Course Overview

Since 2012, artificial intelligence (AI) technology has seen unprecedented advancements due to breakthroughs in deep neural networks. These rapid gains have fueled excitement about AI’s potential to revolutionize businesses across industries, from finance and healthcare to retail and manufacturing. However, while AI applications are becoming increasingly visible, the hype often overshadows the significant challenges in successful implementation. To realize the technology's potential, managers must gain a holistic understanding of the technology, including how its components fit together.

This course explores AI's transformative impact on modern organizations, focusing on Large Language Models (LLMs) and the thriving AI ecosystem driving forward these advances. Through three distinct modules, the course offers an in-depth look at cutting-edge AI technologies, their practical applications in businesses, and the emerging AI supply chains, emphasizing the role of open-source software. Students will gain hands-on experience by prototyping AI solutions and analyzing real-world business cases, mastering both technical skills and the ability to evaluate trade-offs in AI implementation. By the end of the course, participants will confidently navigate AI’s evolving landscape, make informed decisions about its use, and effectively communicate AI-driven projects to external stakeholders.

Prerequisites

• Undergrads at the Juniors and Seniors only.

• Python proficiency at the level of CS 1301 or MGT 3745. Students should be able to independently set up a programming environment, install Python packages, read technical documentation, and debug code.

  • All course assignments can be completed through Google Colab.
  • We will extensively use APIs from OpenAI and libraries from Hugging Face and LangChain throughout the course, but prior exposure to these technologies is not required.

  See Python Prerequisite Details for more information.

Note: prior exposure to machine learning (ML) methods (for example, MGT 4803 “ML for Business”) is helpful but not required.

Learning Objectives

1. Conceptual Understanding.
   • Develop fluency in fundamental AI principles. Understand their key strengths and limitations (e.g., hallucinations, biases, computation challenges).
   • Master essential terminology and concepts, such as Prediction, Generative AI, Language Modeling, Training, Dataset Size, Compute, GPUs, Evaluation, Bias, Tokens, Context Windows, Scaling Laws, Orchestration, and more!
   • Gain insight into the AI ecosystem by becoming familiar with leading firms. Learn to stay current with AI advancements via news and tech media (e.g., Twitter / X, NYTimes).
2. Technical Skills.
   • Gain hands-on experience using high-level libraries / APIs to create business prototypes and understand typical deployment challenges.
   • Understand the trade-offs between prototyping shortcuts and the steps needed for production-ready deployment.
   • Build confidence in navigating online documentation and independent learning resources.
3. Business Skills.
   • Apply a structured framework to identify where AI generates value within business processes and the necessary complements for that value realization.
   • Evaluate AI technologies with attention to ethical implications in deployment.
   • Develop skills to present and advocate for AI-based projects to non-technical stakeholders effectively.

Course Components

See Grades and Policies for how these components fit into your final grade.

Lectures

~30x 75-minute classes focused on both technical content and case analysis. Attendance is mandatory, as participation in discussions is a key component of learning.

• Name Tags. You should print out a name tag with your name on it (large enough to see from the front of the room) and place it in front of you during class. This will facilitate getting to know other students and creating a more familiar atmosphere in the class.
• Sign-in Sheets. I will provide a sign-in sheet for tracking attendance at each of the case study discussions. (It may go without saying, but you may not fill out the sheet for other students — doing so violates the honor code and will be reported if caught.)
• Preparing for Case Study Discussions. The second half of the course will be a case-based discussion You must read 18x case studies and come to class prepared to discuss them. See Resources for instructions on how to access the cases. I will discuss what I’m looking for in these discussions in class, but see also this resource for some additional ideas. (You are not expected to form study groups for case discussions, but are welcome to if you feel it facilitates engagement with the material!)

When applicable, lecture materials will be posted on the ‣.

Weekly Assignments