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In the data-rich digital economy, organizations face unprecedented volumes of market signals that demand rapid interpretation and strategic action. Traditional competitive intelligence approaches, rooted in periodic environmental scanning, are increasingly inadequate for capturing real-time digital signals and converting them into sustainable advantage. This paper synthesizes recent advances in market sensing capabilities and data-driven competitive intelligence to address a critical gap: the lack of an integrated conceptual architecture that links digital signal capture, intelligence interpretation, and strategic decision-making in continuous feedback loops. The analysis reveals how big data analytics, dynamic capabilities, and real-time monitoring systems reshape organizational sensing processes. The paper introduces the Adaptive Market Sensing Intelligence Framework—a novel conceptual model comprising six interlocking layers that enable firms to transform raw digital signals into actionable strategic insights. The framework advances theory by bridging market sensing and competitive intelligence literatures and offers practical guidance for managers seeking to build resilient intelligence systems in volatile, data-saturated environments. Implications for strategic management and information systems research are discussed, emphasizing the need for continuous, adaptive sensing mechanisms.

In data-rich business environments, traditional strategic planning—built on long-term forecasts, annual budgets, and fixed resource allocation—has become increasingly ineffective. Digital markets reward speed, iteration, and real-time adaptation rather than prediction and control. This managerial perspective article argues that digital strategy must be reconceptualized as continuous experimentation: a strategic logic in which hypothesis generation, rapid testing, data-driven learning, and iterative decision-making replace static plans. Drawing on recent scholarship in digital transformation, agile strategy, and organizational learning, the article demonstrates how leading firms operationalize experimentation through A/B testing platforms, real-time analytics, and cross-functional feedback loops. A new strategic framework—the Continuous Experimentation Strategy Loop—is introduced to guide managers in embedding experimentation into core planning processes. The framework highlights six interlocking elements: hypothesis generation, rapid experimentation, data capture and analytics, learning and insight generation, decision and iteration, and scaling with feedback loops. Practical implementation challenges, including organizational structures, cultural barriers, and risks of over-testing, are examined. The article concludes that in volatile, data-abundant contexts, the ability to experiment continuously is not a tactical tool but the central mechanism of strategic renewal. Managers who treat strategy as perpetual experimentation gain superior adaptability, faster innovation cycles, and sustained competitive advantage.

Firms increasingly invest in data infrastructure, analytics tools, digital platforms, and specialist talent in the expectation that these resources will improve decision quality, operational effectiveness, innovation, and market performance. Yet the relationship between analytics investment and business value remains uneven, because data availability does not automatically generate strategic action. This article addresses the persistent data-to-value challenge by examining why analytics capability often remains under-converted into realized business outcomes.The central problem is that prior research has frequently examined analytics capability, decision-making, and business value as related but insufficiently integrated domains. Analytics capability explains what firms can potentially know, while business value explains what firms ultimately gain, but the conversion mechanism between the two is often underdeveloped. This article argues that managerial interpretation is the missing link that determines whether analytical outputs become meaningful, trusted, and actionable.The objective of this article is to develop a new conceptual model, the Data-to-Business-Value Conversion Model. The model links analytics capability to business value and competitive advantage through managerial interpretation as the central mediating mechanism. It explains how firms move from data resources and analytical outputs to decisions, organizational actions, value creation, and strategic advantage.The proposed model identifies four connected elements: data and analytics capability, managerial interpretation, business value creation mechanisms, and competitive advantage pathways. It shows that analytics capability provides decision potential, managerial interpretation converts that potential into action, business value emerges through organizational mechanisms, and competitive advantage depends on whether value is embedded in difficult-to-imitate routines. The article contributes a testable framework for future research and a practical logic for managers seeking to improve analytics value conversion.