AI Assisted Workflow Optimization and Automation

Zhen Zhong

cs.SESep 13, 2026

AI Assisted Workflow Optimization and Automation

Authors: Zhen Zhong

Organizations: Senior Data Engineer, Graduate School of Arts & Sciences, Georgetown University, Washington, DC 20001, United States

Abstract

Against the backdrop of digital transformation and stricter regulation, enterprise compliance work demands higher efficiency and accuracy. The auxiliary compliance process has become an important entry point for optimizing the compliance system due to its strong transactional nature and high degree of repetition. This study focuses on the process characteristics of auxiliary compliance work, sorts out its structural composition and organizational mechanism, proposes an optimization path with process reengineering, system modeling, and technology integration as the core, and focuses on exploring the collaborative application of key technologies such as RPA, rule engine, and semantic recognition in process automation. Research suggests that the systematic optimization and intelligent upgrading of auxiliary processes will help build a modern compliance operation system that is responsive, efficient, structurally clear, and risk controllable.

Workflow Automation

Workflow automation coordinates multi-step tasks across applications, tools, and people using software, AI agents, and explicit process logic.

See all 62 papers

CTRAG: An In-Context Retrieval-based Framework for Automated Compliance Checking using LLMs
Aug 3, 2026Muhammad Roman, Karen Rafferty, Barry DevereuxRetrieval-Augmented Generation
Bristol Research and Innovation Laboratory (BRIL), Toshiba Europe Ltd., Bristol, United Kingdom · Queen’s University Belfast, United Kingdom
The Compliance Gap: Why AI Systems Promise to Follow Process Instructions but Don't
May 3, 2026Kwan Soo ShinAI Agent Benchmarks LLM Honesty
PolymathMinds AI Lab
Evaluating LLM Trade-offs for Enterprise Automation: Lessons from Workflow Generation in a Production Enterprise Platform
Aug 4, 2026Xavier Wrenn, Radoslav Raykov, Aleksandar Angelov +3LLM Evaluation Workflow Automation
IBM

Aug 3, 2026J/K move · Enter open · S save

CTRAG: An In-Context Retrieval-based Framework for Automated Compliance Checking using LLMs

Muhammad Roman, Karen Rafferty, Barry Devereux

Bristol Research and Innovation Laboratory (BRIL), Toshiba Europe Ltd., Bristol, United Kingdom · Queen’s University Belfast, United Kingdom

Retrieval-Augmented Generation

Open paper AskPDF arXiv

Trust is fundamental in modern regulatory ecosystems, and compliance checking plays a critical role in fostering that trust. Regulatory compliance verification is essential for businesses operating in highly controlled environments, as it ensures alignment with sector-specific guidelines across domains such as financial reporting, data privacy, and cybersecurity. Manual compliance testing, however, is often time-intensive and prone to inconsistencies, particularly when compliance depends indirectly on third-party services such as cloud providers, where vendors rely on external providers to meet regulatory standards. In this paper, we present CTRAG, a novel Retrieval-Augmented Generation (RAG) pipeline designed for automated compliance checking. CTRAG employs advanced strategies, including adaptive chunking, dynamic retrieval configurations, and in-context learning, to improve the precision and relevance of compliance assessments. By extracting control questions from regulatory texts and cross-referencing them with unstructured company documentation, CTRAG achieves highly accurate, document-informed compliance verification, even in cases of indirect compliance through third-party services. Empirical evaluations demonstrate significant improvements, with CTRAG achieving an F1-score of 78% and a recall of 85% in the final deployed configuration, ensuring minimal missed non-compliance cases while reducing manual reviewer effort in a real-world deployment. To validate CTRAG value, we developed and deployed a POC within a Big Four professional services firm, applying it to real-world cases and cross-checking results against manual compliance reports. These findings highlight CTRAG potential to streamline compliance workflows, mitigate risks, and enhance regulatory trust in complex, high-stakes environments.

Abstract

Workflow Automation

Explore similar work

CTRAG: An In-Context Retrieval-based Framework for Automated Compliance Checking using LLMs

The Compliance Gap: Why AI Systems Promise to Follow Process Instructions but Don't

Evaluating LLM Trade-offs for Enterprise Automation: Lessons from Workflow Generation in a Production Enterprise Platform