ABSTRACT As artificial intelligence becomes increasingly used by journalists in news production, the question of whether this causes journalists to “deskill” becomes paramount. This paper discovers the core skills that journalists risk losing and the areas in which they must upskill to remain relevant in the AI age. Referencing the theory of disruptive innovation that may lead to struggles within the journalistic field, this study highlights the disruptive power of AI as “cognitive automation” and the potential harms it might cause to journalism as professional identity, institution and practice. Through in-depth interviews with journalists and editors, this paper reveals skills lost in research, writing, note-taking, analyzing, interviewing and independent thinking, and points to the importance of upskilling in the areas of fact-checking, discernment, stylistic writing, personal branding, prompt writing, editing, interpersonal communication, storytelling, mastery of AI, ethical use of AI, and original ideation. These findings reveal an urgent need for journalists to mindfully limit their reliance on AI, while also highlighting that journalism is more than an accumulation of technical skills; it should be accompanied by a moral compass which journalists must not lose, so that the “humanness” of journalism may be retained even in the AI age.

In the Industry 5.0 paradigm, collaborative intelligence, human-machine cooperation, and real-time cognitive automation have increased the dependence of industrial systems on secure and uninterrupted Industrial Internet of Things (IIoT) connectivity. However, this convergence also expands the cyberattack surface and exposes resource-constrained industrial devices to impersonation, replay, man-in-the-middle, rogue gateway, insider, and session-hijacking attacks. Existing authentication schemes mainly focus on initial access verification and often lack continuous Zero Trust enforcement, failure-resilient reconnection, and network-aware runtime validation. To address these limitations, this paper proposes ZT-RIASE, a Zero Trust-resilient identity attestation framework for securing smart industrial IoT environments. ZT-RIASE adopts a hybrid bootstrap-symmetric runtime design, where public-key cryptography is used only during initial device registration and key agreement, while recurring runtime identity attestation, session maintenance, reconnection, and continuous verification rely on lightweight symmetric-key and behavior-based mechanisms. The runtime protocol uses AES-128-GCM, hash/MAC-based integrity verification, nonce-timestamp freshness, and session-continuity tokens to ensure confidentiality, integrity, and replay resistance without repeated public-key operations. To further reduce recurrent authentication overhead, ZT-RIASE introduces Network-Aware Crypto-Behavioral Continuous Authentication (NA-CBCA), which verifies active sessions using token-use regularity, path/gateway consistency, command-access consistency, message-size deviation, request-rate behavior, packet-timing deviation, retransmission/error behavior, and energy/processing deviation. Timing-sensitive behavioral features are normalized using a network condition index based on RTT, jitter, packet loss, and retransmission rate, thereby reducing false positives under changing industrial network conditions. Performance evaluation using representative constrained-device profiles and ns-3 simulations demonstrates that runtime attestation requires 2.400 ms computation time, 0.625 KB communication overhead, 3.800 KB memory, and 1.998 mJ energy, while NA-CBCA requires only 0.350 ms, 64 bytes, 2.100 KB memory, and 0.246 mJ energy. Large-scale scalability analysis from 100 to 1000 IIoT devices further shows predictable aggregate overhead growth with stable per-device runtime delay. These results demonstrate that ZT-RIASE provides lightweight, failure-aware, and behavior-adaptive Zero Trust identity attestation suitable for realistic smart industrial IoT deployments.

Abstract Fears of technological unemployment often portray automation as a force that eliminates occupations. This study offers a different perspective by modelling automation as the sequential erosion of tasks, which reshapes occupational skill bundles and mobility structures. Using data from the Occupational Information Network (O*NET), integrated with two exposure measures—routine task automation and AI-driven cognitive automation—we simulate how the removal of 332 tasks alters skill requirements across 736 occupations. Results suggest that automation increases skill overlap between occupations, promoting structural integration within the occupational network. Yet the nature of integration diverges: routine automation primarily dismantles specialised physical skills, enhancing mobility only within homogeneous manual clusters, whereas AI automation moderates a broader range of cognitive and social skills, creating new bridges across heterogeneous domains. Despite substantial task erosion, most occupations retain residual skills that enable adaptation rather than extinction. By tracing changes in the shares of skills reallocated to machines, we explore how AI-driven automation sustains occupational roles through emerging complementarity rather than substitution. While the model is not designed to forecast labour market outcomes or to conduct counterfactual tests, the results theoretically reframe automation as a process of reorganisation that may expand, rather than constrain, labour mobility. Policy responses must therefore move beyond predicting job loss to supporting workers in navigating newly emerging, and often counterintuitive, mobility pathways.

