how to make experimentation visible, define meaningful lifecycle stages, and establish review points that support informed decision-making. By the end of this module, you'll be able to interpret AI project structures, assess whether work is organized for effective oversight, and conduct retrospectives that surface actionable improvements.

Planning AI sprints with experimentation and uncertainty

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.

Planning AI project costs and staffing

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.

Managing risks and issues in AI projects

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.

Identifying AI opportunities for project leadership

This module builds your ability to recognize where generative AI can genuinely accelerate project leadership work and where it introduces risk that outweighs the efficiency gain. You'll learn how experienced project leaders evaluate their workflows to identify suitable AI use cases, what criteria distinguish good candidates for AI assistance from tasks that require human judgment, and how to explain your AI usage decisions to stakeholders. By the end of this module, you'll be able to assess your own workflows for AI augmentation opportunities and make deliberate choices about where to apply AI assistance.

Applying and evaluating AI for project work

This module develops your ability to evaluate AI-generated project artifacts and maintain quality standards regardless of how the content was created. You'll learn why rigorous evaluation of AI outputs is essential, even when they appear polished, and how to apply consistent validation approaches across different artifact types. By the end of this module, you'll be able to assess AI-generated documentation for completeness and accuracy, identify common errors and hallucinations, and decide when human expertise is required before incorporating AI-assisted work into your projects.

Project : Leading an AI Project from idea to delivery

This module lets you apply AI project leadership skills in a realistic business scenario. You'll develop a project plan for an AI-powered supply chain initiative while navigating stakeholder priorities, governance requirements, delivery constraints, and organizational risks. By the end of the module, you'll be able to assess readiness, address key risks, and make informed recommendations that support successful AI project outcomes.