https://ijarcst.org/index.php/ijarcst/article/view/361/352

Abstract

In the era of digital transformation, data-centric organizations increasingly rely on advanced analytics and artificial intelligence (AI) to gain competitive advantage and support strategic decision-making. Traditional decision support systems (DSS), while effective in handling structured data and routine decisions, often lack the adaptability, predictive power, and real-time intelligence required to address complex and dynamic strategic challenges. This study proposes an AI-driven decision support framework tailored for strategic management in data-centric organizations. The framework integrates machine learning, predictive analytics, natural language processing, and knowledge-based systems to enhance the quality, speed, and accuracy of strategic decisions. 

The proposed framework emphasizes the transformation of raw organizational data into actionable strategic insights through automated data ingestion, intelligent processing, and continuous learning mechanisms. AI models are employed to analyze historical and real-time data, identify patterns, forecast future trends, and simulate alternative strategic scenarios. By embedding explainable AI (XAI) components, the framework ensures transparency and trust in decision outcomes, enabling managers to understand and justify AI-driven recommendations. Additionally, the framework supports collaborative decision-making by combining human expertise with AI-generated insights, fostering a hybrid intelligence approach. 

The study adopts a conceptual and analytical methodology supported by a review of existing literature on AI-based decision support, strategic management, and data-driven organizations. The findings indicate that AI-driven DSS significantly enhance strategic agility, risk assessment, and resource allocation while reducing cognitive bias and uncertainty in managerial decisions. However, challenges such as data quality, ethical concerns, skill gaps, and organizational resistance are also identified. 

The proposed framework contributes to both theory and practice by offering a structured model that aligns AI capabilities with strategic management objectives. It provides a roadmap for organizations seeking to leverage AI for long-term planning, competitive positioning, and sustainable growth. The study concludes that AI-driven decision support systems are no longer optional but essential for strategic management in data-centric organizations operating in volatile and data-intensive environments.