Identifying and Prioritizing Generative AI Use Cases in an Organization: An Industrial Case Study

2026-07-08 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

This study presents an industrial case study conducted within a Nordic energy company, investigating the identification and prioritization of Generative AI (GenAI) use cases. Over four weeks, researchers conducted 16 semi-structured interviews across nine departments, supplemented by internal documents and observations. The analysis identified 41 AI-related use cases, categorized into reporting, retrieval-augmented generation (RAG) solutions, predictive maintenance, anomaly detection, budgeting/forecasting, and uncategorized department-specific needs. Reporting-related use cases received the highest priority due to their direct support for senior management decision-making. Two pilot cases were developed: an "Email Clone System" utilizing RAG, LangChain, and LangGraph for automated customer email responses, achieving 89% accuracy, and a "RAG-Based System for Autonomous Text and Data Retrieval" for internal document management. Employees envisioned GenAI and LLM-based tools being introduced incrementally to support existing workflows in areas like reporting, forecasting, data handling, and maintenance.

Key takeaway

For Directors of AI/ML evaluating GenAI adoption strategies, prioritize solutions that incrementally integrate into existing workflows. Your focus should be on high-impact areas like automating manual reporting, enhancing forecasting, and implementing predictive maintenance. These efforts, supported by RAG-based systems for data integration, reduce immediate workload and build organizational readiness. Start with pilot cases that demonstrate clear value and maintain human oversight to ensure trust and compliance.

Key insights

The study reveals how energy sector employees identify practical GenAI use cases and prefer incremental integration into existing workflows.

Principles

• AI adoption thrives on alignment with existing workflows.
• Prioritize AI use cases by business importance and implementation ease.
• Incremental AI integration reduces organizational resistance.

Method

A qualitative approach using semi-structured interviews, observations, and document review across multiple departments identifies and prioritizes AI use cases based on operational needs and implementation feasibility.

In practice

• Automate repetitive reporting tasks with RAG solutions.
• Implement predictive maintenance for operational stability.
• Use RAG-based chatbots for internal data retrieval.

Topics

• Generative AI Adoption
• Energy Sector
• Retrieval-Augmented Generation
• Predictive Maintenance
• LLM Use Cases
• Organizational Readiness

Best for: Executive, Research Scientist, AI Product Manager, AI Scientist, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.