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当前，具身智能机器人已成为物理 AI 的核心落地形态，推动机器人产业由传统自动化系统向具备感知、学习、决策与行动闭环的智能体演进。产业价值重心不再仅依赖单一算法或硬件性能，而是依托模型、数据、算力、控制与本体的系统级协同能力。整体来看，具身智能机器人技术栈不再沿线性路径演进，而是逐步形成“以模型为中心、软件定义体系、硬件随之重构”的全栈式变革路径。

基于对全球机器人与具身智能产业的持续跟踪，IDC 系统总结了当前具身智能机器人的十大关键技术趋势。这些趋势构成了机器人产业能力跃迁的技术底座，也为厂商、开发者及行业用户把握未来三年的技术方向与竞争格局提供重要参考。

模型为中心——机器人认知与能力泛化的核心驱动

世界模型与具身智能大模型协同驱动认知升级

世界模型构建对环境、自身状态及物理规律的内部表征，为机器人提供预测、规划与连续决策能力，使其从被动响应向主动规划演进。结合仿真与 Sim-to-Real 训练，世界模型降低现实训练风险与成本，是复杂任务工程化与通用人形机器人落地的重要支撑。

虚实融合数据体系成为持续进化核心基础

具身智能机器人对跨场景泛化能力的需求，使训练数据从单一实采向虚实融合体系演进。仿真合成数据成为规模化训练主体，视频学习正在成为潜在扩展路径，遥操作实采数据作为高质量补充，通过闭环训练、仿真微调与在线反馈，支撑机器人在低成本条件下实现能力扩展与持续进化。

快慢系统与技能库协同，提高复杂任务工程化效率
行业普遍采用“快思考 + 慢思考”双系统架构，高层慢系统负责任务规划与世界理解，底层快系统保障高频控制与物理交互实时性。结合模块化技能库与场景专项训练，机器人可实现多任务、多步骤操作的能力复用与稳定落地。

情感理解与个性化智能基座逐步实用化

机器人在家庭、服务和医疗场景中对情感理解、个性化交互和自主决策的需求显著提升。通过情感感知、个性化用户建模、认知决策、情感表达与持续学习，机器人实现端到端闭环的感知、理解与行为生成。大模型和长期学习支撑其从单任务执行向多场景、多任务能力演进，提高陪伴、教育及健康监测价值。

软件定义体系——机器人工程化与系统化的关键支撑

具身智能机器人开发平台走向集成化与开源生态

随着技术栈复杂度提升，具身智能机器人开发平台正形成“软硬件 + 数据 + 模型 + 工具链”的一体化生态。集成化平台通过统一接口、标准化数据与开源生态，降低开发门槛，加速算法、模型与应用在不同机器人本体和场景中的迁移与复用。

机器人操作系统向高可靠分布式架构演进，支撑大规模与高并发应用

随着系统复杂性提升，传统操作系统难以满足多自由度、多传感器、多执行器的实时协作需求。硬实时分布式操作系统通过微内核、模块化服务、任务隔离与分布式调度，保障多节点协同的确定性与可靠性，为高自由度控制、复杂场景适应及自主决策提供底层支撑，加速通用机器人系统开发与产业落地。

IT–OT 融合通信体系成为机器人实时控制关键底座

具身智能机器人对低时延、高确定性通信需求持续增长，推动 IT 与 OT 通信体系加速融合。内部 OT 网络通过 EtherCAT/CAN-FD 与时间敏感网络（TSN）融合，实现高确定性控制；外部 IT 网络借助 Wi‑Fi 7、5G/6G 提供低时延、高可靠通信。分布式控制架构下，统一协议与时间同步保障多机器人协作、云边同步及高自由度运动控制，为大规模部署和系统级协同提供关键支撑。

硬件随之重构——高复杂任务的机器人物理体系升级

端侧算力持续跃升，环境复杂度与系统规模成为核心驱动

随着机器人向具身智能化发展，多模态感知、语义理解、运动控制与实时规划的计算需求大幅增加。算力需求与信息处理复杂度、电机数量及运动控制耦合度高度相关，从家用机器人的十T级跃升至商用服务、四足及人形机器人的百T至千T级，环境复杂度与系统规模成为算力演进的主要驱动因素。

多模态感知全面升级，构建内外协同统一感知体系

机器人感知能力由单一视觉向3D视觉、触觉、力觉、惯性及内部状态感知等多模态融合发展，实现对环境与自身状态的统一理解。多源感知提升空间认知、操作精度与动态稳定性，为复杂非结构化环境下的自主决策与安全执行提供基础支撑。

