11月28日，科锐国际以"AI Links the Future"为主题亮相第三届全国人力资源服务业发展大会全景式呈现"AI×全球×生态"领 域的最新实践成果，用AI重塑效率、用人才链接全球、用生态共筑繁荣，为人力资源行业的数智化转型与全球化实践贡献智 慧。 在自身高质量发展的同时，科锐国际始终秉持开放共赢理念，积极打造人力资源产业互联平台禾蛙，推动全行业发展。目 前禾蛙已聚集了超过18万名专业招聘顾问、3万余家人力资源服务机构，链接海内外400余座城市，近一年协作累计推荐人才超 过40万人。 面向未来，科锐国际表示将持续发挥"AI数智引擎、全球服务网络、开放产业生态"核心优势，推动人力资源服务向专业 化、数智化、全球化、生态化升级，引领人力资源服务行业迈入更高效、更包容、更具价值的"智链时代"。 据悉，本届大会由人力资源社会保障部、湖北省人民政府主办，以"塑造现代化人力资源 促进高质量充分就业"为主题，聚 焦产业结构转型、数智经济发展与人才要素配置，以"研、会、才、聘"四大板块，构建人才、科技与产业深度融合的价值链 路，为行业发展搭建了高效交流与实践展示平台。 生态化协同：从"单向服务"到"产业基础设施"的 ...

Core Insights - The industrialization of artificial intelligence (AI) has surpassed conceptual exploration, fundamentally restructuring various industries through the paradigm of "old scenarios, new solutions" [1] - The focus in the human resources sector is on practical strategies that return to core business processes while seeking disruptive solutions through small-scale validations before scaling [1][4] - The application of generative AI in business is evolving through three distinct stages: knowledge acquisition, multimodal integration, and the agent phase, which emphasizes autonomous execution [2][3] Group 1: AI Application Stages - The first stage involves the ChatGPT phase, which reshapes knowledge acquisition methods, significantly enhancing the efficiency of knowledge-intensive recruitment processes [2][8] - The second stage is the multimodal phase, focusing on the integration of voice and text modalities to optimize communication in recruitment [2][10] - The third stage is the agent phase, where the capabilities of agents in reasoning, long-term planning, and tool utilization are enhanced, transforming short process businesses from assisted decision-making to autonomous execution [2][10] Group 2: Demand Management and Product Design - The introduction of agents fundamentally alters the definition of technical demands and product design logic, emphasizing the need for understanding the essence of demands and their applicability [3][15] - The "problem-solution chain" method proposed by the company clarifies the involved parties, specific issues, and corresponding solutions, ensuring that new solutions can deliver significant improvements [3][15] - In the agent era, product design shifts focus from rigid process nodes to observing the perception and decision-making processes of excellent consultants, necessitating greater involvement from consultants in product development [3][16] Group 3: Future Goals and Innovations - The company aims to enhance its MatchSystem to transition from semantic-level matching to application-level matching by 2025, integrating it with recruitment scenarios to develop a SearchAgent [4][30] - The company is currently testing a more powerful agent product, with applications in automation and self-service label definitions, alongside the development of contextualized applications [4][30] - Innovations in reasoning technology and the CRE-T1 model are being developed to improve the agent's reasoning capabilities, allowing for more effective problem-solving and generalization [13][23] Group 4: AI's Impact on Management and Collaboration - The current wave of AI is reshaping the division of labor and collaboration across all functions, emphasizing the need for interdisciplinary integration among product, data, and engineering teams [18][19] - The management revolution driven by AI is expected to increase standardization and automation in service industries, potentially leading to the reduction or elimination of middle management roles [21][36] - The acceptance and willingness to pay for AI technologies among clients have significantly increased, with many clients seeking to understand AI implementation in recruitment [26][27]

Core Insights - AI is accelerating its implementation across various industries, including the recruitment sector, significantly enhancing candidate screening and client acquisition efficiency [2][5]. Candidate Matching Efficiency - The introduction of AI technology has transformed the candidate tracking process from manual to automated, allowing recruiters to instantly access candidate histories and match new applicants without manual input [3][4]. - The new Candidate Tracking System (CTS) at the company enables real-time updates on candidate activity and generates customized recommendation reports based on job descriptions and candidate profiles [3][4]. Client Acquisition Efficiency - The integration of AI into the company's CRM system has drastically improved client acquisition efficiency, allowing for real-time data retrieval and analysis of job postings across various platforms [5][6]. - The CRM system can now automatically monitor client recruitment needs and send notifications, turning passive client engagement into proactive outreach [6][7]. Technological Advancements - The company is testing an Agent prototype system aimed at enhancing frontline business efficiency rather than merely serving management reporting needs [8]. - Significant improvements have been made in the MatchSystem and CRE model, with the accuracy of job matching increasing by 60% due to the latest updates [8][9]. Future Developments - The company plans to continue advancing Agent technology and reasoning-based embedding models to address complex recruitment tasks, moving beyond traditional semantic embedding [9]. - The ongoing development of the CRE T1 model aims to create a reasoning embedding model that can handle structured reasoning challenges in recruitment scenarios [9].