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.

Learning Objectives:

• Understand key definitions and distinctions of AI, ML, Deep Learning, and Generative AI.
• Identify common AI/ML algorithms and their applications.
• Comprehend the importance of data in AI, including 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 helps participants develop a strategic mindset to identify, evaluate, and prioritise AI opportunities that align with business goals and user needs. It focuses on creating a strong business case for AI initiatives.

Learning Objectives:

• Frame problems for AI solutions and leverage AI for competitive advantage.
• Conduct market research and competitive analysis in the AI landscape.
• Define AI value propositions and build a business case by quantifying value (ROI, cost savings) and assessing risks.
• Develop AI-specific product roadmaps and prioritize initiatives using frameworks.
• Define and set both model-centric and business-centric success metrics for AI-powered products.
• Apply A/B testing and experimentation in AI product development.

Description: This module dives into the practical aspects of building AI products, emphasizing collaboration with technical teams and focusing on user experience and the use of Generative AI.

Learning Objectives:

• Design intuitive and user-friendly AI experiences, including Explainable AI (X AI) to communicate decisions and limitations.
• Conduct user research for AI-specific needs and test AI prototypes effectively.
• Collaborate with AI/ML teams, manage expectations, and understand data/model versioning.
• Evaluate third-party AI solutions and decide whether to build or buy.
• Apply prompt engineering to leverage Generative AI for product ideation and content creation.
• Evaluate AI outputs and iterate on prompts for refinement.

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: This module focuses on the leadership aspects, advanced strategies, and future trends for senior professionals. It covers scaling AI products, fostering an AI-first culture, and identifying future opportunities.

Learning Objectives:

• Manage the complexities of scaling AI models, infrastructure, and costs in production.
• Lead high-performing AI product teams and foster an AI-first culture within an organization.
• Align AI initiatives with business objectives and communicate the vision to stakeholders.
• 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 conceptualize, 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.