Course at a glance
| Item | Detail |
|---|---|
| Course | Management Information Systems (MIS) |
| Programme | Master's Degree in Business Administration, University of Turin (taught in English) |
| Contact hours | 35: 20 of lectures, 4 of business cases, 4 of analytics simulation, 4 of testimonials and 3 for the final exam session |
| Instructor | Prof. Riccardo Bovetti |
| Prerequisites | None. The course starts from the very basics of how a computer works. |
| Calendar | Dates and rooms are published in the university's official course calendar. |
Course description
Course thesis. Information systems are the way a company records what it does, understands what is happening, decides what to do and, increasingly, acts through software agents. Artificial Intelligence is not a chapter of this story: it is where the whole story is heading, and it changes every layer we study.
Every management decision a graduate of this programme will take (closing the books, setting a budget, choosing a supplier, approving an AI pilot) runs through an information system. The course explains how those systems work, from the transistor to the software agent, and how a manager judges whether they create value.
The course is built for students with no technical background. It starts from what a computer is and how it executes instructions, then climbs one layer at a time: the transactional core of the company (ERP, CRM, supply chain), the data and performance layer (data platforms, Business Intelligence, Corporate Performance Management), and the intelligence layer (machine learning, generative AI, agents). The last part asks how to measure the value of these investments, how to manage the transformation and how to keep it under control.
The heart of the course is the pair ERP and CPM: how a company records its economic life and how it turns that record into plans, forecasts and performance measures. Each chapter ends with a block called What changes with AI, supported by live demonstrations of working applications.
Learning outcomes
The outcomes follow the five Dublin descriptors. By the end of the course, students will be able to do the following.
| Descriptor | Students will be able to |
|---|---|
| Knowledge and understanding | Explain how a computer, a network, a database and a machine learning model work, at the level needed to discuss them with specialists; describe the architecture of an enterprise information system from transactions to analytics to AI. |
| Applying knowledge | Map a business process (order to cash, procure to pay, record to report) onto the ERP modules that support it; design a set of KPIs and the data flow that feeds a performance dashboard. |
| Making judgements | Assess an information system or AI investment in terms of alignment, returns, costs and risks; distinguish where AI adds value from where it adds only noise or risk. |
| Communication | Present an IS or AI proposal to a non-technical executive audience, in writing and orally, with a clear argument and evidence. |
| Learning skills | Use AI tools critically to learn, research and verify; keep up with a field whose tools change every year, by knowing which concepts do not change. |
Course architecture
Four layers, from how a machine works (bottom) to how software agents act (top), judged through two frames: value and trust. AI runs through every layer.
Course components
The 35 hours combine five components. Their sequence and dates are set each year in the official calendar: chapters follow the order of the course architecture, while the business cases, the simulation and the testimonials are placed where the calendar allows.
| Component | Hours | Content | Format |
|---|---|---|---|
| Lectures | 20 | Ch.1 to Ch.4 (4 hours each); Ch.5 and Ch.6 (2 hours each) | Lecture, with live demos |
| Business cases | 4 | Two cases on ERP and process redesign, building on Ch.2 | Case discussion (teaching assistant) |
| Analytics simulation | 4 | The lab of Ch.3: data, KPIs, drivers, variances, scenarios | Lab, in groups |
| Testimonials | 2 × 2 | Executives and practitioners; they pair naturally with Ch.5 and Ch.6 | Guest speaker |
| Final session | 3 | Recap, written exam, correction and grades | Exam |
Chapters
Every chapter follows the same grammar: an opening question, the core concepts, a block on What changes with AI, a case or discussion, and the takeaways that the exam tests. ERP (Ch.2) and CPM (Ch.3) carry the most weight.
Ch.1 Foundations: How machines work
Opening question: Why should a manager understand machines?
The course thesis and the value equation used throughout: ICT Value = f(Alignment, Returns, Costs). A short history read as a pendulum between centralisation and distribution, from the mainframe to client-server, to the cloud, to AI models consumed as a utility. What a computer is: bits, logic, the stored program. Hardware today: CPU, GPU and NPU, memory and storage, and why computing power and energy have become strategic issues. Software: operating systems, applications, the shift from installed software to SaaS. Programming languages: from machine code to Python, and now to natural language as a way of programming. Networks, the internet and the cloud.
