Jul 2026· Academic Journal of Applied Sciences· Vol 2, pp. 1-5· 0 citations· 15 references
TL;DR
It is argued that realizing the full potential of AI in instrumentation requires a balanced approach that combines technological innovation with robust governance frameworks, human oversight, and rigorous uncertainty quantification.
Abstract
The integration of artificial intelligence into instrumentation and measurement systems promises transformative improvements in accuracy, efficiency, and operational intelligence. However, this technological convergence also introduces novel risks that challenge established practices in measurement science, legal metrology, and industrial safety. This article provides a balanced analysis of the potential benefits and risks associated with AI-driven instrumentation. We examine key benefits including enhanced measurement accuracy through intelligent compensation, predictive maintenance capabilities, self-diagnostics and adaptive calibration, and operational efficiency gains. Concurrently, we analyze critical risks: the “black box” problem and lack of interpretability, data quality dependence, cybersecurity vulnerabilities, workforce displacement and skills gaps, and regulatory and standardization challenges. We argue that realizing the full potential of AI in instrumentation requires a balanced approach that combines technological innovation with robust governance frameworks, human oversight, and rigorous uncertainty quantification. This analysis serves as a reference for researchers, practitioners, and policymakers navigating the complex landscape of AI-empowered instrumentation.
Artificial intelligence (AI) is increasingly being incorporated into engineering practices, transforming the design, construction, operation, and maintenance of systems. The present study explores recent peer-reviewed literature on three interwoven aspects of this transformation: ethical governance, societal change, and environmental sustainability. The review covers new governance instruments such as the European Union Artificial Intelligence Act, ISO/IEC 42001, and the NIST AI Risk Management Framework, as well as explainable-AI (XAI) methods that engineers are increasingly using to explain to regulators, clients, and the public why their safety-critical decisions are made. Also, it addresses the implications of AI on engineering labor markets, professional education and decision-making power, including the differential exposure of high-skill cognitive tasks to automation, and the need for new skills. Furthermore, it considers the dual nature of AI in terms of its contribution to resource optimization, predictive maintenance and integration of renewable energy, and its contribution to an increasing computational carbon and water footprint that can offset these benefits, a tension that is sometimes referred to as the Jevons paradox. The review suggests an integrated framework of responsible AI engineering that connects the three elements of responsible AI: technical explainability, institutional accountability, and life-cycle environmental auditing. The review concludes that long-term acceptability and acceptance of AI in engineering practice will not only be determined by the performance of the models themselves, but also by the ability of the engineering discipline to govern AI transparently, fairly and within planetary limits.
Artificial intelligence (AI) has become a transformative force in contemporary business management, yet its practical integration remains uneven due to barriers such as technological readiness, competence shortages, and regulatory uncertainty. This study aims to identify the key challenges and development prospects of AI implementation to enhance business efficiency and ensure sustainable development. Employing a foresight research methodology – including expert evaluation, scenario analysis, and semantic modeling – the study utilizes data from a 2025 session at Vilnius TECH University involving interdisciplinary experts. The findings highlight four critical success factors: technologies and innovation; human capital; financing and partnerships; and risk, security, and ethics. While AI’s strategic importance is growing, organizations must address the need for continuous workforce upskilling, stronger ethical governance, and more adaptive regulatory frameworks. Based on these results, a structured roadmap for AI implementation was developed, covering stages from preparation and pilot implementation to scaling and innovation-driven growth. Ultimately, this study contributes an integrated framework for understanding AI in management, providing strategic guidance for organizations to balance technological innovation with responsibility and risk management.
Giedrius Čyras, Vita Marytė Janušauskienė· International Scientific Con...· 1 citation
To improve cybersecurity across industries, Cyber Threat Intelligence (CTI) is becoming increasingly crucial. This systematic review explores how CTI practices are evolving in response to advancements in Artificial Intelligence (AI), particularly in the context of Large Language Models (LLMs). We examined 61 peer-reviewed studies using the PRISMA methodology, which demonstrates a strict selection procedure founded on specified inclusion, exclusion, and quality standards. This approach aligns with the scope of similar systematic reviews in the field of cyber threat intelligence. The review provides a comparative synthesis of CTI research capabilities across threat detection and prediction, attribution, forecasting, and automated reporting. We classify these approaches into three categories: conventional methods, those enhanced by AI and Machine Learning, and those based on LLMs. Our findings indicate that LLMs offer significant advantages in contextual reasoning, processing unstructured threat intelligence, and generating actionable mitigation plans. However, challenges such as model explainability, data privacy, system interoperability, and standardization impede their integration into operational environments. In addition to highlighting the potential and practical limitations of LLMs in CTI, this study identifies research gaps and proposes methods to create scalable, secure, and flexible CTI systems that support real-time cyber defense.
