2026· EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA· 0 citations
TL;DR
It is demonstrated that intelligent automation increases resilience not through extreme autonomy, but through a combination of forecasting, digital twins, hybrid rules, and managed escalation.
Abstract
This article examines intelligent business process automation as a means of increasing the economic resilience of defense industry enterprises. Unlike approaches focused primarily on reducing labor costs, resilience is assessed through a process's ability to prevent disruptions, maintain critical output, recover from failures, and adapt to changing conditions. A process-oriented model is proposed that links the criticality of operations, observability, interdependence, automation potential, and the risk of erroneous decisions. A system of metrics for deviation prediction, continuity, recovery, and adaptation, as well as a metric for avoided economic losses, is developed. The study was conducted using a change management scenario for design and technological documentation (DTD). It is demonstrated that intelligent automation increases resilience not through extreme autonomy, but through a combination of forecasting, digital twins, hybrid rules, and managed escalation.
This article proposes a comprehensive model for assessing the organizational and economic efficiency of business process automation using AI agents. It substantiates the inadequacy of traditional labor cost savings calculations, as agent systems simultaneously generate direct, indirect, and risk-dependent effects, as well as require costs for integration, computing resources, data, monitoring, and managing organizational changes. A multi-level system of metrics has been developed, including economic, process, quality, risk-based, and organizational frameworks. A total cost of ownership model, a procedure for calculating risk-adjusted cash flow, and an integrated performance index for agent-based automation have been developed. The performance assessment model was applied to a simulation scenario for automating the end-to-end process of processing contractual applications at a defense industry enterprise in the Ryazan Region. It has been established that the positive local effect of an individual AI agent does not guarantee an increase in the efficiency of the overall process unless the assessment takes into account the costs of verifying results, handling exceptions, resolving errors, and redistributing personnel functions.
Unknown authors· EKONOMIKA I UPRAVLENIE: PROB...· 0 citations
This article explores the role of intelligent analytics (IA) as a key tool for transforming business processes in modern organizations. It examines the evolution of analytical approaches from traditional business intelligence (BI) to intelligent systems integrating machine learning, process and task mining, predictive modeling, and artificial intelligence-based hypothesis generation. An analysis of empirical cases from Russian and international companies demonstrates that the implementation of IA can shorten decision-making cycles by 42% or more, reduce operational errors by 37%, and identify hidden potential for efficiency unattainable with traditional approaches. The article demonstrates that intelligent analytics not only complements but also fundamentally transforms management processes, shifting organizations from reactive management based on historical data to proactive, scenario-based management in real time. Practical recommendations for IA implementation are provided, including the necessary organizational conditions and typical risks.
Murat V. Agaev, Irina G. Makarova, Karina D. Shakhdullaeva· SOFT MEASUREMENTS AND COMPUT...· 0 citations
Overhead crane operations remain a high-risk activity in heavy manufacturing, yet safety management often relies on static assessments that fail to capture real-time operational dynamics. In developing economies such as Indonesia, a significant digital gap hinders the adoption of high-cost IoT solutions, leaving safety data fragmented and reactive. This study aims to bridge this gap by developing and validating a Business Intelligence (BI) Safety Dashboard that utilizes bridge technologies, defined as cost-effective digital solutions that leverage existing administrative and operational data instead of dedicated IoT infrastructure, to provide real-time predictive risk insights. Following a Design Science Research (DSR) framework, a three-year longitudinal study (2024–2026) was conducted at a metal fabrication facility in West Java. A Weighted Dynamic Risk Score (WDRS) was formulated using Data Analysis Expressions (DAX), integrating incident logs, maintenance records, and operator certification data into a unified star schema model. The results demonstrate a 95% reduction in data processing time and a 30% increase in near-miss reporting. The proposed artifact successfully identified critical risk outliers, such as Crane 08 (WDRS = 8.3), and generated spatiotemporal heatmaps that pinpointed specific risk hotspots within the facility. These findings confirm that the BI Dashboard is a feasible and highly practical solution for resource-constrained environments, providing a scalable blueprint for Indonesian SMEs to achieve Industry 4.0 safety standards by leveraging existing administrative data for predictive maintenance and proactive safety interventions.
