Organizations increasingly operate in environments characterized by information overload, distributed work, rapid technological change, competitive pressure, and growing demand for evidence-based decision-making. In such environments, knowledge is a critical business resource. Organizations rely on prior project experience, lessons learned, client knowledge, employee expertise, operational data, proposals, reports, market intelligence, process documentation, and decision histories to execute work, reduce risk, innovate, and improve performance ([5]; [7]; [11]).

However, many organizations struggle to convert existing knowledge into timely and actionable business insight. Knowledge is often distributed across repositories, emails, shared drives, collaboration platforms, project records, customer relationship management systems, enterprise resource planning systems, business intelligence dashboards, personal files, and informal networks. This fragmentation creates inefficiencies. Employees spend time searching for prior work, teams duplicate effort, lessons are not reused, expertise remains hidden, and managers make decisions without full access to relevant organizational knowledge.

Knowledge management has long addressed these challenges by promoting knowledge capture, sharing, reuse, governance, and organizational learning. Traditional knowledge management systems include repositories, taxonomies, communities of practice, lessons learned systems, and knowledge management systems designed to support knowledge processes ([1]; [15]; [23]). Yet these systems often rely on manual tagging, static repositories, keyword search, voluntary contribution, and inconsistent governance. As organizational knowledge grows in scale and complexity, these approaches become difficult to maintain and apply.

Artificial intelligence offers new opportunities to improve knowledge management and business decision support. AI can support semantic search, summarization, content classification, expertise mapping, knowledge graph development, recommendations, predictive analytics, natural-language querying, and decision-support workflows. These capabilities can help organizations locate, interpret, and apply knowledge more effectively ([8]; [9]; [13]).

At the same time, AI introduces risks. AI-generated outputs may be inaccurate, biased, outdated, unsupported, or disconnected from business context. AI systems may retrieve poor-quality content, obscure source provenance, or create misplaced confidence in automated answers. These risks are especially important because AI evaluation may fail when it treats expert knowledge as fully codified or objective ([14]; [19]). Therefore, AI does not eliminate the need for knowledge management. Instead, it increases the need for trusted sources, governance, human validation, quality management, and performance measurement.

This article proposes an AI-Enabled Knowledge Intelligence Framework for business management. The framework explains how organizations can integrate knowledge management and artificial intelligence to improve decision support, business performance, organizational learning, and knowledge reuse.

The central research question is: How can organizations integrate artificial intelligence into knowledge management to improve business performance, decision support, and organizational learning while maintaining trust, governance, and human judgment?

The theoretical gap is not the absence of research on knowledge management, business intelligence, or decision-support systems individually. Rather, established concepts do not fully explain the organizational capability required to activate heterogeneous knowledge at the point of work through AI while preserving provenance, governance, context, and accountable human judgment. Knowledge management explains how knowledge is created, organized, shared, and reused; business intelligence primarily converts structured data into analytical insight; and decision-support systems assist particular choices. Knowledge intelligence addresses the integrative capability that connects these functions in a governed, recursive system that transforms distributed data, documents, experience, and expertise into validated action and then learns from resulting outcomes.

Accordingly, this article makes three contributions. First, it defines knowledge intelligence as a construct distinct from, but dependent upon, KM, BI, and decision support. Second, it specifies a six-dimension causal architecture in which governed knowledge foundations enable AI augmentation, human validation conditions the reliability of application, and feedback renews the knowledge base. Third, it provides organization-level propositions and observable indicators that make the framework empirically testable.

The practical problem addressed in this article is the persistent gap between organizational knowledge possession and organizational knowledge use. Many organizations have large volumes of information and knowledge assets, but they lack effective mechanisms to convert those assets into business value.

This problem appears in several common business situations. Proposal teams may struggle to find reusable content or past performance examples. Project teams may fail to apply lessons learned from previous work. New employees may struggle to locate trusted guidance and expertise. Managers may make decisions without complete access to relevant organizational knowledge. Operational teams may recreate deliverables that already exist. Experts may be identified through informal networks rather than enterprise systems. Business intelligence systems may present data but lack contextual knowledge.

These issues are not merely information management problems. They affect productivity, quality, cost, risk, innovation, customer responsiveness, and organizational performance.

AI can improve these conditions, but only if implemented within a structured knowledge management environment. Connecting AI tools to disorganized repositories may increase speed without improving accuracy. This concern is consistent with socio-technical research showing that information systems fail when designers focus on technical capability without accounting for organizational context, people, work processes, and governance ([4]). AI-enabled knowledge systems therefore require a managerial framework that integrates technology, governance, business workflows, knowledge quality, human validation, and performance measurement.

This article addresses the following research question: How can AI-enabled knowledge management be structured as a business management capability that improves decision support, knowledge reuse, and organizational performance?

The proposed framework draws on several theoretical foundations relevant to business and management.

