Code
MGYF INF 5751
Level
M2
Field
Informatique
Language
Anglais/English
ECTS Credits
2
Class hours
21
Program Manager(s)
Department
- Management, Marketing et Stratégie
Educational team
Introduction to the module
Le but de ce cours est de permettre aux étudiants de :
● Comprendre comment l’intelligence artificielle transforme le marketing, la vente et le business development sur l’ensemble du parcours client.
● Savoir mobiliser des outils d’IA générative et prédictive pour répondre à des problématiques commerciales réelles (prospection, segmentation, personnalisation, fidélisation).
● Évaluer les impacts, risques et enjeux éthiques (biais, transparence, protection des données, conformité réglementaire).
● Analyser des études de cas d’entreprises utilisant l’IA pour créer de la valeur commerciale.
● Construire une stratégie commerciale pilotée par l’IA.
PROBLÉMATIQUE ET STRUCTURATION DU COURS
Learning objectives/Intended learning outcomes
- 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.
- 6.1 - Design, develop and implement policies and practices conducive to the dynamism of the organisation, in order to resolve identified issues, taking into account the specific characteristics of the business context.
- 6.3 - Produce and analyse key summary documents to ensure optimal, sustainable management, ensuring alignment with the organisation's vision, mission and values.
Rubrics
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
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.
Module Policies
participation active
obligation de remise d'avancée du travail à chaque session
Keywords
IA, marketing, vente