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Strategic alignment between business strategy and digital infrastructure has emerged as a pivotal factor in determining organizational competitiveness in the digital era. This conceptual paper synthesizes recent literature on business-IT alignment, enterprise architecture, and infrastructure-enabled capabilities to propose the Strategic Infrastructure Alignment Model (SIAM). This multi-layer framework elucidates how technology architecture mediates the relationship between strategic intent and competitive outcomes. Drawing on dynamic capabilities theory and resource-based views, SIAM incorporates five core components: (1) business strategy layer, (2) digital infrastructure layer, (3) enterprise architecture alignment mechanisms, (4) capability orchestration systems, and (5) performance and adaptation feedback loop. The model highlights bidirectional influences: aligned infrastructure enables agile execution of strategy, while strategic feedback refines architecture for sustained advantage. By addressing gaps in prior alignment models that overlook recursive dynamics in digital contexts, SIAM offers a novel lens for understanding how modular, scalable technology architectures foster innovation, operational resilience, and market positioning. Implications extend to managerial practice, emphasizing proactive governance of architecture to harness digital infrastructure for competitiveness amid rapid technological change. This framework advances theoretical discourse in digital business and management studies by integrating enterprise architecture as a strategic mediator rather than a mere technical enabler.

The rapid maturation of artificial intelligence and automation technologies is driving a fundamental shift in digital firms, where business processes evolve from human-managed or rule-based automated operations to fully autonomous systems capable of self-execution, adaptation, and decision-making. This theory-development article conceptualizes the emergence of autonomous business processes and delineates their transformative implications for organizational design. Integrating streams of research on robotic process automation, algorithmic management, digital transformation, and human–AI collaboration, we advance a novel theoretical framework that explains the mechanisms of transition and the resulting reconfiguration of structures, authority distribution, and governance. We argue that increasing automation sophistication diminishes traditional hierarchical controls, fosters hybrid human-machine ecosystems, and demands new accountability frameworks to sustain strategic oversight. Six propositions articulate the causal pathways linking AI-enabled execution, process autonomy, decision authority redistribution, and organizational redesign. A conceptual model visually maps the progression from human-controlled to autonomous layers, incorporating feedback loops for continuous learning. Contributions to digital business and management studies include a foundational theory for navigating automation-induced transformations, with implications for leaders seeking to balance machine autonomy with human strategic input while mitigating risks of opacity and power imbalances. This framework provides a platform for future inquiry into the performance and ethical outcomes of autonomous processes in digital organizations.

In the digital economy, firm performance increasingly depends on the ability to convert raw data into strategic knowledge that generates sustained information advantage. This theory-development article re-examines the knowledge-based view through a digital lens, distinguishing data as raw inputs, information as processed signals, and knowledge as contextually applied understanding. We argue that information advantage emerges not automatically from data volume but through deliberate analytics-enabled transformation processes supported by digital infrastructures. We synthesize insights on data as a strategic resource, analytics capabilities, digital information asymmetries, and knowledge accumulation in data-rich environments. The article advances five theoretical propositions that link data resources to knowledge development, strategic action, and superior firm performance while identifying organizational moderators that strengthen or erode information advantage. A conceptual model illustrates the dynamic flow from data to performance with feedback loops. By integrating the knowledge-based view with digital-specific mechanisms, this work offers a novel framework for understanding competitive positioning in digital markets. Theoretical contributions and managerial implications for capability development are discussed.

In technology-driven markets characterized by hyper-velocity change, organizational speed has emerged as the decisive source of competitive advantage. Firms that master rapid strategic response consistently outperform rivals by sensing environmental signals earlier, deciding faster, and executing with precision before opportunities dissipate. This managerial perspective article synthesizes recent scholarship on dynamic capabilities, digital transformation, and high-velocity environments to demonstrate how organizational design can be deliberately engineered for speed. It first examines the strategic challenge of competing through velocity: the pressure to accelerate decision-making while preserving strategic quality, the tension between flexibility and coordination, and the risk of coordination breakdowns in volatile settings. The core contribution is a practical managerial framework—Velocity Engineering—comprising six interlocking components that translate environmental turbulence into sustained performance gains. Digital technologies are shown to act as accelerators across all layers, yet the framework also highlights critical risks of rushed decisions and strategic misalignment. By providing actionable design principles and a visual conceptual model, the article equips senior leaders with a blueprint for building firms that do not merely react but anticipate and shape the pace of industry evolution. The analysis concludes that organizational speed is no longer an emergent property but a deliberate architectural choice in the digital era.