For many years, enterprises have invested heavily in data platforms, dashboards, predictive analytics and machine learning models with the expectation that the accumulation of data and computational capacity would automatically translate into superior decision-making. While these initiatives have delivered important advancements in process visibility and operational efficiencies, the promise of a genuinely intelligent enterprise remains largely unfulfilled. A substantial portion of decision processes continues to rely on human interpretation of dashboards and episodic integration of analytics into management routines, generating a gap between potential and actual business impact. Today, the emergence of explainable artificial intelligence, human-AI collaboration principles, cognitive automation and new digital governance models indicates that organizations are moving beyond a purely data-driven stance toward what can be termed cognitive enterprises. In such organizations, decisions are increasingly supported by transparent models, human oversight becomes structurally embedded rather than incidental, and skills evolve from data usage to cognitive competencies combining digital literacy, domain expertise, and the ability to supervise algorithmic behavior. The shift toward cognitive enterprises entails a rethinking of organizational design, talent strategies, governance frameworks and the underlying notion of enterprise intelligence. It is no longer sufficient to accumulate data and deploy analytics; enterprises are required to orchestrate explainability, trust, human expertise, risk control and strategic alignment of AI-enabled decisions. In addition, enterprises must structure collaboration between humans and AI systems with clear allocation of agency, accountability and transparency. This article introduces the conceptual foundations of the cognitive enterprise, elucidates the transition from data-driven models, and proposes a managerial maturity perspective integrating technology, governance, skills and explainability. The proposed view emphasizes long-term implications for enterprise transformation, strategic resilience and sustainable digital transition.

Abstract.The rapid transition from chalkboard-centered instruction to generative artificial intelligence–supported learning environments marks not merely a technological shift, but an epistemological rupture in the history of education. This article critically interrogates the pedagogical, cognitive, and ethical implications of integrating large language models–particularly ChatGPT developed by OpenAI–into formal and informal learning contexts. Rather than adopting a celebratory or alarmist stance, the study positions generative AI as a transformative mediating artifact that reconfigures knowledge production, authorship, assessment, and academic integrity.Drawing upon socio-constructivist theory (Vygotsky), critical pedagogy (Freire), and postdigital educational frameworks (Jandrić), the paper develops a multi-layered conceptual model termed Pedagogical Co-Agency, which conceptualizes AI not as a tool or substitute teacher, but as a cognitive collaborator operating within dynamic human–machine assemblages. Through qualitative discourse analysis of policy documents, higher education syllabi, and classroom narratives across secondary and tertiary contexts, the study identifies three emergent paradigms: (1) algorithmic scaffolding, (2) distributed cognition, and (3) epistemic outsourcing. These paradigms illuminate both productive affordances – enhanced feedback loops, adaptive explanation, democratized access to expertise–and structural risks, including epistemic homogenization, shallow learning patterns, and the erosion of authorial responsibility.The findings suggest that the uncritical incorporation of generative AI into curricula accelerates what is termed cognitive automation dependency, a condition in which learners increasingly externalize metacognitive labor to algorithmic systems. However, when embedded within critically designed pedagogical frameworks emphasizing reflexivity, transparency, and dialogic engagement, AI-mediated learning can deepen conceptual understanding and foster higher-order thinking.

