The Impact Of Ai And Data Analytics On Enterprise Risk Management And Corporate Governance

Abstract

Artificial intelligence (AI) and data analytics are reshaping enterprise risk management (ERM) and corporate 
governance, yet rigorous empirical evidence from the United Kingdom remains scarce. This study investigates 
the impact of AI and data analytics adoption (AIAD) on ERM effectiveness and governance quality using survey 
data collected from 312 board members, executives, and senior risk professionals across UK financial services, 
technology, manufacturing, energy, retail, and healthcare firms. Data were analysed using descriptive statistics, 
correlation analysis, one-way ANOVA, and three-step hierarchical multiple regression. Controlling for firm size, 
sector, and board digital literacy, the regression reveals that AIAD is the dominant determinant of ERM 
effectiveness (β = 0.689, p < 0.001), contributing an incremental 27.4% of explained variance (full model R² = 
0.514). AIAD likewise predicts governance quality (β = 0.412, p < 0.001) and fully mediates the influence of 
board digital literacy, data-driven culture, and governance quality on ERM outcomes. Sectoral comparisons 
expose significant asymmetries: financial services firms lead adoption (M = 3.91) and exhibit the strongest 
adoption–effectiveness association (r = 0.74), whereas healthcare lags (M = 2.96). High adopters outscore low 
adopters on every governance capability dimension, while legacy systems, skills gaps, and model explainability 
remain critical barriers. The study positions AI-enabled ERM as a strategic governance lever rather than a 
compliance instrument, and the findings carry direct implications for the design of UK corporate governance 
codes, regulatory guidance, and board-level oversight of algorithmic risk. Collectively, the results advance theory 
by showing that technology adoption, rather than structural governance attributes, is the proximate driver of 
modern ERM performance.