Vineet Kumar

Teaching

Artificial Intelligence: Strategy and Marketing (MKTG 68900) Spring 2027

Artificial Intelligence is a general-purpose technology which has the potential to transform many aspects of business and society. In business, the impact ranges from commonplace predictive improvements at one end of the spectrum to opportunities for creating entirely new markets at the other. As background, the course will briefly introduce students to Artificial Intelligence / Machine Learning methods comprising of Unsupervised, Supervised and Reinforcement Learning. Through a combination of lectures and case studies, we will learn how to integrate AI into decision making, focusing on the strategic choices firms face in developing and using AI / ML. We will seek to understand how consumers, decision-makers other stakeholders evaluate decisions made by AI systems and are impacted by it. We will examine the state-of-the-art in explainable and interpretable AI, which aim to make black-box decisions more transparent. Finally, the course will also explore issues at the intersection of AI and Society including fairness and bias, which have proved especially challenging to address.

Course Objectives

The course is designed to provide a strategic perspective on AI technologies, but given the broad scope of the topic, we have to make tradeoffs in selecting areas to examine in depth. My goal is that a student who completes the course successfully should be able to:

• Understand the basics of predictive AI and ML models
• Determine how AI objectives connect to business objectives and strategy
• Understand a framework for decisions on AI / ML and identify the major resources required to implement the chosen AI strategy
• Develop a perspective regarding new emerging AI technologies and how they could reshape markets and firms
• Evaluate the broader societal implications of AI, and how different stakeholders (consumers, employees, firms, regulators, investors and others) are impacted by AI.

Notes:

1. Use of electronic devices will not be permitted in class
2. This is a discussion-based class designed for graduate students. Students will be required to actively participate in class discussions, not serve as passive observers. Students will *not* be permitted to audit this class
3. Masters / PhD students at Purdue who are interested but not eligible to enroll directly can send an e-mail to the instructor with the following: a 1-paragraph description of your interest, Resume and Transcript / Course List with Grades. A select few will be admitted at the discretion of the instructor.
4. We will use Google Colab and code will be provided. Students will be asked to explain the purpose of specific parts of the code (in the first assignment). Students will *not* have to write their own code, but must be comfortable understanding the structure and potentially modifying some code (LLM use permitted). An overview of the code will be provided in class.
5. LLM use is accepted and encouraged in this course. You will have to acknowledge use and reflect on how it was helpful to you.

Textbook(s): None required. Readings, Assignments and Course Notes/Slides will be posted on Brightspace.

---