# On the Evaluation of Intelligent Process Automation

arXiv: https://arxiv.org/abs/2001.02639 · HTML: https://mlsanity.com/paper/2001.02639/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models](https://mlsanity.com/paper/2411.05451/) (2024)
- [ProAgent: From Robotic Process Automation to Agentic Process Automation](https://mlsanity.com/paper/2311.10751/) (2023)
- [Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?](https://mlsanity.com/paper/2109.08634/) (2021)
- [LMV-RPA: Large Model Voting-based Robotic Process Automation](https://mlsanity.com/paper/2412.17965/) (2024)
- [LawFlow: Collecting and Simulating Lawyers' Thought Processes on Business Formation Case Studies](https://mlsanity.com/paper/2504.18942/) (2025)
- [Novel Architecture of RPA In Oral Cancer Lesion Detection](https://mlsanity.com/paper/2603.10928/) (2026)

## Abstract (arXiv)

Intelligent Process Automation (IPA) is emerging as a sub-field of AI to support the automation of long-tail processes which requires the coordination of tasks across different systems. So far, the field of IPA has been largely driven by systems and use cases, lacking a more formal definition of the task and its assessment. This paper aims to address this gap by providing a formalisation of IPA and by proposing specific metrics to support the empirical evaluation of IPA systems. This work also compares and contrasts IPA against related tasks such as end-user programming and program synthesis.