This article uses an integrative conceptual framework-development approach rather than an empirical systematic review. The objective is theory building: to connect established management constructs with recurring organizational problems introduced or intensified by AI-enabled knowledge work. Literature domains were selected purposively when they explained one of four requirements of the focal phenomenon: knowledge as an organizational resource, technology-supported retrieval and decision support, human interpretation and learning, or governance and performance control. The resulting theoretical domains were the knowledge-based view, intellectual capital, dynamic capabilities, organizational learning, business intelligence and management information systems, socio-technical systems, human-AI collaboration, and knowledge governance.

The synthesis proceeded in four stages. First, the literature was examined for established constructs and causal mechanisms relevant to knowledge creation, integration, reuse, sensemaking, decision support, organizational learning, and governance. Second, recurring practitioner problems were used as problem anchors: fragmented content, weak findability, limited expertise visibility, inconsistent reuse, disconnected lessons learned, uncertain source authority, and weak feedback between knowledge use and business outcomes. These problems were retained when they represented an organization-level barrier addressed by more than one theoretical domain and could plausibly be influenced by KM and AI capabilities. Third, AI capabilities—including semantic search, summarization, classification, recommendation, knowledge graphs, expertise mapping, and natural-language interaction—were mapped to the mechanisms they could augment rather than treated as independent solutions. Fourth, the constructs were organized into the smallest coherent sequence that preserved inputs, enabling conditions, transformation mechanisms, application, and recursive learning.

The six dimensions were then checked for conceptual completeness and managerial interpretability. Knowledge sources and knowledge structuring represent the foundation; AI augmentation represents the scalable transformation mechanism; human validation and sensemaking provide contextual and accountability controls; knowledge application represents proximal use; and learning feedback connects outcomes back to the knowledge base. The framework is therefore an analytically derived model intended to support falsifiable propositions and future empirical testing, not a claim of causal effects already demonstrated by the present article.

Traditional knowledge management often focuses on storing, organizing, and sharing knowledge. While these functions remain important, they are no longer sufficient for organizations facing complex business decisions and rapidly expanding knowledge assets. Prior KM research shows that knowledge management systems must support knowledge processes and reuse situations rather than merely store content ([1]; [15]).

This article defines knowledge intelligence as: the organizational capability to convert distributed knowledge into trusted, contextualized, and actionable business insight through the combined use of human expertise, structured knowledge practices, governance, analytics, and artificial intelligence.

Knowledge intelligence shifts KM from a repository-centered activity to a business performance capability. Traditional KM asks: Where is the knowledge stored? Knowledge intelligence asks: How can knowledge improve decisions, execution, learning, and performance? Traditional KM emphasizes access to documents. Knowledge intelligence emphasizes the activation of knowledge in business workflows. Traditional KM often measures system usage. Knowledge intelligence measures reuse, decision quality, operational improvement, innovation, and business impact ([5]; [7]).

Figure 1.Evolution from traditional KM to AI-enabled knowledge intelligence.

The construct is differentiated by its focal object, mechanism, and outcome. KM’s focal object is organizational knowledge and its lifecycle; BI’s focal object is predominantly structured data and analytical patterns; and decision support focuses on improving a decision episode. Knowledge intelligence focuses on the organization-level capability that assembles heterogeneous knowledge, uses AI to retrieve and synthesize it, subjects outputs to governed human sensemaking, embeds the validated result in work, and updates the knowledge environment from observed outcomes (Figure 1). Thus, knowledge intelligence does not replace the established fields. Its theoretical value lies in specifying how their mechanisms must be orchestrated when AI simultaneously expands the scale of knowledge activation and the need for provenance, validation, and accountability.

The AI-Enabled Knowledge Intelligence Framework includes six interdependent dimensions: knowledge sources, knowledge structuring, AI augmentation, human validation and sensemaking, knowledge application, and learning feedback.

Figure 2.Modern knowledge lifecycle and AI augmentation framework.

The dimensions have distinct causal roles rather than forming a simple checklist. Knowledge sources and knowledge structuring are antecedent conditions because they determine the accessible, authorized, and interpretable knowledge available to the system. AI augmentation is the principal transformation mechanism that increases the scale and speed of retrieval, synthesis, classification, and recommendation. Knowledge application is the proximal outcome through which validated insight enters decisions and workflows. Learning and feedback form a recursive mechanism: evidence from use updates content, metadata, models, rules, and practices, thereby changing the antecedent conditions for subsequent cycles.

Governance and human validation operate as cross-cutting controls, but at different levels. Governance is both an antecedent and an embedded control: policies, ownership, source authority, access rules, and quality standards shape every dimension before AI output is produced and while it is used. Human validation is an embedded control and proposed moderator between AI augmentation and knowledge application; stronger expert review, contextual interpretation, and source checking should strengthen the positive effect of AI-generated outputs on decision and work quality, particularly as task uncertainty or consequence increases. Figure 2depicts the process sequence.

