AI Product Management: A Business Masterclass

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 toAI Product Management: A Business Masterclasscourse byUplatz.

Course Description

Artificial Intelligenceis transforming every industry, but building successful AI-powered products requires more than just technical knowledge. It demands a unique combination ofbusiness strategy, data intuition, and product management skills.

This course is designed to help you become anAI-savvy product leaderwho can identify opportunities, design user-centric AI solutions, and manage cross-functional teams to deliver real business value.

Through a mix ofreal-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 completeAI Product Management playbookto take your career or business to the next level.

What You’ll Learn

Understand the fundamentals of AI, ML, and Generative AI in simple business terms

Recognize AI opportunities and evaluate business vs. technical feasibility

Define an AI product strategy aligned with organizational goals

Manage the AI product lifecycle from MVP to production and scaling

Collaborate with data scientists, engineers, and business stakeholders

Apply human-centered AI design principles to build trust and adoption

Measure success using the right KPIs: impact, ROI, and customer trust

Explore AI monetization models and pricing strategies

Address ethics, risk, and compliance in AI product management

Gain insights from case studies of leading AI-driven companies (Netflix, Amazon, Tesla, OpenAI)

Who This Course is For

Product managers who want to transition into AI product roles

Business leaders, entrepreneurs, and consultants exploring AI opportunities

Data scientists, engineers, and designers looking to understand the business side of AI

MBA students and professionals pursuing careers at the intersection of AI, business, and technology

Anyone interested in building, scaling, and managing responsible AI products

Why Take This Course?

Learn from real-world AI product success and failure stories

Master frameworks used by top tech companies to evaluate and launch AI initiatives

Build the skillset that top employers look for in AI Product Managers

Prepare yourself for the future of product management in an AI-first world

Requirements

No coding or advanced technical knowledge required

Basic understanding of product management or business concepts is helpful but not mandatory

Curiosity about how AI creates business value is essential

What is AI Product Management?

AI Product Managementis 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 Works

Identifying Opportunities

Spot business problems where AI can create meaningful value.

Evaluate whether the problem has a good AI fit and if AI is feasible.

Defining Product Strategy

Align AI initiatives with organizational goals and priorities.

Decide whether to build in-house, buy existing solutions, or partner.

Data as the Core

Ensure the availability, quality, and governance of data.

Collaborate with data teams to source, clean, and manage data pipelines.

Cross-Functional Collaboration

Work with data scientists, ML engineers, designers, legal, and operations.

Translate technical concepts into business value for stakeholders.

Designing for Users

Apply human-centered AI design principles: transparency, explainability, trust.

Manage user expectations about what AI can and cannot do.

Building and Scaling

Define MVPs for AI products, which often require iterative experimentation.

Manage pilots and then scale to production with monitoring and governance.

Measuring Success

Move beyond accuracy to measure business impact, adoption, ROI, and trust.

Continuously refine based on feedback and model performance.

Ethics and Compliance

Address 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 Curriculum

Module 1 – Foundations of AI for Business

Introduction: Why AI matters in business today

What AI is (and isn’t) – demystifying buzzwords

AI vs. ML vs. Generative AI explained simply

Myths & misconceptions about AI

AI across industries: banking, retail, healthcare, etc.

Case study: Netflix, Uber, or Amazon’s AI use

Module 2 – The Role of an AI Product Manager

Traditional PM vs. AI PM – what’s different

Core responsibilities of an AI PM

Required skills: business + data intuition + ethics

Working with cross-functional teams (DS, Eng, Legal, Ops)

Success metrics for AI product managers

Career path & opportunities in AI product management

Module 3 – Identifying AI Opportunities

How to recognize AI opportunities in your organization

Problem fit vs. AI fit – frameworks for evaluation

Feasibility vs. business value balance

Example: AI features in consumer apps vs. enterprise solutions

Common reasons AI products fail

Mapping customer pain points to AI-driven solutions

Module 4 – AI Product Strategy

What is AI product strategy?

Aligning AI initiatives with business goals

Build vs. Buy vs. Partner decisions

Roadmaps for AI products – how they differ

Competitive advantage through AI adoption

Case study: Amazon, OpenAI, or Tesla

Module 5 – Data as the Core of AI Products

Why data is the fuel of AI

Data quality and data readiness explained simply

Data acquisition strategies – internal vs. external

Privacy, compliance, and governance issues

The cost of poor data: business implications

Case study: biased AI system failures

Module 6 – Designing AI Products for Users

Human-centered AI design principles

Explainability, transparency, and trust in AI

Managing user expectations of AI systems

UI/UX design considerations for AI features

The “black box” problem explained to business leaders

Case study: ChatGPT’s UX evolution

Module 7 – Building and Scaling AI Products

AI product lifecycle explained (non-technical)

MVPs in AI – what’s different?

Collaboration with data scientists & engineers

Agile product management for AI projects

From pilot to production: scaling challenges

Case study: AI chatbot rollout in a bank/retail firm

Module 8 – Measuring Success in AI Products

Why traditional KPIs aren’t enough for AI

Measuring business impact vs. technical performance

Accuracy vs. adoption vs. ROI trade-offs

Customer trust & adoption as success metrics

Monitoring AI in production – continuous learning

Case study: AI in customer service (success & failure stories)

Module 9 – Monetization and Business Models of AI

AI-native vs. AI-enhanced products

Pricing strategies for AI (subscription, API, usage-based)

SaaS + AI business models

Cost of running AI products (compute, infra, talent)

Ecosystem strategies (platforms, partnerships)

Emerging business models with generative AI

Module 10 – Ethics, Risks, and Regulations

Ethical dilemmas in AI product management

Bias, inclusivity, and fairness explained simply

Risk management frameworks for AI

Regulatory landscape: EU AI Act, US/India/China approaches

Responsible AI as a competitive advantage

Case study: AI ethics failures (facial recognition, hiring bias)

Module 11 – The Future of AI Product Management

The evolution of AI product management role

Generative AI and LLMs shaping products

AI + IoT + Edge AI + Autonomous systems

Skills of the future AI PM

Organizational readiness for an AI-first world

Case study: Microsoft Copilot, Tesla Autopilot, etc.

Module 12 – Capstone & Case Studies

Recap: AI PM playbook

Case 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 idea

Reflection prompts & group exercise design

Closing 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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