Generative AI has demonstrated significant potential to improve knowledge-intensive workflows across enterprises. Despite promising pilot results, organizations operating in regulated environments frequently struggle to transition these capabilities into sustained production use. The primary barrier is not model performance but the absence of deployment patterns that align AI assistance with accountability, governance, and auditability requirements. Technology risk assessment illustrates this challenge. The function requires structured analysis, consistency of evaluation, and clear ownership of decisions. While generative AI can accelerate document review and risk identification tasks, unsupervised use introduces concerns related to accuracy, traceability, and decision authority. As a result, many organizations limit adoption to experimentation rather than operational integration. This article presents a deployment model for a human-supervised digital worker designed to assist technology risk assessment workflows. Human sign-off establishes a control boundary, ensuring AI-generated analysis augments expert judgment while preserving accountability. Supporting controls—including guardrails, validation gates, and auditability—enable the solution to operate within governance and compliance expectations. Applied within technology risk review processes, this approach enables faster review cycles while maintaining oversight and decision ownership. By focusing on operational controls rather than algorithmic innovation, the model offers a repeatable framework for responsible AI adoption in regulated environments.