Companies are increasingly turning to hyperautomation to improve efficiency and decision-making quality by coordinating heterogeneous automation technologies such as robotic process automation, machine learning, and generative AI. However, existing automation approaches primarily focus on task-level automation and offer limited guidance on the systematic design, configuration, and control of hyperautomation in complex, decision-intensive business processes. As a result, companies are struggling to translate the concept of hyperautomation into actionable implementation strategies that are viable as socio-technical systems. Employing a design science research approach, we derived meta-requirements from eleven interviews and a structured literature base, and formulated eleven design principles for implementing hyperautomation, with socio-technical systems theory as the kernel theory and affordance theory as the operationalizing layer. The design principles are intended to help companies plan automation opportunities more holistically, configure human–AI collaboration, and manage automation at scale. The design knowledge covers (1) process and decision decomposition, (2) automation capability mapping, (3) human-in-the-loop configuration, and (4) governance and value assessment mechanisms. We evaluated the results using a mixed-methods design combining a manager survey ( \(n=100\) ) and ex-post expert feedback. This study contributes by presenting an initially validated and comprehensible MR-DP set that covers the entire hyperautomation lifecycle. The resulting design knowledge supports organizations in moving from isolated automations toward governed, observable, and scalable hyperautomation systems.