Core Insights - The article highlights the revolutionary deployment efficiency of the Deep X and AppMall.ai solution, reducing AI model deployment time from 480 minutes to just 8 minutes, representing a 60-fold improvement [5][8]. - The solution addresses significant pain points in traditional AI deployment processes, which often involve lengthy and complex steps, resulting in a low success rate of approximately 40% [5][6]. Industry Pain Points - Traditional AI deployment is likened to a "nightmare marathon," requiring extensive time for hardware selection, environment configuration, framework installation, model downloading, optimization, and testing, with an average total time of 480 minutes [2][3]. - The failure rate in traditional deployment processes is around 60%, leading to wasted computational resources and significant frustration for engineers, especially those less experienced [2][6]. Deep X + AppMall.ai Solution - The Deep X and AppMall.ai solution simplifies the deployment process into a streamlined six-step approach, significantly enhancing efficiency and success rates [3][4]. - The deployment process includes purchasing the hardware, automatic initialization, model selection, and installation, achieving a success rate of 98% and hardware utilization of 85-92% [4][5]. Performance Metrics - The new deployment process results in a time reduction from 480 minutes to 8-10 minutes, a success rate increase from 40% to 98%, and hardware utilization improvement from 50% to 90% [5][8]. - The AppMall.ai platform offers over 1000 pre-trained models, ensuring that each model is optimized for the Deep X hardware, thus enhancing performance by 150-200% [4][6]. Future Plans - The company aims to expand its model offerings from 1000 to 10000 by the end of 2025, with plans for international expansion and the introduction of an enterprise version of the platform [6][8]. - The long-term vision includes creating an "App Store for AI," facilitating easy access to suitable models for various applications and maximizing the value of Deep X hardware [6][8].

作者：Alex Singla、Alexander Sukharevsky、Lareina Yee、Michael Chui、Bryce Hall 企业如何完成生成式AI部署，由谁主导？ AI治理工作涉及构建一系列政策、流程和技术，以确保负责任地开发与部署AI系统。麦肯锡的调查分 析表明，由CEO亲自监督这项工作，是企业借助生成式AI提升财务表现的关键因素之一【1】。尤其在 大型企业中，CEO的直接参与对息税前利润（EBIT）的拉动效果最为显著。在已部署AI的企业中， 28%的受访者称CEO负责AI治理工作，但在年收入超5亿美元的大型企业中，这一比例略低。同时， 17%的受访者称董事会负责AI治理工作。整体来看，这项工作通常由多人共同负责，平均由两位领导牵 头。 AI的真正价值在于重塑企业运作方式。最新调查显示，在针对各类规模企业的25个要素里，重构工作 流程对于组织通过应用生成式AI实现息税前利润增长的作用最为显著。企业正逐步在AI部署的同时调 整流程。在已部署生成式AI的企业中，21%的受访者表示其组织已对部分工作流程进行了彻底重构。 集中管理AI部署的关键环节 生成式人工智能的发展，正推动企业着手构建相应 ...