Essays on Artificial Intelligence and Firm Performance

Abstract

Artificial Intelligence (AI) is widely regarded as the latest General-Purpose Technology (GPT) to emerge. This characterisation places AI alongside transformative innovations like the steam engine, electricity, and the internet. If such characterisation is correct, AI adoption is likely to have significant implications for firm performance. For instance, AI can enhance firms’ ability to innovate and commercialise innovations while improving productivity through reduced labour intensity and operational disruptions. However, as with prior GPTs, realising AI’s full potential requires complementary strategic and organisational adjustments that are inherently lengthy, uncertain, and costly. I assess this theory through two complementary studies. In the first, I investigate whether firms’ strategic focus on revenue growth versus cost efficiency with AI leads to differential performance outcomes. To do so, I analyse over 2 million AI-related contextual windows from firms’ annual reports to infer their AI strategic focus. My results show that focusing on cost efficiency with AI improves firm performance whereas focusing on revenue growth negatively affects performance outcomes. Moreover, I find that marketing and operations capabilities positively moderate the effect of AI revenue focus and negatively moderate the effect of AI cost focus. In the second study, I investigate the effects of hiring AI-skilled employees on firms’ innovation and marketing capabilities. In doing so, I draw on a unique dataset tracking the presence of AI-skilled employees in U.S. firms through textual analysis of their resumes. The results indicate that hiring AI-skilled employees enhances firm performance through its positive impact on innovation capability while simultaneously undermining performance through its negative effect on marketing capability. Moreover, the analysis reveals that financial slack plays a critical role in positively moderating these effects. Overall, my findings suggest AI’s impact on firm performance depends on various complementary resource investments and functional capabilities, making it far more complex than is commonly assumed.