Jul 2026· Audit Financiar· Vol 24, pp. 548-556· 0 citations
TL;DR
The results support the idea that the strategic integration and active use of artificial intelligence contribute to the increase of financial performance, highlighting the role of artificial intelligence as a determining factor of organizational efficiency and medium-term competitiveness.
Abstract
In the context of digital advancement, artificial intelligence
becomes a strategic and significant element for economic
entities, with multidimensional influences on them, namely
on operational and decision-making processes, as well as
on financial performance. The return on equity has
consistently constituted and remains an essential indicator
of organizational performance, being carefully monitored
and analyzed in the managerial decision-making process.
From this perspective, the objective of the research is to
identify and analyze the impact of artificial intelligence
usage on the financial performance of economic entities,
measured through return on equity. The research
approach considers the review of the specialized
literature, a questionnaire-based study applied to
employees working in companies from different industries,
and the construction of a regression model. The
regression results indicate a positive and statistically
significant relationship between the use of artificial
intelligence and the return on equity, while the perceived
impact and digital governance present positive, but more
moderate effects. At the same time, the results support
the idea that the strategic integration and active use of
artificial intelligence contribute to the increase of financial
performance, highlighting the role of artificial intelligence
as a determining factor of organizational efficiency and
medium-term competitiveness.
This study investigates the influence of financial intelligence on strategic decision-making effectiveness in the digital economy. Adopting a quantitative research approach and a cross-sectional survey design, data were collected from 164 respondents drawn from managerial, financial, and strategic roles across diverse industries. The study operationalised financial intelligence into dimensions: financial data analytics, financial reporting quality, financial technology adoption, financial risk intelligence, and financial forecasting capability. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings revealed that al dimensions significantly and positively affect strategic decision-making effectiveness, with FinTech adoption emerging as the strongest predictor. The model explained 62% of the variance in strategic decision-making effectiveness, confirming substantial explanatory power. The results validate the Resource-Based View and Dynamic Capabilities Theory, demonstrating that financial intelligence constitutes both a strategic resource and an adaptive capability essential for organisational competitiveness. The study concludes that financial intelligence is not merely an operational function but a strategic asset that enhances agility, resilience, and innovation in digitally transformed environments. The implications highlight the need for managers to invest in digital financial systems, analytics, and forecasting tools, while policymakers should support enabling infrastructures. Ultimately, financial intelligence is indispensable for organisations seeking sustainable growth and competitive advantage in the digital economy.
Omohefe Israel Ukolobi, B. Igbozulike, F. J. Falope· Journal of Global Interdepen...· 0 citations
This study contributes to the strategic management literature by reconceptualizing financial modelling as a dynamic organizational capability, integrating previously fragmented theoretical perspectives, and establishing a conceptual foundation for future empirical research on AI-enabled financial decision capability.
Iman Sjamsu Rahardjo, Rida Justin Jacobalis, Idha Adhani et al.· Jurnal Minfo Polgan· 0 citations
Finance 5.0 is a revolution in financial management that brings AI and intelligent analytics into the corporate decision-making process. This research paper focused on examining the impact of these digital technologies on financial decision-making in present organizations. A cross-sectional survey approach was used for the quantitative research design. Primary data were gathered from 420 finance professionals such as financial managers, accountants, analysts, banking officers, and finance executives of organizations that have implemented digital financial technologies (DFTs). The questionnaires measured Artificial Intelligence, Intelligent analytics, and financial decision-making on a 5-point scale in a structured manner. The collected data were analysed using descriptive statistics and multiple regression analysis in SPSS 29. The results showed a positive attitude toward all the Study variables. The highest mean (SD = 0.52) was found for financial decision making, followed by intelligent analytics (mean = 4.19, SD = 0.61), and artificial intelligence (mean = 4.27, SD = 0.56). The regression analysis revealed that artificial intelligence had a significant effect on financial decision making (β = 0.463, p < 0.001), as did intelligent analytics (β = 0.387, p < 0.001). The overall model accounted for 68.4 % of the variance explained (R² = 0.684) in financial decision making. The study found success in financial planning and forecasting, efficiency, and strategic decision making with the help of intelligent technologies in Finance 5.0. The result has important implications for financial institutions, corporate organizations, policy and decision makers, as well as technology developers who want to gain a competitive advantage and/or accelerate digital financial transformation by developing smart financial ecosystems.