Hilalah Alturkistani, Abdul Ghafar Jaafar, S. Chuprat et al.· International journal of res...· 0 citations
Artificial Intelligence (AI) often suffers from a "science-to-service gap," where high-performing models fail to translate into effective real-world decision-making. This systematic literature review investigates this divide, identifying three critical barriers: inadequate technical reasoning, organizational resistance, and stringent regulatory compliance. To bridge this gap, we propose a holistic analytical framework anchored in three interconnected pillars: the human–AI relationship, predicated on mutual trust and complementarity; organizational preparedness, necessitating comprehensive cultural transformation and workforce reskilling; and ethical regulation, prioritizing process transparency and robust accountability. Our findings reveal that successful AI integration extends beyond technical optimization, requiring cross-disciplinary strategies such as participative design and collaborative human–AI audits. By synthesizing these dimensions, this study provides a strategic roadmap for enterprises to navigate systemic challenges, fostering a transition from theoretical AI potential to actionable, empowered, and human-centric decision-making systems in complex operational environments. Research indicates that proper application of MCDM techniques can relate AI outputs to real-life decision-making by structuring, enforcing transparency, and justifying AI-scoring results for use within an MCDA (Multi-Criteria Decision Analysis) framework, as demonstrated in complex use cases such as transportation planning.
Karzan Ismael, Ali Mohammed Salih, Zryan Najat Rashid· Knowledge and Decision Syste...· 0 citations
The convergence of Artificial Intelligence (AI) and data analytics is transforming how organizations generate insights, make decisions, and automate complex tasks. This article explores the dynamic interplay between AI's cognitive capabilities and the predictive power of data analytics, framing their synergy as a pivotal force in contemporary technological evolution. It begins by unpacking the foundational principles of each field, then examines their combined impact across sectors such as healthcare, business operations, and environmental monitoring. The discussion further addresses critical ethical concerns, including data privacy, algorithmic bias, and accountability, while acknowledging practical barriers such as data quality, interpretability, and infrastructure limitations. The paper concludes by outlining forward-looking strategies—such as explainable AI, ethical governance frameworks, and edge computing—that can foster responsible innovation. By synthesizing technical depth with societal context, this study aims to chart a roadmap for leveraging AI and analytics not merely for smarter systems, but for more equitable and human-centered outcomes. This review synthesizes findings from 20 peer-reviewed sources published between 2015–2025, retrieved from Scopus, IEEE Xplore, and ScienceDirect, using search terms such as ‘AI + data analytics’, ‘machine learning ethics’, and ‘predictive modeling’.
Sherzodova Fozila· International Journal of Adv...· 0 citations
This paper investigates digital twins as key components of cyber-physical supervision architectures for industrial systems through a systematic analysis of 65 publications (2019-2026). A tripartite taxonomy physical first, data-driven, and hybrid approaches is established and evaluated using operational criteria including data requirements, robustness to process drift, interpretability, and decision latency. The study highlights the evolution of digital twins transitioning from virtual replicas toward intelligent systems through the synergy and the integration of physical models, measured data, and artificial intelligence. Despite these advances, a gap remains between academic developments and industrial deployment due to sensitivity to data quality, limited explainability, and interoperability challenges. Six cross-cutting challenges are identified architectural standardization, physics informed robustness, self-supervised learning, explainable AI, cybersecurity, and scientific reproducibility to support the development of autonomous and prescriptive digital twins for resilient Industry 4.0/5.0 supervision systems. This work contributes through a supervision-oriented perspective combining a unified taxonomy,and quantitative benchmarking framework of digital twins.
A. Amrane, Ali Chabane, Khaled Benfriha et al.· 2026 6th International Confe...· 0 citations
