AI and Agentic Automation
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2026年AI与智能体式自动化趋势报告解锁发展蓝图-UiPath
Sou Hu Cai Jing· 2025-11-21 05:02
Core Insights - The report outlines the future of AI and agentic automation, emphasizing a shift from isolated trials to interconnected governance systems, with a focus on scaling execution by 2026 [1][12][13] Group 1: Key Trends - **Necessity for Reinvention**: AI disruptions are compelling organizations to fundamentally change their operational models, with 78% of executives agreeing on the need for new operating systems centered around agentic capabilities [24][29][41] - **AI ROI Realization**: Enterprises are expected to focus on high-value scenarios, with 73% of executives anticipating significant value from agentic initiatives within 12 months [1][44][50] - **Rise of Vertical Solutions**: Customized agentic solutions are gaining traction due to their rapid deployment and clear ROI, with externally sourced AI projects showing double the effectiveness of internal developments [2][56][62] Group 2: Multi-Agent Systems and Governance - **Multi-Agent Systems (MAS)**: The adoption of MAS is becoming mainstream, with 75% of enterprises planning to implement related frameworks within 18 months, leading to a 25% reduction in operational costs and a 40% increase in execution efficiency [2][65] - **Centralized Command Centers**: Companies are establishing centralized orchestration and governance capabilities to manage agentic systems effectively, with projections indicating that 70% of MAS will utilize centralized platforms by 2028 [2][19] - **Enhanced Security Measures**: With 96% of IT security leaders viewing AI agents as significant risks, organizations are focusing on building comprehensive security frameworks to manage the lifecycle of AI systems [2][20] Group 3: Data and Adaptation - **Data Quality Improvement**: Companies are enhancing data quality through metadata tagging and real-time architectures, which are crucial for the effective operation of AI models [2][21] - **Continuous Adaptation**: The need for operating systems that can adapt in real-time is emphasized, as agentic systems require flexibility to optimize operations continuously [35][36]