Organisations continue to invest in Agentic AI and advanced analytics, yet the conversion of extensive knowledge resources into sustained innovation remains uneven. This difficulty is amplified in the post-truth era, where misinformation, synthetic content, and algorithmic hallucinations undermine information reliability. Within Knowledge Management (KM) research, substantial attention has been given to knowledge creation, sharing, and organisational learning. Less attention has been paid to the structured regulation of knowledge quality as a necessary condition for dependable intelligence production. This paper argues that innovation in digitally mediated environments depends not simply on knowledge availability, but on disciplined management of knowledge quality across the data-information-knowledge-intelligence-wisdom (DIKIW) continuum. The study is grounded in a pragmatist philosophy and adopts an abductive Design Science Research approach, supported by a Systematic Literature Review. It develops a model that links structured knowledge quality to organisational intelligence production. The model comprises three interconnected elements. The first element is a foundational knowledge infrastructure governing data and information organisation. The second element is explicit knowledge quality criteria - validity, authenticity, reliability, currency, and sufficiency, intended to safeguard epistemic integrity. The third element is a structured mechanism for transforming validated knowledge into prioritised organisational intelligence relevant to both human decision-makers and Agentic AI. By clarifying the transition from organised information to prioritised intelligence, the paper extends DIKIW within KM theory to address contemporary challenges of information instability. It shows how structured quality regulation stabilises explicit knowledge repositories and supports more coherent internalisation and organisational learning. Innovation is therefore understood as contingent upon epistemic governance rather than technological capability alone. The paper contributes by conceptualising structured knowledge quality as a distinct KM capability, articulating a formalised pathway between KM and organisational intelligence, and offering a design-oriented framework derived through abductive movement between the reviewed literature, established KM theory and the identified organisational problem.
Lucian De Koker· European Conference on Knowl...· 0 citations
Organisations operating in volatile, uncertain, complex and ambiguous (VUCA) environments are increasingly required to transform expanding volumes of structured and unstructured data into coherent strategic intelligence. While emerging technologies, including artificial intelligence (AI) and large language models (LLMs), expand analytical capability - technological adoption alone does not ensure reliable knowledge transformation or strategic clarity. This paper draws from the qualitative interview component of a broader PhD study to advance a Strategic Intelligence Framework (SIF) conceptualised as a Knowledge Governance Architecture for complex organisations. The broader PhD study adopted a pragmatist research design. For the purposes of this paper, reporting on the qualitative interviews from the broader PhD study are done, because it most directly informed the development of the framework. Semi-structured online interviews were conducted with five South African executives and executive Information and Knowledge Management consultants to examine how strategic decision-makers currently use strategic tools, how they experience complexity, and what they require from a framework capable of governing intelligence in practice. The interview data were thematically analysed in ATLAS.ti through open coding, pattern identification and theme development. The findings indicate that strategic decision-making is generally experienced as complex rather than clear, that strategic tools are used widely but in a fragmented manner, and that there is no consistent standard process for transforming data into actionable strategic intelligence. Participants reported using tools such as Scrum, the Balanced Scorecard, PESTLE analysis, SWOT analysis and Porter’s five forces, yet they emphasised the need for stronger validation, better data quality, ethical boundaries, and preservation of human sense-making. All participants supported the inclusion of LLM capability in a Strategic Intelligence Framework, but only within clear governance boundaries. In response, the SIF comprises four interrelated structural domains: the as-is VUCA environment, the Technologies environment, the NRT methodology, and the to-be VUCA environment. When these domains operate coherently, they converge on the bottom-line of the framework: Strategic Vision, Strategic Understanding, Strategic Clarity and Strategic Agility. The paper contributes to Knowledge Management by reframing strategic intelligence as a governance problem and by linking qualitative evidence from doctoral research to the current proof-of-concept implementation of the framework.
Lucian De Koker, Tanya Du Plessis· European Conference on Knowl...· 0 citations
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