Purpose This paper examines how artificial intelligence (AI), understood as a form of cognitive automation, may reshape the labour structures and social functions of contemporary libraries. It seeks to explain how AI can complement, rather than displace, the library's communal role by supporting its capacity to act as a Third Place. Design/methodology/approach The paper adopts a sociotechnical, conceptual approach, synthesizing research from library automation, social infrastructure and science and technology studies. It develops a theoretical model – the bifunctional institution – to analyse the interaction between algorithmic systems and the social–material dimensions of library work. Findings The study finds that AI may reduce routine interpretive labour and create organisational capacity for librarians to engage in relational, pedagogical and community-oriented activities. However, these benefits are contingent on robust governance, institutional autonomy and professional agency. AI can strengthen the library's social mission when integrated responsibly but may exacerbate inequalities or undermine transparency if adopted uncritically. Research limitations/implications As a conceptual paper, the work does not include empirical data. It highlights the need for future qualitative and organisational studies examining how AI tools are adopted in practice, how labour is redistributed within specific institutions and how governance structures mediate the relationship between automation and community-oriented work. Practical implications The model highlights conditions necessary for responsible AI adoption, including ethical governance, staff training and alignment of AI systems with public values and professional norms. Social implications By enabling a renewed focus on community engagement and shared learning, AI may reinforce libraries as crucial forms of social infrastructure in increasingly digitised and fragmented societies. Originality/value The paper offers one of the first theoretical integrations of automation studies and Third Place scholarship. It advances the concept of functional complementarity and provides a new framework for understanding libraries as bifunctional institutions composed of interdependent algorithmic and social layers.

While artificial intelligence has been widely discussed across various fields, its specific contributions to economics have been rarely explored. This paper investigates the potential applications of generative artificial intelligence in several key areas of economic research, and explores the impact of cognitive automation on economic theory and practice through the use of large-scale language models, aiming to improve productivity through automation.

The aviation industry faces significant operational challenges in accounts payable processes due to high transaction volumes, diverse vendor formats, and complex validation requirements. Traditional manual invoice handling creates bottlenecks that impact cash flow management, increase operational costs, and strain supplier relationships. A cognitive automation framework integrating third-party Intelligent Document Processing platforms with SAP Business Technology Platform and S/4HANA addresses these inefficiencies through machine learning-based data extraction, cloud-native orchestration, and automated validation workflows. Implementation within a major aviation enterprise demonstrates substantial improvements in processing efficiency, with cycle times reduced by 75% and manual effort decreased by 80%. Data accuracy exceeds industry benchmarks while straight-through processing capabilities eliminate human intervention for nearly 75% of incoming invoices. The modular, cloud-based architecture enables scalability across fluctuating transaction volumes without proportional staffing increases. This research contributes to a comprehensive framework validated through industry deployment, demonstrating that intelligent automation combining machine learning with enterprise workflow management delivers superior outcomes compared to traditional robotic process automation approaches for semi-structured document processing scenarios.

Application of Generative AI-Based Robotic Process Automation to Alleviate Pain Points in Last-mile Delivery Operations

Synthetic biology is a rapidly evolving discipline that seeks to understand, modify, design, and build biological systems by applying modular and systemic principles inspired by engineering. Automation in synthetic biology offers significant gains in efficiency, reproducibility, and standardization, enabling more reliable and scalable experiments while reducing human fatigue and health risks. This shift allows researchers to focus on experimental design, data analysis, and innovation rather than repetitive tasks. More recently, artificial intelligence has begun to reshape laboratory work at a cognitive level, enabling machines to analyze data, make decisions, and learn from experience. Artificial intelligence in biology has the potential to accelerate discovery, optimize experimental design, and enhance data analysis by identifying patterns beyond human capabilities. The convergence of robotics and artificial intelligence offers a promising future for synthetic biology but also raises ethical concerns. As the creation of engineered life becomes increasingly automated and shaped by intelligent agents, questions about governance, responsibility, and transparency become more pressing. In this article, we examine the progress and prospects of both physical (robotic) and cognitive (intelligent agent) automation in synthetic biology. We begin with an overview of automation technologies in industrial and laboratory settings, then discuss the objectives and challenges of synthetic biology from an automation perspective. Finally, we propose a dual conceptual framework: one for total automation of the Design-Build-Test-Learn (DBTL) cycle, and another for progressive automation adaptable to diverse laboratory contexts. Our aim is to support the development and responsible implementation of automation systems in synthetic biology laboratories.

