# A Conversational Digital Assistant for Intelligent Process Automation

arXiv: https://arxiv.org/abs/2007.13256 · HTML: https://mlsanity.com/paper/2007.13256/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [SNAP: Semantic Stories for Next Activity Prediction](https://mlsanity.com/paper/2401.15621/) (2024)
- [Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestration](https://mlsanity.com/paper/2212.06564/) (2022)
- [A Case for Business Process-Specific Foundation Models](https://mlsanity.com/paper/2210.14739/) (2022)
- [ProtoNER: Few shot Incremental Learning for Named Entity Recognition using Prototypical Networks](https://mlsanity.com/paper/2310.02372/) (2023)
- [Natural Language Sentence Generation from API Specifications](https://mlsanity.com/paper/2206.06868/) (2022)
- [A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language](https://mlsanity.com/paper/2207.10648/) (2022)
- [TESS: A Multi-intent Parser for Conversational Multi-Agent Systems with Decentralized Natural Language Understanding Models](https://mlsanity.com/paper/2312.11828/) (2023)
- [A Nascent Taxonomy of Machine Learning in Intelligent Robotic Process Automation](https://mlsanity.com/paper/2509.15730/) (2025)

## Abstract (arXiv)

Robotic process automation (RPA) has emerged as the leading approach to automate tasks in business processes. Moving away from back-end automation, RPA automated the mouse-click on user interfaces; this outside-in approach reduced the overhead of updating legacy software. However, its many shortcomings, namely its lack of accessibility to business users, have prevented its widespread adoption in highly regulated industries. In this work, we explore interactive automation in the form of a conversational digital assistant. It allows business users to interact with and customize their automation solutions through natural language. The framework, which creates such assistants, relies on a multi-agent orchestration model and conversational wrappers for autonomous agents including RPAs. We demonstrate the effectiveness of our proposed approach on a loan approval business process and a travel preapproval business process.