A shared services organization is automating a repetitive back-office task with a consistent process across departments. As the CIO, you need to…

The scenario describes automating a repetitive, structured, and consistent back-office task where exceptions are handled manually. This is the classic use case for Traditional Robotic Process Automation (RPA), which is designed to mimic human actions for rule-based processes within existing systems. Traditional RPA excels at uniform execution of high-volume, predictable tasks but lacks the cognitive ability to handle variations or exceptions, which aligns perfectly with the requirement that exceptions are managed outside the automated flow. Intelligent Automation would be an over-specification, as it involves AI to handle more complex, less structured tasks and exceptions.

A. AI agents with contextual planning are for dynamic, complex environments requiring autonomous decision-making, not for a simple, repetitive, and consistent process.
B. Agentic workflows involve multiple autonomous agents collaborating, which is far more complex than the single, structured task described.
C. Intelligent automation combines RPA with AI/ML to handle semi-structured data and manage exceptions, which contradicts the requirement that exceptions are handled externally.

1. Lacity, M. C., & Willcocks, L. P. (2017). Robotic Process Automation and Intelligent Automation. In Robotic Process and Cognitive Automation: The Next Phase (pp. 1-15). Steve Brobst. This source distinguishes between traditional RPA for "rules-based, repetitive tasks" (p. 5) and Intelligent Automation which adds cognitive capabilities to handle exceptions.

2. van der Aalst, W. M. P., Bichler, M., & Heinzl, A. (2018). Robotic Process Automation. Business & Information Systems Engineering, 60(4), 269–272. https://doi.org/10.1007/s12599-018-0542-4. This paper defines RPA as a technology for automating structured, repetitive tasks based on a set of rules (Section 2, "What is RPA?").