What changes with AI: From bits to tokens; from writing code to describing intent.
Ch.2 Systems of Record: Processes and ERP
Opening question: How does the company actually run?
The company seen as processes: order to cash, procure to pay, record to report, hire to retire. The layers of a management information system. ERP in depth: what it is, its history, its modules and how they integrate with Finance (general ledger, payables, receivables, fixed assets, controlling). CRM, SRM and supply chain systems around the ERP core. Integration: APIs, ETL, middleware. Cloud ERP, clean core and composable architectures. Process mining as the X-ray of how processes really run.
What changes with AI: Copilots and agents inside transactional processes, and the controls they need.
Ch.3 Systems of Insight: Data, BI and CPM
Opening question: How do I know what is happening, and why?
Data as an asset and the data value chain. From the relational database (tables, keys, SQL) to the data warehouse, the data lake and the lakehouse. Data quality, master data and data governance. Business Intelligence and reporting. Corporate Performance Management: planning, budgeting, forecasting, consolidation, KPIs and scorecards, and how CPM closes the loop with the ERP. Descriptive, diagnostic, predictive and prescriptive analytics.
What changes with AI: Conversational BI, the semantic layer, continuous forecasting, and four checks before trusting a number produced by AI.
Ch.4 Intelligence and Agency: ML, LLMs, RAG, agents
Opening question: Can machines decide, and should they?
Machine learning explained to a manager: learning from examples, classification, clustering, prediction, and how models are evaluated. From deep learning to large language models: tokens, embeddings, context, why models hallucinate. Retrieval Augmented Generation and company knowledge. Agents: models that plan, use tools and act, with humans in the loop. Where AI works and where it does not: the jagged frontier.
What changes with AI: This is the chapter where AI is the subject.
Ch.5 Value and Transformation: Business cases, sourcing, the ERP journey, change
Opening question: Is it worth it, and how do we get there?
The business case for IS and AI: benefits, total cost of ownership, risks, ROI and NPV, and the productivity J-curve. Benchmarking IT spending. Alignment between business and IT strategy. Sourcing choices: build, buy, SaaS, partner. The ERP journey: software selection, implementation approaches, typical pitfalls, the role of Finance. Managing projects and change, from waterfall to agile; adoption journeys for AI.
What changes with AI: Why time saved is not yet money saved, and how to measure the value of AI.
Ch.6 Trust and Synthesis: Governance in brief, and the course in one picture
Opening question: Who is in control?
The essentials of governance: IT and AI governance, cybersecurity, privacy (GDPR) and the EU AI Act, seen through what a manager must ask rather than through the legal text. Then a synthesis of the course: the integrated information system of a company, from the ledger to the agent, and the questions every manager should be able to ask about it.
What changes with AI: Accountability does not change: a person answers for every decision, whoever or whatever executed it.
Assessment
For attending students the final grade combines a written exam and in-class work.
| Component | Weight | How it is assessed |
|---|---|---|
| Written exam | 80% | Open questions, written in class in the final session (3 hours including recap and correction) |
| Participation and in-class cases | 20% | Quality of contributions to discussions, business cases and the simulation |
The exam asks students to explain concepts in their own words, apply them to a short business situation and argue a judgement. Answers are graded on correctness, precision of language, use of the course frameworks and quality of argument. Grades are communicated at the end of the exam session.
Students who cannot attend take the written exam only, on the official exam dates, with additional questions that replace the in-class component. The attendance threshold that defines an attending student is confirmed at the start of the course.
Use of AI by students
AI tools are welcome in this course, and using them well is part of what it teaches. You may use AI to study, to prepare for cases and to explore topics, provided you verify what you use and can explain it without the tool. In group work, declare the use of AI in one line (which tool, for what). The final exam is written in class without devices, because it assesses what you understand, not what a tool can produce.
Readings
The slides and handouts are the core material. The readings give each chapter a classic and a current voice; some HBR articles require a library login.
Optional textbook for a single reference: Laudon, Laudon and Traver, Management Information Systems: Managing the Digital Firm, Global Edition, 18th edition, Pearson (2025).