How do people and AI work together on a shared project? In this talk, we will explore examples from open-source software and Wikipedia, where many people contribute to shared work. I will introduce a network perspective: a way of understanding collaboration by looking at the connections among people, AI and the work they share. We will first explore how people form teams and bring AI into their work on software projects. We will then look at how bots change who does what on Wikipedia. Through these examples, I will show how networks help us understand choices about work and what makes collaboration with AI work well.

Jingyi Sun is an assistant professor of information systems at Stevens Institute of Technology. Her research focuses on the dynamics of networks in organizing, including virtual communities and teams, health communities, human–AI collaborationand employee mobility. Her research has been published in journals such as Information Systems Research, Production and Operations Management, and the Journal of the Association for Information Systems.

Generative AI is becoming increasingly powerful and widely used, but it also raises important questions about safety, copyright and privacy. This talk will discuss how AI systems can produce harmful or unintended outputs, reproduce copyrighted content and reveal sensitive information. I will also introduce recent approaches for auditing these risks and discuss what is needed to build more trustworthy generative AI systems.

Denghui Zhang is an assistant professor of analytics at Stevens Institute of Technology and is affiliated with the Center for Research toward Advancing Financial Technologies (CRAFT). His research focuses on trustworthy generative AI, particularly AI safety, copyright and privacy risks, and AI agents for fintech and other regulated domains. His research projects have received more than $1 million in funding from the National Science Foundation (NSF). His work has also been recognized with Best Student Paper Awards at ICIS and the Academy of Management (AOM).

This talk explores how advances in artificial intelligence are changing the way we use alternative data in accounting and finance. AI allows us to analyze large amounts of complex and unstructured information that were previously difficult to process systematically. This creates new opportunities to extract meaningful signals from alternative data and use them to better understand firms and markets. The talk will discuss how AI expands what we can learn from alternative data, as well as the challenges of assessing the reliability and economic meaning of these new signals.

Arion Cheong is an assistant professor of accounting at Stevens Institute of Technology. His research focuses on artificial intelligence, data analytics and emerging technologies in accounting and auditing. His work examines how new technologies can transform traditional accounting and audit processes and provide new ways to understand firms and markets. He has collaborated with industry partners on the adoption of data analytics and AI in practice, connecting academic research with real-world applications.

How do investors form portfolios in the era of big data, and what complexities do market participants experience in processing firm and macro news? Between information perception, decision-making, and market pricing, what does this mapping function look like? Is it linear, or perhaps nonlinear? What are the sources of such nonlinearity, and why might we need complex machine learning (ML) algorithms to capture it? In this talk, we look at how we can emulate investors' behavior using ML to assess the complexity they face in pricing firms. The talk provides high-level exposure to XAI and interpretable ML and demonstrates how these tools can be used to assess the complexity of the pricing mechanism that links information, investor decisions and market prices.

Majeed Simaan is an associate professor of financial engineering with tenure at Stevens Institute of Technology. His research focuses on asset pricing, statistical learning, financial risk management and portfolio selection under model risk. He has published 15 articles in ABS-listed journals, including Management Science, and his work has been featured by Bloomberg and supported by NSF funding through CRAFT. He is also an FRM-certified professional and an advocate for open-source, reproducible research using R.

The talk will examine how autonomous AI agents are rewriting the rules of digital platform strategy — reshaping governance, value capture and network effects. Drawing on a comparison of Airbnb and the AI-native travel platform Mindtrip, it will explore how agents that execute tasks, rather than merely recommend, are dismantling the friction-based barriers marketplaces have long relied on. The presentation is structured around three main themes: how AI agents are shifting value capture from the transaction to the moment of consumer intent, how supply differentiation and reputation systems change when an algorithm — not a human — makes the choice, and how incumbent platforms are responding through new governance strategies, from bot detection to listing obfuscation.

Katia Meggiorin is an assistant professor of information systems at the Stevens School of Business and a member of the Stevens Business and AI Lab. She holds a doctorate in strategy from NYU Stern. Her research focuses on digital platform dynamics, technology governance and the strategic and societal implications of emerging digital technologies.

What happens to a job when we automate some of the tasks that make it up? The answer depends on where those tasks sit in a larger process and on what happens to the work around them.

From industrial mechanization and assembly lines to computerized workflows and AI agents, technology has changed how organizations divide, coordinate and oversee work. This lecture traces that development through the business process: the activities, decisionsand handoffs through which people and machines produce outcomes.

Drawing on studies of automation, augmentation and the creation of new work, we will examine how tasks disappear, jobs change and new roles emerge. Automating one person's peripheral work can transform another person's core responsibility.

AI now also coordinates work: it routes cases, hands off tasks, and decides what happens next. We will look at five roles AI can take in a process and the questions each one raises. Who can see what the AI did? Who can override it? How far can we rely on it? Who is accountable when it fails? Participants will gain a process perspective for judging how choices about AI change existing jobs and create new ones.

Michael zur Muehlen is associate dean and associate professor of Information Systems at Stevens Institute of Technology, where his work focuses on artificial intelligence, analytics and digital transformation. His research and teaching examine how AI-enabled technologies reshape decision-making, organizational capabilities and end-to-end business processes in data-intensive environments.

Michael’s career spans academia, industry consulting and executive education. He has advised organizations including the U.S. Department of Defense, Goldman Sachs, JPMorgan Chase, and the Federal Reserve Bank of New York on the design and governance of AI-enabled processes and decision models, as well as data-driven operating models. At Stevens, he is actively involved in developing applied AI curricula and interdisciplinary initiatives that connect research, education and real-world impact.