AI, Data and Value Creation @ESSEC Business School

The “AI, Data, and Value Creation” program at ESSEC Business School is open only to applicants who meet the following criteria:

• Holders of a bachelor’s degree
• At least 3 years of professional experience
• An application for recognition of prior learning (VAPP) may be submitted if the criteria are not met.

A phone interview with an academic advisor to discuss your expectations and career goals in relation to the program you are interested in. Submit your resume and/or cover letter via the online form to the program director, who will review your application and determine whether you meet the program’s prerequisites. You will receive a response within 5 business days.

• Mid-to-senior professionals (5–15 years of experience) who need to orchestrate AI projects and understand strategy, technology, change management, and governance.
• Startup founders, consultants in consulting firms, freelance transformation specialists, Product Owners, Heads of Product, intrapreneurs, and digital transformation leaders: Chief Data Officers, PMOs, Business Unit Directors, and any manager or functional leader interested in AI.
• Strategic-level users — managers who lead and oversee AI initiatives within the organization and who will ultimately define the functional requirements for future AI projects.

Have access to a computer (PC or Mac) with an internet connection.

• Understand the fundamentals and evolution of artificial intelligence, particularly generative AI
• Explore real-world use cases and build a new AI-powered product or service
• Identify AI-driven value creation opportunities within your organization
• Define an AI strategy adapted to your industry and digital maturity
• Master the organizational, cultural, ethical, and regulatory challenges related to AI

• Course videos (15 × 4 minutes)
• Capstone project: development of an AI-powered product or service
• Weekly quizzes
• Case studies / additional readings
• Discussion forum
• Final assessment

• 80% completion rate of the weekly quizzes
• Capstone project

Objectives

Understand the global stakes of AI for the organization and for individuals, and assess the value creation brought by AI at the strategic level.

Learning goals

• The global AI race: Understand what is happening and why big tech companies invest billions of dollars in AI
• Business models for AI and sources of revenue or efficiency: Explore new economic models enabled by AI and how it generates value (ex.: data, AI services).
• Process optimization: Develop a process or workflow mind-set, the necessary ingredient for implementing AI solutions.
• Platforms & network effects: Understand how AI-native platforms operate (and differentiate pre & post gen AI), and the network effects in accelerating the adoption of AI. Understand how far this is from reality for most organisations.

Chapterss

• Why is AI hot?
• The Global AI race: What does it mean? Who is winning?
• Sovereign AI: a matter of national interest
• Business models for AI
• AI value creation examples
• Processes and workflows
• Processes and workflows: two great examples
• AI first: What is it and how to get there?
• AI first companies:Microsoft and JP Morgan
• Scaling with AI to maximize value
• How to find that compelling AI use case and business case?
• Which metrics measure AI value?

Objectives

Understand the strategic importance of data within an organisation, and how external data is critical for better decision making.

Learning goals

• Data as an asset: Understand that viewing data as an asset is helpful in understanding its value.
• Classify data types: Being able to assess which types of data are required to build use cases.
• Data life cycle: Understand the inception of data, its maintenance and different storing architectures.
• Data Governance: Make an action plan for ensuring data quality and best data practices. 

Chapters

• What is a data driven company?
• What happened with big data and why is data still the big problem?
  Data culture and data citizens
• Data types
• An example of data combination to create value
• Retrieval Augmentation Generation (RAG)
• NotebookLM in action
• Where do you store data?
• Data governance
• The Federated Data Governance Model
• The Chief Data Officer
• Boosting data literacy
• Valuing and monetizing data

Objectives

Master the basic principles of machine learning and deep learning, different types of AI.

Learning goals

• L’IA dans une perspective historique : comprendre que l’IA n’est pas nouvelle et pourquoi l’essor de l’IA générative s’explique aujourd’hui.
• Types de machine learning : savoir identifier le type de modèle le plus adapté à un cas d’usage donné.
• Nouvelles fonctionnalités de l’IA : travailler avec l’IA générative et l’IA agentique.
• MLOps : comprendre ce qu’implique le déploiement et le maintien en production d’un produit IA.
• Code : coder en Python avec l’aide d’un assistant IA.

Chapters

• A short history of AI
• The AI hall of fame (Bengio, Ang, Lecun, Hassabis, Altman, Wang)
• Outsmart humans with artificial intelligence?
• Most of value is created with traditional ML models
• Optimising marketing campaigns thanks to supervised learning
• Artificial neural network
• Training an artificial neural network in Python
• Are you a transformer?
• Large language models and generative AI
• The environmental cost of compute and carbon footprint 
• Agentic AI
• AI Agents in action
• Breaking out the sandbox with MLops
• A conversation with python

Objectives

Understand how human cognitive biases interact with AI-driven and algorithmic decision-making, and acquire practical tools for responsible, bias-aware AI adoption in organizations.

Learning Goals

• Human vs. algorithmic decision-making: Distinguish between cognitive and algorithmic sources of error and bias in business decisions.
• Bias detection and auditing: Analyze real-world cases of AI bias in hiring, credit scoring, and other business contexts—drawing on documented litigation and enforcement actions.
• Critical evaluation of AI outputs: Apply checklist-based and structured methods to critically evaluate AI recommendations and LLM outputs, including addressing hallucinations.
• Regulatory context for AI decisions: Recognize the legal responsibilities linked to AI-assisted decisions, including recent regulatory guidance (EU AI Act, NIST AI RMF, OECD Principles).
• Human-in-the-loop design: Develop practical strategies to ensure effective human oversight of automated and semi-automated decisions.

Chapters

• Introductory video: Why decision-making matters in the age of AI
• Human cognitive biases: Classic errors in judgment (confirmation bias, anchoring, etc.)
• Algorithmic biases: How AI systems learn and perpetuate bias
• The Workday case: AI hiring bias and discrimination at scale
• Credit scoring: Real cases of discrimination (Apple Card, SafeRent)​
• LLM hallucinations in enterprise contexts:
• Myths vs. reality: What AI can and cannot do today
• The critical checklist: questions every executive should ask
• Human-in-the-loop: Designing for safe AI deployment
• Critical checklist: Questions every executive should ask before adopting AI
• Regulatory framewok: EU AI Act, NIST AI RMF, Global landscape
• Legal responsibilities and liability
• Synthesis and path forward

Objectives

Learn how to build a pipeline and a roadmap of high value AI projects 

Learning goals

• Lifecycle of innovation in tech: Understand the stakes of each project stage
• Product market fit: Define product market fit and understand why it’s a crucial objective
• Learning from startups methodologies: Apply proven methodologies to your operating playbook
• Vibe coding: Learn how vibe coding can accelerate product market fit discovery

Chapters

• Tech projects often fail. AI projects fail even more
• Stages of tech innovation: from ideation to scaling, what is the maturity of your project?
• The discovery of value: user pain points and needs
• Why product market fit is the go/no-go frontier before scaling
• AI is a tool, not a goal
• Finding PMF: agile or waterfall?
• What is a Minimum Viable Product (MVP)
• Product Requirement Document (PRD): how to communicate effectively with an engineer (or an AI)
• Vibe coding 1: using LLMs to write a PRD
• Vibe coding 2: building a working app from a PRD
• Prioritizing projects: evaluating value, costs, risks at MVP stage
• Killing projects 1: AI cost (“token economics”) estimations in an early stage
• Killing projects 2: AI risks estimations in an early stage

Objectives

Understand the technical stakes behind a technical infrastructure to be able to work with technical teams when building new AI products

Learning goals

• Data pipeline: Understand how raw data is processed into valuable data
• Modern stack and architecture: Learn how all tech companies structure their technical infrastructure
• Cloud fluency: Master the definition and purpose of all main bricks of a cloud service
• AI requirements: Discover specific AI constraints on the technical infrastructure

Chapters

• Anatomy of an AI ecosystem: data, engine, interface
• Data as a factory: the parallel with industrial processing
• Data sources and raw data collection
• Data storage (data lake, data warehouse, vector database) and business impacts
• Cloud services fluency: layers (IaaS, PaaS, SaaS)
• Connecting everything: APIs
• Data operations: orchestrating the flow of data
• Modern data stack principles: assembling all the pieces
• ML/AI stack principles, RAG and orchestration implementation
• Technical debt: the compounding “interest rates” on quick and dirty code
• The quality vs speed compromise: data & AI operations management
• DevOps and MLOps: managing updates and preserving quality

Objectives

Learn how to structure and manage an AI product in production phase

Learning goals

• The wall of production: Understand the critical differences between labs and real environments
• Scalability: Learn how to anticipate new challenges arising with scale
• People management: Discover the main roles in an AI project and how to structure the organization as it grows
• Value management: Learn value creation and sharing strategies

