The limitations of traditional decision-support models have been highlighted by the increasing complexity of organisational systems, driven by sustainability mandates, digital transformation, and global uncertainty. Quantum computing introduces a new computational paradigm capable of handling high-dimensional, nonlinear, and multi-objective decision problems. This research presents a Quantum-Driven Decision Intelligence (QDDI) framework for sustainable organisational management.
The limitations of traditional decision-support models have been highlighted by the increasing complexity of organisational systems, driven by sustainability mandates, digital transformation, and global uncertainty. Quantum computing introduces a new computational paradigm capable of handling high-dimensional, nonlinear, and multi-objective decision problems. This research presents a Quantum-Driven Decision Intelligence (QDDI) framework for sustainable organisational management.
The increasing complexity of financial systems and strategic environments has intensified the need for advanced decision-making models capable of handling uncertainty, multidimensional risk, and dynamic optimization constraints. Classical management science tools—while robust—often face computational limitations when applied to high-dimensional portfolio optimization, capital allocation, and scenario-based strategic planning. This paper explores quantum-driven financial decision-making models for management science and strategic planning, emphasizing hybrid quantum–classical frameworks that integrate quantum optimization, probabilistic sampling, and advanced analytics into managerial decision systems. By examining applications in portfolio management, risk assessment, supply chain finance, and strategic resource allocation, the study evaluates the theoretical advantages and practical constraints of quantum-enhanced models in organizational contexts. The findings suggest that quantum-driven decision architectures may complement traditional financial models by expanding solution-space exploration and improving optimization efficiency, thereby enhancing strategic agility and long-term value creation.
Artificial intelligence has become a transformative technology for predictive modelling across scientific, industrial, financial, and healthcare applications. However, the increasing complexity of large-scale datasets, high-dimensional feature spaces, and computationally intensive learning algorithms has exposed significant limitations in conventional computing architectures. Recent developments in quantum computing and high-performance computing have created unprecedented opportunities to accelerate artificial intelligence by improving computational efficiency, optimization capability, and predictive accuracy. This paper presents a comprehensive study of a quantum-driven artificial intelligence framework that integrates quantum algorithms with high-performance computing infrastructure to enhance predictive modelling performance. The proposed framework combines quantum-enhanced optimization, parallel heterogeneous computing, distributed data processing, and hybrid machine learning architectures to address computational bottlenecks encountered in conventional predictive systems. The study reviews recent developments in quantum artificial intelligence, evaluates emerging computational paradigms, identifies current research challenges, and proposes a scalable architecture suitable for future intelligent decision-support systems. The findings demonstrate that integrating quantum computation with high-performance computing can significantly improve prediction quality, optimization efficiency, scalability, and computational sustainability across diverse application domains while establishing a foundation for next-generation intelligent computing environments.
Sustainable supply chains are becoming increasingly important as businesses strive to reduce environmental impact while maintaining efficiency and profitability. One emerging solution is Quantum Intelligence (Quantum AI)—a powerful combination of quantum computing and artificial intelligence that can handle highly complex and data-intensive supply chain problems. Quantum AI leverages unique quantum principles such as superposition and entanglement to explore a vast number of possible solutions simultaneously. This enables better decision-making in areas like production scheduling, inventory management, and emissions forecasting. As a result, organizations can optimize delivery routes, reduce operational costs, and significantly lower carbon emissions, contributing to more sustainable and environmentally responsible supply chains. A key approach in this field is the hybrid quantum-classical model, which combines the data-processing strengths of classical computing with the advanced computational capabilities of quantum systems. This hybrid approach helps overcome current limitations in quantum hardware, such as noise, limited qubits, and high error rates, making Quantum AI more practical for real-world applications. In addition, integrating technologies like the Internet of Things (IoT), blockchain, and digital twins further enhances supply chain performance. IoT devices provide real-time data from sensors, blockchain ensures transparency and traceability, and digital twins allow simulation of supply chain operations to predict environmental impacts and test different scenarios. Overall, Quantum AI represents a promising step toward building smarter, more efficient, and sustainable supply chains that can adapt to modern challenges while supporting global environmental goals.
