Robotic Process Automation (RPA) is an architectural approach that utilizes software robots or “bots” to automate routine, rule-based digital tasks by mimicking human interactions with digital systems through user interfaces, following defined workflows to execute transactions, manipulate data, and integrate applications without modifying underlying systems.

For enterprise architects, RPA represents a non-invasive automation approach that complements traditional integration methods, particularly valuable for automating processes spanning legacy systems where API access is limited. Modern RPA architectures have evolved beyond simple screen scraping to incorporate intelligent capabilities including computer vision for interface recognition, natural language processing for unstructured data handling, and machine learning for decision support and exception management. Enterprise implementations typically employ multi-tiered architectures: development environments for bot creation and testing; orchestration platforms managing bot deployment, scheduling, and monitoring; credential vaults securing system access; and analytics frameworks measuring automation performance. Technical leaders must address several architectural considerations: scalability for enterprise-wide deployment; resilience to handle interface changes in target applications; security models preventing unauthorized bot activities; and governance frameworks establishing standards for development, testing, and change management. While RPA excels at automating existing processes, the greatest value comes from combining it with broader process optimization efforts, using process mining to identify automation candidates and redesign workflows for maximum efficiency. Organizations typically evolve through maturity phases from isolated tactical automations to enterprise automation platforms supporting citizen development models with appropriate guardrails. For CIOs and CTOs, RPA should be positioned within a broader intelligent automation strategy that includes complementary technologies like business process management, decision management, and AI services, creating an integrated approach that addresses process automation needs across different complexity levels.