- Presented at UBC (2022)
- AI classification scheme: https://misr.sauder.ubc.ca/robotics/
- Research assistant: Raymond Situ
Contrary to the promise that AI will transform various industries, there are conflicting views on the impact of AI on firm performance. We argue that existing AI capability measures have three major limitations, limiting our understanding of the impact of AI in business. First, the definition of AI itself is still elusive in the IS and business literature. With the recognition that AI is a multifaceted problem-solving process different from traditional IT, we present a detailed AI classification scheme using various sources (e.g., PapersWithCode, HuggingFace, ACM). Second, existing measures on AI capability do not distinguish between stated strategies and actual AI implementations. To distinguish “AI washing” and actual AI capability, we collect various AI-related data sources, including AI conferences (e.g., NeurIPS, ICML, ICLR), AI software projects (GitHub repositories), inter-firm transactions related to AI adoption (FactSet), and AI strategies stated in 10-K annual reports. Third, while prior studies identified successful AI implementation factors (e.g., data integrity and intelligence augmentation) in a general context, little is known about the fit of specific AI technologies and different types of tasks. We draw on the theory of task-technology fit to construct a fine-grained AI capability measure that captures the unique characteristics of different industries. Using our newly proposed AI capability measure and a novel dataset, we will study the impact of AI on firm innovation and market reaction, contributing to the nascent literature on managing AI.