AI in Business

AI in Business: Intelligent Transformation and Management

AI in Business aims to bring together academics and practitioners to examine how artificial intelligence is reshaping businesses. Since its inception, AI is now commonly found across operations, customer engagement, innovation, and strategic decision-making. Generative AI opened the door to real productivity gains, but the deeper transformation is happening in autonomous agents that execute complex, multi-step processes without constant human oversight.

This AI symposium provides a platform to explore both the strategic implications of AI as well as the technological implications of AI across modern enterprises.

Topics

• Strategic Planning
• Sales and Demand Forecasting
• Predictive and Prescriptive Analytics
• Operations, Production, and Supply Chain Management
• Automation and Robotics
• Internet of Things (IoT) and AI Integration
• Finance and Economics
• Marketing
• Human Resources and Behavioral Analytics
• LLM Agents
• Ethics and Governance
• Healthcare
• Other AI and business applications

Format of Symposium

The symposium features invited keynote speakers, contributed talks, workshops, panel discussions, and networking activities.

Attendance

Attendance is open to academics, practitioners, policymakers, and graduate students working in AI, machine learning, data science, or AI applications in business.

Submission Requirements & Important Dates

Organizers

Nahid Jafari

Chair

Associate Professor of State University of New York, NY

jnahid@hotmail.com

Mohammed Quazi

Assistant Professor, West Virginia University, WV

mmquazi@hsc.wvu.edu

Mahshid M.Zadeh

Ph.D. Computer Scientist, Sao Paulo University, Brazil

mahshid.msyz@gmail.com

Bruno Kamdem

Assistant Professor of State University of New York, NY

kamdemb@farmingdale.edu

Invited Speakers

Javid Huseynov

Associate Professor of Practice, Applied Analytics
Columbia University, New York, NY

Talk Title:
Ownership Graph Inference with Large Language Models for Governance Risk Scoring

View Abstract ▾

Corporate governance risk is shaped by who ultimately controls a firm and how that control is structured. Yet critical ownership relationships are often embedded in narrative regulatory filings rather than structured data, making large-scale analysis difficult. Concentrated ownership, intermediary entities, trust arrangements, and shared voting relationships can create structural vulnerabilities that simple concentration measures fail to capture.

We formulate ownership modeling as a semantic relation extraction and graph construction problem. We use large language models to identify, normalize, and type beneficial ownership relations from SEC 13D and 13G filings. These relations are assembled into directed ownership graphs, where edges represent stake percentages and entities are linked across firms, enabling analysis of shared ownership patterns and layered control structures.

On this graph representation, we define a Governance Risk Index grounded in agency theory that captures five structural dimensions of governance risk: concentration of control, intermediary depth, ownership opacity, shared major holders, and insider alignment. Because ownership structure evolves slowly while market behavior fluctuates, we use short interest as an external and dynamic signal to examine how structural governance risk relates to market activity. This work operationalizes structural ownership signals from filing text into an interpretable risk metric for consistent comparison of governance vulnerability across firms.

Program Schedule

Note: Each paper/abstract presenter has 20-25 min presentation + Q&A. Room: Harbour B, Lobby Level.

🍴 Social networking dinner - Tue, Apr 7th, 7pm onwards. Location: Mazra, 504 San Bruno Ave W, San Bruno CA 94066.