Artificial Intelligence for Business Decision

Objective

Disclosure Statement: The ideas, analyses, and opinions expressed in this course are solely those of the instructor in an individual capacity and do not represent, nor should they be attributed to, the views, policies, or positions of Amazon or any affiliated institution.

COURSE DESCRIPTION

This course explores how recent advancements in artificial intelligence (AI) are reshaping business strategy, operations, and decision-making. Focusing on real-world applications, it examines technologies such as machine learning, large language models, placing them within the broader context of technological adoption and social transformation. 

Drawing on recent research, the course enables students to assess how AI generates value across various sectors. It is designed for students with a solid grounding in statistical learning and a keen interest in the intersection of emerging technologies and business dynamics.

LEARNING OBJECTIVES

By the end of this course, students will be able to:

1. Analyze developments in AI and assess their strategic implications for business.
2. Identify and evaluate relevant AI applications across different industries.
3. Communicate findings effectively through project work and oral presentation.

ASSESSMENT

Assessment for this course consists of three components:

1. In-class presentations: 35%
2. End-of-lecture quizzes: 5%
3. Final individual project: 60% 

Planning

Week 1 introduces the fundamentals of artificial intelligence (AI) and machine learning (ML), covering supervised, unsupervised, and reinforcement learning, while distinguishing AI from traditional business analytics. The session also draws historical parallels with previous technological shifts to contextualize AI’s transformative impact on industries and society.

Week 2 introduces the task-based economic framework for understanding how AI changes work, distinguishing between automation (task substitution) and augmentation (task creation or enhancement). Students then examine real usage data from ChatGPT, Claude, and Copilot. The session concludes with a practical exercise of how prompt prototypes can be transformed into API workflows for applied analysis.

Week 3 examines how AI is transforming financial decision-making by improving information processing, risk assessment, and investment analysis. We study how large language models extract value from unstructured financial data such as earnings calls and news, reshaping the roles of retail investors and financial professionals. The lecture also discusses key challenges related to governance, transparency, and risk management.

Week 4 explores how AI is reshaping medical decision-making by enhancing diagnosis, clinical reasoning, and documentation. We examine applications ranging from medical imaging and diagnostic support to LLMs and medical agents used for clinical text summarization and reasoning. The lecture emphasizes AI as a tool for augmenting clinicians rather than replacing them, and discusses key ethical challenges related to fairness, safety, accountability, and patient trust.

Week 5 focuses on the challenges organizations face when scaling generative AI from proof-of-concept to enterprise-wide deployment. The lecture introduces an enterprise framework covering data and compute infrastructure, foundation models and tools, security and governance, and repeatable application patterns. We examine business implementation strategies, including organizational design, success factors, and roadmapping, and conclude by emphasizing the role of governance and strategic leadership in enabling sustainable and scalable GenAI adoption.

Week 6 examines how organizations and regulators manage ethical, legal, and cybersecurity risks once AI systems are deployed at scale. The lecture covers internal AI governance, accountability, auditability, and model risk management, alongside adversarial threats such as data poisoning, model extraction, and fraud. We conclude by examining emerging regulatory frameworks and their implications for long-run economic outcomes.