Authors: Dr. Sateeshkumar G
Abstract: Artificial intelligence (AI) is reshaping business functions from marketing and customer service to product development and operations. While practitioner surveys report rapidly rising adoption and measurable benefits, academic evidence shows heterogeneous outcomes driven by complementarities, organizational readiness, and adoption timing. This paper synthesizes recent evidence and proposes a compact empirical strategy to measure how firm-level AI intensity affects performance, which mechanisms mediate effects (automation, augmentation, innovation), and which firm characteristics moderate outcomes (data maturity, industry task content). Using a continuous composite measure of AI intensity (text disclosures, AI-related patents, job-skill signals, and survey indicators) applied to panel firm financials, the proposed identification strategy combines firm and year fixed effects, event-style difference-in-differences, and instrumental variables to address selection and endogeneity. The paper expects positive medium-run effects on profitability and productivity for firms that combine AI with complementary investments, and highlights short-run adjustment costs for others. Managerial recommendations include investing in data infrastructure, governance, and rescaling to capture AI value.
