The 7th International Conference on Modern Management based on Big Data (MMBD 2026)

Abstract: The expanding role of artificial intelligence in organizations has reshaped strategic planning, performance management, and operational decision-making. While advanced machine learning models deliver high predictive accuracy, many operate as opaque systems that offer limited insight into the reasoning behind their outputs. From a management perspective, decision quality extends beyond statistical performance; it encompasses transparency, accountability, governance, and stakeholder trust.
This presentation positions Explainable Artificial Intelligence (XAI) within the broader context of responsible and data-driven management. It argues that interpretability strengthens managerial rationality by clarifying how analytical results are generated, thereby supporting informed judgment under uncertainty. In environments characterized by risk exposure, regulatory oversight, and strategic complexity, explainability becomes a critical mechanism for aligning algorithmic outputs with organizational objectives and ethical standards.
By integrating statistical reasoning with interpretable machine learning approaches, organizations can develop decision-support frameworks that balance analytical power with managerial responsibility. Rather than treating explainability as a purely technical enhancement, the study conceptualizes it as a governance-oriented capability that reinforces legitimacy and long-term sustainability. In the era of big data, building trustworthy AI-enabled management systems requires not only accuracy, but also clarity, accountability, and strategic coherence.

Abstract: Digital Transformation (DT) initiatives increasingly demand a structured approach that bridges strategic intent and operational execution. Enterprise Architecture (EA) has emerged as a critical enabler, providing a holistic framework for aligning business objectives with IT capabilities. This paper explores how EA facilitates the transition from strategy to execution by integrating motivational, strategic, business, application, and technology layers through standardized modeling languages such as ArchiMate. Drawing on empirical evidence from multiple sectors—public administration, finance, energy, and education— the research demonstrates that EA not only mitigates organizational complexity but also enhances communication among diverse stakeholders during DT programs and projects. The speech about the research synthesizes findings from surveys, case studies, and interviews conducted across Czechia, revealing three key contributions, which are generalized globally. First, EA-driven modeling of AS-IS and TO-BE states supports informed decision-making and risk management, enabling organizations to design scalable transformation roadmaps. Second, the integration of EA with project and program management methodologies (PRINCE2, PMI, IPMA, Agile) fosters governance structures that ensure strategic coherence and benefit realization. Third, EA artifacts act as boundary objects, bridging semantic gaps between business and IT communities, thereby improving stakeholder engagement and reducing resistance to change. Emerging AI-driven modeling tools further accelerate architecture design, automate repetitive tasks, and enhance predictive capabilities for transformation planning. Results indicate that sectors with mature EA practices achieve higher digital maturity and operational agility, while organizations neglecting EA face fragmented initiatives and diminished value delivery.

Keywords: Enterprise Architecture, Digital Transformation, ArchiMate, AI in Architecture Modeling, Business–IT Alignment, AS-IS/TO-BE Modeling, Digital Maturity

Abstract: To be updated