Authenticity as a Relational Signal

Authors

Files

Description

As generative AI becomes a fixture of modern life, new questions about authenticity, personal voice, and relational trust are emerging. For business leaders, the challenge is not simply whether or not to use these tools, but how AI-mediated expression shapes their internal sense of authorship, identity, and alignment. This interpretive phenomenological study of eight externally-visible women business leaders examined how AI use in professional communication is experienced internally, as a question of felt alignment rather than technical function. Using asynchronous reflective surveys and semi-structured interviews and drawing on theoretical frameworks in authenticity and leadership identity (Ibarra, 2015; Snyder, 1974), psychological ownership (Pierce, Kostova & Dirks, 2001), and AI-mediated communication (Hancock, Naaman & Levy, 2020; Draxler et al., 2024), the analysis developed an understanding of how leaders make meaning around voice, alignment, and relational presence when AI is involved. The findings extend existing scholarship in three directions: the conditions under which the AI Ghostwriter Effect operates in professional communication contexts (Draxler et al., 2024), the routes through which leaders pursue felt authorship of AI-assisted output (Pierce, Kostova & Dirks, 2001), and the applicability of professional identity orientation as a predictor of AI engagement behavior (Ibarra, 2015). From these observations, the study proposes the AI-Engagement Orientation (AI-EO), a typology framework for understanding how a leader's identity orientation and pursuit of psychological ownership shape their characteristic pattern of engagement with generative AI.

Publication Date

2026

Keywords

generative AI; AI-mediated communication; AI-Engagement Orientation; authenticity; psychological ownership; authorship; professional identity; personal voice; trust; AI Ghostwriter Effect; AI disclosure; leadership communication; qualitative research; interpretive phenomenological analysis