AI-enabled KM has several applications in business and industrial management (Figure 3).

Figure 3.AI-enabled KM use case matrix.

AI-enabled KM requires governance because AI systems depend on the quality and authority of the knowledge they access. Governance helps ensure that AI-supported outputs are reliable, ethical, secure, and useful. This aligns with knowledge governance research and ISO 30401 guidance for establishing, implementing, maintaining, reviewing, and improving knowledge management systems ([10]; [12]). Figure 4 depicts governance as a cross-cutting layer spanning the AI-enabled knowledge-management system.

Figure 4.Knowledge governance layer for AI-enabled KM.

Key governance requirements include: Knowledge ownership, where each major knowledge domain should have an accountable owner; source authority, where organizations define approved and trusted sources; metadata standards, ensuring content is consistently tagged and classified; lifecycle management, so knowledge is reviewed, updated, archived, or retired; access control, protecting sensitive information; AI validation, ensuring high-risk AI outputs receive human review; ethical use, so AI is used transparently and responsibly; performance monitoring, evaluating AI-enabled KM against business outcomes; and risk management, monitoring bias, hallucination, outdated information, and misuse ([8]; [10]; [12]).

Governance should not slow knowledge flow unnecessarily. Instead, it should create the trust necessary for employees and managers to use AI-enabled knowledge systems confidently.

The primary unit of analysis for all propositions is the organization. A semi-autonomous business unit may be used as an equivalent unit when it possesses its own knowledge sources, governance, AI-enabled KM practices, workflows, and performance measures. Individual perceptions or behaviors may supply respondent-level data, but they are aggregated to characterize the organizational unit; the propositions do not infer organization-level effects from isolated individual use. Unless a study explicitly models multiple levels, predictors, mediators, moderators, and outcomes should be measured at this same organizational level.

Proposition 1: At the organizational level, higher knowledge-governance maturity will be associated with greater workforce trust in AI-enabled KM outputs.

Proposition 2: At the organizational level, stronger AI-enabled knowledge structuring will be associated with higher knowledge reuse, and this relationship will be stronger when metadata standards, source authority, and human-validation controls are more mature.

Proposition 3: At the organizational level, the positive relationship between AI-enabled KM capability and business performance will be mediated sequentially by knowledge quality and effective knowledge application, conditional on knowledge-governance maturity.

Proposition 4: At the organizational level, AI-enabled KM will be more positively associated with decision-support effectiveness when it is integrated into business workflows than when it is deployed as a stand-alone technology.

Proposition 5: At the organizational level, human validation will positively moderate the relationship between AI augmentation and knowledge-application quality, with the moderating effect increasing in complex or high-consequence decision contexts.

Proposition 6: Organizations with more active learning-feedback loops will demonstrate greater longitudinal improvement in knowledge quality, reuse, and organizational-learning capability.

Proposition 7: At the organizational level, greater use of AI-enabled expertise discovery will be associated with improved workforce utilization through reduced dependence on informal networks.

Proposition 8: At the organizational level, AI-enabled lessons-learned systems will be more positively associated with aggregate project performance when relevant lessons are delivered during planning and execution than when lessons are stored only after closeout.

Table 1. Organization-level operationalization of the research propositions.

The indicators in Table 1 are illustrative operationalizations rather than a fixed measurement instrument. Future studies should establish construct validity, reliability, temporal ordering, and appropriate aggregation statistics before testing the propositions.

AI-enabled KM should be evaluated through both activity measures and business outcome measures. Table 1 links each research proposition to an organization-level unit and at least one observable indicator, providing a starting point for future scale development and empirical testing.

Activity measures include number of searches, AI summaries generated, knowledge assets classified, lessons captured, reusable assets accessed, expertise profiles created, and user feedback ratings. These measures help determine whether the system is being used.

Figure 5.AI-enabled knowledge intelligence maturity model.

However, activity measures alone are insufficient. Business outcome measures are more important. Outcome measures may include reduced time to find information, reduced duplication of work, faster proposal development, faster project startup, improved onboarding speed, improved decision cycle time, increased reuse of prior deliverables, improved quality of project outputs, reduced operational risk, improved customer or client responsiveness, better workforce utilization, increased innovation activity, and improved organizational learning maturity.

A maturity model (Figure 5) can help organizations assess progress from fragmented knowledge practices to enterprise-level knowledge intelligence. At lower maturity levels, knowledge is scattered and AI use is experimental. At higher maturity levels, knowledge is governed, AI is integrated into workflows, and feedback loops continuously improve knowledge quality and business performance.

