Code
MGYF INF 5751
Level
M2
Field
Informatique
Language
Anglais/English
ECTS Credits
2
Class hours
21
Total student load
40
Program Manager(s)
Department
- Management, Marketing et Stratégie
Educational team
Introduction to the module
INDIQUER ICI UN MINI RESUME DU COURS
Learning goals
- 1. S’approprier les usages avancés et spécialisés des outils de l’intelligence digitale en s’assurant de leur impact durable et responsable
Learning objectives
- 1.1 - Audit advanced and specialised uses of digital intelligence tools in order to deploy them appropriately, taking into account the strategic context of organisations.
- 1.2 - Use digital intelligence tools efficiently to support the societal, digital, energy and environmental transformations of organisations, ensuring their sustainable and responsible impact.
Course Learning objectives
The concepts presented in this course allow students to:
● Understand how AI is transforming marketing and business development
● Apply generative and predictive AI tools to real commercial challenges
● Evaluate the impact, risks, and ethical considerations of AI in business
● Analyze real-world case studies from leading companies
● Build a practical, AI-driven strategy that creates measurable value
PROPOSITION:
The concepts presented in this course allow students to:
-Explain how artificial intelligence is transforming modern marketing and business development models.
-Apply generative and predictive AI tools to solve real-world commercial and operational challenges.
-Evaluate the strategic impact, business risks, and ethical considerations of AI deployment.
-Deconstruct and analyze real-world case studies from leading companies implementing AI.
-Design a practical, AI-driven strategy that creates measurable business value.
Content : structure and schedule
Session 1: Foundations of AI-Driven Business Strategy Introduces how AI is transforming marketing, sales, and business development, and helps students identify high-impact use cases in commercial environments. Learning Outcomes: By the end of this session, students will be able to: ● Define AI and distinguish it from past digital shifts (mobile, cloud…) ● Explain the BUILD framework ● Understand how AI accelerates competition ● Identify key AI use cases across the customer and sales journey ● Analyze how companies use AI to create value
Session 2: AI Technologies and Business Applications Explores key AI capabilities, especially generative AI, and how they enhance content creation, prospecting, and proposal development across marketing and business development. Learning Outcomes: By the end of this session, students will be able to: ● Describe foundation models and their role in commercial use cases ● Use prompt engineering to support content generation in sales and marketing ● Evaluate GenAI tools for strategic and operational tasks ● Understand how AI integrates into commercial workflows
Session 3: AI-Powered Customer Intelligence Applies AI capabilities to customer and prospect intelligence, focusing on lead scoring, segmentation, and recommendation systems in B2B contexts. Learning Outcomes: By the end of this session, students will be able to: ● Distinguish supervised, unsupervised, and reinforcement learning in commercial applications ● Apply AI models for segmentation, scoring, and recommendations ● Analyze how leading platforms leverage AI for personalization and prioritization ● Evaluate ethical implications of AI-driven commercial decisions
Session 4: Data-Driven Customer Journey & AI Execution Transforms the complete commercial funnel by integrating AI at every stage, from initial awareness through deal closure and expansion. Learning Outcomes: By the end of this session, students will be able to: ● Map AI opportunities across marketing and sales journeys ● Implement predictive techniques for customer engagement ● Leverage first-party data for competitive advantage ● Design AI-powered workflows for commercial teams
Session 5: Strategic Integration & Assessment Synthesizes all concepts into a unified AI strategy while addressing governance challenges and future-proofing commercial approaches. Learning outcomes: By the end of this session, students will be able to: ● Build comprehensive AI go-to-market strategies ● Make strategic decisions on positioning and enablement ● Navigate ethical and regulatory requirements ● Design adaptive strategies for continuous evolution
Sustainable Development Goals
ODD 4 – Quality Education; en permettant aux étudiants l'usage utile et efficace des outils de l'IA
● ODD 8 – Decent Work and Economic Growth en permettant aux étudiants d'agir efficacement sur le monde grace à la connaissance de leurs environnements et des outils à leur disposition
● ODD 9 – Industry, Innovation and Infrastructure.en permettant une meilleure adaptation aux situations actuelles évolutives
Number of SDG's addressed among the 17
3
Learning delivery
synchrone
Pedagogical methods
La pédagogie combine :
● Apports théoriques structurés : courts exposés magistraux pour introduire les
concepts clés.
● Études de cas et exemples réels : analyse de pratiques d’entreprises utilisant l’IA
dans leurs stratégies marketing et commerciales.
● Travaux pratiques sur outils d’IA :
○ tests de modèles génératifs pour la création de contenus (emails,
présentations, propositions commerciales),
○ utilisation d’outils pour le scoring de leads, la segmentation et le design de
workflows IA (CRM + GPT, etc.).
● Travail de groupe : élaboration d’une stratégie commerciale B2B pilotée par l’IA sur
un cas fictif.
● Réflexion critique individuelle : rédaction d’un papier sur l’éthique, les risques et
Evaluation and grading system and catch up exams
Continuous assessment – 60% of final grade:
● Group project – “AI-Powered Go-to-Market Strategy” (40%). Students work in teams on
a fictional B2B case. They must:
○ Map the customer journey,
○ Identify AI use cases for marketing and business development,
○ Propose an AI-driven commercial strategy (tool stack, organisation, governance).
Deliverables: a structured report (slide deck) and a short in-class presentation.
● Individual reflection paper – “AI, Ethics & Commercial Impact” (20%). Short essay in
which each student:
○ Describes how they used generative AI tools during the course,
○ Discusses ethical issues, bias, transparency, and privacy,
○ Proposes best practices for responsible AI use in marketing and sales.
Final exam – 40% of final grade:
● Individual written exam (40%) – individual work. End-of-course exam (case study + short
questions) assessing:
○ Understanding of key concepts (AI foundations, BUILD framework, types of
algorithms, customer journey, governance),
○ Ability to propose recommendations for a B2B company,
○ Identification of risks, limitations, and ethical issues.
CATCH UP EXAMS ??
Module Policies
participation active
obligation de remise d'avancée du travail à chaque session
Keywords
IA, marketing, vente