The digital firm

Digital technology and business strategy are now so deeply interwoven that it no longer makes sense to discuss one without the other. Yet few business leaders are technology-literate, and few technology experts fully grasp the strategic and organisational consequences of the systems they build. This course aims at bridging that gap. It provides business engineering students with an operational understanding of information systems, digital infrastructure and artificial intelligence, and of the way they reshape the organisation, the processes and the competitive position of a firm.

The course is built around a single question, followed from the first session to the last:

"This company wants to get there. What has to be transformed, and how do digital technology and AI contribute?"

That question is broken down into a transformation chain — Ambition → Strategy → Processes → Data → Technology → Programme → Change — which structures the twelve theory sessions, the weekly work sessions and the final group report alike. Every session opens on the same diagram, with the link of the day highlighted, so that students always know where the material fits.

Artificial intelligence is treated in two ways: transversally, through a dedicated segment in each session on what AI changes in the domain being discussed (AI-native processes, agentic automation, data quality as an AI prerequisite, embedded copilots in enterprise software, the real cost of inference, predictive versus descriptive analytics, the AI business case, adoption and responsible AI); and in one dedicated session on AI in the enterprise.

The course is organised in five blocks.

The main objective of the course is to make business engineering students capable of leading, or credibly challenging, a digital and AI transformation: to connect a firm's strategic ambition to the processes, data, systems, infrastructure, programmes and organisational changes that would deliver it, and to assess critically what digital technology and AI can and cannot contribute.

More specifically, at the end of the course students should be able to:

• reconstruct a firm's digital ambition and translate it into measurable business objectives;
• conduct an external and internal strategic diagnosis of a firm and its industry, and identify where digitalisation and AI create — and where they do not create — competitive value;
• describe and critically assess the main categories of digitally enabled business models, including digital channels, data-driven strategies, platforms and ecosystems;
• identify, characterise and model a firm's core and support business processes, including As-Is and To-Be process modelling;
• assess what can and cannot usefully be automated, across the continuum from workflow automation to agentic automation, and estimate the associated value;
• model enterprise master data in an entity-relationship diagram and assess data quality, ownership and governance requirements;
• recognise and criticise the functional scope of the main families of enterprise systems;
• describe and criticise high-level application and infrastructure architectures, including cloud service and deployment models, and the integration choices that underpin them;
• recognise and evaluate technology and cybersecurity risks, continuity requirements and compliance obligations, and prioritise them in a business context;
• design a KPI set and an actionable dashboard, and specify the data sources and transformation rules required to produce them;
• build and prioritise a portfolio of AI use cases, assess its prerequisites and risks, and identify the obligations arising from the European AI Act;
• structure a transformation programme: business case and benefits realisation, milestone planning, governance and partner management;
• anticipate the organisational and change management conditions on which the transformation depends, and propose a digital governance model adapted to the firm;
• use generative AI tools as a professional working instrument, appraise their output critically, and document that use transparently;
• argue, in writing and orally, the competitive advantage expected from a proposed transformation.

Required and Corequired knowledge and skills

The course is designed to be accessible to students coming from a management background as well as to students coming from a technical background. No formal prerequisite course is required. Students are, however, expected to be comfortable with the following, and remedial material is made available on the Université Virtuelle for each of them:

• general familiarity with computing and information technology;
• basic concepts of business strategy and of corporate finance, including the valuation of an investment (NPV, IRR, payback);
• basic concepts of organisational behaviour and change management;
• basic understanding of relational data models and of entity-relationship diagrams.

 

The course is organised as a flipped classroom: sessions require reading and preparation beforehand, both for the theory and for the work sessions.

Theory — one two-hour session per week ⟦Thursday, 10:00-12:00, per the official timetable⟧:

• preparatory readings and video capsules made available on the Université Virtuelle;
• interactive lectures, with case discussions conducted in plenary or in small groups;
• preparation of case discussions with the assistance of a generative AI tool, submitted together with the students' own critique of that output;
• guest lecture(s) where the schedule allows.

Hands-On Digital — work sessions and project — one two-hour session per week ⟦Thursday, 18:00-20:00, per the official timetable⟧:

• a short deliverable (indicatively 2 to 4 slides) prepared by each group on the topic of the week
• designated groups present their preparation, which is then discussed with the class and with the teaching assistants;
• tutoring and guidance by the teaching assistants, illustrated on a common example company;
• a final written report and an oral presentation.

Attendance at the work sessions is strongly recommended: the deliverable of the week is discussed there, and the feedback given is the main mechanism by which groups improve their final report.

