Aug 2026· Journal of Intelligent Decision Making and Information Science· Vol 3, pp. 1822-1830· 0 citations· 21 references
TL;DR
The findings indicate that the model will have a high predictive accuracy and reliability, with ANOVA showing statistically significant differences among models (p < 0.05), and the results of cross-validation confirm the stability and generalizability of the model to various data subsets.
Abstract
The concept of Artificial Intelligence (AI) is rapidly changing the way strategic decision-making processes in engineering and technology-driven enterprises are conducted, by facilitating data-driven insights and predictive analysis. This paper suggests an artificial intelligence-based strategic decision model with the Random Forest model, which is supported by statistical validation methods, such as ANOVA, correlation analysis, regression analysis, cross-validation, and feature importance analysis. The findings indicate that the model will have a high predictive accuracy and reliability, with ANOVA showing statistically significant differences among models (p < 0.05). Both correlation and regression results show that the risk level and technological uncertainty have a negative effect on the results of decision making, and team experience has the most significant positive impact. The results of cross-validation also confirm the stability and generalizability of the model to various data subsets. The feature importance analysis identifies the most important variables that determine the strategic decisions. The suggested framework will improve the quality of decisions made, facilitate risk management, and enhance resource allocation. The current study is an addition to AI-based decision systems that incorporates predictive modeling in conjunction with statistical validation of enterprise applications.
The article examines the potential of artificial intelligence to optimize management processes and improve decision-making in large organizations and proposes a conceptual AI-based management framework that integrates data sources, analytical models, and decision support systems into a unified adaptive cycle with a feedback mechanism.
Serhii Kubitskyi, Yevhen Kozlovskyi, K. Balabukha et al.· Human Resources Management 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
Artificial Intelligence (AI) driven Decision Support Systems (DSS) help transform the field of strategic management through rapid, accurate and data-driven decision-making in increasingly complicated business environments. This article is centered on a comprehensive machine learning approach for AI-enabled strategic decision support involving all aspects of data acquisition, predictive analytics models, intelligent recommendation systems and collaboration between humans and AI. The framework suggested consists in the integration of data coming from the enterprise, customer relationship management systems, financial databases and market intelligence services and results obtained through machine learning technologies. The process of decision-making involves both the utilization of recommendation systems to develop possible strategies based on analytical results and human involvement ensuring that the outcome is ethical, clear and considerate. The research shows that AI-enabled DSS helps to enhance decision-making quality, operational efficiency, competitive edge, innovation capabilities, and organizational agility as a result of continuous learning. In addition, the paper argues that explainable AI, data governance, organizational preparedness, and responsible AI governance are important factors for successful implementation. Despite numerous advantages available, there are some challenges connected with data quality, transparency of algorithms, data confidentiality, safety, and managerial acceptance that should also be taken into account. The conceptual framework proposed emphasizes a focus on effective cooperation of sophisticated AI technologies with rational judgments that assist decision-makers while staying accountable and trustworthy. The results imply that AI-enabled decision support systems can be essential for businesses striving to adopt digitalization, ensure sustainability, and maintain long-term competitiveness in the context of intelligent organizations. Further studies should investigate target industries, Explainable AI (XAI), ethical frameworks of governance, and the integration of new technologies such as Digital Twins, Generative AI, and autonomous decision-making systems.
VIRENDRA S. GOMASE, SUHAS B. DHANDE, PANKAJ R. NATU· International journal of com...· 0 citations
The study concludes that AI is not replacing managerial judgment but augmenting human decision-making through intelligent data-driven insights, and organizations that strategically embrace responsible AI adoption while investing in digital capabilities and ethical governance are likely to achieve sustainable competitive advantage.
Peter Stone· Research Journal in Business...· 0 citations
In the era of data-driven decision-making, selecting the appropriate analytical model is critical for improving business outcomes. This study aims to compare the effectiveness of machine learning (ML) techniques and conventional statistical models in terms of predictive accuracy, robustness, and decision-making precision. Using the Telco Customer Churn dataset comprising 7,043 observations, a quantitative comparative research design was adopted. Logistic Regression was used to represent traditional statistical modelling, while Decision Trees, Random Forests, and Support Vector Machines (SVM) represented machine learning approaches. Model performance was evaluated using Accuracy, Precision, Recall, F1-score, and RMSE, and a paired t-test was conducted to examine the statistical significance of performance differences.
The results indicate that machine learning models outperform the traditional Logistic Regression model in predictive accuracy, with Random Forest achieving the highest accuracy (91.3%), followed by SVM (89.5%) and Decision Tree (84.9%), compared to Logistic Regression (78.6%). However, Logistic Regression demonstrated greater interpretability, providing clear insights through model coefficients.
The study is limited to a single classification dataset and relies on secondary data, suggesting the need for cross-industry validation in future research. The findings highlight that while machine learning models are more suitable for complex predictive tasks, traditional statistical models remain valuable in contexts requiring transparency and explainability. This study contributes by offering a balanced framework for selecting appropriate analytical models in business decision-making.
Divya K, S. K· International Journal For Mu...· 0 citations
In response to the complex decision-making requirements during the process of business digital transformation, this paper constructs an AI-driven intelligent decision-making model and a path framework. Based on the "driver-constraint" quantitative model and stage efficiency indicators S and Etotal, the transformation motivation, constraints, and path evolution are parameterized and characterized; further, around multisource heterogeneous business data, feature engineering and standardized preprocessing procedures are designed, a regression prediction model with L2 regularization is established, and a cost-time multiobjective optimization model is constructed by combining genetic algorithms, forming a "prediction-optimization-execution" closed-loop decision-making mechanism; at the same time, through decision process modeling, the system integration of the data layer, model layer and business logic is achieved. Experiments show that compared with rule engines and traditional regression models, the intelligent decision-making model achieves accuracy of 0.92, recall rate of 0.89 and F1 score of 0.90 in terms of precision, recall rate and F1 score, and the prediction error decreases from 12.5% to 3.5% in continuous 10-day tests, verifying the stability and engineering application value of the proposed model in complex business scenarios.
Guobin Guan· The 2026 International Confe...· 0 citations