Bridging Cultural and Linguistic Gaps with AI-Based Knowledge Management: A Solution for Language and Cultural Barriers at PT. STI

In today’s globalized economy, multinational corporations like PT. STI face significant challenges in enabling seamless knowledge sharing across culturally and linguistically diverse teams. This study proposes an Artificial Intelligence-based Knowledge Management (KM) system to address language and cultural barriers causing operational inefficiencies such as project delays and reduced productivity at PT. STI. Using a mixed-methods approach, the research applies the Asian Productivity Organization (APO) KM Framework to evaluate the company’s KM maturity, the Analytical Hierarchy Process (AHP) to prioritize AI features, and the Technology Acceptance Model (TAM) to assess employee perceptions of the system's usefulness and ease of use. Quantitative data from APO KM and AHP surveys were complemented by qualitative insights from semi-structured interviews with key personnel. The APO KM assessment shows PT. STI at the "Expansion" KM maturity level with a score of 135.14, highlighting weaknesses in knowledge processes, technology, and outcomes. AHP analysis identifies Real-Time Translation (RT) as the top feature (44%), followed by Automated Tagging/Categorization (AT) and Contextual Q&A/Summarization (CQ). Employee interviews corroborate these findings, demonstrating strong willingness to adopt the system due to its potential to overcome cross-lingual communication barriers and improve knowledge accessibility. The proposed AI-based KM system provides PT. STI with a framework to reduce language barriers, minimize knowledge silos, and boost operational efficiency. It is expected to shorten project timelines, lower costs, and increase client satisfaction, offering valuable insights for multinational corporations facing similar challenges.