DIGITAL TWINS FOR LEADERSHIP CONTINUITY: A CONCEPTUAL MODEL LINKING NPC PEDAGOGY AND SUCCESSION

Authors: Giammarco Tosi, Silviu Gabriel Szentesi

Vol. 11 • Special Issue • 2026

Abstract

This paper presents a conceptual design framework that explores how AI-powered digital twins can be designed to sustain organizational culture during leadership transitions, one of the most delicate challenges for contemporary organizations. Transitions often expose firms to crises of continuity, erosion of culture, and the loss of tacit knowledge embedded in the communicative style and cognitive framing of outgoing leaders. Traditional mechanisms of succession planning, such as mentorship, leadership pipelines, and governance codification, capture explicit procedures but rarely preserve the distinctive ethos and narrative identity that founders and executives imprint on their firms. At the same time, artificial intelligence is opening new possibilities for continuity by enabling the construction of digital twins: AI-driven representations of leaders that simulate reasoning, communicative tone, and value preferences.
To address this challenge, we propose designing digital twins as organizational NPCs, agents equipped with adaptive memory and narrative coherence that transmit culture and scaffold value-consistent decision-making during transitions. Drawing on insights from NPC pedagogy in games and responsible tourism, where similar agents foster empathy, reflection, and ethical reasoning, we develop a framework integrating stakeholder orientation, ethical responsibility, organizational exploration, systemic awareness, and narrative coherence. Grounded in a TREES-inspired (Tourism, Responsibility, Exploration, Environment, Story) design framework, digital twins become cultural continuity agents that preserve not only what leaders decided, but how they decided, ensuring organizational ethos and identity survive succession intact.

Keywords: Artificial Intelligence; Digital Twins; Leadership Continuity; NPC Pedagogy; TREES Framework.

JEL Classification: M10; M12; O33; I21; L86.

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