AI+ Product Manager TM Training | CourseMonster

- Product Managers and Associate PMs

- AI and ML Enthusiasts transitioning into PM roles

- Startup Founders and Innovation Leads

- Strategy & Business Intelligence Professionals

- UX/Product Designers wanting AI context

- Understanding the basics of digital technologies and their influence on various aspects of professional life.

- Interest in learning about the integration of AI technologies within product development.

- Participants should have an open mindset towards learning new concepts and technological developments.

Module 1: Introduction to Artificial Intelligence (AI) for Product Managers

Understanding the Basics of AI

• Defining AI: Key terms and concepts (e.g., machine learning, deep learning, NLP, computer vision, generative AI).
• The Product Manager's Lens: Shifting from traditional product management to AI-driven products.
• AI vs. Automation: Differentiating between rules-based systems and predictive models.

Importance of AI in Product Strategy

• Identifying AI-native opportunities vs. enhancing existing products with AI.
• Strategic Frameworks: The AI Product Strategy Canvas and the AI Value Proposition.
• Competitive Advantage: Using AI to create moats, network effects, and defensible product value.

Module 2: Fundamentals of Machine Learning

Introduction to Machine Learning

• Types of Learning: Supervised, Unsupervised, and Reinforcement Learning with business examples.
• Key Terminology: Understanding models, features, training data, and prediction.
• The Role of the PM: Bridging the gap between business needs and data science capabilities.

Data Preparation in ML Models

• The Importance of Data: "Garbage in, garbage out" and the PM's responsibility for data quality.
• The Data Flywheel: How product usage creates data that improves the product.
• Data Sourcing and Labeling: The process and challenges of acquiring and preparing data for training.

Module 3: AI Product Development Lifecycle

Leveraging AI in Ideation and Conceptualization

• Problem Framing: Defining the problem and identifying if AI is the right solution.
• Discovery Phase: Conducting user research to uncover pain points that AI can solve.
• Feasibility, Desirability, Viability: A modified framework for AI products.

Prototyping and Testing AI-driven Products

• The "Wizard of Oz" Method: Simulating AI functionality with human intervention.
• MVP for AI: Building a minimum viable product that demonstrates AI's core value.
• Iterative Development: Fast, data-driven cycles for model improvement and product refinement.

Module 4: AI Ethics and Bias

Ethical Implications of AI Products

• The PM's Role in Responsible AI: Defining ethical guidelines and principles.
• Trust and Transparency: Communicating AI's decisions to end-users.
• Harmful AI: Understanding potential societal and individual harms (e.g., privacy, surveillance, manipulation).

Identifying and Addressing AI Bias

• Sources of Bias: Understanding how bias can enter through data, algorithms, and human decision-making.
• Bias Audits: Implementing checks and balances to identify and measure bias.
• Mitigation Strategies: Techniques like diverse data collection, algorithmic fairness, and human oversight.

Module 5: AI Implementation Strategies

Integrating AI into Existing Products

• Augmentation vs. Replacement: How to enhance human workflows without removing human agency.
• Technical Implementation: APIs, microservices, and deploying models to production.
• Change Management: Preparing users and stakeholders for new AI-powered features.

Communicating AI Initiatives to Stakeholders

• Crafting the Narrative: Explaining the business value and impact of AI projects to executives.
• Demystifying the Technology: Translating complex data science concepts into understandable language for marketing, sales, and support teams.
• Managing Expectations: Setting realistic goals and timelines for AI projects, which can be unpredictable.

Module 6: AI Metrics and Performance Evaluation

Identifying Relevant KPIs

• Business Metrics: Measuring the impact on revenue, user engagement, and operational efficiency.
• Model Metrics: Understanding and translating technical metrics like accuracy, precision, recall, and F1-score into business outcomes.
• Holistic Evaluation: Creating dashboards that combine product KPIs and model performance metrics.

Evaluating AI Model and Product Performance

• A/B Testing with AI: Best practices for testing AI models in a production environment.
• Model Monitoring: Setting up systems to track model drift and performance degradation over time.
• Feedback Loops: Creating a continuous cycle for users to provide feedback that improves the AI.

Module 7: AI Regulation and Compliance

Exploring AI Regulatory Frameworks

• Global Regulations: An overview of key frameworks like the EU AI Act, and other national and industry-specific regulations.
• Privacy by Design: Integrating data privacy and security measures from the beginning.
• Compliance vs. Ethics: Differentiating between legal requirements and ethical responsibilities.

Developing Compliance Strategies

• Risk Assessment: Identifying and prioritizing regulatory risks for your AI product.
• Documentation and Auditing: Creating a transparent record of the model development and deployment process.
• Cross-functional Collaboration: Working with legal, compliance, and security teams.

Module 8: Future Trends in AI and Product Management

Upcoming AI Technologies

• Multi-modal AI: The rise of models that can understand text, images, and audio.
• Edge AI: The shift of AI processing from the cloud to on-device.
• Personalized AI: The future of hyper-personalized user experiences.

Planning for Future AI Integration

• Skill Up: Identifying the skills a modern product manager needs to stay relevant.
• Strategic Foresight: Developing a long-term vision for how AI will transform your industry and product portfolio.
• The Human-in-the-Loop: Understanding the evolving partnership between humans and AI.

Optional Module: AI Agents for Product Management

Understanding AI Agents

• Definition: Autonomous systems that can reason, plan, and execute multi-step tasks.
• Agentic Frameworks: The key components of an AI agent (planning, memory, tools).

Case Studies and Hands-on Practice

• Market Research Agent: Using an agent to scan competitors, analyze trends, and identify new opportunities.
• User Feedback Agent: An agent that sifts through user reviews and support tickets to identify key pain points.
• Data Analysis Agent: Automating data queries and generating insights to inform product decisions.