阿里云AI Landing Zone正式发布，助力企业从“上好云”到“用好AI”的战略升级

砍柴网2025-12-19

随着人工智能技术进入体系化突破的新阶段，企业内部迅速涌现的各类 AI 应用，正对云治理提出前所未有的挑战：成本、安全、稳定与效率之间的平衡难题被急剧放大。尽管企业拥抱 AI 的意愿普遍高涨，但不同成熟度企业之间逐渐拉开的“能力鸿沟”，正成为决定其 AI 发展成败的关键因素。12月16日，在2025年第六届中国信通院IT新治理领导力论坛上，阿里云正式发布AI Landing Zone白皮书，并升级...

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AI 的意愿普遍高涨，但不同成熟度企业之间逐渐拉开的“能力鸿沟”，正成为决定其 AI 发展成败的关键因素。12月16日，在2025年第六届中国信通院IT新治理领导力论坛上，阿里云正式发布AI Landing Zone白皮书，并升级AI云采用框架，系统性介绍了企业如何从直面治理挑战，到构建清晰蓝图，再到实现智能化运营的完整路径，并分享了前沿客户在 AI Landing Zone 落地过程中的实践经验。图一：阿里云智能集团开放平台负责人何登成做发布演讲在 AI 时代，云治理正面临五大“能力鸿沟”。会上，阿里云与埃森哲联合发布《云治理企业成熟度发展 2025年度报告》，重点分析了企业在 AI 浪潮下面临的核心治理问题。报告显示，当前近九成企业积极拥抱 AI，但在加速技术应用的同时，普遍对数据主权、系统稳定性等衍生风险心存顾虑，整体仍缺乏一套将“技术应用”与“风险防御”并行推进的双轨治理体系，例如：•稳定性的严重短板：仅14.3%的低成熟度企业在云资源部署中采用多可用区架构，其 AI 业务在高并发与关键场景下面临较大稳定性风险。•安全防线的致命缺口：高达77.3%的低成熟度企业数据库仍允许公网 IP直接访问，安全基础极为薄弱。•成本管理的价值迷失：云成本治理仍停留在“单纯降本”，而非以业务价值为导向，难以支撑持续攀升的 AI 投入。•自动化水平的普遍滞后：超过 60% 的企业仍通过人工方式创建云资源，效率低下，难以支撑 AI 业务的敏捷迭代。面对这些严峻的治理挑战，企业所需要的不仅是更强大的 AI 算法能力，更是一套能够驾驭复杂性的系统化方法论。这正是阿里云推出全新 AI 治理框架的出发点。为此，阿里云正式发布AI Landing Zone(AI LZ)白皮书，并升级AI 云采用框架(AI Cloud Adoption Framework，简称 AI CAF)。在AI CAF中将复杂的 AI 落地过程清晰拆解为AI 战略、AI 准备、工程化构建AI应用与运营治理四个可执行阶段，并通过端到端的方法论体系指导企业跨越从 AI 概念验证(PoC)到规模化生产的关键鸿沟。图二：阿里云AI云采用框架在至关重要的“AI 准备”阶段，阿里云强调，企业必须构建一套通往生产环境的“数字登陆区”——AI Landing Zone(AI LZ)。它既是一个基于云计算最佳实践构建的标准化、自动化、可治理的 AI 基础设施平台，也是一套融合组织协同、流程规范与自动化治理的系统方法，确保企业在 AI 项目启动之初，就能在安全、稳定、合规与成本管控等关键维度建立完善的治理能力。在通用 Landing Zone 的基础上，AI Landing Zone 进一步补齐了面向 AI 场景的关键能力，涵盖安全合规治理、AI 成本精细化管理，以及覆盖训练与推理场景的可观测性能力。图三：阿里云AILandingZone架构图例如，某全球运动服饰领军企业在引入“PAI + GPU 算力 + 通义模型”体系时，发现其既有的标准 Landing Zone 已难以完全适配 AI 平台的新治理需求。通过引入 AI Landing Zone 方案，该企业在原有基础上进行了能力强化：借助云 SSO、操作审计、配置审计等服务，实现了精细化权限管控与全链路操作审计，有效解决了 AI 场景下的身份管理、分账与安全合规等关键问题。再如，某国内头部新能源汽车品牌在将核心 AI 训练业务迁移上云时，明确要求同时满足高性能计算能力与企业级治理要求。AI Landing Zone 为其提供了体系化解决方案：一方面满足其对高性能算力与存储的需求;另一方面，通过内置治理框架，构建了以“安全合规”与“高性能”双轮驱动的 AI 基础设施，为其智能驾驶技术的持续领先奠定了坚实基础。通过构建 AI Landing Zone，企业得以建立可治理、可扩展、可持续的 AI 能力体系，真正实现用好 AI、管好 AI，并从中持续释放业务价值。当坚实的“数字登陆区”构建完成，云治理的旅程并未止步，而是迈入更高阶的“智能化运营”阶段。通过将 AI 能力反哺于 IT 运维(AIOps)，领先企业正在树立云治理的新范式，实现效率与价值的双重跃升。例如，某全球消费品巨头通过落地 AIOps，打造了以钉钉机器人为入口的“智能运维助手”，可实现站内信智能摘要、日志告警智能解读等能力，将运维人员从繁杂信息中解放出来，大幅提升问题处理效率。国内某头部新势力车企则通过建设“AI 全栈可观测”体系，将 AI 应用与非 AI 应用统一纳入端到端监控，使 AI Agent 的运行不再是“黑盒”，显著提升问题定位效率，并支撑其 AI 平台整体性能实现量级提升。从直面治理挑战，到发布AI Landing Zone这一坚实蓝图，再到迈向AIOps 驱动的智能化运营，阿里云正通过一套系统完整、层层递进的“组合拳”，为企业在AI 时代的云治理演进提供清晰指引。这不仅是一次技术能力的升级，更是一场面向 AI 时代的治理与管理范式革新，旨在帮助每一位客户在波澜壮阔的智能化浪潮中行稳致远。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"1","news_tag":"productRelease","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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