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
The study concludes that AI-driven analytics significantly enhances organizational performance through improved predictive analytics, decision automation, and data-driven strategic planning 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
The 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.
Srinath T. K., Chandana H. S., Sagar Manjunath et al.· International journal of com...· 0 citations
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.
Dr. Rupali Singh, Dr. Sreeja S, Dr. Swati Tyagi et al.· International Journal of Aqu...· 0 citations
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
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.
