TVS Sidekick: Challenges and Practical Insights from Deploying Large Language Models in the Enterprise

2025-09-30

AI Summary

Enterprises face significant challenges in responsibly and compliantly integrating large language models (LLMs) into core business processes amid rapid technological evolution and underdeveloped ethical infrastructure. Method: Drawing on a longitudinal case study of TVS Supply Chain Solutions, this paper systematically analyzes ethical risks, regulatory alignment challenges, and socio-technical integration barriers in LLM assistant deployment, and proposes a lightweight, enterprise-oriented AI governance framework that tightly couples ethical principles and compliance requirements with existing IT architectures and operational workflows. Contribution/Results: The framework bridges a critical gap in actionable, auditable, and scalable LLM deployment practices within real-world industrial settings. Empirical validation demonstrates its effectiveness in concurrently enhancing process efficiency and mitigating key risks—including bias, hallucination, and accountability ambiguity—thereby offering a reusable methodology and implementation pathway for AI governance in manufacturing supply chains.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• 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
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Many enterprises are increasingly adopting Artificial Intelligence (AI) to make internal processes more competitive and efficient. In response to public concern and new regulations for the ethical and responsible use of AI, implementing AI governance frameworks could help to integrate AI within organisations and mitigate associated risks. However, the rapid technological advances and lack of shared ethical AI infrastructures creates barriers to their practical adoption in businesses. This paper presents a real-world AI application at TVS Supply Chain Solutions, reporting on the experience developing an AI assistant underpinned by large language models and the ethical, regulatory, and sociotechnical challenges in deployment for enterprise use.

Problem

• Addressing ethical AI adoption barriers in enterprises
• Implementing governance for responsible large language model deployment
• Overcoming sociotechnical challenges in enterprise AI integration

Innovation

• Deploying large language models for enterprise AI assistant
• Addressing ethical AI governance and regulatory challenges
• Implementing practical sociotechnical solutions for business adoption