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Leading AI integration as a Product Owner
AI can enhance a product when it starts with the expected value. Clarify use cases, define priorities and adapt your Product Owner role to AI project uncertainty. Develop an approach to coordinate teams and discuss choices with stakeholders.
- Duration
- 3 days 21 hours
- Code
- MGMT002FR Code
Presentation
The emergence of cognitive technologies is transforming software design methods. To remain competitive, the Product Owner can no longer overlook the specifics of data: they need to identify sources of value and translate complex technical concepts into concrete business opportunities. This urgent business need requires rapid skills development to stay in control of technological transformation.
This immersive 3-day course equips you to navigate the artificial intelligence ecosystem confidently. Through real use cases in healthcare, finance and retail, alongside practical work, you will learn to engage with data scientists, define relevant acceptance criteria and structure a project lifecycle suited to predictive model uncertainty.
By the end of the programme, you will be able to build a robust product strategy incorporating generative AI or machine learning components. You will have methodological tools to move from idea to deployment while ensuring ethical compliance and sound investment choices.
Objectives
By the end of this AI for Product Owners course, you will be able to:
- Understand fundamental machine learning and deep learning concepts without unnecessary jargon;
- identify data-driven innovation opportunities within existing products;
- frame the complete data project lifecycle, from collection to model deployment;
- define a Minimum Viable Product (MVP) suited to AI constraints;
- streamline communication and collaboration with technical teams and data scientists;
- build an agile product roadmap incorporating performance and ethical considerations.
Program
Module 1: Understanding fundamentals and use cases
- Demystifying essential terms: algorithms, machine learning and deep learning.
- Distinguishing traditional symbolic approaches from modern statistical models.
- Overview of current technologies and their practical applications.
- Exploring AI types: predictive analytics, natural language processing (NLP), computer vision and generative AI.
- Analysing examples from healthcare, finance and HR to inspire your own features.
Practical exercises
- Map potential use cases within your own product backlog and prioritise them by feasibility.
Module 2: Framing the AI project and adapting the PO role
- Specific features of the data project lifecycle compared with conventional software development.
- The data journey: collection, cleaning, training and production deployment.
- Defining the MVP concept in a probabilistic context.
- Adapting user stories to include data and performance dimensions.
- Defining success indicators (KPIs) and expected business value.
Practical exercises
- Write data-oriented user stories and define model acceptance criteria.
Module 3: Deploying an agile, ethical strategy
- Governance issues: GDPR compliance, algorithm explainability and bias management.
- Ensuring user acceptability and transparency of automated decisions.
- Integrating AI initiatives into an overall value-driven agile roadmap.
- Implementing validation processes through user testing.
- Monitoring model performance over time and continuous improvement.
Practical exercises and case study
- Develop a complete product roadmap incorporating intelligent components and identify associated risks.
Audience
This course is intended for product management professionals seeking to unlock value from their data, including:
- Product Owners managing backlogs to integrate high-value intelligent features;
- Product managers defining the strategic vision to anticipate technological market disruption;
- Business analysts translating business needs into functional specifications for data teams;
- Innovation managers exploring new growth opportunities to transform the business model;
- UX designers creating human–machine interfaces suited to AI interaction.
Prerequisites
This course requires the following prerequisites:
- Professional experience: proven experience with agile methods (Scrum, Kanban) and the Product Owner role is essential to apply the concepts.
- Basic knowledge: awareness of current technological challenges.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
Course highlights
- No-code approach: business-value-focused learning without requiring programming skills.
- Operational tools: leave with user story and roadmap templates specifically adapted to data projects.
- 360° strategic perspective: master the complete lifecycle, from technical design to AI ethical and legal challenges.
- Practical grounding: immediately apply methods to your product challenges through three concrete, immersive workshops.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.