The proposed framework advances the argument that AI-enabled KM should be understood as a business management capability rather than a technology project. AI can improve the speed and scale of knowledge retrieval and synthesis, but it cannot replace the managerial work of defining knowledge priorities, governing trusted sources, validating outputs, and measuring business value ([4]; [19]).

The framework also bridges knowledge management and business intelligence. Business intelligence provides data-driven insight, while knowledge management provides context, experience, lessons, and expertise. AI-enabled knowledge intelligence integrates these functions by connecting structured data, unstructured knowledge, human expertise, and decision-support processes ([1]; [5]; [7]).

The discussion also highlights the importance of human judgment. AI can generate recommendations and summaries, but managers and experts must determine whether those outputs are relevant, accurate, ethical, and actionable. This is especially important in complex business environments where decisions involve uncertainty, tradeoffs, relationships, and risk ([13]; [14]).

Finally, the framework suggests that organizations should begin with business problems rather than AI tools. High-value starting points include proposal reuse, project startup, lessons learned, expertise discovery, onboarding, quality management, and decision support.

This article offers several implications for managers. First, leaders should treat AI-enabled KM as a strategic business capability. It should be connected to organizational goals such as improved decision-making, faster execution, better quality, innovation, and operational efficiency ([11]; [20]).

Second, organizations should identify priority use cases before selecting technology. AI-enabled KM should solve real business problems, not simply introduce new tools.

Third, managers should invest in knowledge foundations. These include taxonomies, metadata, trusted repositories, content ownership, lifecycle management, and quality controls ([10]; [12]).

Fourth, human expertise should remain central. AI should augment expert judgment by reducing search burden and improving access to relevant knowledge ([13]; [19]).

Fifth, organizations should establish governance before scaling AI. Employees need clarity about approved sources, validation requirements, security rules, and appropriate use.

Sixth, organizations should measure outcomes rather than activity alone. The success of AI-enabled KM should be evaluated by whether it improves business performance and strengthens intellectual capital, knowledge reuse, and organizational learning ([3]; [15]; [6]).

This article presents a conceptual framework and does not empirically test the proposed relationships. The framework is most applicable when an organization has recurring knowledge-intensive work, a sufficiently accessible digital knowledge base, identifiable business workflows, and authority to govern both knowledge and AI use. Its propositions therefore require empirical examination through case studies, surveys, design science research, longitudinal designs, and mixed-methods studies.

Several boundary conditions may weaken or alter the proposed relationships. In highly regulated or safety-critical settings, mandatory controls, documentation, and approval requirements may reduce speed benefits while increasing the value of provenance and validation. In domains containing classified, personal, proprietary, or otherwise sensitive knowledge, access restrictions may limit the sources available to AI and require isolated technical environments. Where work depends heavily on tacit, embodied, relational, or locally negotiated expertise, codified sources and AI synthesis may represent only part of the knowledge required for action. The framework may also yield limited value when source quality is poor, the task is genuinely novel, feedback is delayed or ambiguous, or the organization lacks sufficient scale and resources to maintain governance, metadata, and evaluation.

These conditions do not necessarily make knowledge intelligence inapplicable; they change its expected mechanisms and outcomes. In high-consequence or tacit-intensive contexts, the framework predicts greater dependence on expert participation and more conservative automation. The proposed performance effects would be weakened or falsified if governed knowledge foundations, human validation, workflow integration, and feedback did not improve—or if they imposed costs that consistently exceeded gains in decision quality, reuse, learning, or operational performance.

Future studies could test whether AI-enabled KM reduces search time, improves proposal efficiency, increases lessons learned reuse, accelerates onboarding, or improves decision quality. Researchers could also examine how knowledge governance maturity affects trust in AI outputs.

Additional research is needed across different industries, including consulting, manufacturing, healthcare, logistics, education, public sector organizations, and research-intensive firms. Comparative studies could identify which industries benefit most from AI-enabled knowledge intelligence and which governance models are most effective.

Future research should also examine the relationship between AI-enabled KM and business intelligence. This includes how organizations integrate structured data, unstructured documents, tacit expertise, and AI-generated insight into decision-support systems.

Artificial intelligence is changing the future of knowledge management, but it does not replace the need for KM. Instead, AI increases the importance of knowledge governance, structure, quality, human validation, and business alignment ([1]; [10]; [12]; [14]).

This article proposed an AI-Enabled Knowledge Intelligence Framework for business performance, decision support, and organizational learning. The framework includes six dimensions: knowledge sources, knowledge structuring, AI augmentation, human validation and sensemaking, knowledge application, and learning feedback.

The central contribution of the article is the concept of knowledge intelligence. Knowledge intelligence extends traditional KM by emphasizing the conversion of distributed organizational knowledge into trusted, contextualized, and actionable business insight. Organizations that develop this capability will be better positioned to improve decision-making, reduce duplication, accelerate work, strengthen innovation, and learn continuously from experience.