The rapid integration of artificial intelligence and algorithmic systems into core organizational processes has transformed decision-making, yet it has simultaneously exposed critical deficiencies in traditional corporate governance mechanisms. Algorithmically mediated organizations now confront unique challenges in maintaining accountability for opaque automated decisions, ensuring transparency in high-stakes outcomes, and exercising strategic oversight amid rapid technological evolution. This conceptual manuscript synthesizes contemporary scholarship to map these tensions and introduces the SOAR Framework—Strategic Oversight for Algorithmic Responsibility—as a novel multi-layered governance architecture. Developed through a systematic review of peer-reviewed sources, the framework comprises six interdependent layers: the algorithmic decision core, transparency and explainability systems, accountability assignment protocols, strategic oversight bodies, risk and compliance shields, and adaptive feedback loops. By embedding human-AI hybrid controls and continuous audit mechanisms, SOAR enables organizations to align algorithmic mediation with ethical, legal, and strategic imperatives. The model addresses pressing gaps identified in existing literature, including the diffusion of responsibility in AI-driven environments and the insufficiency of conventional board-level oversight. Contributions to digital business and management studies include a practical blueprint for implementation and a conceptual foundation for future empirical testing. Ultimately, the SOAR Framework equips corporate leaders to govern algorithmic systems responsibly while preserving competitive advantage in digitally transformed enterprises.

Digital business ecosystems powered by platform networks are defined by profound interdependence among participating firms, where success depends on coordinated actions across autonomous actors rather than hierarchical control. Traditional management approaches fall short in these settings, as firms must simultaneously manage complementarities, network effects, and coopetition while enabling value co-creation. This manuscript introduces the Strategic Coordination of Interdependence in Platform Ecosystems (SCIPE) framework. This novel multi-layer conceptual model integrates actor roles, interdependence structures, coordination mechanisms, non-hierarchical governance, and adaptive feedback loops. Synthesized from peer-reviewed publications spanning strategic management, information systems, and innovation journals, the framework addresses critical gaps in the literature on ecosystem orchestration and governance. By delineating how platform owners and complementors can strategically align without formal authority, the SCIPE Framework advances theory on interdependence management and offers practical guidance for sustaining ecosystem vitality. The model emphasizes dynamic adaptation through feedback, balancing cooperation and competition to enhance resilience and competitiveness in digital markets. Theoretical contributions bridge fragmented streams on platform dynamics, while managerial implications equip leaders with actionable layers for coordination. Future extensions include cross-industry validation and longitudinal studies of adaptation processes.

In today’s hyper-connected economy, technological volatility and market uncertainty have become persistent features rather than episodic shocks. Digital organizations must therefore move beyond traditional efficiency-driven designs to embed resilience as a core strategic capability. This managerial perspective article synthesizes insights from dynamic capabilities theory, digital transformation research, and organizational design literature to identify actionable principles for building resilient digital firms. Drawing on peer-reviewed studies, the analysis highlights how sensing, robustness, adaptation, and learning mechanisms interact with digital infrastructure to create self-reinforcing resilience cycles. Key challenges—rapid technological obsolescence, platform ecosystem shifts, and demand volatility—are examined alongside practical design levers such as modular architectures, real-time data orchestration, and cross-functional reconfiguration routines. A novel conceptual framework is introduced to visualize the continuous resilience cycle and specify the managerial actions required at each stage. The article concludes that resilience is not an emergent property but a deliberate strategic design outcome. Managers who proactively invest in anticipation capabilities, buffering mechanisms, and learning loops can convert volatility into sustained competitive advantage while safeguarding performance under disruption. These principles offer executives a pragmatic roadmap for redesigning digital organizations that thrive amid uncertainty rather than merely survive it.

