Overview

By the end of the course, you must develop a prototype of an AI application that addresses an important business problem. The goal for this assignment is to integrate across the various learning objectives of the class, challenging you to think holistically about how AI can solve key business problems. A few remarks about the design of the assignment:

• The assignment is intentionally open ended, so that you can choose a problem that is fun for you to work on. Choose a problem that you think is important or valuable. Spend time up front carefully considering your options!
• The project is intended to be done over a period of time, so that you have time to explore the software libraries introduced in class. Please don't leave this to the last minute, or you will miss out on an opportunity to learn these powerful technologies.
• My intention is for you to develop your project using generative AI technologies, as that is what we covered in the class. However, if you have prior experience with predictive AI technology and feel passionate about a project based on that approach, you are welcome to pursue that approach as well. Please be clear about your intention to do this in your initial proposal.
• Final project work can be done in teams of up to three; grading standards will be commensurate to the number of teammates (a higher standard for more people).
  • If you can form a larger team, I encourage you to do so. You will have a more enjoyable project experience and implement a more complete prototype.
  • Teams of four may be permitted with instructor approval, particularly for cross-disciplinary teams (e.g., MBA + MSCS students). Please reach out early if you are interested in forming a larger team.
• You are responsible for sourcing your own dataset.
  • You may use publicly available data from Kaggle, HuggingFace, or other common data science sources.
  • You may collect the data yourself.
  • You may use data that you have access to through other activities, assuming that you have permission to do so.
• The use of generative AI is highly encouraged in the process of developing these prototypes. [See below for the course's AI policy.] We will only have time to engage the relevant AI development libraries at a superficial level in class, but you will likely need them to implement interesting solutions. This project is your chance to learn how to work with these technologies at a deeper level.

Instructor Consultation Sessions

In addition to regular office hours, the following sessions are dedicated to project feedback. The instructor will be available in the classroom for one-on-one or team discussions about your project:

• February 26 — No class; instructor available in classroom for consultation
• Week of March 3 — No weekly assignment; use this time to work on your project
• April 21 — No class; instructor available in classroom for consultation

If there is sufficient interest, a sign-up schedule will be provided so that teams can reserve specific time slots.

Sample Projects

[just to give you a sense of what is possible — not intended to limit you]

• Generative AI
  • Customer Support Chatbot: Build an AI-driven chatbot for automating customer support using a pre-trained language model. Show how it reduces operational costs by handling common inquiries.
  • Automated Resume Screening Tool: Create a resume screening tool using natural language processing. Showcase its ability to speed up the hiring process and improve candidate selection.
  • AI-Based Recommendation System: Build a recommendation system for an e-commerce site, demonstrating how it enhances user experience and increases sales by suggesting relevant products.
  • Data Analysis Assistant: Integrate custom information into a chatbot that can facilitate business analysis.
  • AI-Powered Research Agent: Build an agent that can search, summarize, and synthesize information from multiple sources to answer complex business questions.
• Prediction-based AI
  • Demand Forecasting System: Develop a system that uses historical sales data to predict future demand, demonstrating how it can reduce inventory costs and optimize supply chain management.
  • Fraud Detection in Transactions: Use AI to detect fraudulent transactions in financial data, explaining how it improves security and reduces financial losses for a business.
  • Predictive Maintenance for Machinery: Implement a predictive maintenance system using sensor data to predict equipment failure. Demonstrate how it can save costs by reducing downtime and repair expenses.

Grading

Final projects will be graded based on five components: