International Workshop on Artificial Intelligence and Impact to Firms

10:00 - 11:00

論題

People, AI and Professional Boundaries: A Qualitative Meta‑Analysis of Human–AI Collaboration

報告者

Yingying ZHANG ZHANG（国際大学国際大学研究所）

概要

Artificial Intelligence (AI) is increasingly reshaping professional tasks, leading to the decline of some roles and the emergence of new ones. Yet AI's position as an "invading actor" within professional systems remains underexplored. Existing research typically treats AI as an exogenous factor in human–AI collaboration, overlooking the jurisdictional conflicts that arise as AI challenges the authority of human professionals. These tensions give rise to various forms of boundary work, but the underlying mechanisms remain unclear. To address this gap, we conduct a qualitative meta‑analysis synthesizing 42 case studies. Our findings show that the introduction of AI into the workplace triggers jurisdictional conflicts, prompting professionals to adopt four distinct modes of boundary work: boundary struggling, boundary bridging, boundary retreating, and boundary creating. The tension between professional knowledge and professional practice shapes these modes. Our research develops a conceptual framework explaining how boundary work forms in human–AI collaboration. By incorporating the emergence of AI agency within professional contexts, the framework offers a novel lens for understanding the increasingly complex and dynamic relationships between humans and AI.

11:00 - 12:00

論題

The Intelligence Loop and Value Creation in the Age of AI Agents

報告者

Yong LI（Troesh Center for Entrepreneurship and Innovation, University of Nevada）

概要

This paper addresses a central question in strategy and entrepreneurship: how does artificial intelligence (AI) create value for organizations? Prior research emphasizes demand-side mechanisms, most notably data network effects, whereby user interactions generate data that improve algorithmic prediction and reinforce adoption. The emergence of AI agents capable of autonomously performing goal-directed tasks introduces a qualitatively different mechanism. We propose the intelligence loop, a process wherein agentic capability, comprising self-learning, self-adapting, and self-improving, drives continuous intelligence improvement for autonomous task execution. We define intelligence as the demonstrated competence in perceiving, reasoning, and acting to achieve goals across relevant environments, and introduce minimum viable intelligence as the threshold at which the intelligence loop activates. The loop may decay, self-sustain, or self-accelerate. Entrepreneurial opportunity derives not only from underserved customer demand but also from supply expansion, i.e., deployment of autonomous agents to address limited supply of human capital. The framework offers theoretical insights into AI-driven value creation and has implications for entrepreneurship and strategy in the age of AI agents.