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Sarmad Bin Saeed, Saba Mahmood, Abid Manzoor· Journal of Business Insight...· 0 citations
Financial performance represents a fundamental benchmark for ensuring long-term business sustainability, particularly within the rapidly expanding, capital-intensive, and innovation-driven global Artificial Intelligence (AI) industry. While competitive pressures and financing decisions are widely acknowledged as determinants of profitability, there remains a notable research gap concerning the interaction between market mechanisms, debt structures, and corporate governance in AI firms that require substantial capital investment. This study is designed to investigate the effects of Product Market Competition (PMC), capital structure, and leverage on financial performance, while simultaneously assessing the moderating role of corporate governance in AI companies worldwide. The research population comprises 13 global AI firms, all of which are included as a census sample. Employing a quantitative approach, the study utilizes secondary data extracted from financial statements covering the period 2020–2024. Analytical techniques involve panel data regression and Moderated Regression Analysis (MRA), conducted with the assistance of Stata 19 software. The empirical findings reveal that both PMC and leverage exert a positive and statistically significant influence on financial performance, whereas capital structure does not demonstrate a significant effect. Furthermore, moderation analysis indicates that corporate governance strengthens the impact of capital structure and leverage on financial outcomes. However, governance does not moderate the relationship between PMC and financial performance, largely due to the strong substitutive role of external market discipline.
M. Septriawan, Zuliana Zulkarnen, R. N. Fonna et al.· Jurnal Investasi Islam· 0 citations
The diffusion of artificial intelligence in recent years has reshaped how organizations process information, automate tasks and sustain competitive advantage, although its effects on small and medium-sized enterprises remain heterogeneous. This article analyzes the mechanisms through which artificial intelligence adoption influences the economic performance of small and medium-sized enterprises, considering productivity, costs, innovation and organizational barriers. The study follows a qualitative, exploratory and descriptive design, based on an integrative bibliographic and documentary review of studies published mostly between 2021 and 2026, complemented by data from Brazilian and international official bodies. The results indicate that artificial intelligence can expand productivity and operational efficiency among small and medium-sized enterprises, particularly through operational applications such as automation and data analysis, although these gains do not follow automatically from technology acquisition alone. The conversion of use into performance depends on the articulation of digital infrastructure, data quality, human competencies, organizational integration and governance. The findings also reveal persistent asymmetries between small firms and large corporations, both in access to financial and technical resources and in the outcomes obtained. The article concludes that claims of technological democratization through low-cost generative solutions should be treated with caution, since internal organizational capabilities remain a determining factor in the economic appropriation of artificial intelligence by Brazilian and international small and medium-sized enterprises.
Cassio Gainett Cardoso Silva· Revista de Estudos Interdisc...· 0 citations
Artificial intelligence (AI) in today’s reality is no longer merely a technological tool – it is becoming the centerpiece of modern innovation management models. In Kazakhstan, 2026 has been officially declared the Year of Digitalization and Artificial Intelligence, lending the topic particular strategic significance. Meanwhile, existing theoretical models were developed for advanced markets and do not account for the specificities of transition economies. The purpose of this study is to conduct a systematic theoretical and methodological review of the international scholarly literature at the intersection of AI and enterprise innovation management, with a focus on the applicability of global approaches to the Kazakhstani context. The paper analyzes six theoretical lenses: the resource-based view, dynamic capabilities, diffusion of innovations theory, the TOE framework, the institutional perspective, and digital maturity models. The review covers publications from 1983 to 2026 retrieved from Scopus, Web of Science, and regional journals. A theoretical gap is identified between global models’ postulates about the decentralizing effect of AI and the phenomenon of “digital centralism” in Kazakhstan. Based on critical analysis, a research framework – “AI Resources – Dynamic Capabilities – Institutional Filter – Innovation Outcomes” – is proposed, which for the first time directly incorporates the institutional context of a transition economy. Six testable hypotheses are formulated for subsequent empirical research on a sample of Kazakhstani enterprises. The findings are of interest to innovation management researchers, digital transformation policymakers, and enterprise management in Kazakhstan.
A. K. Toleubayeva, B. Spychalski, M. T. Akhmetova· Bulletin of "Turan" Universi...· 0 citations