TITLE:
Artificial Intelligence Adoption and Corporate Financial Decision-Making: The Roles of Financial Analytics Capability, Organizational Agility, and Corporate Governance
AUTHORS:
Igwe Chukwuebuka Prince, Yingchun Song
KEYWORDS:
Artificial Intelligence Adoption, Financial Analytics Capability, Organizational Agility, Corporate Governance, Financial Decision-Making, Dynamic Capabilities, PLS-SEM
JOURNAL NAME:
Open Access Library Journal,
Vol.13 No.10,
October
13,
2026
ABSTRACT: Artificial intelligence (AI) is reshaping how firms sense, interpret, and act upon financial information, yet the mechanisms through which AI adoption translates into superior corporate financial decision-making remain theoretically under-specified and empirically contested. Drawing on the resource-based view, dynamic capability theory, and organizational information processing theory, this study advances and tests an integrated model in which financial analytics capability and organizational agility jointly mediate the effect of AI adoption on financial decision-making outcomes, while corporate governance moderates the strength of that effect. Using survey data from 412 senior finance and technology executives across listed and large private firms, and applying partial least squares structural equation modeling (PLS-SEM) with complementary robustness procedures, the study finds that AI adoption exerts a modest direct effect on financial decision-making outcomes but a substantially larger indirect effect operating through financial analytics capability and organizational agility, which together account for roughly two-thirds of the total effect. Corporate governance positively moderates the AI-outcome relationship, indicating that governance quality functions as a complementary asset that amplifies rather than merely constrains AI-enabled value creation. The findings reconcile competing accounts of the AI-performance relationship by identifying the capability-based pathways through which value is realized, extend information processing theory to the AI context, and reframe governance as an enabling condition. The study offers actionable guidance for executives, boards, regulators, and technology providers seeking to convert AI investment into disciplined financial decisions.