Leveraging Generative AI for Adaptive Compliance Workflows in Enterprise Platforms

Abstract

The integration of Generative Artificial Intelligence (GenAI) into enterprise compliance operations allows businesses to create flexible, compliant, real-time workflows that can respond to changing regulations with ease [2]. Traditional compliance practices are primarily based on human interpretation of policies, manual drafting of compliance documents, and the subsequent re-configuration of these documents and workflows — all of which cannot sustain a compliant business in an environment where regulatory changes occur continuously [3]. This paper presents the Generative Adaptive Compliance Orchestration Framework (GACOF) [10], which is integrated into the ServiceNow Governance, Risk, and Compliance (GRC) solution [5]. This framework utilizes large language models (LLMs) [7][8], natural language processing (NLP) [7] and workflow orchestration to automatically analyze and interpret regulatory updates and generate compliance policy drafts based upon those updates, along with changing compliance workflows as regulations change (almost) in real-time. A financial services example demonstrates the efficacy of the GACOF framework in reducing policy update cycles by 40%, manual policy drafting workload by 93% and regulatory interpretation accuracy rate by 95%. The research presents a new model of AI-based compliance orchestration that combines GenAI with retrieval-augmented generation (RAG) [9] and human-in-the-loop validation, which provides enterprise businesses with a scalable and auditable AI-based solution for compliance globally. Based upon these findings, GenAI will act as a multiplier of compliance professionals, not as a substitute for them, but rather as a technology that will allow for compliance ecosystems to be proactive and to continually self-adapt to meet new compliance requirements [10].

Introduction

The text addresses the growing challenge of rapidly increasing regulatory change, which has rendered traditional, manual compliance workflows ineffective for large, multi-jurisdictional organizations. Despite advances in Governance, Risk, and Compliance (GRC) platforms, compliance teams still rely heavily on manual interpretation of regulations, policy drafting, approvals, and workflow updates. This results in long delays between regulatory issuance and enforcement, higher compliance risk, audit findings, and enforcement exposure.

To address these limitations, the paper proposes the Generative Adaptive Compliance Orchestration Framework (GACOF)—a ServiceNow GRC–native, AI-driven framework that integrates generative AI, Natural Language Processing (NLP), and automated workflow orchestration into the entire compliance lifecycle. GACOF transforms compliance from a reactive, audit-driven function into a continuous, intelligence-driven enterprise capability. Regulatory updates are ingested in real time, analyzed using NLP to extract obligations and intent, and evaluated against existing policies and controls. Context-aware policy drafts are then generated using generative AI grounded in enterprise knowledge (via retrieval-augmented generation), validated through automated checks and human-in-the-loop review for high-risk changes, and operationalized through automated workflows.

The framework was evaluated using a mixed-method research approach, including analysis of 12,000 regulatory documents (2023–2025) and deployment in a live ServiceNow production environment. A case study of a Tier-1 investment bank demonstrated significant benefits: policy update cycle times reduced by ~40%, human drafting effort reduced by up to 90%, semantic accuracy improved to 95%, and audit findings related to policy-control gaps reduced by ~87%. These improvements resulted from faster regulatory interpretation, consistent policy generation, and tighter alignment between regulations, policies, and operational controls.

The paper formalizes the AI-driven policy generation process, emphasizing traceability, governance alignment, and audit defensibility. It also outlines an implementation blueprint within ServiceNow and presents best practices, including trusted data sources, human oversight, version control, confidence thresholds, and immutable audit trails.

Overall, the study demonstrates that AI-powered adaptive compliance orchestration can deliver scalable, accurate, and auditable compliance operations, enabling organizations to keep pace with volatile regulatory environments. Future work includes multimodal regulatory ingestion, conflict detection across jurisdictions, and AI-generated control-testing scripts for continuous compliance monitoring.

Conclusion

Generative Adaptive Compliance Orchestration Framework [10] shows generative AI enhances compliance teams instead of eliminating them [2]. By integrating generative AI into Governance, Risk Management, and Compliance (GRC) Platforms [5], businesses can move from a reactive to a proactive and self-adjusting compliance ecosystem and implement AI-enhanced interpretations of regulations, create new policies, and manage compliance within an infrastructure that allows for rapid and accurate deployment of policies via artificial intelligence [8]. These results demonstrate how compliance is being transformed through adaptive processes and tools, providing faster and more accurate interpretation of policies, creating more accurate policies, significantly reduced number of audit findings [6], and as increased use of adaptive compliance becomes a necessity throughout the business due to the increasing complexity of regulatory environment [4]. This means that AI based compliance orchestration will become an increasingly integral and foundational component of enterprise compliance platforms [2].

References

[1] Thomson Reuters, \"Regulatory Intelligence Report 2025,\" 2025. [2] Deloitte, \"Future of Compliance: AI and Automation,\" 2025. [3] NAVEX, \"Global Ethics and Compliance Benchmark,\" 2025. [4] EY, \"Global Regulatory Outlook 2024,\" 2024. [5] ServiceNow, \"GRC with AI: Technical Guide,\" Vancouver Release, 2025. [6] PwC, \"AI in Risk and Compliance,\" 2025. [7] J. Devlin et al., \"BERT: Pre-training of Deep Bidirectional Transformers,\" in Proc. NAACL, 2019. [8] Anthropic, \"Claude 3.5 Technical Report,\" 2024. [9] P. Lewis et al., \"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,\" in NeurIPS, 2020. [10] S. K. Prasad, \"GenAI for Adaptive GRC Workflows,\" in ServiceNow Knowledge 2025, 2025.

Copyright

Copyright © 2026 Saikrishna Tarakampet, Raghunath Reddy Koilakonda, Subhash Tatavarthi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.