Projects & Product Management with AI

Project and product management are no longer just about task lists—they’re about turning business needs into clear deliverables, prioritizing outcomes, and communicating value. AI tools now act as powerful assistants, helping product managers translate high-level goals into structured requirements, estimate effort more consistently, and surface risks early in the process. This module emphasizes how to combine traditional PM frameworks with AI-augmented workflows to deliver projects faster and with stronger alignment to business value.

Objectives

By the end of this module, students will be able to:

• Translate a business problem into a Product Requirements Document (PRD).
• Plan sprints, risks, and success metrics, while using AI for story drafting and estimation cross-checks.
• Build a lightweight ROI model to evaluate the financial viability of a project.

Lecture & Discussion

The lecture introduces common product management frameworks (Agile, Scrum, Lean) and situates them within broader project execution models. Students will discuss success metrics such as OKRs (Objectives and Key Results), ROI (Return on Investment), and customer-centric KPIs. Key debates include the build vs. buy decision (when to develop internally vs. procure externally) and the role of change management in ensuring adoption. Case studies will highlight how AI can assist in backlog grooming, risk detection, and strategic prioritization.

Hands-On Exercise

Students will practice with Notion AI and ClickUp AI to break down large initiatives (epics) into actionable user stories. Elevate AI will be used to generate acceptance criteria and quality checks for those stories, while estimation models help students validate resource needs. Students will also create a simple Gantt chart in Google Sheets, linking milestones to dependencies, risks, and metrics.

Lab 9: PRD + Roadmap

In this lab, students will prepare a combined “PRD + Roadmap” deliverable, which includes:

• Scope Definition – clear boundaries of what is and isn’t included.
• Risks Register – prioritized risks with mitigation strategies.
• KPI Tree – mapping high-level business goals into measurable outcomes.
• ROI Assumptions – lightweight financial projections to justify the initiative.

This lab reinforces the full cycle: from problem framing → requirements → execution plan → measurable impact, showing how AI can act as a co-pilot for modern project and product managers.

Lesson Summary

Project and product management have evolved to focus on translating business needs into clear deliverables, prioritizing outcomes, and communicating value with the assistance of AI tools. This module emphasizes combining traditional PM frameworks with AI-augmented workflows for faster project delivery and stronger alignment to business value.

• Key Objectives of the Module:
• Translate business problems into Product Requirements Documents (PRDs).
• Plan sprints, risks, and success metrics using AI for story drafting and estimation cross-checks.
• Build a lightweight ROI model to evaluate the financial viability of a project.

The lecture discusses common product management frameworks like Agile, Scrum, and Lean within broader project execution models. It covers success metrics such as OKRs, ROI, and customer-centric KPIs, including debates on the build vs. buy decision and the role of change management.

Hands-On Exercises involve using Notion AI and ClickUp AI for breaking down initiatives, Elevate AI for generating acceptance criteria, quality checks, and estimation models. Students also create a simple Gantt chart in Google Sheets.

In Lab 9, students work on a "PRD + Roadmap" deliverable that includes scope definition, risks register, KPI tree, and ROI assumptions. This lab reinforces the project cycle from problem framing to measurable impact, showcasing how AI can assist as a co-pilot for modern project and product managers.