Email as the Interface to Generative AI Models: Seamless Administrative Automation

TL;DR

This paper presents an email-based framework integrating LLMs and OCR to automate administrative tasks, significantly reducing processing time and costs while enhancing accessibility for non-technical staff.

Contribution

It introduces a novel email interface system that leverages LLMs and OCR for seamless administrative automation, improving usability and efficiency in enterprise environments.

Findings

Automated form filling in under 8 seconds.

Reduced staff time by a factor of 3-4 with human supervision.

Lowered cost per form by 64% compared to manual processing.

Abstract

This paper introduces a novel architectural framework that integrates Large Language Models (LLMs) with email interfaces to automate administrative tasks, specifically targeting accessibility barriers in enterprise environments. The system connects email communication channels with Optical Character Recognition (OCR) and intelligent automation, enabling non-technical administrative staff to delegate complex form-filling and document processing tasks using familiar email interfaces. By treating the email body as a natural language prompt and attachments as contextual information, the workflow bridges the gap between advanced AI capabilities and practical usability. Empirical evaluation shows that the system can complete complex administrative forms in under 8 seconds of automated processing, with human supervision reducing total staff time by a factor of three to four compared to manual workflows. The top-performing LLM accurately filled 16 out of 29 form fields and reduced the total cost per processed form by 64% relative to manual completion. These findings demonstrate that email-based LLM integration is a viable and cost-effective approach for democratizing advanced automation in organizational settings, supporting widespread adoption without requiring specialized technical knowledge or major workflow changes. This aligns with broader trends in leveraging LLMs to enhance accessibility and automate complex tasks for non-technical users, making technology more inclusive and efficient.