AHP 기반 중소기업 경영컨설팅에서의 인간-AI 협업모델 우선순위 분석

Recent advances in artificial intelligence (AI) have increased the use of AI-based tools in knowledge-intensive professional services such as management consulting. In small and medium-sized enterprise (SME) consulting, AI holds particular potential given SMEs' limited data infrastructure and analytical capabilities. However, since responsibility for consulting outcomes rests with human experts, AI adoption should be understood as a collaboration design problem regarding role and responsibility allocation between humans and AI. This study structures human-AI collaboration in SME consulting as a multi-criteria decision-making problem using the Analytic Hierarchy Process (AHP). Based on research regarding knowledge management, service quality, task-technology fit, human-AI collaboration, and AI governance, the model comprises four evaluation criteria, twelve factors, and four alternatives: AI-led with human verification, joint creation, human-led with AI assistance, and human-only execution. Surveys were conducted with certified management consultants. Results show that 'acceptability and explainability' is the most important criterion, with 'client trust,' 'accuracy,' and 'task structure and repetitiveness' as key factors. The 'AI-led with human verification' model achieved the highest priority across most factors, whereas 'human-led with AI assistance' was preferred for risk-related factors. Sensitivity analysis confirmed ranking stability, suggesting SME consulting requires a contingent collaboration structure adapting models to task characteristics and accountability.