Ridwan Kurniaji, Nur Azizah, Mohamad Rakhmansyah et al.· IAIC Transactions on Sustain...· 0 citations
The accelerating pace of digital transformation has fundamentally redefined the strategic role of corporate finance. Financial management is no longer confined to budgeting, reporting, and capital allocation; it has evolved into an intelligent decision-making function that integrates artificial intelligence, automation, advanced analytics, and real-time digital information to support enterprise-wide strategy. As organizations operate within increasingly volatile economic environments characterized by technological disruption, geopolitical uncertainty, regulatory complexity, and rapidly changing customer expectations, traditional financial planning models have become insufficient for sustaining long-term competitiveness. Static financial systems, historical reporting practices, and periodic decision cycles are gradually being replaced by adaptive financial architectures capable of generating predictive insights, supporting continuous planning, and enabling executives to make faster and more informed strategic decisions.
This article examines how digital transformation is reshaping financial strategy through the integration of artificial intelligence, intelligent automation, predictive analytics, and executive decision intelligence. Rather than treating digital technologies as isolated operational tools, the study argues that they collectively represent a new strategic infrastructure that transforms finance into an organizational intelligence center. The discussion explores the evolution of financial strategy from retrospective financial control toward predictive and autonomous decision support while analyzing the growing importance of data governance, digital leadership, organizational resilience, cybersecurity, and ethical AI in financial management. Particular attention is given to the changing role of executive leadership, where Chief Financial Officers increasingly function as strategic architects responsible for integrating technological innovation with long-term value creation. The article further proposes a comprehensive framework for intelligent financial strategy that combines human expertise with machine intelligence to strengthen organizational adaptability and sustainable competitive advantage. The findings suggest that organizations capable of integrating AI-driven financial intelligence with strategic executive leadership are better positioned to improve decision quality, enhance financial resilience, accelerate innovation, and create enduring enterprise value in the digital economy.
Efe Calguner· International Journal of Res...· 0 citations
This study develops an integrated generative artificial intelligence (GAI) framework for improving business process performance in industrial enterprises. The framework treats GAI not as the isolated use of generative tools, but as a governable information systems capability embedded in recurring workflows, enterprise architectures, documented knowledge, and human decision roles. It integrates four functional subframeworks—manufacturing, marketing and sales, accounting and finance, and human resource management—with a shared orchestration and governance layer. This layer coordinates process architecture, approved data and knowledge sources, reusable GAI capabilities, human-in-the-loop validation, traceability, escalation, and performance measurement. A proof-of-concept maturity-readiness validation is conducted in an electronics company using maturity-readiness logic inspired by the Smart Industry Readiness Index (SIRI). The assessment shows an increase in the overall readiness score from 41.60 in the pre-GAI baseline to 79.08 in the post-GAI implementation scenario. Accordingly, the score increase is interpreted as expert-assessed maturity-readiness evidence rather than as a measured causal effect on operational performance. This study contributes a process-centric reference architecture designed for technical implementability, traceability, auditability, and human-supervised enterprise-scale GAI adoption.
Digital economies face increasing risks from cyber threats, operational disruptions, and market uncertainties. Traditional resilience approaches are often reactive and insufficient for dynamic environments. This paper proposes an AI-Driven Business Resilience Framework (AIBRF) that integrates data acquisition, AI analytics, risk prediction, adaptive decision-making, resilience orchestration, and continuous learning. By leveraging machine learning, predictive analytics, and intelligent automation, the framework enables proactive risk management and real-time adaptation. Experimental results demonstrate improvements in risk detection, recovery time, decision-making efficiency, resource utilization, and business continuity. The proposed framework provides a scalable and intelligent approach for enhancing organizational resilience in digital economies, while future enhancements may incorporate generative AI, federated learning, explainable AI, and blockchain technologies.
Anita Verma· International Journal of Art...· 0 citations