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

2025-06-30

AI Summary

Non-technical administrative staff in enterprises face accessibility barriers when leveraging AI to automate complex administrative tasks. Method: We propose an LLM-integrated framework embedded within the email interface, uniquely treating email bodies as natural-language instructions and attachments as contextual inputs—combined with OCR, document understanding, and intelligent automation—to enable end-to-end form filling and document processing. Contribution/Results: Our approach introduces a zero-code, low-friction email-based interaction paradigm, eliminating the need for API integration or GUI-based configuration and substantially lowering the AI adoption barrier. Experiments show the system processes a single form in 8 seconds on average, achieves 16/29 field-filling accuracy, reduces human supervision time by 3–4×, and cuts per-form processing cost by 64%. This provides a deployable, scalable pathway for lightweight AI-powered office automation in enterprise settings.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Automate administrative tasks via email using LLMs
• Bridge AI capabilities with practical usability for non-technical staff
• Reduce time and cost in form processing with automation

Innovation

• Integrates LLMs with email interfaces
• Uses OCR for document processing automation
• Treats email as natural language prompts