从“老场景”的“新解法”下手，突破Agent落地难题| 2025 ITValue Summit前瞻WAIC现场版：AI落地指南系列

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]