The AI Integration Gap™ (AIG)

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 & Davenport, 2026). Drawing on Siegel's (2024) Interpersonal Neurobiology framework, attachment theory (Bowlby, 1969), and current human-AI interaction research, the AIG framework formally measures three dimensions: (1) a leader's dominant Motivational Vector - Agency, Bonding, or Certainty - and its predictable AI misuse pattern; (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.