In just a few years, generative artificial intelligence has changed the terms of the strategic problem. Producing a PESTEL analysis, filling in a Business Model Canvas or drafting a transformation plan now takes an AI only a few minutes. What makes the difference today is what comes before and after: asking the right question, choosing the right framework, steering the tool with discernment and critically assessing what it produces. This is precisely the competence this course aims to develop.
Information systems now occupy a central place in organisations, both private and public. Digital strategy is no longer limited to information management: it encompasses business models, platforms, the organisation of work, data and customer experience. AI holds a dual position in this landscape. It is a major strategic issue for companies: what happens to competitive advantages when AI agents compress the costs of creation and execution? It has also become the everyday working instrument of the analyst and the decision-maker.
The objective of the INFO M421 course remains to provide a set of frameworks (models and case studies) that can be applied within an organisation to design or revise a digital strategy, and to foster an entrepreneurial mindset along the way. To this it adds an ambition specific to the AI era: learning to mobilise these frameworks judiciously, that is, knowing how to select them, combine them, calibrate them and recognise their limits, in an environment where their execution can be delegated to the machine.
Upon completion of the course, students will be able to:
understand and master the key concepts of information systems strategy and digital strategy;
map the main strategic analysis frameworks: the question each one answers, its inputs, its blind spots, its complementarities;
select and combine the relevant frameworks when facing a given strategic question, and justify that choice;
analyse major technological shifts (platforms, generative and agentic AI) and new business models;
assess the impact of AI on competitive advantage, business models and the organisation of companies;
use generative AI as an instrument of strategic analysis, with a critical eye: steering the tool, verifying its output, identifying what it does not see;
apply all of the above to the analysis of a recent strategic sequence of a real company.
The course runs as a flipped classroom and favours judgement-based exercises: confronting frameworks on the same case, critically auditing AI-generated analyses, structured debates. The use of AI is explicitly permitted, embraced and assessed. Topics covered:
foundations of strategy and IS strategy: competitive advantage, trade-offs, the evolution from IT strategy to digital strategy;
the analytical toolbox (PESTEL, SWOT, Porter's five forces, BCG matrix, Business Model Canvas), put to the test of the AI era;
digital transformation of organisations: business models, platforms, orchestration of work by AI agents, articulation of IT and AI functions;
AI and strategy: the impact of generative and agentic AI on competitive advantages, strategies of established companies facing AI-native attackers;
a group case study on a well-known company and a recent strategic sequence.
Examples of companies covered in the course: Tesla, IKEA, Michelin, Volkswagen, AXA, Schneider Electric, Revolut, Netflix.
The course unfolds in two phases.
Phase 1: workshops
Four 2-hour sessions run on the flipped classroom principle. At least ten days before each session, a compact set of readings (articles, case studies, book chapters) is distributed together with a debated thesis: a sharply framed proposition on strategy in the digital and AI era. All readings must be prepared before the session, whatever side a student is assigned to.
The class is divided into two stable groups. For each workshop, one group defends the thesis and the other refutes it, with sides alternating from one session to the next. Within each group, roles (speakers, rebutters, cross-questions and restating the opposing side's best argument) rotate, so that every student takes the floor at least twice over the semester. Each session combines opposing presentations, rebuttals, cross-examination and a debriefing by the teaching staff, who revisit the concepts and frameworks the debate mobilised or missed.
The use of generative AI to prepare readings and arguments is explicitly permitted and embraced: summarising is taken for granted. What is assessed is the ability to defend a position live, withstand an unanticipated objection and acknowledge the opposing side's strongest argument, which no AI can do in a student's place.
Phase 2: group work and oral defence
Working in pairs, students receive a business scenario: a real company facing a strategic situation calibrated for the course. Each group designs a digital strategy proposal by mobilising the frameworks covered in the course, explicitly justifying its choice of models and documenting its use of AI (what was delegated to the tool, what was corrected, what the tool failed to see). Students may also propose a subject outside the suggested list.
The work results in a written report and an oral defence held during the last two sessions. Each group presents its analysis and then answers questions; every other group prepares in advance a question to be put to the presenting group. During the defence, each student individually answers questions on the course concepts and on the justification of the report's choices.
| Training | Study programme | Block | Credits | Mandatory |
|---|---|---|---|---|
| Master in Computer Science | Standard | 1 | 5 | No |
| Master in Computer Science | Finalité spécialisée en data science | 2 | 5 | No |
| Master in Computer Science | Finalité spécialisée en software engineering | 2 | 5 | No |