安全性体系从局部防护走向系统级冗余，实现全链路稳健运行

随着应用场景拓展，安全性和稳定性成为具身智能机器人大规模落地的核心要求。行业正推动从单点防护向系统化设计转型，通过感知冗余、约束控制、运行时监控和长尾风险验证构建可信赖系统，确保机器人在动态复杂环境中可安全降级、即时干预并长期稳定运行。

IDC中国研究经理李君兰表示，当前，具身智能机器人正处于技术高度复杂且潜力巨大的交汇点：一方面，大模型、世界模型、多模态感知等 AI 能力持续突破；另一方面，机器人在真实环境中面临的物理约束、实时控制及安全可靠性远比数字世界复杂。IDC 认为，产业正沿着“模型为中心、软件定义体系、硬件随之重构”的路径演进，标志着机器人产业迈入全栈竞合的新阶段。

IDC 建议产业参与者采取六大行动方向：

  • 提前布局世界模型与具身智能大模型的协同能力，
  • 构建虚实融合的数据生产与训练体系，
  • 升级端侧算力与分布式操作系统架构，
  • 应从单一产品思维转向平台与生态思维，
  • 将安全能力内生为系统级基础模块，
  • 结合自身定位选择差异化的技术或应用突破路径。

报告信息：

本文核心观点来源于 IDC 报告《具身智能机器人技术趋势与品牌推荐，2025》（Doc# CHC53183725，2025年12月），报告不仅系统梳理了具身智能机器人十大技术趋势，也对奥比中光、地瓜机器人、NVIDIA、擎朗智能、微亿智造、银河通用、智元机器人等典型厂商进行了深入分析与品牌推荐，为产业参与者提供战略参考与厂商选择指南。欲了解更多详情或进行深度交流，请联系IDC中国机器人与具身智能领域研究经理李君兰（邮箱：lyli@idc.com）

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Lily Li - Research Manager - IDC

Lily Li is a research manager for emerging technologies in IDC China. She is responsible for conducting research and analysis for Internet of Things (IoT) in the same country. She is also involved in global and regional consulting as well as business development in related markets. Prior to joining IDC, Lily has had in-depth working experiences in the urban digital transformation (DX) field and a wide range exposure to Smart City developments. She has a deep understanding of the status quo and is knowledgeable about the market's future trends. Lily holds a master's degree from the Graduate University of Chinese Academy of Sciences (GUCAS).

AI 正改变医疗服务方式与价值逻辑

长期以来，数字化已成为中国医疗健康行业持续演进的方向。从信息系统建设、影像辅助诊断到线上医疗服务，技术在不断改善效率与覆盖面，其中AI 技术的引入也给行业发展带来了更多的机遇。但 IDC 指出，真正的分水岭并不在于“是否引入 AI”，而在于 AI 是否开始改变医疗服务方式与价值逻辑。

随着大模型逐步进入医疗场景，行业正在发生一场更深层次的变化：医疗不再只是围绕单次诊疗展开，而是开始围绕 人群、流程与长期健康结果 进行重构；AI也不再只是作为辅助工具，而是逐步成为能够“协同工作、参与决策”的智能体。这一转变，标志着医疗AI正从“工具增强”走向“智能体协作”，从“以治疗为中心”迈向“以预测和预防为导向”。

为什么这份 FutureScape，对医疗决策者具有现实参考意义？

在《IDC FutureScape：全球医疗健康行业2026年预测——中国启示》（2025年12月）中，IDC 明确指出：未来五年，中国医疗健康行业将面临三重结构性变化——人口健康压力持续上升、医疗服务向价值导向转型，以及公众对可信 AI 的期望显著提高。

这三重变化相互叠加，使得医疗 AI 的成败不再取决于算法性能本身，而取决于 治理能力、信任机制与系统性整合水平。在这一背景下，FutureScape 提供的并非单点趋势，而是一张描绘未来医疗运行逻辑的路线图。

十大预测：智能体协作如何重塑医疗健康体系

预测 1｜人口健康管理策略升级

到2027年，40%的医疗机构将采用先进的风险分层工具来应对人群健康问题，重点关注慢性病负担和老龄化人口。

这一预测反映出医疗体系正在从“以患者为单位”的诊疗模式，转向“以人群为对象”的风险管理模式。风险分层将成为分级诊疗、资源配置和医保支付的重要基础能力，也为预防性医疗提供数据支撑。

