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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.

Data governance has become a central organisational concern as firms depend increasingly on data for analytics, digital transformation, regulatory compliance, and operational coordination. Yet many organisations still locate data governance within information technology departments and define it primarily through technical controls, data quality rules, security standards, and regulatory obligations. This article argues that such a framing is too narrow for contemporary data-intensive organisations. When data governance is treated only as an IT compliance function, it is separated from strategic decision-making, business model innovation, customer trust, managerial accountability, and competitive renewal. The objective of this perspective article is to reconceptualise data governance as a strategic management capability. This means understanding data governance not merely as the administration of data assets, but as the managerial capacity to define decision rights, allocate accountability, shape data use, manage risk appetite, and convert data into organisational value. The perspective shows that strategic data governance changes the role of managers, the meaning of risk control, and the metrics by which governance success should be judged. It concludes that organisations seeking to compete through data must elevate data governance from a technical support function to a core management capability owned by senior leaders.

Organizations are investing in artificial intelligence with expectations of improved efficiency, stronger decision quality, better customer experiences, and new sources of competitive advantage. Yet many AI initiatives do not progress beyond experimentation or fail to produce durable business value after deployment. A central reason is that firms often treat AI deployment as a technical implementation challenge rather than as an organizational readiness problem. Existing readiness models help organizations evaluate digital capability, while business value frameworks help managers estimate the strategic and financial promise of technology investments. However, these two assessments are frequently conducted separately. As a result, decision-makers may pursue AI initiatives that appear strategically attractive but exceed the organization’s practical capacity to implement, govern, and absorb them. This article develops the AI Readiness–Business Value Matrix as an original conceptual model for assessing organizational preparedness before artificial intelligence deployment. The matrix maps organizational readiness against expected business value potential, enabling firms to classify AI initiatives into practical decision categories. It is designed to support managerial judgment before major resources are committed. The article synthesises peer-reviewed publications published across AI adoption, organizational readiness, digital transformation, business value, and strategic management. The resulting model defines readiness dimensions, value categories, and four strategic quadrants that guide whether organizations should proceed, prepare, pivot, or postpone AI deployment. Four tables operationalise the model by detailing preparedness dimensions, business value potential, matrix design, and application scenarios. The AI Readiness–Business Value Matrix contributes a practical diagnostic tool for aligning AI ambition with organizational preparedness. It encourages organizations to move beyond technology enthusiasm toward structured evaluation of capability, governance, culture, and strategic value. By doing so, it can reduce deployment failure risk and improve the allocation of scarce managerial, technical, and financial resources.