The impact of intelligent digital systems on the development of analytics and value-added management in the economy

The rapid development of intelligent digital systems has significantly reshaped economic decision-making, shifting analytical practices from retrospective evaluation to predictive and prescriptive modeling. Technologies such as machine learning, big data analytics, and real-time monitoring enhance the speed, accuracy, and adaptability of managerial processes, transforming both operational efficiency and the structure of value creation.

Intelligent systems replace static, periodic analytical frameworks with continuous data collection, automated pattern recognition, and dynamic modeling of complex environments. By integrating heterogeneous information flows, these systems provide deeper insight into consumer behavior, production performance, and market dynamics, supporting more informed and resilient strategic decisions [1, p. 162-174].

At the same time, value-added management evolves as digital tools enable more precise assessment of value drivers, including intangible assets and cross-functional interactions. The convergence of intelligent analytics and value-centered management creates the foundation for flexible resource allocation, proactive risk mitigation, and innovative economic models. This article explores the impact of intelligent digital systems on analytics and value-added management in the modern economy [2, p. 66-69].

Transformation of analytical practices under intelligent digital systems

The implementation of intelligent digital systems fundamentally changes the methodological foundations of economic analytics. Traditional analytical models relied on aggregated, retrospective, and often delayed information, which limited their ability to capture dynamic interactions and emerging anomalies within complex economic environments. Intelligent systems, by contrast, operate on continuous data streams generated by sensors, enterprise platforms, digital marketplaces, and user interactions [3, p. 122-129]. This enables real-time analytical processing, where patterns, correlations, and deviations are identified as they appear, dramatically improving the timeliness and reliability of managerial insights.

Machine learning algorithms further enhance analytical capabilities by enabling automated model refinement and adaptive learning. Instead of relying solely on predefined assumptions, intelligent systems update analytical parameters in response to new data, allowing organizations to detect hidden trends, nonlinear dependencies, and emerging risks [4, p. 128-136]. This form of adaptive analytics supports scenario modeling and predictive forecasting at a level of sophistication previously unattainable with conventional statistical tools. As a result, firms are able to anticipate shifts in consumer demand, optimize production processes, and mitigate operational risks with greater precision.

Moreover, intelligent digital systems integrate heterogeneous sources of information - including financial indicators, operational metrics, customer behavior data, and external market signals – into a unified analytical ecosystem [5, p. 16-25]. This integration expands the analytical horizon beyond single-department data silos and enables cross-functional evaluation of value drivers. The transformation of analytical practices under intelligent systems thus leads to a more holistic understanding of economic activities, supporting strategic decision-making grounded in comprehensive, real-time evidence.

Intelligent systems as drivers of value-added management

Intelligent digital systems significantly expand the conceptual and practical possibilities of value-added management by introducing new mechanisms for identifying, quantifying, and optimizing value across economic processes [6, p. 3-8]. Whereas traditional value analysis relied on standardized financial metrics and periodic performance reviews, intelligent systems use advanced analytics to model value creation as a dynamic, multidimensional construct. Through continuous monitoring of operational efficiency, customer experience, resource utilization, and intangible asset performance, organizations can detect underexplored value reserves and redesign processes to maximize long-term returns.

One of the key contributions of intelligent systems lies in their capacity to quantify aspects of value that were previously difficult to measure. Machine learning models and semantic analytics make it possible to capture non-financial drivers such as innovation intensity, service personalization quality, supply-chain resilience, and employee engagement - factors that increasingly determine competitive advantage in digital economies [7, p. 103541]. By integrating these intangible value dimensions into decision-making frameworks, firms can shift from cost-oriented optimization to strategic value enhancement, aligning resource allocation with long-term economic potential rather than short-term efficiency alone [8, p. 47-52].

Furthermore, intelligent systems support proactive value management by enabling predictive modeling of value dynamics under different internal and external conditions. Organizations can simulate alternative scenarios, evaluate the impact of technological upgrades, regulatory shifts, or market fluctuations, and determine which interventions generate the greatest incremental value [9, p. 611-616]. This predictive capacity enhances strategic agility and strengthens investment decision-making, ensuring that managerial actions are consistently aligned with the firm’s value-creation trajectory. As a result, intelligent systems act as critical catalysts for building resilient, innovation-oriented, and value-driven economic models.

Conclusion

Intelligent digital systems fundamentally transform the methodological foundations of economic analytics and the mechanisms through which organizations manage value creation. By integrating real-time data processing, adaptive machine learning models, and automated decision-support tools, these systems enhance the precision, responsiveness, and depth of analytical processes. As a result, firms gain the ability to detect emerging trends, anticipate market fluctuations, and optimize operations based on continuously updated evidence, strengthening their strategic resilience in dynamic economic environments.

The growing convergence of intelligent analytics with value-added management enables organizations to identify and quantify new sources of value, incorporate intangible assets into strategic planning, and design more flexible, innovation-oriented economic models. Intelligent digital systems contribute to improved resource allocation, proactive risk mitigation, and the development of long-term competitive advantages. Their widespread adoption signals a systemic shift toward a more data-driven, adaptable, and value-focused economic structure, shaping the future trajectory of managerial and analytical practices.