Free Download AI Product Management A Business Masterclass
Published 9/2025
Created by Uplatz Training
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 12 Lectures ( 10h 35m ) | Size: 2.34 GB
Master the skills to identify, build, and scale AI products that drive business impact, innovation, and growth.
What you'll learn
Explain the fundamentals of AI, ML, and Generative AI in simple business terms
Identify myths, misconceptions, and industry applications of AI
Distinguish the role of an AI Product Manager from a traditional PM
Demonstrate the skills required to manage AI products (business acumen, data intuition, ethics)
Evaluate organizational opportunities for AI using problem-fit and feasibility frameworks
Align AI initiatives with business goals and define an AI product strategy
Make informed Build vs. Buy vs. Partner decisions for AI solutions
Assess the importance of data quality, governance, and compliance in AI projects
Apply human-centered design principles to AI features and manage user expectations
Collaborate effectively with cross-functional AI teams (data science, engineering, legal, ops)
Navigate the AI product lifecycle from MVP to production and scale
Define and track success metrics that go beyond accuracy (ROI, adoption, trust)
Understand monetization models and pricing strategies for AI products
Recognize ethical, regulatory, and risk management issues in AI adoption
Develop a forward-looking perspective on the future of AI Product Management
Apply frameworks and case study insights to evaluate their own AI product ideas
Requirements
Enthusiasm and determination to make your mark on the world!
Description
A warm welcome to AI Product Management: A Business Masterclass course by Uplatz.Course DescriptionArtificial Intelligence is transforming every industry, but building successful AI-powered products requires more than just technical knowledge. It demands a unique combination of business strategy, data intuition, and product management skills.This course is designed to help you become an AI-savvy product leader who can identify opportunities, design user-centric AI solutions, and manage cross-functional teams to deliver real business value.Through a mix of real-world case studies, practical frameworks, and actionable insights, you will learn how AI product management differs from traditional PM roles, how to align AI initiatives with business goals, and how to navigate the challenges of data, ethics, and scaling AI systems.By the end of the course, you will have a complete AI Product Management playbook to take your career or business to the next level.What You'll LearnUnderstand the fundamentals of AI, ML, and Generative AI in simple business termsRecognize AI opportunities and evaluate business vs. technical feasibilityDefine an AI product strategy aligned with organizational goalsManage the AI product lifecycle from MVP to production and scalingCollaborate with data scientists, engineers, and business stakeholdersApply human-centered AI design principles to build trust and adoptionMeasure success using the right KPIs: impact, ROI, and customer trustExplore AI monetization models and pricing strategiesAddress ethics, risk, and compliance in AI product managementGain insights from case studies of leading AI-driven companies (Netflix, Amazon, Tesla, OpenAI)Who This Course is ForProduct managers who want to transition into AI product rolesBusiness leaders, entrepreneurs, and consultants exploring AI opportunitiesData scientists, engineers, and designers looking to understand the business side of AIMBA students and professionals pursuing careers at the intersection of AI, business, and technologyAnyone interested in building, scaling, and managing responsible AI productsWhy Take This Course?Learn from real-world AI product success and failure storiesMaster frameworks used by top tech companies to evaluate and launch AI initiativesBuild the skillset that top employers look for in AI Product ManagersPrepare yourself for the future of product management in an AI-first worldRequirementsNo coding or advanced technical knowledge requiredBasic understanding of product management or business concepts is helpful but not mandatoryCuriosity about how AI creates business value is essentialWhat is AI Product Management?AI Product Management is the discipline of defining, building, and scaling products powered by artificial intelligence (AI) while balancing business goals, customer needs, data constraints, and ethical considerations.It is not just traditional product management with AI added in - it focuses on bridging business, technology, and data science to turn AI capabilities into real-world, user-friendly, and valuable products.AI Product Managers serve as translators between business, data, and technology, ensuring AI products are not only technically sound but also usable, valuable, ethical, and scalable.How AI Product Management WorksIdentifying OpportunitiesSpot business problems where AI can create meaningful value.Evaluate whether the problem has a good AI fit and if AI is feasible.Defining Product StrategyAlign AI initiatives with organizational goals and priorities.Decide whether to build in-house, buy existing solutions, or partner.Data as the CoreEnsure the availability, quality, and governance of data.Collaborate with data teams to source, clean, and manage data pipelines.Cross-Functional CollaborationWork with data scientists, ML engineers, designers, legal, and operations.Translate technical concepts into business value for stakeholders.Designing for UsersApply human-centered AI design principles: transparency, explainability, trust.Manage user expectations about what AI can and cannot do.Building and ScalingDefine MVPs for AI products, which often require iterative experimentation.Manage pilots and then scale to production with monitoring and governance.Measuring SuccessMove beyond accuracy to measure business impact, adoption, ROI, and trust.Continuously refine based on feedback and model performance.Ethics and ComplianceAddress risks such as bias, fairness, and regulatory compliance.Position responsible AI as part of the product's competitive edge.AI Product Management: A Business Masterclass - Course CurriculumModule 1 - Foundations of AI for BusinessIntroduction: Why AI matters in business todayWhat AI is (and isn't) - demystifying buzzwordsAI vs. ML vs. Generative AI explained simplyMyths & misconceptions about AIAI across industries: banking, retail, healthcare, etc.Case study: Netflix, Uber, or Amazon's AI useModule 2 - The Role of an AI Product ManagerTraditional PM vs. AI PM - what's differentCore responsibilities of an AI PMRequired skills: business + data intuition + ethicsWorking with cross-functional teams (DS, Eng, Legal, Ops)Success metrics for AI product managersCareer path & opportunities in AI product managementModule 3 - Identifying AI OpportunitiesHow to recognize AI opportunities in your organizationProblem fit vs. AI fit - frameworks for evaluationFeasibility vs. business value balanceExample: AI features in consumer apps vs. enterprise solutionsCommon reasons AI products failMapping customer pain points to AI-driven solutionsModule 4 - AI Product StrategyWhat is AI product strategy?Aligning AI initiatives with business goalsBuild vs. Buy vs. Partner decisionsRoadmaps for AI products - how they differCompetitive advantage through AI adoptionCase study: Amazon, OpenAI, or TeslaModule 5 - Data as the Core of AI ProductsWhy data is the fuel of AIData quality and data readiness explained simplyData acquisition strategies - internal vs. externalPrivacy, compliance, and governance issuesThe cost of poor data: business implicationsCase study: biased AI system failuresModule 6 - Designing AI Products for UsersHuman-centered AI design principlesExplainability, transparency, and trust in AIManaging user expectations of AI systemsUI/UX design considerations for AI featuresThe "black box" problem explained to business leadersCase study: ChatGPT's UX evolutionModule 7 - Building and Scaling AI ProductsAI product lifecycle explained (non-technical)MVPs in AI - what's different?Collaboration with data scientists & engineersAgile product management for AI projectsFrom pilot to production: scaling challengesCase study: AI chatbot rollout in a bank/retail firmModule 8 - Measuring Success in AI ProductsWhy traditional KPIs aren't enough for AIMeasuring business impact vs. technical performanceAccuracy vs. adoption vs. ROI trade-offsCustomer trust & adoption as success metricsMonitoring AI in production - continuous learningCase study: AI in customer service (success & failure stories)Module 9 - Monetization and Business Models of AIAI-native vs. AI-enhanced productsPricing strategies for AI (subscription, API, usage-based)SaaS + AI business modelsCost of running AI products (compute, infra, talent)Ecosystem strategies (platforms, partnerships)Emerging business models with generative AIModule 10 - Ethics, Risks, and RegulationsEthical dilemmas in AI product managementBias, inclusivity, and fairness explained simplyRisk management frameworks for AIRegulatory landscape: EU AI Act, US/India/China approachesResponsible AI as a competitive advantageCase study: AI ethics failures (facial recognition, hiring bias)Module 11 - The Future of AI Product ManagementThe evolution of AI product management roleGenerative AI and LLMs shaping productsAI + IoT + Edge AI + Autonomous systemsSkills of the future AI PMOrganizational readiness for an AI-first worldCase study: Microsoft Copilot, Tesla Autopilot, etc.Module 12 - Capstone & Case StudiesRecap: AI PM playbookCase study 1: Success story (e.g., Spotify personalization)Case study 2: Failure story (e.g., Microsoft Tay chatbot)Framework to evaluate your own AI product ideaReflection prompts & group exercise designClosing thoughts: AI PM mindset shift for leaders
Who this course is for
Aspiring product managers wanting to enter AI product roles
Current product managers transitioning into AI-focused careers
Experienced PMs looking to strengthen AI, data, and business strategy skills
Business leaders and executives exploring AI adoption and innovation
Entrepreneurs and startup founders building AI-powered solutions
Consultants advising clients on AI strategy, productization, and digital transformation
MBA and business school students preparing for careers at the intersection of business and technology
Data scientists, ML engineers, and developers aiming to move into product management
UX/UI designers and operations managers working on AI-enabled products
Professionals interested in AI ethics, governance, and compliance
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