Chapters

• The wall of production: why something works in the lab but breaks in real life
  Garbage in,
• garbage out: real world inputs are not controllable
• LLM models hallucinations consequences in production
• Reliability and SLAs using AI in production
• Quality monitoring and AI drift
• Anticipating malicious behaviours: the example of LLM prompt injections
• Crisis management: when your product breaks
• Roles and jobs in an AI product team
• Drafting your org chart
• Hiring and growing your team
• The augmented workforce in practice

Objectives

Equip leaders with practical knowledge to ensure AI projects comply with evolving global regulations (EU AI Act, GDPR, NIST, OECD), and to establish transparent, accountable governance structures in their organizations.

Learning Goals

• Regulatory landscape: Understand the key provisions and timelines of the EU AI Act, including risk classification and obligations for high-risk AI systems.​
• GDPR and AI: Recognize how GDPR requirements apply to AI systems processing personal data, including the interplay with the EU AI Act.​
• Global frameworks: Compare approaches from OECD AI Principles, NIST AI RMF (including the GenAI Profile), and US/China initiatives.​
• Ethics-by-design: Integrate ethical principles and ESG compliance throughout the AI project lifecycle.
• AI governance structures: Establish or participate in an AI governance committee, with real authority over project approval, risk assessment, and compliance.​

Chapters

• Introductory video: Why AI governance matters now
• Overview of the EU AI Act: Structure, risk levels, and key obligations​
• Timeline and inplementation : what we need to do and when
• High-risk AI systems: What counts, and what’s required?
• GDPR and AI: How data protection and AI regulation intersect
• NIST AI Risk Management Framework & Global governance approaches
• Ethics-by-design: integration ethical principles throughout the AI lifecycle
• Building AI governance committees: structure, authority and accountability
• Case study: Algorithmic discrimination in credit scoring – Compliance lessons
• Explainability, transparency and algorithmic audits​
• Critical checklist: questions every executive should ask

Objectives

Prepare participants to lead and thrive in AI-augmented organizations, fostering high adoption, user trust, and well-being. Develop capabilities to plan change management, coaching, and skills transformation using leading research and multinational deployment cases.

Learning Goals

• New job categories: Recognize emerging AI-related roles (AI trainers, prompt engineers, AI ethics officers) and understand the structural transformation of work.​
• Upskilling and reskilling: Apply practical frameworks for building AI skills across the organization, including manager training and continuous learning pathways.​
• Change management: Design and execute change management strategies for AI adoption, addressing resistance and fostering adoption champions.​
• Workforce analytics: Use data to measure AI adoption, user engagement, and transformation outcomes.​
• Digital well-being: Anticipate and address risks of overload, burnout, and the need for inclusive adoption.​

Chapters

• Introductory video: New ways of working in the AI era
• The transformation of work: 170 million new jobs by 2030 (WEF), 92 million displaced​
• Hybrid transformation: Humans and AI working together
• Upskilling imperatives: 66% faster skills change in AI-exposed jobs, 56% wage premium
• AI adoption in the workplace: 75% of knowledge workers now use AI
• Upskilling platforms and best practices​
• Change management for AI: Principles and pitfalls​
• Building adoption champions and AI ambassadors
• Measuring adoption: KPIs for productivity, engagement, well-being​
• Case: Multinational upskilling initiative—lessons learned
• Digital well-being: Avoiding overload and burnout
• Critical checklist: leading AI transformation

Objectives

After having learned conceptually and concretely about AI, it is important to continue learning and being aware of the new trends.

Learning goals

• AI developments: Awareness about current AI research and experiments.
• AI challenges: Assess when AI does not serve the intended purpose anymore.
• Robots: Realize that most AI is not yet in the real world, but will be soon
• Quantum computing: Understand the state of the art, and first successful commercial applications
• Energy and climate Tech: Have a clear view of what is at stake climate wise.

Chapters

• Where is AI going?
• Company to follow: Google (Cat paper, Attention paper)
• Company to follow: Nvidia (Cuda, game cards to GPU)
• Company to follow: USML
• What can go wrong? Law zero initiative.
• AI/data centers and demand for electricity
• Robotic process automation
• The robots are coming
• Ready to be fuzzy?
• Examples of quantum computing
• Q-day
• Renewable energy storage
• Capturing carbon

• 80% completion rate of weekly quizzes
• Capstone project