Business+AI Lab at Stevens

Events

• Oct. 8, 2026Stevens AI Conference 2026: Leveraging AI for Business Value

  Sold out. A full-day executive conference on turning AI into measurable business value — October 8, 2026 at Stevens Institute of Technology, Hoboken, NJ.

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• Sept. 25, 2026Business + AI Hackathon 2026: Leveraging AI for Innovation in Business

  One day, one real business challenge — student teams from every discipline build AI-enabled solutions and make the business case. Sept. 25, 2026 at Stevens Institute of Technology, Hoboken, NJ.

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• Sept. 23, 2026Business+AI Forum, September 2026

  The second Stevens School of Business for the Business+AI Forum, a conversation with senior leaders who are integrating and scaling AI to discuss what’s really working, where teams and companies are pivoting, and how other forward-thinking leaders are making AI-related decisions.

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• May 14, 2026Delivering Value with AI: Business Model Transformation

  Katia Meggiorin explores how AI is rewriting the rules of competition by analyzing two rival companies in the same industry—one traditional, and one heavily AI-driven.

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• May 7, 2026Data: The Next Frontier

  Aleksi Aaltonen explores what data are (or is), why their importance is going to grow, and what are the emerging trends and issues that organizations will need to tackle in the AI-driven economy.

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• April 30, 2026AI Entrepreneurship: Bootstrapping Autonomous Robotaxi Services

  Jordan Suchow looks at a key strategic decision that companies in the autonomous navigation space have all wrestled with: how to bootstrap a product when the data needed to train the system requires deploying it.

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• April 23, 2026AI in Finance: Rewiring Robo-Advising for the Autonomous Portfolio

  Zachary Feinstein explores the transition toward truly autonomous wealth management by bridging artificial intelligence with decentralized market structures.

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• April 16, 2026Responsible AI: Foundations of Ethics and Algorithmic Fairness

  Violet Chen introduces the AI ethics landscape and examines how principles of fairness, transparency, privacy, and accountability apply in real-world business decisions.

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• April 9, 2026Managing AI Technologies: AI-in-the-Loop, AI-on-the-Loop, Human-out-of-the-Loop?

  Michael zur Muehlen introduces a framework for understanding three AI deployment postures: AI-in-the-loop, AI-on-the-loop, and human-out-of-the-loop, raising hard questions about the point at which augmentation becomes automation.

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• March 31, 2026Miaoze Han: “Generative AI and Firm-Candidate Dynamics in the Labor Market”

  The rapid proliferation of generative artificial intelligence (GenAI) is significantly reshaping the labor market. While existing literature primarily focuses on GenAI’s isolated effects on either labor demand (e.g., task automation) or labor supply (e.g., skill development), research exploring the combined, interactive effects on firm-labor dynamics remains limited. This study addresses this gap by examining how the interplay between firms and job candidates has changed since the advent of GenAI. Leveraging data from a leading bi-directional job matching platform in China, we investigate key pipeline metrics, from initial communication (measured by the volume of chats, resumes, and contact information exchanged) to final conversion (operationalized as the successful scheduling of an interview), across job categories differentially exposed to GenAI. Our findings demonstrate that positions highly exposed to AI experience a significant increase in overall communication volume, yet no corresponding change in interview conversions. Further analyses reveal that these observations stem from divergent changes on the part of firms and candidates. Specifically, among highly exposed positions, candidates initiate significantly more communication but achieve fewer interview conversions, while firms initiate less communication yet secure more conversions. A dynamic regression analysis confirms the persistence of these divergent trends throughout our observation period. We provide strong evidence for the mechanism where GenAI technology improves skill transferability while displacing domain-specific knowledge, therefore reducing the entry barrier for the ex-ante ineligible applicants. We discuss implications for both firms and individuals navigating this evolving job market landscape.

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• March 13, 2026Dominik Molitor: “Customer Responses and Economic Consequences of Data Breach Announcements

  Cybersecurity incidents and data breaches are increasingly common and pose significant financial risks for firms. This study examines how consumers respond to major data breach announcements and evaluates their economic consequences for affected companies. Using a difference-in-differences research design, we analyze a large-scale data breach in the hospitality industry that exposed over 380 million customer records. The results show a short-term decline in the firm’s revenue following the breach disclosure, lasting approximately two months before recovering. These findings suggest that while data breaches initially affect consumer behavior, long-term financial consequences may be more limited than commonly assumed, potentially due to consumers’ limited attention to privacy concerns.

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• March 11, 2026Business+AI Forum, March 2026

  The first Business+AI Forum on how AI is transforming business strategy, operations, and decision-making took place on March 11, 2026.

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• March 6, 2026Jinan Lin: “Delegating Returnless Refund Authority to Sellers: Evidence from an E-Commerce Platform”

  Balancing consumer protection with seller welfare is a central challenge in platform governance. This study examines returnless refunds—an innovation that reimburses buyers without requiring product returns—and analyzes a policy shift that delegated refund authority to highly rated sellers on a leading e-commerce platform. Using a difference-in-differences framework, we find that delegated sellers reject opportunistic requests more often while improving service performance. While delegation strengthens reputation-based incentives and benefits smaller, high-quality sellers, it also increases negotiation costs, highlighting tradeoffs in decentralized platform governance and the need for trilateral mechanisms.

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• Feb. 6, 2026Sagit Bar-Gill: “LLMs and Consumer Bias: ‘Paying Not to Go to the Gym’ with an LLM Advisor?”

  Large language model (LLM)–based advisors increasingly interact directly with consumers, raising the question of whether they mitigate or exacerbate well-documented consumer choice biases. We study this question in the context of gym membership purchases, where consumers are known to systematically overestimate future usage. We address three research questions: (1) Under what conditions do LLM advisors mitigate versus reinforce consumer choice bias; (2) What is the resulting consumer harm or benefit when biased consumers interact with LLM advisors; and (3) How can such harm be detected and mitigated through regulation and LLM design. To answer these questions, we conduct a field experiment in collaboration with NYU Athletics. In this experiment, prospective gym members are randomized to interact with one of two LLM advisor variants or assigned to one of three informational control conditions without LLM interaction, prior to choosing among gym membership plans. Preliminary results suggest that LLM advisors mitigate choice bias when consumers’ overestimation of future gym attendance is modest, but reinforce bias when overestimation is large. This work-in-progress will be extended to additional choice settings and aims to inform the design and regulation of consumer-facing LLM agents in markets characterized by asymmetric information, uncertainty, and systematic consumer biases.

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