预测 2｜基于 Agentic AI 的患者交互

到2028年，45%的医疗机构将优先基于对数字公平、文化一致与信任的考虑，推动基于 Agentic AI 的医患交互方式，从而实现个性化且富有同理心的交互。

IDC 强调，医疗交互的价值不只在效率，更在信任。Agentic AI 能够结合临床数据与社会健康决定因素，提供更具情境感知与同理心的沟通方式。

预测 3｜沉浸式行为治疗平台

到2028年，15%的行为健康服务提供者将配备具备 AI Agent 的 XR 平台，实现沉浸式引导治疗的自动化，减少30%的面对面就诊次数。

在行为健康资源长期短缺的背景下，XR 与 AI Agent 的结合为扩大服务覆盖、降低就诊门槛提供了新的可能性，同时也推动治疗方式从“诊室中心”走向“持续陪伴”。

预测 4｜AI 推动支付方主导模式

到2028年，“payvider”模式将在中国医疗保健领域实现20%的渗透率，将医疗服务与保险相结合，并加速基于价值的医疗和数字健康的发展，以改善患者预后。

支付方与服务方的融合，将推动医疗激励机制从“按项目付费”转向“按结果付费”，也对数据共享、跨机构协同提出更高要求。

预测 5｜AI 驱动的智慧病房

到2029年，20%的新建医院或重大改造项目将配备由人工智能驱动的智慧病房，其可根据患者病情严重程度及临床背景动态调整监测参数、工作流程、查房安排及病房环境。

智慧病房代表着护理流程的智能化重构，通过持续监测与动态调整，提升患者安全性并缓解护理人员压力。

预测 6｜自主化医疗设备

到2029年，50%的新型医院医疗设备将采用 AI Agent、先进传感器和边缘计算技术，实现自我优化、故障预测，并将运行时间提升50%。

医疗设备正从“被动资产”转向“主动系统”，这不仅提升运行效率，也对设备安全、运维和合规提出更高要求。

预测 7｜AI 就绪型医疗基础设施成熟度

到2029年，40%的医疗机构将因社会、文化和政治层面对数据滥用或可解释性缺失的负面反应，推迟部署人工智能就绪的医疗基础设施。

这一预测提醒行业：技术成熟并不等于社会接受。可解释性、伦理与公众信任，将直接影响 AI 投资节奏。

预测 8｜预防性医疗

到2030年，多模态人工智能将在症状出现前预测45%的慢性病和罕见病，通过更广泛的健康数据（包括可穿戴设备和多组学数据）使预防性医疗成为现实。

医疗价值链正在前移，疾病预测与早期干预将成为核心能力。

预测 9｜医疗 Agent 崛起

到2030年，50%的中国等级医院将部署 AI Agent，以提供实时决策支持和自主工作流程，准确率超过80%，同时将异常情况上报至临床工作人员。

医疗 Agent 的核心价值在于重构临床工作流，让医生将精力集中于高复杂度决策。

预测 10｜量子医学

到2030年，15%的顶级医疗机构将采用量子平台，实现诊断、模拟及数字孪生技术的100倍加速，以精准驱动复杂医疗护理。

尽管仍处早期阶段，量子计算已开始在复杂疾病建模与精准诊疗中展现潜力。

这些预测共同指向什么？

IDC FutureScape 2026 反复传递出一个重要信号：医疗健康的未来，并不是“更多 AI”，而是“更系统、更可信、更以人为中心的智能协作体系”。
技术只有在治理、信任和组织能力同步演进的前提下，才能真正释放价值。

IDC 中国高级分析师林红表示，中国医疗健康行业正站在从“数字医疗”迈向“智能体医疗”的关键节点。能够在政策的指引下将信息系统、大模型技术、基础设施与医院业务统一规划的机构，将更有能力在提升诊疗质量的同时改善患者体验。

一个面向未来的行动性判断

IDC 并不建议医疗机构“追逐所有新技术”。相反，更重要的是 优先投资那些能够同时提升医疗质量、增强信任的 AI 能力——包括可信医疗智能体、人机协同决策机制，以及真正 AI 就绪的医疗基础设施。只有将这些能力视为医疗体系的长期资产，而非短期项目，医疗 AI 才能走向可持续的规模化应用。

进一步推荐

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Erin Lin - Senior Market Analyst - IDC

Erin Lin serves as a senior market analyst of the IDC China Health Insights group. She is responsible for conducting research and analysis about the health industry for both domestic and regional markets. She is also involved in consulting and business development in related markets. Before joining IDC, Erin had four years of experience in medical administration; then served as an analyst in the health industry for two years. Erin is familiar with healthcare institutions and related businesses, and has been gathering deep insights into the health industry and emerging medical technologies. