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This conceptual preprint aims to analyze how decision-making can be improved through a framework integrating AI and organizational processes.

June 20, 2026Open Access

Decision Intelligence: A Conceptual Framework for AI-Augmented Organizational Decision Systems

Key Points

This conceptual preprint aims to analyze how decision-making can be improved through a framework integrating AI and organizational processes.
Introduces the concept of Decision Intelligence Capital (DIC) to enhance organizational decision-making.
Proposes a framework addressing interactions among data ecosystems and predictive technologies.
Examines the roles of governance mechanisms and human-AI collaboration in decision environments.
Suggests that effective decision architecture is crucial for leveraging intelligence in organizations.
Argues that competitive advantage stems from operationalizing intelligence rather than merely possessing information.
Highlights the transformation from data-centric to decision-centric frameworks in organizational models.

Abstract

The growing adoption of artificial intelligence, predictive analytics and data-driven technologies has transformed how organizations generate insights and manage information. Despite substantial investments in data infrastructure and analytical systems, many organizations continue to experience difficulties translating information into effective decisions. This challenge highlights an important limitation within contemporary digital transformation initiatives: the assumption that improved analytics automatically leads to improved decision-making. In practice, organizational decisions emerge through complex interactions among data resources, computational systems, human expertise and institutional processes. Consequently, the quality of organizational outcomes depends not only on the availability of information but also on the mechanisms through which intelligence is converted into action. This conceptual preprint introduces the concept of Decision Intelligence Capital (DIC), defined as the accumulated organizational capability generated through the integration of data assets, predictive systems, computational infrastructures and human expertise to improve the quality, speed and adaptability of decisions. The paper argues that decision-making should be understood as a strategic organizational capability rather than merely a managerial activity. As artificial intelligence becomes increasingly embedded within enterprise environments, organizations are transitioning from data-centric and analytics-centric models toward decision-centric operating systems. A conceptual framework is proposed to explain how Decision Intelligence Capital emerges through interactions among information ecosystems, predictive technologies, organizational knowledge and adaptive learning processes. The framework further examines the role of AI-augmented decision systems, human–AI collaboration, governance mechanisms and organizational readiness in supporting intelligent decision environments. The manuscript suggests that future competitive advantage may depend less on information possession and more on the ability to operationalize intelligence through effective decision architectures.

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