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.
