Artificial intelligence in Business
Information systems management
Contact hours
27 H
Number of spots
Open to visitors

Pedagogical contribution of the course to the program

LEARNING GOAL 1 : Students will master state-of-the-art knowledge and tools in management fields in general, as well as in areas specific to the specialized field of management.

Students will identify a business organization’s operational and managerial challenges in a complex and evolving environment.
Students will understand state-of-the-art management concepts and tools and use them appropriately.
Students will implement appropriate methodologies to develop appropriate solutions for business issues.
LEARNING GOAL 2 : Students will develop advanced-level managerial skills.
Students will work collaboratively in a team.
Students will participate in a decision-making process in a critical way.
Students will communicate ideas effectively, both orally and in writing, in a business context.
LEARNING GOAL 4: Students will study and work effectively in a multicultural and international environment.
Students will demonstrate written and oral competency in two foreign languages.
Students will analyze business organizations and problems in a multicultural and international environment


Today, artificial intelligence and data systems are pervading all systems in organizations. Management and business sectors are integrating artificial intelligence techniques and algorithms into all activities at all levels. Many decisions in managers' daily tasks are now supported and automated or semi-automated by AI. Artificial intelligence" is a paradigm that covers many techniques and approaches. Some of them are used more in business and management, such as marketing, finance, merchandising, manufacturing, logistics, human resources, etc.

Teaching methods


- Lectures
- Tutorials

In group

- Exercises
- Oral presentations
- Case studies/texts


- Discussions/debates


No items in this list have been checked.

Learning objectives

Cognitive domain

Upon completion of this course, students should be able to
  • - (level 1) identify the main algorithms and techniques of Artificial Intelligence that are known in the business sector
  • - (level 2) give examples about the different AI techniques in business cases
  • - (level 3) manipulate some datasets in AI integration cases
  • - (level 4) analyze situations where automated decisions are needed and thus AI maybe applied
  • - (level 5) interpret situations and results when applying AI in some business cases

Affective domain

Upon completion of this course, students should be able to
  • - (level 1) choose and apply the main concepts about Artificial Intelligence in the Business contexts
  • - (level 2) discuss the business models to integrate AI techniques
  • - (level 3) explain some relevant cases of use that may need AI integration


I. Business data 1. Turing machine 2. Automated decision-making 3. Data-driven systems II. Analytics 1. What and why 2. Analytics mutations 2.1. Traditional analytics 2.2.1. OLAP 2.2.2. Business Intelligence 2.2. Advanced analytics 2.2.1. Data Mining 2.2.2. Artificial Intelligence III. Artificial intelligence 1. What and why 2. History 3. Design patterns 4. AI branches 5. Machine learning 6. Deep learning 7. Applications 7.1. in marketing: association rule algorithm 7.2. in warehousing: decision tree algorithm IV. The future of data and artificial intelligence in business? 1. Existing challenges and opportunities 2. Future challenges and opportunities

No prerequisite has been provided

Knowledge in / Key concepts to master

Fundamentals in organizations and management – Skills about business concepts - Skills in MS Excel

Teaching material

Mandatory tools for the course

- Computer
- Calculator
- Reference manuals

Documents in all formats

- Case studies/texts
- Worksheets

Moodle platform

- Upload of class documents
- Interface to submit coursework
- Assessments
- Coaching/mentoring


No items in this list have been checked.

Additional electronic platforms

- Other :

Recommended reading

Book: Rose, D. (2018). Artificial Intelligence for Business What You Need to Know about Machine Learning and Neural Networks. (available in Moodle) Book: Deshpande, A., & Kumar, M. (2018). Artificial intelligence for big data: Complete guide to automating big data solutions using artificial intelligence techniques. Packt Publishing Ltd. (available in Moodle) Book: A. J. Gutman, J. Goldmeier (2021). Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning (available in Moodle)

Book: Finlay, S. (2021). Artificial intelligence and machine learning for business: a no-nonsense guide to data driven technologies (No. 4th ed). Relativistic. Book: Panda, S. K., Mishra, V., Balamurali, R., & Elngar, A. A. (Eds.). (2021). Artificial Intelligence and Machine Learning in Business Management: Concepts, Challenges, and Case Studies. CRC Press.

EM Research: Be sure to mobilize at least one resource

Textbooks, case studies, translated material, etc. can be entered
CHEHBI GAMOURA S. (2021). Predictive Reinforcement Learning Algorithm for Unstructured Business Process Optimization: Case of Human Ressources Process. International Journal of Spatio-Temporal Data Science, 1 (n° 2). CHEHBI GAMOURA S., DERROUICHE R., DAMAND D., BARTH M. (2020). Insights from Big Data Analytics in Supply Chain Management: An All-Inclusive Literature Review Using the SCOR Model. Production Planning and Control, 31 (n° 5) [CNRS cat.2, FNEGE cat.2, HCERES cat.A] Impact Factor. 4. Review. Artificial Intelligence and Applied Mathematics in Engineering Problems, Cham, Switzerland, Springer Nature, 1-16.


List of assessment methods

Intermediate assessment / continuous assessment 1Other (date, pop quiz, etc.) : automared tests at the end of each session case study at the 4th session
Written and oral (30 Min.) / Group / English / Weight : 30 %
Details : Automated tests individually a case study in group
This evaluation is used to measure LO1.1, LO1.2, LO1.3, LO2.1, LO2.3, LO4.1, LO4.2
Final evaluationLast class
Written (120 Min.) / Individual / English / Weight : 70 %
This evaluation is used to measure LO1.1, LO1.2, LO1.3, LO2.3, LO4.1, LO4.2
No assessment methods have been attributed to this course yet.