The evolution of cloud infrastructure management represents a fundamental transformation in how organizations approach operational excellence, moving from reactive manual intervention to proactive intelligent automation. This article examines the convergence of artificial intelligence and human expertise in creating collaborative intelligence frameworks that redefine the role of infrastructure engineers in modern distributed systems. As cloud environments generate exponentially increasing volumes of operational data from thousands of interdependent components, traditional monitoring methodologies prove inadequate, necessitating machine learning-driven observability platforms that establish dynamic baselines, detect anomalies before they impact users, and trigger automated remediation actions. The transformation unfolds across three evolutionary phases: the shift from reactive to proactive operations, repositioning engineers as architects of prevention, the transition from procedural to cognitive work, elevating human contribution to decision logic design, and the movement from isolated to collaborative models, establishing synergistic human-machine partnerships. This evolution creates a new professional archetype—the automation architect—whose expertise lies in designing resilient systems, encoding domain knowledge into learning models, and supervising continuous improvement processes. Contemporary frameworks integrate cognitive automation layers with governance structures that preserve human oversight, knowledge integration mechanisms that enable continuous learning from operational patterns, and transparent control systems that establish explainability and accountability standards. The integration of natural language processing capabilities further enhances collaboration by enabling conversational interfaces that reduce cognitive overhead while maintaining human authority over critical decisions. This article demonstrates that successful autonomous operations depend not on replacing human judgment but on architecting frameworks where computational analytical power amplifies human creativity, ethical reasoning, and contextual understanding, creating operational paradigms that leverage the complementary strengths of human insight and machine intelligence.

The convergence of Large Language Models (LLMs) and data engineering ushers in a new era of cognitive automation, which can greatly improve data pipeline reliability, efficiency, and governance. This paper provides a comprehensive framework for using structured prompt engineering to perform three critical data engineering functions: (1) context-aware SQL query generation and optimization, (2) automated schema compatibility validation and drift detection, and (3) proactive data anomaly detection using statistical and semantic analysis. We offer novel prompt design patterns, such as Meta-Context Retrieval, Multi-Agent Validation Chains, and Feedback-Aware Prompt Tuning that use dynamic metadata from data catalogs to generate correct, production-ready results. Furthermore, we propose a layered safety architecture that includes Prompt Sanitization, Semantic Guardrails, and Human-in-the-Loop (HITL) checkpoints to reduce the risks associated with LLM hallucinations and logical errors. Empirical discussion, supported by current literature and real scenarios, shows that systematic rapid engineering can cut development time by up to 60% for common activities while enhancing data quality adherence. We conclude that prompt engineering is evolving from an auxiliary skill into a core data-engineering competency, essential for building resilient, self-documenting, and intelligent data systems in the AI-augmented era.

Abstract This is a study of skill heterogeneity in technology’s impact on laborers transitioning from physical automation to cognitive automation. The findings indicate that cognitive skills are a crucial determinant of the extent of technological influence. Considering both technological substitution and technological control, high-skilled and low-skilled workers experience limited technological substitution. However, high-skilled workers are not significantly affected by technological control, whereas low-skilled workers are subjected to stronger technological control. A comparison between automation technology and large language models (LLMs) reveals that the former primarily affects the secondary sector, while the latter mainly affects the tertiary industry. Furthermore, LLMs disproportionately influence women, younger demographics, professional skilled laborers, and higher-income groups. In the context of a new technological revolution led by artificial intelligence, analyzing and exploring the impact of technology holds significant theoretical and practical implications.

