Barriers and enablers of AI implementation in corporate finance: a study of digitalization challenges in the chemical industry

Abstract

The digital transformation of corporate finance, driven by artificial intelligence (AI), presents a significant opportunity for the capital-intensive chemical industry. Success stories from other sectors, particularly banking, manufacturing and healthcare, have already demonstrated that AI can enhance forecasting, risk assessment, process efficiency and data-driven decision-making. This strengthens its strategic relevance for corporate finance in the chemical industry. This study systematically analyses the key barriers and enablers that determine the nature and success of AI implementation in the industry’s corporate finance functions. A synthesis of contemporary academic literature and industry analytical sources was conducted to develop a diagnostic conceptual model, comprising a Barrier Index (BI) and an Enabler Index (EI). The proposed model was tested on a sample of 94 publicly listed chemical companies using publicly available corporate data. The results of the empirical analysis reveal a strong and statistically positive relationship between the BI and EI indices, indicating the systemic role of organizational enablers—strategic leadership, mature data infrastructure, and organizational agility—in mitigating institutional and operational barriers to digital transformation. The application of cluster analysis to companies according to their digital maturity levels has resulted in the identification of four qualitatively distinct profiles. It is essential to note that each of these profiles requires a tailored strategic development trajectory. The principal contribution of the study lies in operationalizing the concept of dynamic capabilities into an applied diagnostic tool, thus providing managers with a structured approach to assessing AI readiness and prioritizing strategic investments for its sustainable integration.