AI for Product Management

During the first week of this programme, we cover off two critical modules to help develop your understanding of how ML works:

Module 1: ML Fundamentals (neural networks, deep learning)

• ML development pipeline

• Data pipelines & labeling

• ML model evaluation

Module 2: Latest ML tools for PMs

• Which ML tools for each purpose

• Case studies and examples

Application Work: Participants will be required to practice their ML skills using a set of sample data.

During these modules, we will deep dive into:

Module 3: Generative AI crash course

• The AI landscape

• How LLMs work

• The AI value chain for Product Leaders

Module 4: Latest generative AI tools for PMs and the Generative AI flywheel

• open-source & commercial tools

• No- and low-code tools

• AI models

• Data

• User feedback

Application Work: Apply the Generative AI Flywheel on your AI product

During these modules, we will deep dive into:

Module 5: Specialised topics in ML

• computer vision

• reinforcement learning product examples

Module 6: Learn and implement LLM enhancement strategies

• Retrieval-Augmented Generation (RAG) strategies

• Prompt engineering strategies for PMs

• Instruction tuning

• Fine tuning

• Implementation of LLM enhancement strategies on product use case

Application Task: Participants continue to refine their ideas using these more advanced topics.

During these modules, we will explore:

Module 6: AI Product Management

• What AI Product Management is

• core AI Product Management skills

• Successful AI product case studies & applications

Module 7: AI Product Lifecycle

• Incubate (AI MVP: data, product ideation, product validation & discovery, technical feasibility, prioritisation & roadmapping)

• Hatch (launchpad: A/B testing, monitoring & maintenance)

• Grow (GTM strategy: scalability, optimisation, distributed training for large datasets, security & compliance, data privacy, AI GTM playbook)

Application Task: Participants build out product roadmap, centred on the implementation of AI

During these modules, we will explore:

Module 8: User experience for AI products

• Key UX principles

• How AI can assist the UX

• User testing

Module 9: AI Product Strategy & Product Vision

• Connecting product strategy to business goals

• OKR’s

• AI Governance & Ethics in product development

Application Task: Develop the AI product strategy for your product

During these modules, we will explore:

Module 11: AI strategy for product leaders

• Leading innovation - AI product maturity matrix

• Risk management for AI products

• Data organization

Module 12: People & processes

• Cross-functional team collaboration

• Communication with data engineers, researchers and machine learning engineers

• People management & culture

• AI cultural shift

• Managing stakeholders and product teams

Application Task: Apply the AI Product Lifecycle and develop your AI Product Requirements Document (PRD). Practice presenting your PRD.

Each participant presents their PRD and what they have learned throughout the programme.