Governing generative AI in organizations: a design theory and quasi-experimental field study of sociotechnical guardrails

Abstract

Generative AI adoption has outpaced organizational governance capabilities. We conceptualize AI guardrails as sociotechnical governance mechanisms, comprising policy, technical, and workflow components that embed organizational norms in deployed AI systems. Extending norm-based coordination accounts, we specify three mechanisms (norm encoding, output monitoring, and escalation) targeting four properties: predictability, fairness, safety, and auditability. We instantiate the theory in a three-lay

Categories

@article{leon2026governing,
  title = {{Governing generative AI in organizations: a design theory and quasi-experimental field study of sociotechnical guardrails}},
  author = {Maikel Leon},
  year = {2026},
  month = aug,
  journal = {Journal of Supercomputing},
  doi = {10.1007/s11227-026-08760-7},
  url = {https://www.semanticscholar.org/paper/955a0cafb871b88276f7ce9fde7b1bf0a2f68fdf}
}