The digital economy has fundamentally altered the foundations of competitive strategy, shifting firms from reliance on scarce physical and intangible resources toward data-rich environments where advantage stems from continuous data accumulation, analytics, and platform-mediated interactions. This theory-development article synthesizes recent literature on digital platforms, big data analytics, and ecosystem dynamics to propose new logics of competitive advantage characterized by speed, scale, learning, and connectivity. Traditional resource-based and positioning views are transformed as firms leverage data as a generative resource, platforms as coordination mechanisms, and algorithms for real-time adaptation. The article identifies key mechanisms driving this shift: data-driven resource reconfiguration, network effects amplifying scale advantages, algorithmic competition enabling dynamic strategic adjustment, and ecosystem positioning fostering connectivity. Five theoretical propositions articulate these relationships, culminating in a conceptual model illustrating the evolutionary trajectory from traditional to data-driven strategies. By highlighting feedback loops of continuous learning and adaptation, the framework explains how firms sustain advantage in volatile, data-intensive markets. Contributions include a reconceptualization of competitive logics in the digital era and implications for strategic management in platform-dominated ecosystems.

The integration of artificial intelligence (AI) into strategic management has transformed how organizations design decision systems and pursue competitive advantage in digital environments. This narrative literature review synthesizes peer-reviewed studies, focusing on AI-enabled strategic decision-making, algorithmic organizational systems, and the mechanisms through which AI generates sustained performance gains. Drawing on top-tier journals such as Strategic Management Journal, MIS Quarterly, and Technological Forecasting and Social Change, the analysis identifies recurring patterns of augmentation rather than replacement. It surfaces persistent tensions in human–AI collaboration and governance. Key findings show that AI augments predictive accuracy and resource allocation, yet introduces novel risks related to algorithmic bias, ethical oversight, and the erosion of traditional sources of advantage. The review traces the field’s evolution from early conceptual explorations of human–AI symbiosis to more recent examinations of generative AI’s disruptive potential and firm-level outcomes. Conceptual overlaps emerge around the centrality of hybrid decision architectures, while inconsistencies appear in assessments of long-term competitive sustainability. By mapping these streams and their interrelationships, the manuscript offers a structured foundation for understanding AI’s strategic role. It highlights critical gaps in cross-industry generalizability, ethical frameworks, and the interplay between technological affordances and organizational adaptation. This synthesis equips scholars and executives with an integrated lens on how AI is reconfiguring strategic management in the digital age.

The digital economy has fundamentally altered how firms innovate, shifting the emphasis from linear value chains to platform-based architectures that unlock multi-sided interactions and ecosystem-wide value creation. This narrative literature review synthesizes conceptual insights from peer-reviewed studies published in leading management and information systems journals. It examines platform-driven mechanisms that enable novel forms of value co-creation, data monetization, and ecosystem orchestration while navigating inherent tensions between value creation and capture. Core themes include the architecture of multi-sided markets, value co-creation practices, AI and data-enabled transformations, governance and monetization strategies, and evolutionary pathways in digital ecosystems. By comparing theoretical perspectives across these streams, the review reveals how platforms reduce transaction costs, amplify network effects, and integrate artificial intelligence to build dynamic capabilities. It also surfaces unresolved challenges such as platform governance in complex ecosystems, ethical implications of AI-driven revenue models, and the sustainability of value capture amid rapid technological change. The synthesis concludes by identifying promising research directions, including cross-ecosystem interactions and hybrid governance models. This conceptual overview equips scholars and practitioners with an integrated understanding of platform-based value creation as the cornerstone of competitive advantage in the digital era.

Algorithmic decision systems (ADS) are rapidly transforming organizational decision-making by automating routine and complex processes across strategy, operations, human resources, and customer management. Drawing on peer-reviewed literature, this research agenda article examines the evolution of ADS from traditional human-centric models to hybrid human–algorithm configurations and increasingly autonomous systems. It highlights substantial opportunities, including enhanced efficiency, scalability, and data-driven precision, alongside critical risks such as algorithmic bias, opacity, reduced accountability, and erosion of human judgment. Governance challenges—encompassing fairness, transparency, explainability, and ethical oversight—remain unresolved and demand new theoretical and managerial frameworks. The paper first traces the historical and technological trajectory of algorithmic integration in organizations, then analyzes emerging dynamics, including bias amplification, tensions in human–AI interaction, and dependence risks. A conceptual roadmap visualizes these interrelationships and pathways for governance intervention. By synthesizing insights from leading journals in management, information systems, and strategy, the article identifies persistent theoretical gaps in organizational adaptation, legitimacy, and long-term societal impact. It concludes with a structured future research agenda comprising twelve targeted questions to guide scholars and practitioners toward responsible ADS deployment. This work contributes a comprehensive foundation for advancing theory and practice in digital business and management studies.

Digital transformation has become a central construct in business and management studies, but its conceptual boundaries remain unstable. The term is used to describe technological adoption, strategic renewal, organizational redesign, business model innovation, and performance transformation, often without clear differentiation among these levels of analysis. This review addresses the problem that digital transformation research has expanded faster than its theoretical integration. In particular, the relationships among strategic alignment, organizational capability, and performance outcomes remain inconsistently theorised and unevenly measured. The objective of the article is to critically review the digital transformation literature through three interrelated lenses: strategic alignment, organizational capability, and performance outcomes. The article treats digital transformation not as a purely technological phenomenon but as a strategic and organizational process whose value depends on fit, readiness, and execution. The review finds that the literature suffers from conceptual ambiguity, weak integration between alignment and capability perspectives, and considerable heterogeneity in performance measurement. It concludes that future research requires stronger conceptual integration, more rigorous longitudinal designs, and more critical attention to the conditions under which digital transformation creates, fails to create, or destroys organizational value.

Digital technologies are reshaping how small and medium-sized enterprises design, deliver, and capture value. Yet SMEs do not experience digital transformation in the same way as large firms because they often operate with narrower financial margins, limited managerial bandwidth, weaker digital infrastructures, and fewer specialised capabilities. This systematic review examines the literature on digital business model innovation in SMEs from. It focuses on three interrelated themes: value creation mechanisms, resource constraints and capability barriers, and market scalability pathways. The findings show that SME digital business model innovation is not a single phenomenon but a family of related changes involving digital channels, platforms, data-driven services, digitalised customer interfaces, digitally enabled operations, and ecosystem participation. Across the literature, value creation is most often linked to efficiency gains, enhanced customer access, improved responsiveness, digital service augmentation, and new market reach. The review concludes that the field remains fragmented and insufficiently cumulative. More integrated research is needed to explain how SMEs convert limited resources into digital capabilities, how these capabilities reshape business models, and under what conditions digital business model innovation supports scalable and sustainable growth.

Digital transformation has intensified the need for leadership that can guide organizations through technological, strategic, and cultural change. Although digital technologies create opportunities for innovation and performance improvement, their organizational value depends on how leaders mobilize people, capabilities, and change processes. Digital leadership has therefore emerged as a distinct managerial concern rather than a simple extension of conventional leadership. The literature on digital leadership remains fragmented across leadership studies, organizational change, human resource management, and information systems research. Some studies emphasize managerial competencies and strategic direction, while others focus on employee readiness, organizational agility, or technology-enabled performance outcomes. This fragmentation limits the development of an integrated understanding of how leadership enables successful transformation. This integrative review synthesises peer-reviewed journal articles. It examines digital leadership as a managerial capability, employee readiness as a condition for change acceptance, and transformation success as a multidimensional outcome. The review does not present new empirical data but integrates conceptual and empirical findings across related research streams. The synthesis identifies digital leadership as a multi-level capability involving strategic vision, digital literacy, change communication, empowerment, learning orientation, and the capacity to align technology with organizational purpose. Employee readiness emerges as a central mediating condition shaped by trust, perceived usefulness, digital self-efficacy, organizational support, and involvement in change processes. Transformation success depends not only on technology adoption but also on leadership alignment, cultural adaptation, agile structures, and meaningful performance measurement. The review contributes an integrative framework linking managerial capability, employee readiness, organizational change mechanisms, and transformation outcomes. It argues that digital leadership should be studied as a processual and relational capability rather than as a static set of traits. Future research should develop multi-level models that connect leadership behaviour, employee psychology, organizational routines, and measurable transformation success.

Digital transformation has become a central strategic challenge for firms operating in competitive and uncertain markets. Yet the ability to transform successfully depends not only on adopting digital technologies but also on aligning digital strategic intent with the organizational capabilities required to enact it. This article develops a conceptual framework for understanding this alignment problem. Existing digital transformation research has generated important insights into digital strategy, innovation, business models, and dynamic capabilities. However, these streams often remain fragmented, treating strategic direction and organizational capability as related but insufficiently integrated domains. As a result, firms may articulate ambitious digital strategies without possessing the capabilities needed to implement, adapt, and sustain them. The objective of this article is to propose the Digital Strategy–Capability Alignment Framework. The framework explains how firms can manage business transformation by aligning strategic digital choices with capability configurations under conditions of uncertainty, competitive pressure, and technological change. It positions alignment as a dynamic managerial process rather than a static planning exercise. The article is based on a conceptual synthesis of  peer-reviewed journal articles published. These studies are integrated across strategic management, digital innovation, digital transformation, dynamic capabilities, business model innovation, and competitive strategy. The synthesis identifies the core dimensions of digital strategy, the organizational capabilities required to support them, and the market contingencies that shape the value of alignment. The framework contributes to theory by specifying how profile fit, contingency fit, and temporal fit connect digital strategy to transformation outcomes. It contributes to practice by offering managers a diagnostic lens for identifying strategic misalignment, capability gaps, and transformation risks. The article argues that sustained transformation success depends on continuously recalibrating the relationship between what the firm seeks to become digitally and what it is organizationally capable of doing.

Digital maturity has become a widely used concept for assessing how firms progress in digital transformation. In both managerial practice and academic research, maturity is often represented as a measurable condition that indicates how far an organization has advanced in adopting digital technologies, redesigning processes, and developing digital capabilities. This view has helped organizations diagnose digital gaps, compare current and desired states, and structure transformation initiatives. However, the dominant maturity logic also creates theoretical limitations. Many maturity models imply that firms move through relatively stable and sequential stages, even though digital transformation unfolds under uncertainty, competitive pressure, technological discontinuity, and organizational resistance. As a result, maturity scores may describe the presence of digital resources but fail to explain whether firms can adapt strategically when external conditions shift. This article reconceptualises digital maturity as strategic adaptability. Rather than treating maturity as a static stage, it defines digital maturity as the firm’s ongoing capacity to align digital strategy, organizational capabilities, resource configurations, and managerial action with changing technological and market conditions. This reinterpretation positions maturity as a dynamic, adaptive property of the firm rather than a checklist of completed digital initiatives. The contribution is threefold. First, the article critiques the static assumptions of existing digital maturity models. Second, it defines strategic adaptability as the central mechanism that translates digital maturity into transformation outcomes. Third, it offers a conceptual foundation for future empirical research and managerial diagnosis by shifting attention from maturity as status to maturity as adaptive capacity.

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.

Despite substantial investment in digital technologies, many digital transformation initiatives fail to deliver the strategic, operational, and organizational outcomes expected of them. The persistence of such failures suggests that the problem cannot be explained only by technology selection, implementation delays, or budget overruns. Instead, digital transformation failure reflects a deeper managerial misunderstanding of what transformation actually requires. This perspective article argues that digital transformation fails when it is treated as a conventional management project rather than led as a long-term process of organizational change. Project thinking encourages managers to focus on systems, milestones, budgets, and delivery schedules, while underestimating leadership commitment, cultural adaptation, and strategic coherence. As a result, organizations may install digital tools without transforming how decisions are made, how people work, or how value is created. The objective of this article is to diagnose three interconnected managerial failure mechanisms: leadership gaps, cultural resistance, and strategic misalignment. Leadership gaps weaken vision, commitment, role modelling, and digital literacy. Cultural resistance blocks behavioral change, reinforces legacy routines, and reduces employee willingness to engage with new digital ways of working. Strategic misalignment disconnects digital initiatives from the core business agenda, producing fragmented efforts that rarely scale. The article develops an evidence-based perspective by synthesizing peer-reviewed and practitioner-oriented articles published. It does not present new empirical data. Instead, it uses critical synthesis, conceptual reasoning, and practical interpretation to explain why digital transformation failure is fundamentally a management problem rather than a technological inevitability. The article concludes that digital transformation requires a shift from project management to organizational leadership. Managers must move beyond treating digital transformation as an implementation programme and instead lead it as a continuous process of strategic renewal, cultural adaptation, and capability development. Only then can digital initiatives become embedded in the organization’s operating logic and contribute meaningfully to transformation outcomes.

