2025-05-23-Moveworks-100＋企业生成式AI用例_46页_5mb

Agentic AI in Enterprise: Analysis Summary

Agentic AI represents a shift from traditional, rule-based AI systems to those that can operate autonomously. It involves AI that reasons, learns, and makes decisions without explicit human intervention, distinguishing itself from non-agentic AI, which relies on predefined instructions. This autonomy allows agentic AI to pursue complex goals, adapt to changing environments, and execute multi-step processes, providing enterprises with enhanced flexibility, efficiency, and scalability.

How Agentic AI Works

At its core, agentic AI functions through a perception-reasoning-action loop: it perceives environmental data (e.g., via APIs or sensors), reasons using advanced models (often LLMs), and takes action through software integrations. This loop enables continuous learning and optimization, allowing AI agents to handle dynamic tasks independently.

Key Differences from Non-Agentic AI

• Autonomy: Agentic AI sets and follows its own goals, whereas non-agentic AI requires human prompts for each task.
• Goal-Oriented Behavior: Agentic AI focuses on achieving specific objectives, improving efficiency and reducing manual effort.
• Adaptability: It can adapt to new situations and learn from experiences, unlike non-agentic systems.

Enterprise Benefits and Use Cases

Agentic AI drives significant advantages, including cost savings, automation of repetitive tasks, and improved decision-making. By 2028, 33% of enterprise software is expected to use agentic AI for complex workflows. It is applied across various sectors:

• HR: Automates onboarding, compliance, and employee self-service (e.g., Palo Alto Networks saved 350K hours).
• IT: Manages incidents, password resets, and troubleshooting (e.g., Power Design won Service Desk of the Year).
• Finance: Handles expense approvals, payroll, and contract monitoring.
• Sales: Enhances lead scoring, quote issuance, and customer engagement.
• Customer Service: Provides 24/7 support and personalization (e.g., Mercari reduced ticket volume by 74%).
• Other industries, such as Law, Facilities, Marketing, and Productivity, also leverage agentic AI for tasks like document review, predictive maintenance, and campaign optimization, with numerous case studies illustrating real-world impacts.

Case Study Highlights

• Palo Alto Networks: Achieved substantial productivity gains by automating employee support issues.
• Mercari: Reduced routine IT support tickets by 74%, improving efficiency and scaling operations.

Call to Action

Agentic AI adoption is growing rapidly, offering enterprises the tools to streamline operations and achieve better outcomes. Solutions like Moveworks enable quick deployment of AI agents, helping organizations stay agile and competitive in a data-driven world.