This paper investigates the transformative potential of Robotic Process Automation (RPA) and its integration with Artificial Intelligence (AI) in business process management. Drawing on recent empirical studies and industry literature (2019–2025), it explores how RPA enhances operational efficiency while AI expands its adaptability and decision-making scope. The research synthesizes critical themes across ten seminal academic and industry sources, identifying key benefits, application cases, and implementation frameworks. The outcome is an integrative conceptual model termed the Cognitive Automation Capability Framework (CACF)—a student-developed framework that explains how human–bot collaboration, process intelligence, and AI-based learning create strategic value in digital enterprises.

In this paper, we introduce two systems: Variants as Open Queue Network (OQN) Creation, and Automated Storage and Retrieval Systems (AS/RS). To assess their performance, we utilize a performance analyzer named MPA, which is commonly employed for analyzing OQNs. In our analysis, we employ the AVS/RS tool to swiftly evaluate different configurations. Through our experimentation, we demonstrate that MPA outperforms simulation methods for efficiently evaluating the performance of these systems. We provide experimental results to support this conclusion. The AMHS layout is uniquely configured, incorporating features Turntables, turnouts and high speed express lanes etc. The assumptions underlying the simulation model were verified using real-world data sets. Behavioral analysis was conducted, primarily focusing on intertribal times. It was assumed that intermediate points were accessible for all stockers. The distribution of stacker behavior followed basic patterns, with interatrial times predominantly exhibiting exponential or Weibull distributions. Cognitive automation refers to the use of computerized systems that mimic human cognitive functions to provide relevant information and reduce the workload for operators. This type of automation aims to streamline processes by handling tasks that require human-like decision-making, problem-solving, and learning. It can analyze data, interpret patterns, make predictions, and even interact with users in a more intelligent and human-like manner. This document originates from Memorial Hospital (GMH) in Greenville, South Carolina, USA, focusing on research utilizing data to explore the implementation of Automated Guided Vehicle Systems (AGVS) within discrete flow systems. These AGVS systems utilize both unidirectional and bidirectional guide routes networks. The article primarily centers on two key objectives. Firstly, it introduces a mathematical optimization technique aimed at enhancing material flow efficiency. Secondly, it proposes a modeling approach to address the challenges associated with implementing AGVS within discrete flow systems A 300-inch wafer fabrication facility can be designed using a general model that considers the dimensions of Automated Material Handling System (AMHS) solutions to support decision-making. This model utilizes ratio analysis and dimensionless metrics to assess various options. The Multiple Objective Optimization by Method of Ratio Analysis (MOORA) employed for objective measurement, ensuring that contractor bias does not influence decisions. This method categorically ranks different solutions based on their performance in meeting specific criteria. In the typical progression of a manufacturing system, various components such as product design, facility placement, facility layout, suppliers, materials, and technology need to be carefully considered and implemented at appropriate stages to ensure efficiency. Ultimately, assessing the performance of the machinery can be effectively accomplished using the MOORA method. To validate this approach, a test scenario was devised for verification purposes. Process data is ranked first, whereas inventory management data is ranked lowest.

This article examines how SAP Joule and SAP Intelligent Technologies can transform Order-to-Cash (OTC) and Supply Chain Management (SCM) processes through cognitive automation, predictive insights, and human-in-the-loop decision support. The article presents a comprehensive framework for integrating AI capabilities into critical business processes, addressing both technical architecture and organizational considerations. The article identifies implementation patterns that maximize value realization while mitigating risks. The article reveals that successful implementations follow a phased approach that balances quick wins with sustainable capability building, supported by robust data governance and change management practices. Beyond operational efficiency gains, the article highlights how AI augmentation enables more sophisticated decision-making through multivariate analysis, constraint-based reasoning, and dynamic optimization. The article gives particular attention to human-AI collaboration models that appropriately balance automation with human judgment, ethical frameworks that ensure responsible deployment, and governance structures that maintain alignment with organizational values. This comprehensive article on intelligent automation provides a roadmap for organizations seeking to leverage SAP's AI capabilities to create more adaptive, efficient, and customer-centric business processes.

The present study explores the enablers for the integration of Industry 5.0 principles within the automotive industry, emphasizing the transition towards human-centric, sustainable, and resilient manufacturing. This research utilized a three-round Delphi method involving a panel of experts to identify, evaluate, and prioritize key enablers associated with the adoption of Industry 5.0. In order to enhance the analytical depth, consensus trajectory mapping was employed to track opinion convergence across rounds. Fuzzy ranking was applied to provide a more nuanced evaluation of item prioritization. The results indicate a substantial degree of consensus on subjects such as collaborative robotics, cognitive automation, and circular manufacturing. The present study offers theoretical and practical implications, providing a roadmap for researchers and automotive stakeholders seeking to operationalize Industry 5.0 values.

This article introduces a novel framework for understanding the convergence of cognitive automation and advanced process mining in financial operations. Moving beyond traditional rule-based systems, the framework explores how intelligent document understanding, behavioral analytics, and real-time process discovery collectively reshape the automation landscape. The current limitations of conventional automation approaches become evident, particularly regarding unstructured data handling and adaptation to process variations. Merging cognitive technologies with process mining creates adaptive financial systems that continuously refine operations. This fusion identifies inefficiencies, enhances regulatory responsiveness, and sustains improvement protocols. Evidence from mortgage processing and compliance reporting shows gains in transaction speed, regulatory alignment, and client satisfaction. Banking executives increasingly recognize this convergence as essential infrastructure supporting operational excellence within contemporary financial environments. The resulting operational resilience equips financial institutions for increasingly complex environments where conventional automation proves insufficient for addressing evolving market demands and expanding regulatory frameworks. Contemporary financial executives recognize this technological integration as a strategic imperative for maintaining competitive positioning within the evolving financial services landscape.

The rapid acceleration of digital transformation and artificial intelligence (AI) is fundamentally reshaping medicine. Much like previous technological revolutions, AI-driven by advances in computer technology and software including machine learning, computer vision, and generative models-is redefining cognitive work in healthcare. Radiology, as one of the first fully digitized medical specialties, is at the forefront of this transformation. AI is automating workflows, enhancing image acquisition and interpretation, and improving diagnostic precision, which collectively boost efficiency, reduce costs, and elevate patient care. Global data networks and AI-powered platforms are enabling borderless collaboration, empowering radiologists to focus on complex decision-making and patient interaction. Despite these profound opportunities, widespread AI adoption in radiology remains limited, often confined to specific use cases, such as chest, neuro, and musculoskeletal imaging. Concerns persist regarding transparency, explainability, and the ethical use of AI systems, while unresolved questions about workload, liability, and reimbursement present additional hurdles. Psychological and cultural barriers, including fears of job displacement and diminished professional autonomy, also slow acceptance. However, history shows that disruptive innovations often encounter initial resistance. Just as the discovery of X-rays over a century ago ushered in a new era, today, digitalization and artificial intelligence will drive another paradigm shift-this time through cognitive automation. To realize AI's full potential, radiologists must maintain clinical oversight and safeguard their professional identity, viewing AI as a supportive tool rather than a threat. Embracing AI will allow radiologists to elevate their profession, enhance interdisciplinary collaboration, and help shape the future of medicine. Achieving this vision requires not only technological readiness but also early integration of AI education into medical training. Ultimately, radiology will not be replaced by AI, but by radiologists who effectively harness its capabilities.

The banking sector has experienced significant growth, driven by technological advancements that provide faster, more secure, and reliable services. As competition intensifies, particularly with the rise of virtual banking, banks are focusing on enhancing user experiences, improving efficiency, reducing costs, and optimizing back-office operations. This paper explores the transformative potential of Robotic Process Automation (RPA) in banking, covering its applications from customer service and account management to risk assessment and compliance. It highlights the evolution from traditional RPA to Cognitive Automation, where technologies like AI, ML, and NLP enhance automation capabilities. By automating complex tasks such as data analysis and decision-making, Cognitive Automation improves accuracy and innovation. The study also addresses the practical implications of RPA, including data governance, ethical AI practices, and workforce upskilling, offering valuable insights for banking professionals and policymakers navigating digital transformation in finance.