Speakers
Description
Artificial intelligence (AI) adoption among startups is accelerating, yet systematic understanding of how founders approach AI-related risks, justify implementation decisions, and establish governance frameworks remains limited. This study examines how startup founders interpret AI risks and opportunities, develop justification strategies for adoption, and negotiate boundaries of responsibility through applied sociological analysis. Drawing on survey data from 72 startup founders collected in September 2024, we analyzed quantitative patterns and qualitative insights using sociological frameworks of technological framing, institutional logics, and boundary-work. Founders predominantly view AI through dual lenses, recognizing innovation opportunities while acknowledging operational risks including data privacy, algorithmic bias, and integration challenges. Despite widespread risk awareness (95% have learned or intend to learn about AI risks), actual governance measures remain limited, with 85% having no specific protective measures. Founders primarily employ market-oriented justifications emphasizing efficiency and competitive advantage, while ethical considerations remain largely symbolic. Most favor shared responsibility between AI developers and implementers (39%), though practical boundary-setting is rare. These sociological insights inform technical implementation strategies and governance system design for AI deployment in resource-constrained environments. Our findings offer evidence-based frameworks for risk prioritization, resource allocation, and governance implementation that can inform startup strategies and ecosystem-level support programs.
