Tag Archives: LLM

Designing for Designers: A Multi-Agent Multi-Representational AI System to Enhance Automotive Design

Zhang, Xiaoke, Angela Kwon, Mi Zhou, Gene Moo Lee “Designing for Designers: A Multi-Agent Multi-Representational AI System to Enhance Automotive Design,” Work-in-progress.

Organizations increasingly seek to use large language models (LLMs) to support knowledge-intensive work. However, effective deployment requires systems that ground LLM reasoning in heterogeneous, domain-specific knowledge. In collaboration with the vehicle design team of a major automotive manufacturer, we develop Design Insight Atlas, a multi-agent, multi-representational retrieval-augmented generation (RAG) AI system for automotive design intelligence. The system grounds LLM responses in three complementary knowledge representations: structured vehicle specifications for factual analysis, automotive news for temporal and market intelligence, and a knowledge graph for relational reasoning. A central designer assistant orchestrates a news retrieval tool, a data analysis agent, and a knowledge graph analysis agent, integrating their outputs into unified, evidence-grounded responses. We evaluate the system using 160 designer-oriented questions across eight task categories and four backbone LLMs. Design Insight Atlas achieves an average overall win rate of 89.2% against vanilla LLM baselines and consistently improves comprehensiveness, accuracy, and actionability across models and question types. Our study demonstrates how multi-representational knowledge grounding and multi-agent retrieval can enhance LLM support for domain-specific organizational knowledge work.

Large Language Models in the Institutional Press: Investigating the Effects on Information Sourcing and News Production (MISQ 2026)

Zhang, Xiaoke, Myunghwan Lee, Mi Zhou, Gene Moo Lee. “Large Language Models in the Institutional Press: Investigating the Effects on Information Sourcing and News Production,” Conditionally Accepted by MIS Quarterly.

  • Presentations: UBC (2024), DS (2024), CIST (2024), BIGS (2024), JUSWIS (2025), UIUC (2025), SWIS (2026), KAIST (2026)
  • Industry partner: Muhayu

Large language models (LLMs) are transforming journalism by directly entering journalistic workflows, creating new opportunities and challenges for the institutional press. This study investigates how LLM assistance affects journalists’ information sourcing and news production using a mixed-methods approach. We begin with a qualitative study of 43 journalists to identify three core dimensions central to journalistic production: publication promptness, information source quantity, and information source originality. We then compile a large-scale dataset of 1,073,742 news articles from 111 South Korean news outlets and collaborate with industry experts to detect articles with undisclosed LLM assistance. Our event-level analysis shows that articles with disclosed or detected LLM assistance are published more quickly but cite fewer information sources, with a particularly pronounced reduction in visible primary sources. Heterogeneity analyses and a randomized experiment provide convergent evidence for two underlying mechanisms: an LLM generation mechanism that narrows the set of retrieved and represented sources, and a metacognitive regulation mechanism that reduces journalists’ active search and evaluation of information. Moreover, a journalist-level difference-in-differences analysis indicates that LLM adoption is associated with persistent reductions in journalists’ visible source usage over time. Our findings offer managerial implications for LLM system design, newsroom practices, and institutional policy.

Unpacking AI Transformation: The Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective

Park, Jaecheol, Myunghwan Lee, J. Frank Li, Gene Moo Lee “Unpacking AI Transformation: The Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective,” Work-in-Progress.

  • Presentations: UBC (2024), CIST (2024), INFORMS (2024), SNU (2024), UMass (2024), BIGS (2024), KrAIS (2024), CityU Hong Kong (2025), NTU (2025), AIM (2025), ISR-PDW (2025)
  • Best Paper Award at BIGS 2024
  • Best Student Paper Award at KrAIS 2024

Artificial intelligence (AI) technologies hold great potential for large-scale economic impact. Aligned with this trend, recent studies explore the adoption impact of AI technologies on firm performance. However, they predominantly measure firms’ AI capabilities with input (e.g., labor/job posting) or output (e.g., patents), neglecting to consider the strategic direction toward AI in business operations and value creation. In this paper, we empirically examine how firms’ AI strategic orientation affects firm performance from the dynamic capabilities perspective. We create a novel firm-year AI strategic orientation measure by employing a large language model to analyze business descriptions in Form 10-K filings and identify an increasing trend and changing status of AI strategies among U.S. public firms. Our long-difference analysis shows that AI strategic orientation is associated with greater operating cost, capital expenditure, and market value but not sales, showing the importance of strategic direction toward AI to create business value. By further dissecting firms’ AI strategic orientation into AI awareness, AI product orientation, and AI process orientation, we find that AI awareness is generally not related to performance, that AI product orientation is associated with short-term increased operating expenses and long-term market value, and that AI process orientation is associated with long-term increased costs and sales. Moreover, we find the negative moderating effect of environmental dynamism on AI process orientation. This study contributes to the recent AI strategy and management literature by providing the strategic role of AI orientation on firm performance.