By completing this course, learners will:

• Gain business fluency in AI concepts and how they apply to products.

• Lead cross-functional AI product teams.

• Design AI product strategies aligned with business goals.

• Manage data, ethics, and governance challenges.

• Develop KPIs and success metrics for AI adoption.

• Build confidence as an AI Product Manager for global organizations.

Course Syllabus

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

Learners will receive a Certificate of Completion from Uplatz, validating their expertise in AI Product Management. This certification demonstrates readiness for roles bridging business strategy, AI technology, and ethical leadership.

AI Product Management prepares learners for roles such as:

• AI Product Manager

• Product Leader (AI-driven organizations)

• AI Program Manager

• Business Strategist (AI adoption)

• Entrepreneur / Startup Founder (AI-first products)

With AI adoption accelerating worldwide, skilled AI PMs are in demand across tech, finance, healthcare, retail, and startups.

1. What is the role of an AI Product Manager?
   An AI PM balances business strategy, technical feasibility, and ethics, guiding AI products from idea to deployment.

2. How is AI Product Management different from traditional PM?
   AI PMs need data intuition, model understanding, and risk management beyond typical product ownership.

3. What makes data critical in AI products?
   AI performance depends on data quality, diversity, and governance.

4. What are success metrics for AI products?
   Beyond accuracy, AI PMs measure adoption, ROI, trust, and user satisfaction.

5. What are common reasons AI products fail?
   Poor data, lack of alignment with business goals, weak UX, or overpromising results.

6. What is the “black box” problem in AI?
   It refers to the lack of explainability in AI decisions, which impacts trust and adoption.

7. How can AI PMs ensure ethical AI?
   By applying fairness, inclusivity, transparency, and regulatory compliance frameworks.

8. What is database branching in the context of AI product dev?
   (Not applicable directly, but branching in AI PM means experimentation and iteration on product features.)

9. How do AI PMs work with cross-functional teams?
   They collaborate with data scientists, engineers, legal, design, and operations for holistic delivery.

10. Where is AI PM most in demand?
    Across tech, healthcare, retail, finance, autonomous systems, and generative AI startups.