From Automation to Agentic Intelligence: A Framework for AI-Driven Digital Transformation, Innovation Capability, and Strategic Decision-Making in Contemporary Enterprises
Aug 2026· Journal of business and management studies· 0 citations
TL;DR
The authors present a conceptual framework for the relationship between AI capability inputs and organizational absorption processes and downstream effects, which are moderated by the regulatory environment and innovation industry context and mediated by innovation capability and employee AI literacy.
Abstract
From descriptive analytics to generative systems and, most recently, autonomous agentic architectures, artificial intelligence (AI) is transforming how enterprises achieve digital transformation and develop innovation capabilities and how they shape strategic decision-making. This study highlights an AI adoption-value paradox; despite significant investments, many organizations report a lack of business value from AI. The authors present a conceptual framework for the relationship between AI capability inputs (generative, predictive, and agentic) and organizational absorption processes (culture, skills, and governance) and downstream effects (digital transformation maturity, innovation performance, and decision quality), which are moderated by the regulatory environment and innovation industry context and mediated by innovation capability and employee AI literacy. Based on decision intelligence theory, the technology-organization-environment (TOE) framework, and dynamic capabilities theory, this study follows a mixed-methods approach with a structured survey of mid-to large-sized enterprises, followed by semi-structured interviews with C-suite and senior IT executives that will be analyzed through partial least squares structural equation modelling (PLSEM) and thematic analysis. This study extends the dynamic capabilities theory to the domain of agentic AI, provides managers with practical guidelines for governing AI decisions, and provides regulators with insights into responsible AI adoption. The implications, limitations, and directions for empirical validation are discussed.
Purpose: This paper investigates the potential of Agentic Artificial Intelligence (AI) to be a driver of innovation and change management in organizations undergoing technology-enabled transformation. It addresses the impact of Agentic AI features, such as autonomy, reasoning, planning, tool use, memory, and multi-agent coordination, on innovation processes, organizational readiness, human–AI interaction, as well as responsible governance.
Methodology: The methodology used in this paper was a desk-based conceptual review design, which was supported by framework development. The literature was identified through a concept-driven structured search process using ScienceDirect and Emerald Insight. The concepts reviewed were the five interrelated concepts: Agentic AI/AI agents, AI and innovation management, change management/AI readiness, digital transformation/technology-enabled transformation, and responsible AI governance. A conceptual and thematic synthesis method of analysis was used to analyze the selected literature.
Findings: The review revealed that Agentic AI is able to assist with ideation, knowledge recombination, experimentation, process redesign, business model innovation, and workflow coordination. However, transformation does not occur automatically. The factors of organizational readiness, data quality, leadership support, employee trust, and role clarity, along with human-AI interaction and responsible governance, are essential for transformation based on Agentic AI. The paper also noted that Agentic AI presents a governance paradox because while it can have value due to autonomy, the greater the autonomy, the more likely there will be a need for human oversight, audit trails, escalation protocols, transparency, and restrictions on autonomous activity.
Unique Contribution to Theory, Practice and Policy: The paper is unique in bringing together, in a single conceptual framework, socio-technical, dynamic capability, AI-readiness, change-management, and responsible AI governance views on the topic of Agentic AI-enabled transformation. It makes a contribution to practice by demonstrating that Agentic AI can be used as an organizational transformation capability, not as a technology with a narrow scope of practice. It helps shape policy by highlighting the importance of establishing real-world, reliable governance standards, accountability measures, controlled testing spaces, and sector-specific guidance for the responsible adoption of Agentic AI.
Fatma Albalooshi· International journal of tec...· 0 citations
A framework in which the microfoundations of dynamic capabilities operate through organizational readiness to shape AI-driven industrial management capability and, in turn, operational and managerial performance outcomes is developed.
Hoogendijk Ha· Journal of Economic, Finance...· 0 citations
The rapid development of Artificial Intelligence (AI) has significantly transformed the operations and strategies of digital businesses. Initially, AI adoption focused on efficiency, automation, and extracting insights from large-scale data, but the lack of systematic innovation frameworks limited its full integration into enterprise ecosystems. This paper provides an in-depth analysis of AI-based innovation models for smart digital firms, focusing on foundational approaches. AI-driven innovation frameworks utilize machine learning, data analytics, cognitive computing, and automation to enhance business processes, customer interaction, and decision-making, enabling scalable and responsive enterprise systems. It also highlights the role of data-centric architectures, cloud computing, and algorithmic intelligence in digital transformation. The study examines the evolution from traditional IT systems to predictive, automated smart ecosystems and reviews key frameworks such as data-driven models, knowledge-based systems, and enterprise intelligence architectures. A multi-layered AI innovation model is proposed, including data acquisition, processing, intelligence, and business integration, supported by feedback mechanisms and continuous learning for adaptability and scalability. Results indicate that AI frameworks improve efficiency, reduce costs, and enhance decision accuracy and innovation. The paper concludes that structured AI frameworks are essential for sustainable innovation in digital enterprises and highlights future directions, including deep learning and autonomous systems.
Tharindu Jayasinghe, Nimal Perera· International Journal of Art...· 0 citations
The findings suggest that AI creates enterprise value through cognitive automation, decision intelligence, and business model innovation, but their effectiveness depends on data governance, digital leadership, human capital, financial readiness, and regulatory support.
Diep Van Vu· Tạp chí Khoa học Đại học Côn...· 0 citations
An extended theoretical framework that integrates AI-specific trustworthiness and organizational digital maturity as contingency mechanisms is proposed and offers actionable directives for policymakers and business managers seeking to accelerate digital maturity.
Nor Fazalina Salleh, N. H. Asnawi, Norfazlina Ghazali et al.· International journal of res...· 0 citations
Artificial intelligence (AI) has become a central element of digital transformation, yet strategic-management research still lacks a sufficiently precise explanation of when AI constitutes a strategic resource rather than a widely accessible technology. This conceptual study extends the Digital Resource View (DRV) by theorizing AI as a configured, multi-layered digital resource architecture composed of AI infrastructure, AI capability, AI-driven decision systems, and AI-orchestrated digital platforms. Following a theory-adaptation design, the article integrates resource-based theory, resource orchestration, dynamic capabilities, digital transformation, organizational AI capability, platform, and institutional perspectives. The resulting framework specifies how the four AI layers are connected and how they activate three DRV transmission mechanisms—digital value creation, digital rareness, and uncertainty-driven imitation barriers—to generate competitive advantage. Normative digital pressure is introduced as a contextual condition that shapes the conversion of AI-enabled decision systems into realized digital value. Ten propositions clarify the causal logic and make the framework empirically testable. The study contributes by distinguishing AI resource layers from AI outcomes, replacing a direct technology–performance assumption with an orchestration-based mechanism, and clarifying how configurational uniqueness and learning uncertainty can sustain advantage even when core AI technologies are widely available. The framework also identifies boundary conditions for platform dependence, institutional context, and governance capacity, thereby providing a more disciplined foundation for future empirical research on AI-enabled competitive advantage.
A. Abubakar· PERFECT EDUCATION FAIRY· 0 citations
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