Jul 2026· International Journal of Aquatic Research and Environmental Studies· 0 citations
TL;DR
The study provides empirical evidence on the impact of AI on shaping organizational processes and reiterating the need to connect the implementation of AI to organizational and process capabilities of an organization to achieve considerable outcomes.
Abstract
The relationship between artificial intelligence (AI) introduction and the organizational performance in the firms existing in more than one country and industry. The researcher in the study applies quantitative approach, which is founded on such extensive data as 100,000 firm-level observations, to investigate the patterns of AI adoption and their impact on productivity, organizational change. Descriptive, correlation and regression analysis was conducted to evaluate the effect of the variables of AI adoption, year of adoption, training hours, workforce impact, and role creation. The findings indicate that the direct effect of the variables of AI adoption on productivity is not critical. However, the workforce-related factors, such as the number of workers impacted and the creation of new jobs are highly correlated with organizational change. This means that AI indirectly impacts firms in terms of the structural and process level changes, but not directly impacting on performance. The study contributes to the body of knowledge by providing empirical evidence on the impact of AI on shaping organizational processes and reiterating the need to connect the implementation of AI to organizational and process capabilities of an organization. The findings suggest that businesses ought to be concerned with adopting AI strategically to achieve considerable outcomes.
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.
The study concludes that AI adoption serves as a strategic organizational capability that significantly enhances strategic planning effectiveness and suggests that organizations leveraging AI technologies are more likely to develop effective strategies, improve decision quality, enhance forecasting accuracy, and strengthen organizational adaptability.
Mark Ian C. Abrias, Nerissa M. Revilla· World Journal of Advanced Re...· 0 citations
The application of artificial intelligence (AI) and the improvement of environmental, social, and governance (ESG) performance have become important concerns for contemporary firms. Understanding whether AI can be effectively integrated into corporate ESG practices has significant implications for sustainable development. Using panel data from Chinese A-share listed firms from 2009 to 2025, this study empirically examines the effect of AI application on corporate ESG performance through a fixed-effects model. The results show that AI application significantly improves corporate ESG performance, indicating that firms with higher levels of AI adoption tend to achieve better ESG outcomes. The heterogeneity analysis further reveals that this effect varies according to firms’ technological intensity, industry pollution characteristics, and the strength of regional environmental regulation. Specifically, the positive effect of AI application is more pronounced among firms with lower technological intensity, firms operating in non-heavily polluting industries, and firms located in regions with stricter environmental regulation. The mediation analysis shows that AI application enhances ESG performance by promoting human capital upgrading and green technological innovation, thereby strengthening firms’ internal capabilities and technological foundations for sustainable development. This study contributes to the literature by integrating AI application and ESG-oriented sustainable development within a unified analytical framework.
This study investigates how AI adoption enhances organizational innovation capability and, in turn, improves economic, environmental, and social dimensions of business performance, and links digital transformation with sustainability outcomes.
S. P, Sriharan M, S. P et al.· International Journal for Re...· 0 citations
The rapid integration of artificial intelligence (AI) into organizational processes has fundamentally altered the landscape of managerial accounting, yet empirical evidence on its behavioral adoption and financial consequences in emerging industrial markets remains limited (Vărzaru, 2022; Secinaro et al., 2024). This study examines AI acceptance in managerial accounting and assesses its strategic impact on the financial performance (FP) of industrial firms listed on the Amman Stock Exchange (ASE) in Jordan. The technology acceptance model (TAM) serves as the theoretical lens through which perceived usefulness (PU), perceived ease of use (PEU), behavioral intention (BI), and actual use (AU) are examined. A quantitative research design was adopted, with data collected from 228 managerial accountants across listed industrial firms. Partial least squares structural equation modeling (PLS-SEM) was employed to test the hypothesized relationships. The results confirm that PEU and PU positively influence BI, which in turn drives AU of AI systems. Furthermore, the AU of AI significantly enhances decision-making (DM) quality, which subsequently improves FP. These findings depict AI as a strategic enabler in managerial accounting, with important implications for organizations in emerging markets seeking to leverage AI use for sustainable competitive advantage.
Huthaifa Al-Hazaima, Mohammad Barakat, Mahmoud Mahmoud et al.· Corporate & Business Strateg...· 0 citations
AI capability is a new strategic capability in the organization that goes beyond operational efficiency and can support the quality strategic decision-making, sustainable performance of an organization, and high decision quality. Though AI capability is evolving, current research remains disparate in how to transform an AI capability to a organizational value with the role of governance, leadership, and organizations capability. To solve this, in this study, a integrated conceptual framework grounded in the theory of resource-based view(RBV), dynamic capabilities theory(DCT) and the AI Governance literature is developed and empirically tested. In the model, the sequential relation between AI capability, AI governance, strategic decision quality, organizational agility, and organizational performance was proposed and the moderating role of digital leadership was examined. An explanatory sequential mixed-methods research design was used. The empirical analysis includes two phases. In the first phase, a cross-sectional survey of 446 senior executives and strategic decision makers of public and private organizations was conducted to empirically test the proposed integrated model using Partial Least Squares Structural Equation Modeling (PLS-SEM). In the second phase, qualitative data from 30 semi-structured interviews with senior executives was collected to gain a deep understanding of AI governance, digital leadership and organizational agility practices. Multi-group analysis further revealed differences in the proposed relationships for public and private organizations. Findings revealed that AI capability not only significantly strengthens the AI governance, and consequently the strategic decision quality, but it also improve the organizational agility, resulting in improved performance. Furthermore, digital leadership has a positive effect on reinforcing the association between AI governance and the strategic decision quality. Overall, this study integrates the technology capability, the organizational capability and the leadership capability to establish an AI-enabled strategic decision-making and performance management framework, and provides strategic insights for organizations that aim to realize greater value from their AI investments.
Dareen Alshamsi, Dr. Mohamed Manea Almansoori, Dalal S. Almansoori et al.· Journal of Intelligent Decis...· 0 citations