Master AI Product Integration and lead the future of product development.

Description: 

This module provides a solid, non-technical understanding of AI and its core concepts, focusing on what product managers need to know to effectively work in AI teams and build AI products.

Learning Objectives:

• Understand key definitions and distinctions of AI, ML, Deep Learning.
• Identify common AI/ML algorithms and their applications
• Understand the importance of data in AI, including its collection, cleaning, and quality.
• Explain the role of data infrastructure, privacy, and security in AI.
• Grasp fundamental AI technical concepts from a PM perspective, such as model training, evaluation, and performance metrics.
• Understand the roles and collaboration with data scientists and ML engineers.

Description: 

This module explores how AI product development differs from traditional product development, focusing on the unique challenges of building custom ML models versus using Foundation models. You will master the AI Product lifecycle: Discovery → Design → Develop → Deploy and ♻️Iterate fast.

Learning Objectives:

• Understand AI vs. Traditional Products – Distinguish between ML products, Foundation models, and traditional software development approaches
• Master the AI Product Lifecycle: Discovery → Design → Develop → Deploy and iterate with confidence
• Build Effective Prototypes & MVPs – Create fast feedback loops and test assumptions quickly
• Introduction to AI Evaluations
• Handle Real-World Challenges – Solve common AI product issues like dataquality problems, model staleness, and metric misalignment
• Apply through case studies – Analyze real AI product examples and learn from both successes and failures

Description: 

This module teaches participants how to develop AI product strategies that drive business value while managing the unique challenges of AI/ML systems. Participants will learn to bridge AI capabilities with business objectives through strategic thinking, data-driven decision making, and stakeholder alignment.

Learning Objectives:

• Identify customer and business problems that can be effectively addressed with AI, and articulate how AI can create competitive advantage.
• Understand customer needs, user behaviors, and market dynamics, and conduct targeted market research and competitive analysis in the AI landscape.
• Develop compelling AI value propositions and quantify impact through ROI, cost savings, and risk assessment.
• Create and prioritize AI initiatives using structured frameworks, accounting for data readiness, feasibility, and business impact.
• Align cross-functional teams and leadership around the AI product vision through clear communication and stakeholder engagement strategies.

Description: 

This module explores the practical aspects of building AI products, focusing on how product managers collaborate with technical teams, design intuitive user experiences, and make strategic trade-offs. It emphasizes the use of ML models and Foundation Models to build products, explainability for user trust, and effective iteration. Participants will practice bridging technical complexity with UX and business needs.

Learning Objectives:

• Design intuitive and user-friendly AI experiences, incorporating Explainable AI (XAI) to communicate decisions and limitations.
• Conduct user research for AI-specific needs and test AI prototypes.
• Define and set both model-centric and business-centric success metrics for AI products.
• Collaborate with AI/ML teams, manage expectations, and understand data/model versioning.
• Evaluate and iterate AI outputs to refine performance (AI evals)
• Apply A/B testing and experimentation to AI product development.

Description: This module addresses the critical ethical implications of AI, focusing on how to build products responsibly. It covers principles of fairness, accountability, and transparency (FAT), and prepares participants to navigate emerging regulations.

Learning Objectives:

• Apply principles of fairness, accountability, and transparency in AI product development.
• Identify and mitigate bias in AI models and data.
• Design for user trust and control through explainability and opt-out mechanisms.
• Understand emerging AI regulations and develop ethical guidelines for product development.
• Establish processes for responsible AI deployment and monitoring.

Description: In the AI era, this module explores leadership, advanced strategies, and emerging trends for senior professionals. It covers scaling AI products, driving organizational AI transformation, building an AI-first culture, and identifying future opportunities.

Learning Objectives:

• Lead high-performing AI product teams and foster an AI-first culture within an organisation.
• Organizational AI Transformation
• Align AI initiatives with business objectives and communicate the vision to stakeholders.
• Manage the complexities of scaling AI models, infrastructure, and costs in production.
• Stay abreast of the latest advancements in AI and identify future opportunities and challenges for AI PMs.

Project Goal: 

Participants will work individually or in self-formed groups to conceptualise, design, and roadmap an AI-powered product or feature. This involves problem definition, AI opportunities, data requirements, a proposed AI model approach (conceptual), UX/design, success metrics, a high-level roadmap, and ethical considerations.