2025协作自动化的崛起_自主式AI智能体如何重塑业务流程研究报告_17页_4mb

Summary of "The Rise of Collaborative Automation"

Core Content

This document explores the transformative role of AI agents in redefining business automation. It outlines how AI agents, powered by Generative AI (GenAI), are changing the landscape of automation by offering greater flexibility, intelligence, and adaptability compared to traditional Robotic Process Automation (RPA). The report emphasizes the importance of integrating AI agents with RPA to create a more efficient, intelligent, and scalable automation ecosystem.

Main Points

• AI Agents vs. RPA: AI agents can understand context, learn dynamically, and make decisions autonomously, whereas RPA is limited to rule-based, repetitive tasks.
• Collaborative Automation: AI agents and RPA can work together to enhance productivity and scalability. RPA handles structured tasks, while AI agents manage more complex, adaptive workflows.
• Enhancing RPA: AI agents can extend RPA by processing unstructured data, adapting to new formats, and enabling contextual decision-making and exception handling.
• Phased Transition: Organizations should adopt a phased approach to integrating AI agents, starting with enhancing existing RPA systems and gradually replacing them as the technology matures.
• Future of Automation: AI agents are expected to evolve into autonomous, multiagent systems that can manage complex, cross-functional tasks and drive strategic decision-making.

Key Takeaways

• AI agents are a breakthrough in automation, enabling smarter and more adaptive workflows.
• They can improve strategic decision-making, process optimization, and user experience through contextual understanding and dynamic learning.
• AI agents can operate independently, reducing the need for human intervention in many scenarios.
• Combining RPA with AI agents allows businesses to balance cost, efficiency, and adaptability.
• Organizations should not abandon RPA but rather use it as a foundation for more advanced automation.

AI Agents and RPA Integration

Benefits of Combining AI Agents and RPA

• Efficiency and Intelligence: RPA handles repetitive, structured tasks, while AI agents manage more complex, ambiguous, and language-dependent tasks.
• Reduced Manual Intervention: AI agents can handle exceptions autonomously, minimizing the need for human oversight.
• Scalability and Flexibility: RPA scales with task volume, while AI agents scale with task complexity, adapting to new formats and rules.
• Resilience and Flexibility: AI agents can process outputs from RPA, analyze data, and provide decision support, making automation pipelines more robust.

Use Case Examples

Future Trends in AI Agents

Current Trends (NOW)

• AI agents are becoming more context-aware and adaptive, enabling them to understand business environments and user intent.
• They offer personalized automation and decision-support capabilities, making them ideal for tasks requiring flexibility and reasoning.

Mid-Term Trends (NEXT)

• Multiagent Collaboration: AI agents will work together autonomously, reducing the need for human oversight.
• Self-Healing Automation: AI systems will identify and correct inefficiencies and errors without human intervention.
• Enterprise Orchestration: AI agents will manage large-scale processes such as supply chains and customer experiences.

Long-Term Trends (FUTURE)

• Generalist AI Systems: These will serve as strategic advisers across multiple domains, autonomously designing and optimizing automation frameworks.
• Autonomous Negotiation and Strategic Planning: AI agents will manage contracts, optimize business models, and drive corporate strategy.
• Autonomous Decision-Making: They will support investment planning, economic forecasting, and competitive analysis.

Smart Plays for Better Automation

• For Organizations with Existing RPA Programs: Use RPA as a foundation and identify areas where AI agents can enhance automation. A hybrid approach is recommended.
• For Organizations without RPA Programs: Start with RPA to build a scalable and standardized automation base, then gradually introduce AI agents.
• Phased Approach: Begin with enhancing RPA with AI, then selectively replace it, and finally transition to full AI agent and multiagent systems.
• Consideration of Risks: Ensure AI agents can act autonomously while maintaining control and accountability through human oversight.

Conclusion

The rise of AI agents marks a new era in automation, offering businesses the ability to handle complex, dynamic processes with greater intelligence and adaptability. While RPA remains a valuable tool, the integration of AI agents is essential for future-proofing automation strategies. Organizations should strategically transition to agentic automation, leveraging existing RPA capabilities while preparing for the next wave of AI-driven processes.

References

• Contributors: Jim Rowan, Parth Patwari, Sanghamitra Pati, Ed Van Buren
• Report: "Now decides next: Generating a new future," Deloitte AI Institute, January 2025

Contact

• Prakul Sharma – Principal, AI & Data, Deloitte Consulting LLP – praksharma@deloitte.com
• AJ Maxwell – Principal, AI & Data, Deloitte Consulting LLP – amaxwell@deloitte.com
• Patricia Henderson – Principal, AI & Data, Deloitte Consulting LLP – pahenderson@deloitte.com
• Camille Chicklis – DC Specialist Leader, Deloitte Consulting LLP – cchicklis@deloitte.com

For more information, visit Deloitte AI Institute.