Aug 2026· World Journal of Advanced Research and Reviews· 0 citations
TL;DR
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.
Abstract
This study examined the influence of Artificial Intelligence (AI) adoption on strategic planning effectiveness among selected IT companies in the Philippines. As organizations increasingly operate in dynamic and data-intensive environments, AI technologies have emerged as critical tools for improving decision-making, forecasting, and strategic management. Despite the growing implementation of AI across industries, empirical evidence regarding its contribution to strategic planning effectiveness remains limited, particularly in developing economies. Using a quantitative descriptive-correlational research design, data were collected from 105 managers and executives employed in selected technology firms. A structured questionnaire measured AI adoption and strategic planning effectiveness using a seven-point Likert scale. Descriptive statistics, Pearson product-moment correlation, and linear regression analyses were employed to analyze the data.
The findings revealed that AI adoption was perceived to be at a very high level, while strategic planning effectiveness was assessed as high among participating organizations. Correlation analysis indicated a strong and statistically significant positive relationship between AI adoption and strategic planning effectiveness (r = 0.78, p < 0.01). Regression analysis further demonstrated that AI adoption significantly predicted strategic planning effectiveness (β = 0.47, p < 0.001), explaining a substantial proportion of the variance in strategic planning outcomes (R² = 0.72). These results suggest that organizations leveraging AI technologies are more likely to develop effective strategies, improve decision quality, enhance forecasting accuracy, and strengthen organizational adaptability.
The study concludes that AI adoption serves as a strategic organizational capability that significantly enhances strategic planning effectiveness. The findings contribute to the growing literature on AI-enabled management and provide practical implications for organizations seeking to improve strategic decision-making and achieve sustainable competitive advantage in the digital era.
This study examines the role of Artificial Intelligence (AI) in enhancing supply chain project management and operational performance in a dynamic business environment. As supply chains become increasingly complex, data-intensive, and disruption-prone, organizations are adopting AI-driven tools to improve forecasting accuracy, optimize inventory, streamline logistics, and strengthen decision-making. The purpose of this research is to assess the level of AI adoption, identify key application areas, and examine the relationship between AI familiarity and AI adoption while considering the broader roles of organizational readiness and governance mechanisms. A quantitative research design was employed using a structured questionnaire administered to 42 respondents, including supply chain professionals, project managers, data/AI analysts, students, and other business or technology-related participants. Data were analyzed using descriptive statistics, correlation analysis, and regression techniques. The findings indicate that approximately 57% of respondents reported current AI adoption within their organizations, while the mean AI familiarity score was 3.6 on a five-point scale, reflecting moderate awareness. Correlation analysis revealed a positive relationship between AI familiarity and AI adoption (r = 0.61), suggesting that increased knowledge supports adoption behavior. The results also highlight the perceived importance of AI training, organizational preparedness, and governance frameworks in maximizing implementation benefits. This study contributes to business analytics, operations management, and decision sciences by providing empirical insight into AI-enabled supply chain transformation. The findings offer practical implications for managers, policymakers, and industry stakeholders seeking to strengthen AI readiness, improve operational efficiency, and promote responsible AI adoption for sustainable supply chain excellence.
Denise Nalini, Dr. S.Barathi, Dr. Rubidhadevi· The Journal of Theoretical A...· 0 citations
This study examined the impact of artificial intelligence (AI)-driven analytics on organizational performance. Specifically, the study investigated the influence of predictive analytics, decision automation, and data-driven strategic planning on organizational performance. The study adopted a survey research design. Data were collected from 320 managers and senior staff of selected manufacturing, banking, telecommunications, and service organizations in Nigeria. A sample size of 178 respondents was determined using Taro Yamane's formula, while data were analyzed using descriptive statistics and multiple regression analysis. Findings revealed that predictive analytics significantly improves organizational profitability (β = 0.381, p < 0.05), decision automation positively influences organizational productivity (β = 0.294, p < 0.05), and data-driven strategic planning has a significant positive impact on organizational efficiency (β = 0.427, p < 0.05). The study concludes that AI-driven analytics significantly enhances organizational performance through improved predictive analytics, decision automation, and data-driven strategic planning. The study recommends increased investment in AI infrastructure, employee training, and data governance systems to maximize organizational benefits from AI technologies.
