International Journal of Knowledge-based and Intelligent Engineering Systems

Atoum, Ibrahim

doi: 10.1177/13272314261456990pmid: N/A

The convergence of Quantum Computing (QC) and Artificial Intelligence (AI) ushers in a transformative era of computational capability, with profound implications for science and industry alongside significant technical, ethical, and environmental challenges. This article explores quantum-enhanced AI (QAI), focusing on foundational quantum principles—superposition, entanglement, and quantum parallelism—and their applications in molecular simulation, financial optimization, and climate modeling. While quantum algorithms offer exponential speedups for Machine Learning (ML) and optimization, persistent barriers such as qubit decoherence, scalability limitations, and high energy demands hinder progress. Hybrid quantum-classical architectures are identified as critical transitional tools, combining quantum processors for specialized tasks like combinatorial optimization with classical systems for error correction and data preprocessing. Near-term commercial healthcare, finance, and logistics applications are highlighted, including pilot projects using quantum neural networks (QNNs) for drug discovery, portfolio risk analysis, and supply chain optimization. However, dual-use risks—such as quantum-powered surveillance and threats to classical encryption—emphasize the need for post-quantum cryptography (PQC) and ethical frameworks. Environmental concerns, particularly the carbon footprint of superconducting qubits, drive calls for sustainable innovations like photonic qubit designs and renewable-powered quantum data centers. Anticipated milestones include fault-tolerant QNNs, standardized benchmarks for quantum advantage, and democratized cloud access. Achieving these goals requires addressing interdisciplinary talent shortages, algorithmic biases, and global disparities in quantum infrastructure. This work advocates for governance models integrating sustainability metrics and equity-focused policies to steer QAI development responsibly. By harmonizing technical innovation with ethical foresight, the quantum-AI synergy can advance solutions to global challenges while mitigating misuse and ecological harm risks.

Kedziora, Damian

doi: 10.1177/13272314251410348pmid: N/A

Organizations adopt process automation software to support digital transformation, but leaders managing diverse Information Systems (IS) face challenges in developing automation capabilities that deliver both strategic advantage and operational resilience. This study examines how organizations build and sustain process automation competencies through effective Information Technology (IT) governance, strategic alignment, and systematic capability development. Drawing on Technology-to-Performance Chain (TPC) theory and the Dynamic Capabilities framework, this research analyses IS management practices to demonstrate how process automation initiatives can evolve from tactical implementations into sustainable and resilient sources of competitive advantage. A multi-method research design was employed, combining survey data from 286 IT and business leaders across manufacturing, retail, logistics, financial services, and healthcare organizations in the Nordic region, together with 18 qualitative interviews with Chief Information Officers (CIOs), IT directors, and automation leaders. The study identifies pathways for developing three critical capability dimensions: (1) technical capabilities through platform selection and integration practices, (2) governance capabilities through portfolio management and performance measurement mechanisms, and (3) organisational capabilities through change management and continuous learning. The analysis shows that governance capabilities explain 61% of return on investment variance, while balanced capability development across all three dimensions produces returns 2.5 times higher than single-dimension focus. Organizations with mature capabilities establish governance before scaling these initiatives, adopt hybrid governance models, and invest approximately 35% in governance, 35% in technical, and 30% in organisational capabilities. The study provides a capability development framework, empirical evidence of development and performance outcomes, as well as practical guidance for building automation capabilities.