AI on AI in Retail Filings

Format: Online
Number of Interns: 1 student
Duration of the Internship: Summer term, approximately 4-6 weeks (full-time)
Start Date: June 22, 2026
Finish Date: July 31, 2026
Application Deadline: May 25, 2026
Project Supervisor: Asst. Prof. Ayşe Çetinel

Project Description: This project uses AI both as a tool and as the topic of analysis. The student will build a Python + agentic AI pipeline that ingests 10-K/10-Q/annual reports of major retailers and automatically finds, classifies, and summarizes all passages where companies talk about AI, machine learning, data platforms, and analytics. The system will split filings into sections and chunks, label AI-related text (e.g., use cases, functions, claimed benefits), and extract structured fields such as “AI use case,” “business function,” “deployment stage,” and “claimed impact.” The final outputs will be an AI-generated catalog of retailers’ AI narratives and use cases, company-level summaries of how AI is positioned in their strategy, and simple Q&A and visualization tools to explore “how retailers say they use AI” across firms and over time.

Research Intern Responsibilities: The primary responsibility of the intern is to design and implement a Python-based workflow that leverages agentic AI to process and analyze large volumes of corporate text. This involves writing scripts to ingest annual reports, segment them into appropriate chunks, and programmatically query Large Language Models (LLMs) to identify specific mentions of AI and analytics. The student will be responsible for refining the extraction logic to ensure the system accurately captures structured data points—such as use cases and claimed benefits—from the unstructured narratives. Additionally, the intern will work on synthesizing these results by creating a catalog of AI narratives and developing basic visualization or Q&A tools to display the trends in how retailers discuss their technological strategies.

Required Skills and Qualifications: Undergraduate student (Business, Data Science, Computer Science, Industrial Engineering, or Economics) with solid basic Python skills (e.g., functions, working with CSVs) and some familiarity with Pandas. Strong interest in AI and its real-world business applications, especially in retail and digital commerce. Comfortable reading English-language business documents and curious about how to turn unstructured text into structured data using large language models.

Expected Learning Outcomes: Upon completion of this project, the student will have acquired advanced technical skills in building AI-driven data pipelines and applying "agentic" workflows to solve real-world research problems. They will learn the intricacies of transforming unstructured text data into structured databases suitable for quantitative analysis, a critical skill in modern data science. Furthermore, the student will gain deep domain expertise in the intersection of retail strategy and technology, understanding not only how major retailers are deploying AI but also how they communicate these innovations to stakeholders in their financial disclosures.

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