颠覆认知！重金买大模型搞不定业务？高手都在布局这个“老古董”

Core Insights - Many companies invest heavily in large models but struggle to address practical business issues, revealing that traditional foundational technologies and data governance methods are crucial for cost reduction and efficiency [1][14]. Workflow: Revitalizing Old Concepts - Workflow has evolved from rigid processes to a vital tool for managing AI, acting as a bridge between AI technology and real business needs [3][5]. - The presence of workflow ensures that AI operates within a defined scope, preventing it from making unverified commitments that could lead to significant risks [5][7]. Integration with Existing Systems - AI must be integrated into the complex and mature systems of enterprises; otherwise, it remains an isolated high-performance accessory [9]. - Workflow serves as a "business integrator," embedding AI capabilities into existing standard operating procedures (SOPs) [9][11]. Effective Workflow Implementation - Proper implementation of workflow involves maintaining existing management processes while allowing AI to assist, ensuring compliance with financial management principles [11][13]. - AI operates within the parameters set by the workflow, with decision-making authority retained by management, thus creating an efficient automated processing loop [13][16]. Conclusion - Companies often focus on selecting advanced models and high-end hardware while neglecting workflow design and optimization, which is essential for effective AI application [14][16]. - The essence of workflow lies in operational guidelines, emergency response plans, and cross-departmental collaboration, which are critical for successful AI integration [16].