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[100% Off] Ai Product Management: A Business Masterclass

Master the skills to identify, build, and scale AI products that drive business impact, innovation, and growth.

Added on September 30, 2025 Business 6 min read

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

Course Description

Artificial 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.

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 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 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 Works

1. 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.

2. Defining Product Strategy

   • Align AI initiatives with organizational goals and priorities.

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

3. Data as the Core

   • Ensure the availability, quality, and governance of data.

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

4. Cross-Functional Collaboration

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

   • Translate technical concepts into business value for stakeholders.

5. Designing for Users

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

   • Manage user expectations about what AI can and cannot do.

6. Building and Scaling

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

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

7. Measuring Success

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

   • Continuously refine based on feedback and model performance.

8. 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

1. Introduction: Why AI matters in business today

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

3. AI vs. ML vs. Generative AI explained simply

4. Myths & misconceptions about AI

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

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

Module 2 – The Role of an AI Product Manager

1. Traditional PM vs. AI PM – what’s different

2. Core responsibilities of an AI PM

3. Required skills: business + data intuition + ethics

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

5. Success metrics for AI product managers

6. Career path & opportunities in AI product management

Module 3 – Identifying AI Opportunities

1. How to recognize AI opportunities in your organization

2. Problem fit vs. AI fit – frameworks for evaluation

3. Feasibility vs. business value balance

4. Example: AI features in consumer apps vs. enterprise solutions

5. Common reasons AI products fail

6. Mapping customer pain points to AI-driven solutions

Module 4 – AI Product Strategy

1. What is AI product strategy?

2. Aligning AI initiatives with business goals

3. Build vs. Buy vs. Partner decisions

4. Roadmaps for AI products – how they differ

5. Competitive advantage through AI adoption

6. Case study: Amazon, OpenAI, or Tesla

Module 5 – Data as the Core of AI Products

1. Why data is the fuel of AI

2. Data quality and data readiness explained simply

3. Data acquisition strategies – internal vs. external

4. Privacy, compliance, and governance issues

5. The cost of poor data: business implications

6. Case study: biased AI system failures

Module 6 – Designing AI Products for Users

1. Human-centered AI design principles

2. Explainability, transparency, and trust in AI

3. Managing user expectations of AI systems

4. UI/UX design considerations for AI features

5. The “black box” problem explained to business leaders

6. Case study: ChatGPT’s UX evolution

Module 7 – Building and Scaling AI Products

1. AI product lifecycle explained (non-technical)

2. MVPs in AI – what’s different?

3. Collaboration with data scientists & engineers

4. Agile product management for AI projects

5. From pilot to production: scaling challenges

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

Module 8 – Measuring Success in AI Products

1. Why traditional KPIs aren’t enough for AI

2. Measuring business impact vs. technical performance

3. Accuracy vs. adoption vs. ROI trade-offs

4. Customer trust & adoption as success metrics

5. Monitoring AI in production – continuous learning

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

Module 9 – Monetization and Business Models of AI

1. AI-native vs. AI-enhanced products

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

3. SaaS + AI business models

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

5. Ecosystem strategies (platforms, partnerships)

6. Emerging business models with generative AI

Module 10 – Ethics, Risks, and Regulations

1. Ethical dilemmas in AI product management

2. Bias, inclusivity, and fairness explained simply

3. Risk management frameworks for AI

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

5. Responsible AI as a competitive advantage

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

Module 11 – The Future of AI Product Management

1. The evolution of AI product management role

2. Generative AI and LLMs shaping products

3. AI + IoT + Edge AI + Autonomous systems

4. Skills of the future AI PM

5. Organizational readiness for an AI-first world

6. Case study: Microsoft Copilot, Tesla Autopilot, etc.

Module 12 – Capstone & Case Studies

1. Recap: AI PM playbook

2. Case study 1: Success story (e.g., Spotify personalization)

3. Case study 2: Failure story (e.g., Microsoft Tay chatbot)

4. Framework to evaluate your own AI product idea

5. Reflection prompts & group exercise design

6. Closing thoughts: AI PM mindset shift for leaders