Strategic Management of LLM-Based Chatbots: Transforming Internal Collaboration and Decision-Making in Organizations

The article develops a strategic management framework for LLM based chatbots that explains how these systems reshape internal collaboration and managerial decision making and the conditions that enable reliable use. The background is the shift from scripted chatbots to assistants that retrieve and synthesize organizational knowledge, sustain context aware dialogue, and support knowledge work. The methodology is an analysis of peer reviewed scientific literature retrieved from major academic platforms, using targeted keyword searches and selective inclusion of studies with organizational relevance. The data collecting process relied on database searches and screening of titles, abstracts, and full texts. Expected results indicate five practical roles for LLM based chatbots, namely Librarian, Analyst, Coordinator, Scribe, and Coach, which accelerate access to knowledge, bridge silos, improve coordination, and strengthen onboarding and meetings. Mapped to decision processes, these assistants support the intelligence, design, choice, and learning stages. The conclusions underline that value depends on human in the loop oversight, sound data management, simple usage protocols and training, and transparency through basic audit trails, while a small set of metrics can guide pilots and scaling.