Working with AI: A Practice-Based Study of Knowledge Sharing in a Multinational Enterprise (MNE)

Abstract

Knowledge sharing is central to intraorganisational collaboration in multinational enterprises (MNEs), where employees work across functions, locations and linguistic contexts. Existing research has highlighted the potential of AI to enhance knowledge management and organisational performance, but there is still limited understanding of how employees use AI in their everyday knowledge sharing practices in MNEs. This study therefore examines how AI influences these practices within an anonymised MNE, referred to as GlobalMFG. The study adopts a qualitative single-case study design and draws on six semi-structured interviews with managers and knowledge workers from different organisational functions. The findings demonstrate that AI supports knowledge sharing by improving access to information, assisting communication across geographically dispersed teams and supporting meeting follow-up. Its effectiveness remained dependent on employees’ ability to formulate appropriate prompts and assess how AI-generated outputs could be applied within specific organisational contexts. AI was primarily used before and after interpersonal interactions. Experienced colleagues continued to be the preferred source of specialist and contextual knowledge. Although organisational documents reflected a strong strategic commitment to AI adoption, interview data revealed inconsistencies in access to AI tools, system integration, and governance, resulting in uneven implementation across the organisation. These findings highlight the need to connect AI investment with clear governance, consistent access to approved tools and task-specific employee support. Establishing ongoing employee feedback mechanisms can further support the responsible, effective, and contextually appropriate adoption of AI-enabled knowledge sharing practices within such MNEs.