O. Enyinnaya, O. Onwuegbule, K. M. Amasiatu et al.· British journal of managemen...· 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
Artificial Intelligence (AI) has emerged as a transformative technology that is reshaping organizational processes, strategic decision-making, and competitive advantage across industries. In today's dynamic and highly competitive business environment, organizations must continuously enhance their agility to respond effectively to technological disruptions, changing customer expectations, and market uncertainties. This study examines the relationship between Artificial Intelligence Adoption (AIA) and Organizational Agility (OA) while assessing the influence of Organizational Context (OC) from a management perspective. The research aims to investigate how AI adoption contributes to organizational agility and whether organizational context significantly influences this relationship. The study adopts a quantitative research approach using a structured questionnaire administered to managerial and professional employees across various industries. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate both the measurement and structural models. The measurement model was assessed through Cronbach's Alpha, rho_A, Composite Reliability (CR), and Average Variance Extracted (AVE), while the structural model was examined using bootstrapping techniques to test the proposed hypotheses. The findings demonstrate that all constructs exhibit excellent reliability and convergent validity, with Cronbach's Alpha values exceeding 0.94, Composite Reliability values above 0.95, and AVE values greater than 0.70. Structural model analysis reveals that Artificial Intelligence Adoption has a significant positive effect on Organizational Agility (β = 0.708, p < 0.001) and Organizational Context (β = 0.962, p < 0.001). Furthermore, Organizational Context significantly influences Organizational Agility (β = −0.378, p = 0.013), indicating that certain contextual organizational characteristics may constrain agility despite increased AI adoption. These findings suggest that while AI serves as a strategic capability that enhances organizational responsiveness and innovation, its effectiveness depends on the presence of supportive organizational structures, leadership, and an adaptive culture. The study contributes to the literature by integrating perspectives from the Technology–Organization–Environment (TOE) Framework, Resource-Based View (RBV), and Dynamic Capabilities Theory to explain the role of AI in improving organizational agility. From a practical perspective, the findings emphasize that organizations should complement AI investments with flexible organizational structures, transformational leadership, employee capability development, and innovation-oriented cultures to maximize the benefits of digital transformation. The study concludes that Artificial Intelligence is not merely a technological innovation but a strategic organizational capability that enables firms to achieve sustainable competitiveness and long-term organizational agility in an increasingly digital business environment.
Srinath T. K., Chandana H. S., Sagar Manjunath et al.· International journal of com...· 0 citations
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.
Dr. Rupali Singh, Dr. Sreeja S, Dr. Swati Tyagi et al.· International Journal of Aqu...· 0 citations
The rapid advancement of Artificial Intelligence (AI) technologies has transformed the way organizations operate,
innovate, and create value in an increasingly competitive business environment. AI adoption enables firms to automate
processes, enhance decision-making, improve operational efficiency, and deliver personalized customer experiences. However,
the extent to which AI contributes to sustainable business performance depends not only on technological implementation but
also on an organization's ability to innovate. This study examines the relationship between Artificial Intelligence adoption and
sustainable business performance, with innovation capability serving as a mediating variable. Specifically, it investigates how AI
adoption enhances organizational innovation capability and, in turn, improves economic, environmental, and social dimensions
of business performance. The study adopts a quantitative research approach using structured questionnaires administered to
managers and professionals from manufacturing and service organizations. Data will be analyzed using Structural Equation
Modeling (SEM) to examine the direct and indirect relationships among the study variables. The proposed conceptual
framework posits that AI adoption positively influences innovation capability, which subsequently enhances sustainable business
performance. The findings are expected to provide empirical evidence on the strategic role of AI in fostering innovation and
achieving long-term organizational sustainability. Furthermore, the study offers practical implications for business leaders,
policymakers, and practitioners by highlighting the importance of integrating AI technologies with innovation-driven strategies
to improve competitiveness and sustainable growth. By linking digital transformation with sustainability outcomes, this research
contributes to the growing body of knowledge on AI-enabled business innovation and supports the achievement of the United
Nations Sustainable Development Goals, particularly SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry,
Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production).
S. P, Sriharan M, S. P et al.· International Journal for Re...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
