Product Management & GenAI: Strategic Fundamentals
The integration of Generative AI is radically transforming the role of the Product Manager, shifting from "Feature Orchestrator" to "Architect of Uncertainty". Unlike traditional deterministic projects, GenAI projects introduce a probabilistic element that must be properly framed. This intensive session equips Product Managers with a critical framework to validate, design, and manage a product integrating Generative AI. It provides an understanding of the ecosystem, mastery of new technological building blocks (LLM, RAG, Agents), and helps avoid common "Hype" pitfalls by focusing on creating real and secure value.
What you will learn
- Distinguish the specificities of a Generative AI project compared to traditional software development (uncertainty management, prior validation).
- Identify relevant use cases and disqualify "false good ideas" (Anti-patterns) where AI is not necessary.
- Understand key technical components (LLM, RAG, Fine-tuning, Agents) to communicate effectively with technical teams.
- Master strategic decision criteria: compliance (GDPR, AI Act), data security, and cost management (FinOps/Tokens).
- Drive AI product quality and performance through adapted metrics (evaluation, hallucination management).
Prerequisites
- Significant experience in product management.
- Basic digital and data literacy.
- No coding skills required.
- A computer with internet connection to access materials and collaboration tools.
Target audience
- Product Managers (Confirmed/Senior), Head of Product, experienced Product Owners, Anyone in charge of product strategy looking to integrate generative AI
Training Program
4 modules to master the fundamentals
Topics covered
- →The "Reality Check" in Discovery: From determinism (Code) to probabilism (Model). Understanding why technical feasibility must precede need validation (Proof of Value).
- →Data as a product: The critical importance of "Data Readiness" (Quality, structure, accessibility).
- →Anti-Patterns & Eligibility Matrix: Knowing when to say "No" (the Calculator, Workflow, and Ground Truth traps). The golden rule: Use AI to create/transform, not to execute strict rules.
- →The Run: Introduction to qualitative monitoring and drift management.
Topics covered
- →The model landscape: Choosing the right tool. LLM (Powerful generalists) vs SLM (Fast specialists). Introduction to multimodality (Text, Image, Audio).
- →RAG (Retrieval Augmented Generation): Connecting AI to enterprise knowledge to limit hallucinations and cite sources.
- →Fine-Tuning vs Prompting: Distinguishing instruction (Prompt) from education (Fine-tuning). Knowing when to invest in training.
- →Assistant vs Agent: Understanding the autonomy scale, from the human-validated "Copilot" to the action-executing "Agent".
Topics covered
- →Regulation and Data (The legal filter): GDPR (Anonymization and least privilege), AI Act (Transparency obligations and high-risk system documentation).
- →Infrastructure and Security (The technical filter): Decision matrix SaaS Public (Speed/Performance) vs Private Cloud/On-Premise (Sovereignty/Control).
- →FinOps and Performance (The economic filter): Token economics (estimating and controlling variable costs), managing Latency (impact on user experience and Streaming).
Topics covered
- →Summary of key points.
- →Questions and Answers.
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
No date suits you?
We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.
Register for a custom dateTrain multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote