2026· International Journal of Latest Technology in Engineering, Management & Applied Science· Vol 15, pp. 2105-2122· 0 citations
TL;DR
A structured framework for the literature and theory is built, showing how AI capability fuels innovation via interconnected knowledge processes across the organization, putting human judgment, epistemic governance, and knowledge validation at the center of how organizations learn and innovate with AI.
Abstract
Artificial Intelligence (AI) is reshaping how organizations handle knowledge from the moment they acquire it, to how they generate, validate, integrate, learn from, and put it to work. Research has touched on many of these pieces: AI capability, knowledge management, organizational learning, and innovation, but these areas often feel disconnected in theory. This paper pulls those threads together by building a structured framework for the literature and theory, showing how AI capability fuels innovation via interconnected knowledge processes across the organization.
The approach draws from the Knowledge-Based View (KBV), Nonaka and Takeuchi’s SECI model, Organizational Learning Theory, Dynamic Capability Theory, and recent work on AI capability. Here, AI capability is conceptualized as a higher-order, formative organizational capability. It’s an ensemble of technological infrastructure, data resources, skilled AI professionals, strong coordination and change management, and robust AI governance.
One notable theoretical advance in this work is the introduction of Knowledge Validation and Epistemic Governance. These act as a bridge between AI-powered knowledge acquisition or creation and its integration within the organization. The idea is simple: just because AI creates content doesn’t mean it’s automatically part of the organization’s knowledge. That content must first be checked for accuracy, origin, relevance to context, clarity, bias, and accountability before it’s fully integrated.
The framework presented sees knowledge acquisition and creation as parallel tracks that come together through validation and integration. From there, organizational learning, smarter decision making, and finally, innovation performance follow. It’s not a one-way street, either innovations feedback into new knowledge creation, and what the organization learns helps shape AI capability itself.
The paper offers eight propositions based in theory, and touches on what these mean for future research: how to test these ideas, ways to measure them, the importance of context, and tracking change over time. In sum, this contribution links AI capability to core theories in knowledge and organization, putting human judgment, epistemic governance, and knowledge validation at the center of how organizations learn and innovate with AI.
As organizations integrate artificial intelligence into knowledge‐intensive work, a phenomenon is emerging that knowledge management theory has yet to adequately explain: institutional knowledge can become progressively encoded into AI‐enabled organizational infrastructure through sustained patterns of human use. This paper develops that phenomenon into a formal construct, organizational AI acculturation, defined as the process through which an organization's collective knowledge, values, and decision heuristics become embedded in AI‐enabled systems through repeated human interaction, such that the systems' outputs increasingly reflect the organization's distinctive ways of framing problems and evaluating alternatives. The paper makes three contributions. First, it defines the construct and distinguishes it from adjacent concepts, including organizational memory, absorptive capacity, and transactive memory systems. Second, drawing on organizational learning theory, the resource‐based view, and dynamic capabilities, it explains how institutional knowledge encoded into AI‐enabled systems through sustained human use can become a source of competitive advantage when organizations govern the quality and renewal of what their systems encode. Third, it advances four testable propositions linking the quality of human‐AI interaction to competitive advantage, knowledge persistence under employee turnover, and adaptive capacity. Published organizational cases illustrate the construct in practice, showing that deliberate governance of human‐AI interaction can build defensible knowledge assets, whereas poor governance can stabilize error, bias, and outdated assumptions. The paper concludes with a research agenda focused on measurement, mechanism, governance, and scope conditions. The central claim is that knowledge management theory must expand to account for a new kind of knowledge repository: AI‐enabled systems whose behavior is shaped by the cumulative judgments of the organizations that use them.
Adrienne N. Moore Terrell, Antwon D. Woods· Knowledge and Process Manage...· 0 citations
The pace of artificial intelligence (AI) across industries has created a paradox: numerous organizations purchase AI technologies and even build up an arsenal of algorithms, yet many fail to transform this purchase into sustainable benefit. The construct that is most frequently called upon to give an explanation of this gap- AI capability- is still conceptually disjointed. Earlier literature has focused on adoption, preparation, maturity, and resource collection, and a parallel line has started to consider algorithmic assets as a specific strategic resource (algorithmic capital). The transformation, i.e., how organizations transform algorithmic capital and other resources into a higher-order organizational AI capability, and why that capability should be able to create value, is what is unspecified. The present paper builds up an integrative transformation theory based on the Resource-Based View, the Knowledge-Based View, the Dynamic Capabilities perspective, and Organizational Learning theory. We characterize the organizational AI capability as a higher-order, multidimensional construct; present a three-level architecture that bridges AI resources (including algorithmic capital), mid-level capability constructs, and an emergent organizational capability; describe a formation mechanism; develop seven propositions; and state the boundary conditions. The contribution is a coherent account of how firms move from possessing algorithmic capital to possessing an AI-based organizational capability, and from that capability to competitive advantage.
M. Abuhaimed· International Journal of Res...· 0 citations
The findings show that AI-related competency change extends beyond technical skills toward hybrid and portfolio-based configurations that integrate technical understanding, managerial judgment, learning agility, governance capabilities, and psychological readiness, and demonstrates that no single framework sufficiently explains these shifts.
As artificial intelligence (AI) tools become embedded in everyday work, employees increasingly engage in adaptive, experiential collaboration with AI systems. While such collaboration often generates valuable employee-level tacit knowledge, organizations struggle to translate this learning into strategic capabilities. This study examines how organizations can harness the knowledge emerging from employee–AI collaboration to build knowledge-based dynamic capabilities (KBDCs).
We conducted an inductive, qualitative study based on 29 semi-structured interviews with frontline employees, middle managers and senior leaders across multiple industries.
We develop a process model showing how tacit knowledge generated through micro-level human–AI collaboration is externalized as articulated experiential insights, validated and simplified into shared heuristics, and ultimately codified and embedded in organizational routines. This bottom-up transformation may help firms to renew internal knowledge resources and enhance KBDCs. The process is contingent on enabling conditions such as managerial support, organizational culture and employees' perceived agency in interacting with AI.
Organizations seeking to build dynamic capabilities from AI use should create structures that facilitate articulation, validation and dissemination of employee-level AI insights. Leaders play a critical role in translating individual experimentation into collective learning. Firms can assess their position in the transformation process and invest in systems that support phase-to-phase transitions.
While prior research has emphasized acquiring codified or external knowledge for dynamic capabilities, this study shifts attention to the internal, tacit and evolving knowledge that arises from employee–AI collaboration. We advance theory by unpacking a bottom-up pathway of KBDC formation through which such knowledge is articulated, validated and embedded over time. In doing so, the study also identifies a recursive organizational learning mechanism suited for high-velocity, AI-enabled environments.
Erica Wen Chen, Hongkun Tang, Ben Nanfeng Luo et al.· Management Decision· 0 citations
This study aims to develop a conceptual framework explaining how artificial intelligence-enabled knowledge management (AI–KM) contributes to dynamic organisational capabilities through the socio-cognitive role of Generation Z knowledge behaviours.
Drawing on the dynamic capability view and socio-technical systems theory, this study adopts a theory-building conceptual design based on a structured literature search across Scopus, Web of Science, ScienceDirect and Emerald Insight, supplemented by selected foundational and measurement sources. A final set of 64 scholarly sources informed the conceptual synthesis using a hybrid deductive-inductive thematic coding approach.
This study proposes a multilevel socio-technical framework in which AI–KM contributes to organisational sensing, seizing and reconfiguring through both a complementary direct pathway and an indirect pathway mediated by Generation Z knowledge behaviours, namely, digital collaboration, self-directed learning and openness to innovation. The framework also identifies technostress, trust in AI, digital competence, organisational learning culture, governance, industry risk and workforce composition as boundary conditions.
As a conceptual study, this framework requires future empirical validation, particularly through multilevel research designs that distinguish individual- and team-level knowledge behaviours from organisational-level capabilities.
The framework suggests that organisations should align AI-enabled knowledge infrastructures with employee learning, collaborative routines, governance mechanisms and human oversight.
This study contributes to AI–KM, human–AI collaboration and strategic management literature by refining the notion of Generation Z as a “digital bridge” into a set of contingent socio-cognitive knowledge behaviours through which AI-enabled knowledge becomes strategically consequential.
H. Aulawi, Ayu Latifah, Risa Aisyah et al.· The International Journal of...· 0 citations
Artificial intelligence (AI) is transforming digital knowledge systems (DKS), reshaping how organizations generate, interpret, and apply knowledge in support of organizational learning and strategic decision-making. While AI-enabled systems enhance analytical capability, predictive insight, and information processing speed, their implications for learning quality and epistemic judgment remain insufficiently understood. This study addresses this gap through a systematic literature review that synthesises research across knowledge management, information systems, and organizational learning. Adopting a concept-centric approach, the review analyses peer-reviewed studies published between 2000 and 2026, focusing on how AI-enabled digital knowledge systems are conceptualised and how they influence knowledge processes, organizational learning, and strategic decision-making. The findings indicate a shift from viewing digital knowledge systems as passive repositories toward conceptualising them as mediating infrastructures that augment, generate, and orchestrate knowledge. These systems expand knowledge creation through data-driven insights, enhance knowledge sharing through algorithmic mediation, and increasingly shape how knowledge is applied in decision contexts. However, the analysis also reveals a tension between informational efficiency and authentic learning. While AI-enabled systems support sensemaking and improve decision support under conditions of uncertainty, they may encourage overreliance on algorithmically generated outputs, reduce opportunities for critical reflection, and narrow interpretive perspectives. These dynamics highlight the importance of governance conditions, including epistemic transparency, trust calibration, and learning-oriented organizational practices. The study develops an integrative framework that positions AI-enabled digital knowledge systems as mediating infrastructures linking knowledge processes to organizational learning and strategic decision-making under specific governance and learning conditions. The findings contribute to knowledge management and information systems research by providing a coherent conceptualisation of how AI-enabled systems reshape the epistemic foundations of learning and decision-making in contemporary organizations.
Peter L. Mkhize· European Conference on Knowl...· 0 citations
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