2025年代理型人工智能AI智能体终极指南报告：100＋企业生成式AI用例（英文版）-Moveworks

The Ultimate Agentic AI GuideEnterprise AI adoption is skyrocketing. Just a few years ago, only 48% of organizations were experimenting with AI. Today, that figure has jumped to 72% — and it’s only growing.Why? Because AI delivers efficiency, automation, and scale like never before. As more companies recognize the long-term value of AI, the number of real-world use cases in production has surged. However, we’re now moving into a new phase of AI — one driven by agentic AI. Unlike non-agentic AI models that rely on predefined rules, agentic AI systems can reason, learn,and make decisions on their own without human intervention.100+ Real World Use Cases of Agentic AI for the EnterpriseWhy does this matter for your organizationIn this guide, we’ll break down:• What agentic AI is and how it differs from non-agentic AI • 100+ real-world use-cases showing how enterprises are already leveraging agentic AI• Practical examples across industries — including HR, IT, finance, sales, customer service, and more• How to get started with agentic AI in your organization todayIf you’re looking for ways to automate decision-making, streamline workflows, and unlock new levels of productivity — this guide is for you.What is agentic AI?Agentic AI refers to AI systems that can autonomously pursue complex goals, make decisions, and execute multi-step processes — all without explicit human supervision or interven-tion. These systems can plan, adapt, and take action, much like a human employee. How does agentic AI work?Agentic AI can plan, reason, take action, and learn to adjust its behavior over time. In this way, AI agents operate through a cyclical perception-reasoning-action loop process: • Perception: They utilize sensors (such as APIs, cameras, or data feeds) to gather environmental informationBy 2028, 33% of enterprise software applications will incorporate agentic AI to help manage complex tasks and workflows.That’s because agentic AI provides the flexibility, efficiency, and scalability necessary to achieve these goals while helping your business stay agile to accommodate shifting needs and operational demands• Reasoning: They employ reasoning (often powered by Large Language Models or LLMs) to process this data and make decisions• Action: They use actuators or software actions to execute those decisions. Consider a real-world example of a bank’s AI customer service assistant that operates autonomously through a perception-reasoning-action loop:• Perception (Data Collection): The chatbot uses Natural Language Processing (NLP) to understand customer queries like “What is my account balance?” or “How do I reset my password?” • Reasoning (Data Processing and Decision Making): Machine Learning models powered by Large Language Models (LLMs) interpret the query, retrieve relevant information from the bank’s database, and determine the best response. • Action (Executing Decisions): The AI assistant provides the response in natural language, such as