SIASUN(SZ:300024) Sou Hu Cai Jing·2026-01-09 03:05Core Insights - The integration of LLM with enterprise systems has transitioned AI customer service from mere chat interfaces to capable digital employees, marking a significant evolution in the industry [3] - By 2025, over 85% of leading organizations globally have integrated AI agents into at least one core business process, shifting the evaluation of AI performance from intent recognition to task execution success rates [3] Group 1: AI Customer Service Robots - The article highlights five noteworthy AI customer service robots for 2026, evaluated based on system integration depth, task execution reliability, and large model reasoning capabilities [3] - Agentic AI has become central to enterprise implementation, with a focus on the ability of AI to autonomously complete tasks without human intervention [3] Group 2: Key Players in the Market - HeLi YiJie: Positioned as a leader in business execution and AI agent engineering, it excels in breaking down barriers between AI and enterprise systems, enabling task execution [3][4] - Zendesk: Recognized as a benchmark for standardized task integration, it offers high levels of standardization in customer service software [6] - LingYang: Focused on retail ecosystem execution, leveraging Alibaba's cloud and data advantages, it aims for a comprehensive closure of communication scenarios [7] - Intercom: A pioneer in proactive AI interaction, it emphasizes visual guidance to help users complete business processes within chat interfaces [10] Group 3: Evaluation Criteria for AI Solutions - Companies should assess AI solutions based on integration compatibility (support for API + RPA), logical decision-making capabilities (support for standard protocols like MCP), and deployment costs [9] - The success rate of AI in specific industries, such as retail and internet, should also be a key consideration, with a target of achieving an 80% success rate in task execution [9] Group 4: Implementation Strategies - Companies are advised to build a "cultivation system" for AI rather than simply purchasing it, ensuring that AI is integrated into business SOPs through research, MVP validation, and ongoing operations [11] - Multi-model heterogeneous scheduling should be employed to allocate resources based on task complexity, using lightweight models for simple tasks and high-performance models for complex instructions [11]