Digital disruption has become a persistent condition of contemporary business rather than an exceptional event. Firms face cybersecurity risks, platform shifts, supply-chain volatility, workforce reconfiguration, data overload, and rapid changes in customer behaviour. These pressures require resilience capabilities that are explicitly suited to digital environments. Existing resilience research has clarified how organizations anticipate, absorb, recover from, and adapt to disruption. However, digital business resilience requires more than general robustness or crisis response. It depends on the capacity to use digital systems, strategic flexibility, and human adaptability as mutually reinforcing sources of continuity and renewal. This article develops an original Digital Business Resilience Framework. The framework integrates three pillars: strategic agility, workforce adaptability, and data-driven decision infrastructure. It argues that resilience emerges when these pillars interact as a coherent system rather than operating as separate managerial initiatives. The central contribution is to reposition digital business resilience as a triadic capability. Strategic agility enables rapid direction-setting, workforce adaptability enables human execution under uncertainty, and data-driven decision infrastructure enables timely sensemaking and coordinated action. Together, these elements help firms anticipate disruption, withstand shocks, recover operationally, and reconfigure for future competitiveness

Digital business research has generated a wide range of capability concepts to explain how firms compete, innovate, adapt, and learn in technology-intensive environments. Yet these concepts often appear under overlapping labels, including digital capabilities, analytics capabilities, platform capabilities, operational capabilities, and dynamic capabilities. This proliferation has enriched the field but has also made it difficult to compare findings across studies. The central problem addressed in this article is the absence of a coherent taxonomy for digital business capabilities. Without clear classification rules, researchers may treat different capabilities as equivalent, while managers may invest in digital resources without understanding which capability category they are actually developing. This ambiguity weakens conceptual precision, measurement design, and strategic capability development. The objective of the article is to develop an original taxonomy that classifies digital business capabilities into four distinct categories. These categories are Customer Intelligence, Operational Agility, Platform Coordination, and Strategic Learning. Each category is defined by a distinct value-creation logic, resource orientation, temporal focus, and managerial purpose. The resulting taxonomy identifies four mutually exclusive and collectively exhaustive categories of digital business capabilities. It defines the sub-dimensions of each category, explains how the categories differ, and provides tables that support classification, comparison, and managerial application. The taxonomy offers a shared language for scholars and practitioners seeking to analyse, measure, and develop digital business capabilities with greater precision.

Many organizations are now several years into continuous digital transformation, yet the managerial mood surrounding transformation has changed. What was once framed as renewal, modernization, and strategic reinvention is increasingly experienced as another wave of pressure. Employees, middle managers, and business units are often asked to absorb new systems, new routines, new data practices, and new operating models before earlier initiatives have stabilized. This article argues that digital transformation has reached a point where fatigue itself must be treated as a serious management concern. Digital transformation fatigue refers to a state of organizational exhaustion that emerges when digital change becomes excessive, fragmented, and insufficiently connected to strategic purpose. It is not simply resistance to technology or a temporary decline in motivation. Rather, it reflects the cumulative effect of initiative overload, employee resistance, and declining strategic focus. When these forces interact, organizations may continue launching digital projects while losing the energy, attention, and coherence required to benefit from them. The objective of this viewpoint article is to define and diagnose digital transformation fatigue as a distinct management problem. The article argues that fatigue is often self-inflicted because managers expand the transformation agenda faster than the organization can meaningfully absorb it. It also shows why employee resistance should not be dismissed as irrational opposition, but should be read as a signal that the pace, volume, or meaning of transformation has become mismanaged. The article therefore shifts attention from technology adoption alone to the managerial conditions under which digital change remains sustainable. The article concludes that digital transformation fatigue is real, diagnosable, and manageable. Organizations do not need less ambition, but they do need more disciplined prioritisation, stronger transformation governance, greater employee involvement, and clearer strategic anchoring. The central managerial lesson is that sustainable digital transformation depends not on adding more initiatives, but on orchestrating organizational energy, focus, and commitment. Recognising fatigue is therefore not a retreat from transformation, but a condition for making transformation effective.