Passing the baton: an AI-powered leader digital twin to capture and operationalize organizational memory

Digital twins have predominantly targeted physical assets, while the ‘human in the loop’—and leadership in particular—remains underexplored [1][2]. We propose an AI-powered Leader Digital Twin (LDT) that models a leader’s memory, routines, and decision heuristics to (i) delegate routine decisions under policy constraints, (ii) preserve and disseminate organizational memory, and (iii) provide what-if analysis for complex scenarios. The LDT integrates five key architectural layers: a data ingestion layer ensuring privacy compliance [3], an organizational knowledge graph encoding people, roles, processes, decisions, and policies [4], a hybrid behaviour model combining LLM-based reasoning with imitation learning [5], a governance and guardrails layer implementing oversight and drift detection [6], and a Decision Theatre UI enabling human-AI collaboration [7]. Multi-perspective evaluation protocols, including shadow-mode pilots and NASA-TLX workload metrics, demonstrate promising scalability, robustness, and usability [8]. This work addresses critical gaps in leadership succession planning and organizational memory preservation.