AIML 901

Syllabus

AIML-901OP-5 AI Foundations for Managers - Lab

course_canvas_vignette Kellogg School of Management
Instructor: Sébastien Martin
Email: sebastien.martin@kellogg.northwestern.edu
Website: sebastienmartin.info

Note: this syllabus is subject to change until the course starts.

Course Overview

AI is advancing at an unprecedented pace. The best way to understand its potential is not just by studying it, but by using it. This immersive course provides Kellogg students with extensive hands-on experience building with generative AI, equipping you with practical skills to immediately apply these technologies in business contexts. In just five weeks, you will go from AI outsider to AI insider, taking your first steps toward building solutions while developing the essential vocabulary and conceptual framework to navigate the rapidly evolving AI landscape. Let’s get building!

Course Features

  • State-of-the-Art: While “AI” has many meanings, the focus of this course is on the latest AI developments. We will mostly study generative and agentic AI.
  • Hands-On: The best way to understand AI’s strengths and limitations is to use it. The main deliverable is a project and each lecture includes hands-on activities.
  • Leveraging AI Agents in n8n: AI agents are the perfect vehicle for this journey—practical enough to build, sophisticated enough to reveal how AI really works, and relevant enough to spark discussions about operations, human collaboration, and organizational transformation. n8n is “high level” enough to be accessible to beginners, yet “low level” enough to allow for deep technical exploration.
  • Preparing You for the Future: This course is designed to be a stepping stone in your AI journey, enabling you to stay current as AI continues to evolve.
  • AI-Powered Learning Support: This course will heavily leverage AI. This will include AI homework, AI teaching assistants, and AI-powered case studies.

    Content and Relationship to AIML-901 Courses

    The course is a “Lab” version of AIML-901. Its specific focus is to be as hands-on as possible: we will actually build AI products and figure out how to create value with them. We will also target the latest AI developments. We will therefore focus mostly on using and leveraging Agentic AI (the latest and most important genAI development).

However, all AIML-901 courses are also a general introduction to AI. Therefore, in this spirit of “learning by doing,” we will use this experience to more broadly introduce the AI space, touching on the following general topics:

  • AI and Machine Learning history.
  • Machine Learning basics.
  • Generative AI.
  • Accountability, ethics, fairness, governance, and future considerations.
  • AI applications in business and the manager’s role.

Overall, I highly recommend taking other versions of the AIML-901 course. They all choose very different approaches to introduce AI, and I believe that the more perspectives you get, the better.

Requirements

This course is designed to be accessible to all Kellogg students without prerequisites (no need to know how to code!), regardless of technical background. If you have any doubts, please reach out.

Instructor

Professor Martin is an associate professor of operations, whose research focuses on designing and implementing AI/automation systems that solve real-world business challenges. He designed Lyft’s reinforcement learning approach to matching drivers and passengers and also works on a similar project at Waymo. He is passionate about incorporating AI in education: he created Kai, Kellogg’s teaching assistant, and introduced the first AI-powered case studies. He was named P&Q 40 under 40 MBA professor in 2025. Outside research and teaching, he also consults with companies on AI strategy and is currently an AI advisor to the CEO of ESAB Corporation.

Recitations

The course includes five in-person recitations, each lasting 60 minutes. The recitations will be focused on learning how to build AI agents using n8n, which is the main technical knowledge that you will have to learn. The recitations will be hands-on and interactive, each one focused on a specific business use case and gradually introducing more complex agentic workflows.

Attendance to the recitations is not required but strongly recommended. In particular, the final exam is solely focused on what is covered during recitation as it will evaluate your ability to use n8n.
In case you cannot make it, the recitations will be recorded and made available to you.

Individual Project

The main deliverable for this course is an individual project where you will build your own AI agent using n8n. This is by far the most important deliverable of the course. The project’s goal is two-fold: showcasing that you can build impressive AI agents, and also showcasing that you can design agentic workflows that actually create value.

In a nutshell, the project (much more than the final exam) is the true evaluation mechanism of the course and can leverage everything we will learn.

  • The goal of the project is to design an AI/agentic workflow that would create value in a business context you are deeply familiar with.
  • The target of the project must be either a previous company/organization you worked for, or a startup/product idea that you have and want to work on.

Deliverable:
A recorded presentation video (8–10 minutes), together with a working MVP of your AI workflow. The video must include:

  • An explanation of the business context and the problem you are trying to solve.
  • A live demo of your AI agent in action, featuring n8n.
  • A demo of the evaluation pipeline to measure the performance of your agent.
  • A discussion of the implementation path you suggest and why you think this change can truly add value.

Grading:

  • 40% idea/implementability/business value of your proposal
  • 40% technical implementation
  • 20% quality of the presentation

Final Exam

The project is really the main deliverable, the final exam is more of a “check-in” to make sure you learned the technical skills.
The exam will be virtual and open-book, timed (1.5 hours), and will feature a set of practical exercises where you will have to use n8n to build AI agents.

If you attended the recitations, practiced them, and did the homework, you should get close to full grade.

Homework

Before each class, a “Kai homework” will be assigned. You will have a guided conversation with an AI teaching assistant (Kai) to prepare you for the next class, and you will submit this conversation on Canvas.

Homework is designed to be quick (no more than 30 minutes) and will help you get the most out of the class, and they can be quite fun!

  • Homework is graded for effort, not results.
  • You will get full credit if you took it seriously and spent the time, regardless of accuracy.

Grading Rubric

The final grade will be based on:

  • 45% Individual project
  • 25% Final Exam
  • 25% Participation (attendance, engagement, and homework effort)

Attendance and Participation

Attendance, timeliness, and in-class contributions are extremely important to me, as everyone benefits from a positive learning environment.

  • In-class contributions consist mainly of voluntary participation.
  • Occasionally, I also use “warm calling,” where I give you a heads up and ask you a question about something you already wrote in a class preparation homework.
  • On-time attendance is required (when feasible).
  • You have one free pass for unexcused absences; more will impact your grade.
  • Per Kellogg policy, 3 unexcused absences may result in a failing grade.

AI Policy

The use of AI in this course is encouraged and you will be given access to many AI tools. We will both learn about how to use AI and also use AI to help us learn.

However, AI is a double-edged sword; while it may be tempting, try not to use it as a “black box” that does the work for you without seeking to understand how and why it produces its results.

Classroom Etiquette and Honor Code

We follow the Kellogg Honor Code and the Code of Classroom Etiquette.

You may not:

  1. Engage in cross-talking.
  2. Engage in disruptive movement (e.g., arriving late or leaving class for a coffee or snack).
  3. Use a smartphone, laptop, or other device outside of designated times.
    • Tablets are allowed for note-taking, but only if used flat on the table.
    • Laptops only with prior approval.
    • Laptops required during designated hands-on times.
  4. Engage in any other inappropriate or disruptive behavior.

Software, Tools and Costs

I’ve done my best to keep costs low. Expected costs/resources:

  • ChatGPT/Claude/Gemini subscription recommended but not required.
  • n8n: free (course sponsored by n8n).
  • Lovable: free (1 month sponsored access).
  • Kai (AI TA) and AI-powered case studies: free (covered by Kellogg).
  • Required cost: OpenAI (or Anthropic) API usage for your AI agents.
    • Expect less than $5–$10 for the whole course.

Class and Recitation Content

See Canvas for the most up-to-date class-by-class content description.