Quantum computing is increasingly viewed as a transformative technology for optimization, cybersecurity, analytics, simulation, and organizational decision-making (Kar et al., 2025). Yet the Management Information Systems field has not fully articulated what a quantum-informed MIS research agenda should look like. We introduce Quantum MIS (Q-MIS) as an emerging domain concerned with how organizations prepare for, adopt, govern, and create value from quantum-enabled information systems. We argue that Quantum MIS should not be understood only as the technical application of quantum computing. Rather, it should be theorized as a socio-technical transformation in which quantum capabilities interact with organizational routines, managerial cognition, governance structures, data infrastructures, and institutional readiness. We ground this agenda in three IS theoretical traditions: sociotechnical systems theory, IT capability and dynamic capabilities perspectives, and affordance theory (Larsen et al., 2025). First, drawing on sociotechnical systems theory, we argue that quantum value will emerge from aligning technology, people, tasks, structures, and organizational processes. Second, building on IT capability and dynamic capabilities perspectives, we conceptualize quantum readiness as an organizational capability involving sensing quantum opportunities, seizing viable use cases, and transforming digital infrastructures. This view aligns with prior IS research that conceptualizes IT capability as a strategic organizational resource linked to firm performance (Bharadwaj, 2000). Third, using affordance theory, we suggest that quantum technologies may create new action possibilities for optimization, simulation, security, and decision support, but these affordances will be realized only when firms possess the knowledge, resources, and governance mechanisms to actualize them. We position Quantum MIS around five research domains: quantum readiness, quantum-enabled decision systems, hybrid classical and quantum infrastructure, quantum cybersecurity governance, and the societal implications of quantum capability gaps. Our contribution is threefold. First, we define Quantum MIS and distinguish it from the technical study of quantum computing. Second, we connect quantum computing to core IS theories, showing how existing theory can guide early inquiry into post-classical information systems. Third, we outline a preliminary research agenda and practical implications for managers preparing for quantum-enabled digital transformation. As organizations experiment with quantum technologies under uncertainty, MIS scholars have an opportunity to shape the conversation before quantum technologies become mainstream (Kar et al., 2025).
AI and quantum computing are increasingly transforming corporate control. This study incorporates quantum artificial intelligence (QAI) into commercial decision-making, operations, and strategic planning. Quantum algorithms' immense processing capacity and probabilistic nature can improve business analysis, modeling, and prediction. QAI may change business analytics, risk management, supply chain optimization, market forecasting, and consumer behavior analysis. Quantum-improved machine learning, neural networks, and data-driven insights help companies uncover patterns and anomalies faster. In addition, case examples and theoretical models demonstrate how quantum-based algorithms improve real-time uncertainty-based decision-making. Multidisciplinary cooperation, morality, and digital infrastructure development are all studied in QAI technology's organizational preparation and strategic ramifications. This chapter discusses how quantum computational electricity and AI-driven systems may revolutionize company management as industries prepare for quantum technology.
The convergence of quantum computing and artificial intelligence represents a transformative technological paradigm with unprecedented potential for business intelligence applications. This comprehensive review critically examines the revolutionary capabilities of quantum computing-enhanced AI systems in addressing complex computational challenges across multiple business domains. Through systematic analysis of emerging research, implementation frameworks, and interdisciplinary case studies, we investigate how quantum computing's unique computational mechanisms can fundamentally reshape data analysis, strategic decision-making, and predictive modeling. Our comprehensive examination reveals that quantum AI systems demonstrate remarkable potential to reduce computational complexity by up to 90%, enhance predictive accuracy by 60-75%, and provide unprecedented insights across financial, logistical, and strategic business intelligence domains. The research synthesizes evidence from multiple technological domains, highlighting the transformative potential of quantum-enhanced AI in solving previously intractable computational problems. By exploring technological capabilities, implementation challenges, and future research directions, this review provides a critical framework for understanding the emerging intersection of quantum computing and artificial intelligence in advanced business intelligence applications.