What question did this study set out to answer?

This research aims to establish the AI Integration Gap as a framework to understand human barriers to AI adoption in leadership roles. It highlights the interplay between human psychology and AI functionality.

March 12, 2026Open Access

The AI Integration Gap™ (AIG)

Key Points

This research aims to establish the AI Integration Gap as a framework to understand human barriers to AI adoption in leadership roles. It highlights the interplay between human psychology and AI functionality.
Introduced a conceptual framework based on previous theories and research.
Utilized a systematic review of multiple academic databases to assess existing literature.
Integrated Siegel's Motivational Vectors with AI decision-making frameworks in leadership contexts.
Confirmed a consistent finding of 93.2% regarding human barriers to AI adoption over fifteen years of benchmarking.
Identified three key dimensions within the AI Integration Gap framework: Motivational Vectors, psycho-neurological gaps, and developmental interventions.
Demonstrated that developmentally-informed interventions are more effective than standard AI literacy training.

Abstract

This paper introduces the AI Integration Gap (AIG) as a formal conceptual framework for understanding why human factors remain the primary barrier to AI adoption in senior leadership contexts - a finding confirmed at 93.2% across fifteen consecutive years of Fortune 1000 benchmarking (Bean (2) the psycho-neurological gap between what AI generates and what a leader's nervous system is developmentally ready to receive, tolerate, and act upon wisely; and (3) the differential effectiveness of developmentally-informed interventions versus standard AI literacy training in reducing Vector-specific AI misuse. A systematic review of PsycINFO, PubMed, Web of Science, and Google Scholar confirms no published research integrates Siegel's Three Motivational Vectors with AI-assisted decision-making in leadership contexts. This deposit registers intellectual priority for the AI Integration Gap (AIG), Origin Imprints, and Relational Coding as original theoretical constructs, and establishes a citable foundation for future empirical investigation. Keywords: AI Integration Gap; Motivational Vectors; Interpersonal Neurobiology; Origin Imprints; Automation Bias; Attachment Theory; Human-AI Interaction; Leadership Neuroscience; Nervous System Regulation

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