Master AI to scope, prioritize, and drive data-driven products
This training course enables Product Owners to understand the fundamentals of artificial intelligence (AI), its use cases, limitations, and implications for product development. It gives them the keys to collaborating effectively with technical teams, framing AI projects, prioritizing features, and integrating AI into a user-centered agile approach.
Is it for you ?
Product Owners
Prerequisites
Knowledge of the Product Owner role and agile methods (Scrum, Kanban). No technical AI skills required.
What You'll Walk Away With
- ✓ Identify and prioritize relevant AI use cases from an existing product backlog
- ✓ Scope AI projects from data collection to deployment, accounting for model-specific constraints
- ✓ Write AI-focused user stories and define appropriate acceptance criteria for models
- ✓ Define KPIs and measure business value and performance of AI solutions
- ✓ Build an AI-driven product roadmap while managing risks, dependencies, and ethical considerations
Training content
1 Introduction to artificial intelligence
- Definitions: AI, machine learning, deep learning
- Overview of technologies and algorithms
- Symbolic AI vs. statistical AI
2 Use cases for AI in products
- Recommendation, classification, prediction, NLP, vision
- Examples in healthcare, finance, retail, HR
- Generative AI: uses and limitations
3 Workshop 1: Mapping AI use cases in your product
- Identifying AI opportunities in an existing backlog
- Prioritization based on value and feasibility
4 The lifecycle of an AI project
- From data collection to production
- Specific features of an AI project vs. a traditional software project
- The concept of AI MVP
5 The role of the PO in an AI project
- Writing AI user stories
- Defining business value and KPIs
- Collaborating with data scientists
6 Workshop 2: Writing AI user stories
- Examples of data-driven user stories
- Defining acceptance criteria for an AI model
7 Governance, ethics, and risks
- Algorithmic bias, explainability, Law 25
- User acceptability and transparency
- Best practices in AI governance
8 Product and AI roadmap
- Integrating AI into an agile roadmap
- User testing and value validation
- Continuous measurement of model performance
9 Workshop 3: Building an AI roadmap
- Developing a product roadmap that integrates AI building blocks
- Identifying dependencies and risks
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📌 Practical information
Our training sessions are offered in Montreal or Quebec City, in person or in a virtual classroom. Dates and locations are specified when you select your session below. If you have any questions, check out our FAQ.
