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Master Product Management with AI-The Skillset Hiring Managers Are Looking For

Microsoft AI-Powered Product Management Certification

Think Like a Product Leader, Solve Real Product Problems and Build AI-Native Products

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AI-powered Product Management Certification

Prerequisites and Eligibility for the AI-Powered Product Management Certification

Prerequisites and Eligibility

Whether you're transitioning into product management or advancing in your current role, this program is designed to support learners at different stages.

There are no strict eligibility criteria. To maximize your learning experience, you should have:

Prerequisites image
  • 500k+
    Career Transformations
  • 250+
    Workshops every month
  • 100+
    Countries and counting

Who Should Enrol in the AI for Product Managers Course

Who is this Course for
  • Looking to break into product roles with strong fundamentals
  • Aiming to strengthen product thinking and execution skills
  • Looking to integrate AI into product workflows and decision-making
  • Aiming to move into strategic and leadership roles in product
  • Exploring AI-powered product innovation and system design
  • Looking to create and scale AI-first products and workflows
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AI-Powered Product Management Training Highlights

Course Highlights

Live Instructor-Led Sessions to learn product thinking frameworks through interactive discussions and real-world product examples.

Product Thinking Frameworks to develop structured thinking using proven approaches to identify problems, evaluate solutions, and prioritize features.

Product Discovery and Design to learn how to identify real user problems, design solutions, and create meaningful product experiences.

Root Cause Analysis & Metrics Thinking to understand how product managers analyze metrics and diagnose performance drops or engagement issues.

Product Requirement Document (PRD) Writing to develop industry-standard documentation skills for clearly communicating product ideas and aligning stakeholders.

Hands-On Product Case Exercises to practice solving real-world product cases in every session by breaking down problems and designing effective solutions.

AI Foundations for Product Managers to understand how AI, Machine Learning, and LLMs are transforming modern product development.

AI Product Workflows to learn how AI-powered features are designed using LLMs, APIs, prompts, and automation pipelines.

Portfolio Building and Mock Interviews to build a strong product portfolio with teardown analyses, case studies, and PRDs while practicing interviews with peers.

* Please note - Microsoft certification is available only with the Pro plan.

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Why Choose Our AI-Powered Product Management Program

upGrad KnowledgeHut Edge

First-Principles Product Thinking

Learn how great product managers approach problems starting from user needs and real-world constraints.

Practical Learning Approach

Each topic follows a structured format: Framework explanation → Case exercise → Individual assignment → Mock interview This ensures you build practical product thinking skills, not just theoretical knowledge.

AI-Focused Product Skills

Understand how AI is changing product development and learn how to design AI-enabled product features and workflows.

Product Portfolio Creation

Build tangible artifacts including Product Requirement Documents (PRDs), Product teardown analysis, and Product improvement case studies

Structured Interview Preparation

Participate in interviews and portfolio reviews to prepare for product management roles.
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Ready to unlock your full potential as an AI Product Manager?

AI Product Management Course Curriculum

Curriculum

1. Module 1: AI -Augmented PM Fellowship 

Week 1: Product Thinking, Discovery & Data Foundations

  • Product mindset
  • Why products exist
  • Product thinking vs feature thinking
  • Product discovery frameworks
  • AI-powered market research using Perplexity and Genspark
  • Synthetic persona building with Claude
  • User segmentation and customer segmentation
  • AI-assisted SQL for user data retrieval to Visualize app performance with data

Case Study

  • Define success metrics for a productivity app using AI-simulated behavior

Week 2: AI-Powered Design & Root Cause Analysis

  • Product design framework
  • User journey mapping
  • Feature prioritization
  • Solution tradeoffs
  • Text-to-wireframe conversion using Figma AI
  • UI component generation with AI
  • Root Cause Analysis
  • RCA frameworks
  • Metrics drop investigation using Mixpanel Spark AI
  • Natural-language analytics querying
  • Automated RCA Boards and property-level breakdowns
  • Anomaly Detection and Session Replay correlation

Hands-on: Designing a new feature for a learning platform using AI-driven solution tradeoffs. The Mixpanel Lab: Diagnosing a simulated 25% drop in user engagement by correlating Anomaly Detection alerts with Session Replays to see exactly where users are struggling 

Week 3 : PRD Automation & Product Improvement

  • Product gap identification
  • User feedback analysis
  • Scope definition
  • Prioritization
  • Success metrics
  • PRD structure & PRD generation using ChatPRD and WriteScribe
  • Automated documentation from raw notes
  • Sentiment-tracking pipelines for feedback loops

Case Study

  • Improve a grocery delivery experience by identifying product gaps with AI

Week 4: Product Teardown + Job Profiling

Hands-on Product Teardown:

  • Spotify, Notion, Airbnb
  • AI-powered competitor UX pattern analysis and AI-generated portfolio teardown reports. Portfolio building workshop and portfolio structure
  • Resume review using AI-powered ATS simulators
  • LinkedIn optimization

2. Module 2: Building AI-Native Products

Week 5: AI Foundations & Feasibility for PMs

  • AI vs ML vs LLM and AI use case discovery
  • AI feasibility thinking, AI feature design
  • Understanding Tokenization and context windows and the cost-benefit analysis of LLM providers

Week 6: LLM APIs & Agentic Workflows

  • Prompt engineering and LLM fundamentals
  • Introductions to LLM APIs and HuggingFace models
  • Hands-on lab: building an automation pipeline connecting an LLM to external data sources

Week 7: AI Architecture & Agentic Design

  • AI agents, AI copilots, AI system design
  • Architecture tech for PMs Understanding RAG (Retrieval-Augmented Generation) vs. Fine-tuning and data pipelines and model deployment basics.
  • Hands-on lab: : Designing a multi-agent system (e.g., using Flowise or n8n) where agents collaborate on complex tasks.

Week 8: Capstone – Building the AI MVP

  • Finalize AI MVP and prepare for the job market
  • Capstone Presentation: Demonstrating a functional AI prototype built with low-code tools (e.g., an AI-driven assessment tool or personalized tutor).
  • Final Review: Mock interviews and portfolio finalization focusing on "AI-First" product management.

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