salesforce(US:CRM) Sou Hu Cai Jing·2025-12-31 04:22Core Viewpoint - Salesforce is shifting its strategy from reliance on generative AI to more predictable "deterministic" automation technologies in its flagship product, Agentforce, due to operational challenges and customer feedback regarding AI performance [1][4][8]. Group 1: Strategic Shift - Salesforce has reduced its customer support team from 9,000 to approximately 5,000 as part of its AI deployment strategy [1]. - The company is now introducing basic automation techniques in Agentforce to enhance reliability, moving away from the previously favored generative AI models [3][4]. - This strategic pivot contrasts sharply with Salesforce's earlier "AI-first" approach, indicating a significant change in product strategy [4]. Group 2: Operational Challenges - Agentforce has faced issues with stability and reliability, leading to customer dissatisfaction, such as missed satisfaction survey prompts [5]. - The CTO of Agentforce acknowledged that basic automation could lower operational costs but also highlighted limitations, such as the risk of missing instructions when the number exceeds eight [4][5]. - The phenomenon of AI "drift" has been noted, where AI systems deviate from their intended tasks when faced with unrelated queries [5][6]. Group 3: Market Reaction and Financial Impact - Salesforce's stock has dropped approximately 34% from its peak in December 2024, reflecting market concerns over the company's AI strategy [7]. - CEO Marc Benioff has indicated a shift in focus towards data infrastructure rather than AI models, emphasizing the risks associated with unreliable AI outputs [7]. - There is speculation about potentially rebranding the company to "Agentforce" due to declining interest in cloud computing topics among customers [7]. Group 4: Industry Implications - Salesforce's retreat from generative AI may have ripple effects on thousands of companies currently utilizing this technology [8]. - The company emphasizes the need for AI to be integrated with reliable data and governance frameworks to ensure predictable business outcomes [8].