Author(s): Naga Venkata Chaitanya Akula

Abstract: Revolutionizing invoice processing, this paper presents a next-generation intelligent automation framework powered by Pega GenAI Bots and Pega Robot Manager, delivering cognitive automation at enterprise scale. Traditional accounts payable systems are plagued by manual inefficiencies, diverse invoice layouts, and unstable ERP interfaces. To address these challenges, we propose a hybrid solution that combines generative AI, large language models (LLMs), and hyper automation to construct a self-optimizing invoice pipeline. Leveraging OpenAI-driven data extraction through Pega’s Connect GenAI, the system interprets both structured and unstructured documents with high precision. Seamless orchestration of workflows is achieved via attended digital workers for exception handling and unattended RPA bots for fully automated invoice lifecycle management. Key innovations include real-time adaptability to legacy UI changes in systems like SAP, dynamic workflow coordination through Robot Manager, and self-healing mechanisms to mitigate AI hallucinations and UI drift. The framework was validated using a synthetic dataset of 1,000 varied invoice formats, achieving over 90% reduction in processing errors, 70% acceleration in cycle times, and 98% SLA compliance. Less than 10% of invoices required human intervention, indicating a high level of autonomy. Furthermore, performance remained consistent across different invoice types, including PO-based, utility, and scanned formats. These results demonstrate not only technical feasibility but also practical scalability, positioning the system as a robust solution for enterprise-wide deployment. By fusing AI reasoning with deterministic automation, the proposed architecture sets a new benchmark for AI-human collaboration in financial operations and provides a blueprint for trustworthy and resilient AI adoption in document-centric workflows.

DOI: https://www.doi.org/10.33545/26633582.2025.v7.i1c.180

Pages: 204-211 | Views: 3229 | Downloads: 2536

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