References, bibliography, and recommended reading

The course uses no specific textbook but the following references are recommended as supporting documentation and/or for a deeper understanding:

• Westerman, Bonnet and McAfee, Leading Digital: Turning Technology into Business Transformation, Harvard Business Press, 2014.
• Austin, R., R. Nolan and S. O’Donnell, The Adventures of an IT Leader, Harvard Business Press, 2009.
• Laudon, C. and J. Laudon, Management Information Systems, Managing the Digital Firm (16th edition), Pearson, 2018.
• Ross, J.W., Weill, P. and Robertson, D.C., Enterprise Architecture as Strategy: Creating a Foundation for Business Execution, Harvard Business School Press, 2006
• Davenport, T. and J. Harris, "Competing on Analytics: The New Science of Winning", Harvard Business School Press, 2006.
• Darnton, G., Business Process Analysis: including Architecture, Engineering, Management, and Maturity, Requirements Analytics, 2012
• Downey, A.  D. (2015), "Think Python", 2nd edition, Green Tea Press. https://greenteapress.com/wp/think-python-2e/
• Sweigart A. (2020), "Automate the boring stuff using Python", 2nd edition, No Starch Press. https://automatetheboringstuff.com/

Course notes

• Université virtuelle
• Podcast

Additional information

Main resources for the course:

• Course space on Université Virtuelle: Resources, handouts, readings, assignments
• Course team on Microsoft Teams: Live sessions (theory+exercises) and class discussions
• Course page on Microsoft Stream: Video content (theory+exercises)
• Hands-On Digital website: Exercises explanations and assignments

Contacts

Prof. in charge of the course and main instructor

• Vincent Lion
  • vincent.lion@ulb.be
  • Expert in Strategic Digital Transformation
  • Founder, VLC - Management & IT Advisors
  • Co-Founder, Internal Consulting Services
     
• Nicolas van Zeebroeck
  • Nicolas.van.Zeebroeck@ulb.be
  • http://www.vanzeebroeck.net/
  • R42.5.205

Author of the main contents of the "Hands-on Digital" Part

• Charles HOFFREUMON
  • http://homepages.ulb.ac.be/~choffreu

Lecturers of the "Hands-on Digital" Part for this academic year

• Valentin Dirken (coordinator)
  • IT Architect, ULB
  • Expert in IT Architecture 
  • https://www.valentindirken.com/
• Christophe Castan
  • Co-Founder, Penbox
  • Expert in AI
  • https://www.linkedin.com/in/castanchristophe/
• Michaël Dawant
  • Co-Founder, Vespucci Analytics
  • Expert in Analytics and UX/UI
  • https://www.linkedin.com/in/micha%C3%ABl-dawant-a84814151/
• Gaël Rabier
  • Founder, Niboo
  • Expert in ERP Systems and Odoo
  • https://www.linkedin.com/in/gaelrabier/

Campus

Solbosch

Method(s) of evaluation

• Personal work
• written examination
• Group work
• Other

Personal work

written examination

Group work

Other

The course involves active participation, both in the theory and exercise sessions.
The theory part of the course will be evaluated through class participation and an exam.
The practical part (hands-on digital) will be evaluated during exercise sessions (at specific milestones), as well as through the group project, and through personal contributions to the class discussions.

Mark calculation method (including weighting of intermediary marks)

CORE COMPONENTS

The course grade is distributed as follows:

• Multiple Choice exam (theory): 10 points
• Hands-on digital project : 10 points 
  • The individual hands-on digital project grade is made of the group grade and the individual contribution to the project.
• Individual class participation : Ip to 2 bonus points
• The participation bonus rewards documented, substantive contributions to the theory sessions and to the work sessions. It can only increase a mark: the final mark is capped at 20/20, and the absence of a participation bonus is never in itself a penalty.

GENERAL RULES

Please review the following rules carefully:

• Note that assignments (e.g. projects and milestones) will be associated with formal requirements and a strict deadline. Failure to comply with the formal requirements and/or with the deadline will lead to a null grade for that assignment.
• Note as well that plagiarism is a very serious offense and will be punished severely. If you submit a piece of work that contains plagiarized passages, you risk receiving a null grade for the entire piece of work.
• The course forms an integral whole and active components (exercises, assignments, etc.) may change from one year to the next. As a result, partial grades are carried over from first to second sitting (session) but normally not from one year to the next.
• Note also that the 10 points coming from "active" components of the class (participation, assignments, etc.) are final and offer no possibility for a second sitting. These components of the course are set in January and will be carried over to September in case of a second sitting. With other words, there is no second chance for this part of the course.
• The project grade will be taken into account if and only if the individual grade for the exercises is at least 1 out of 2. Should the individual exercises grade be lower than 1/2, the group grade will count as 0/3.
• Any form of cheating will lead to a null grade at least for the focal component of the course.
• The teaching team reserves the right to invite any student to an additional oral interview in order to verify that the student personally masters the content of the work they submitted, in particular where inconsistencies in that work prevent the teaching team from properly assessing the level of knowledge and skills the course is intended to develop.
   

  The use of generative AI tools is expected and encouraged in this course, under three conditions.

• Declaration. Every deliverable states which tools were used and for what. The final report includes an appendix — not counted in the 10-page limit — presenting the tools used, the main prompts, the output that was rejected and the reasons for rejecting it.
• Responsibility. Students are fully responsible for the content they submit, whatever its origin. A factual error, a fabricated reference, an invented figure or an inapplicable recommendation is graded as an error of the group, not of the tool.
• Critical appraisal. The quality of the critical appraisal of AI output is an explicit criterion in the grading grids of the deliverables and of the final report.

• Using generative AI without declaring it is treated as academic fraud within the meaning of the ULB regulations, and is sanctioned as such. The same applies to the submission of AI-generated content presented as original analysis when the declaration appendix is absent or knowingly incomplete.

Language(s) of evaluation

• english