American Journal of Advanced Multidisciplinary Research and Innovation

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Advanced Decision Support Models for Business Intelligence and Strategic Management

Author(s) Bruce Schneier
Country United States
Abstract The rapid growth of digital technologies, big data, and intelligent analytics has fundamentally transformed how organisations make strategic decisions. Traditional decision-making approaches, which largely relied on historical reports and managerial intuition, are increasingly inadequate for addressing today's complex, dynamic, and data-intensive business environment. Modern organisations require intelligent decision support models capable of integrating large-scale structured and unstructured data, generating predictive insights, optimising strategic planning, and supporting evidence-based management. Advances in Business Intelligence (BI), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Big Data Analytics, Cloud Computing, Internet of Things (IoT), Blockchain, Digital Twin Technology, and Explainable Artificial Intelligence (XAI) have significantly enhanced the capabilities of contemporary Decision Support Systems (DSS).
This study presents a comprehensive analysis of Advanced Decision Support Models for Business Intelligence and Strategic Management. A qualitative analytical research methodology based on secondary data is employed to examine intelligent decision support architectures, predictive analytics, executive dashboards, knowledge management systems, risk analysis, business forecasting, strategic optimisation, and performance management. The research investigates how advanced decision support models contribute to organisational agility, operational efficiency, competitive advantage, and sustainable business growth.
The findings indicate that AI-powered Business Intelligence platforms substantially improve forecasting accuracy, customer analytics, financial planning, supply chain optimisation, resource allocation, and strategic decision-making. Explainable AI enhances managerial trust by providing interpretable recommendations, while cloud-based analytics and digital twin technologies enable real-time simulation of strategic scenarios. Furthermore, integrating blockchain technology improves data integrity and transparency, supporting trustworthy business intelligence ecosystems.
Despite these opportunities, challenges remain regarding data quality, cybersecurity, organisational readiness, ethical AI governance, interoperability, implementation costs, and regulatory compliance. Future research should investigate explainable business intelligence, autonomous decision support systems, federated enterprise analytics, quantum-enhanced optimisation, and AI-driven strategic governance.
The study concludes that advanced decision support models integrating Business Intelligence, Artificial Intelligence, and predictive analytics provide a robust framework for intelligent, transparent, and data-driven strategic management capable of supporting digital transformation and long-term organisational competitiveness.
Keywords Business Intelligence, Decision Support Systems, Strategic Management, Artificial Intelligence, Predictive Analytics, Business Analytics, Explainable Artificial Intelligence, Digital Transformation, Big Data, Executive Decision Support
Field Engineering
Published In Volume 2, Issue 3, May-June 2020
Published On 2020-05-05
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