Behavioral Factors as Determinants of Successful Scaling of Artificial Intelligence Pilot Projects in a Corporate Environment

The article examines behavioral factors that shape the transition of corporate artificial intelligence (AI) initiatives from pilot deployments to scalable, sustained use. Relevance follows from the growing gap between rapid experimentation with AI tools and limited organizational capability to institutionalize them in everyday workflows. Novelty lies in integrating adoption frameworks (TAM and TOE) with evidence on human-AI interaction regarding trust calibration, cognitive load, and affective reactions, and in translating these constructs into a scaling-oriented conceptual framework. The paper aims to synthesize recent research and propose an analytical structure for diagnosing disengagement and "pilot-to-production" failure patterns, with special attention to AI coding assistants as a high-visibility class of corporate AI. Methods combine targeted literature synthesis, comparative conceptual analysis, and framework building. Recent scholarly and institutional sources are reviewed to derive constructs, hypothesized links, and governance implications. The paper concludes by articulating expected outcomes for management practice and a research agenda for future mixed-methods validation. The results inform leaders and designers of workplace AI.