2026年智能体式自动化与AI智能体趋势报告_25页_3mb

2026 Agentic Automation and AI Agent Trends Summary

Core Content

2026 is positioned as the year where agentic automation and AI agents move from promise to proof, emphasizing ROI, governance, and strategic implementation over mere technological capability. The focus is on orchestrating AI agents within business workflows, ensuring human-AI collaboration, and rethinking organizational structures to support AI integration.

Main Trends

1. ROI Proof Over Promise

• Key Focus: Measuring real business impact through AI implementation.
• Challenges: Organizations are under pressure to justify large AI investments with tangible ROI.
• Recommendations:
  • Start with well-defined, manageable use cases.
  • Measure baseline processes before AI deployment.
  • Communicate successes to build awareness and trust.
• Statistics:
  • Only 15% of AI decision-makers reported EBITDA lift in the past year.
  • By 2028, AI agents will handle over 90% of B2B buying processes, driving $15 trillion in spend.

2. Org Readiness Is Kicking Up a Notch

• Key Focus: Reengineering business models and workforce skills for AI integration.
• Challenges: Many organizations still operate in the industrial age, lacking agile AI frameworks.
• Recommendations:
  • Rethink business functions and structures.
  • Choose a methodology for AI implementation (e.g., SS&C | Blue Prism® EOM).
  • Upskill employees to work effectively with AI.
• Statistics:
  • Only 1% of organizations have agile or digital operating models.
  • Ungoverned AI could cost B2B companies over $10 billion in enterprise value by 2027.

3. AI-Human Collab Is Taking Shape

• Key Focus: Hybrid teams where humans and AI agents collaborate.
• Challenges: The need for clear roles, responsibilities, and continuous support for AI agents.
• Recommendations:
  • Use user-friendly AI tools and prebuilt models.
  • Realign roles and responsibilities to enable better collaboration.
  • Orchestrate workflows to enhance AI-human synergy.
• Statistics:
  • By 2028, 38% of organizations will have AI agents as team members.
  • 75% of hiring processes will include AI proficiency testing by 2027.

4. Orchestrating AI Agents Becomes Increasingly Essential

• Key Focus: Integrating AI agents into end-to-end workflows for maximum impact.
• Challenges: Managing the complexity of multi-agent systems and ensuring coordination.
• Recommendations:
  • Implement human-in-the-loop verification.
  • Use specialized, vertical AI agents.
  • Integrate orchestration into workflows for seamless collaboration.
• Statistics:
  • By 2028, 80% of customer-facing processes will use multi-agent AI.
  • By 2027, process-oriented contracts will lose 50% of their value due to agentic AI.

5. Trusting the Tech With AI Governance

• Key Focus: Ensuring AI is used responsibly, securely, and in compliance with regulations.
• Challenges: Regulatory pressures (e.g., EU AI Act) and the risk of unregulated AI.
• Recommendations:
  • Design applications with governance in mind.
  • Use AI gateways to ensure compliance and explainability.
  • Monitor and audit AI processes for transparency.
• Statistics:
  • 60% of Fortune 100 companies will appoint a head of AI governance by 2026.
  • By 2027, fragmented AI regulation will cost $5 billion in compliance.

6. Creating AI Projects That Actually Scale

• Key Focus: Building scalable, sustainable AI initiatives.
• Challenges: Many AI projects fail due to lack of clear strategy and governance.
• Recommendations:
  • Plan with measurable goals and stakeholder involvement.
  • Build multi-agent systems with narrowly specialized agents.
  • Ensure platform interoperability and reuse of components.
• Statistics:
  • Over 40% of agentic AI projects will be canceled by 2027.
  • 70% of multi-agent systems will use specialized agents by 2027.

7. RPA Isn't Gone; It's Supercharged

• Key Focus: RPA and AI agents complement each other in the automation stack.
• Challenges: AI agents are not a replacement for RPA but require a strong foundation.
• Recommendations:
  • Use RPA for structured, repetitive tasks.
  • Integrate RPA with AI agents for complex, unstructured processes.
  • Consider orchestration and governance to enhance both.
• Statistics:
  • By 2027, AI agents will challenge mainstream productivity tools, causing a $58 billion market shift.
  • RPA remains critical for high-volume, repeatable processes.

Key Information

• AI Agents: Are becoming essential in enterprise workflows, especially in customer-facing and high-compliance environments.
• Human-AI Collaboration: Is evolving into a standard practice, with clear roles and continuous support needed.
• Governance: Is now a core component of AI implementation, with frameworks like SS&C | Blue Prism® AI Gateway playing a vital role.
• ROI: Must be proven through measurable outcomes, not just pilot projects.
• Org Readiness: Is improving, with more companies rethinking their operating models and upskilling employees.
• Orchestration: Is becoming a key enabler for integrating AI agents with other systems and workflows.

2026 Checklist

• Create and track measurable goals for AI projects
• Develop a clear roadmap for AI implementation
• Upskill employees on working with AI agents
• Integrate orchestration to connect workflows and people
• Set up a governance framework before deploying AI
• Define AI roles and responsibilities across the business
• Consider which tasks are better suited to RPA versus AI agents

Conclusion

2026 marks the turning point where AI and agentic automation are no longer just possibilities but operational realities. Success will depend on strategic planning, governance, ROI proofing, and effective human-AI collaboration. Organizations that embrace these principles will lead the way in leveraging AI for sustainable, scalable, and secure transformation.