Feasibility of Generative Artificial Intelligence and Large Language Model Adoption to Enhance Organisational Knowledge Management Practices
Generative artificial intelligence (Gen AI) and large language models (LLMs) offer substantial potential to improve how organisations capture, organise, retrieve and reuse knowledge. Existing knowledge management (KM) frameworks, however, seldom integrate Gen AI/LLM-specific processes, data governance, and ethical requirements in highly regulated public-sector settings, and few have been empirically evaluated. This study assesses the feasibility of adopting GenAI and LLMs for organisational KM and develops a corresponding Knowledge Management and Artificial Intelligence (KMAI) framework for a national communications regulator. Guided by Diffusion of Innovation (DOI) theory, the study employed a multi-method qualitative design comprising a literature review, semi-structured interviews with six KM representatives, a focus group discussion involving fourteen participants using the LEIQ™ model for SWOT analysis, and a content-validity evaluation by three experts. Interview data were analysed thematically, SWOT findings were transformed into strategies through TOWS analysis, and framework relevance was assessed using item- and scale-level content validity indices. The findings indicate that adoption is feasible in this setting: participants perceived clear relative advantage, compatibility, trialability and observability, while complexity was manageable when supported by adequate skills, data classification, secure infrastructure and governance. The principal risks concerned data quality, privacy, security, misinformation, over-reliance on AI, and organisational resistance. The proposed KMAI framework achieved acceptable content validity across all clusters, with S-CVI/Ave values ranging from 0.89 to 1.00, and was refined into four strategic thrusts: People, Process, Technology and Data. Because the evidence derives from one organisation and a three-member expert panel, the framework is presented as an empirically grounded and internally validated proposition whose extension to other agencies is analytic rather than statistical; the boundary conditions governing such extension are stated explicitly, and confirmatory validation with a larger expert panel and independent organisational sites is identified as the necessary next step. The study extends DOI-based adoption analysis by showing that data governance and ethics condition Gen AI/LLM adoption in organisational KM and provides a practical, staged framework for regulated public-sector organisations.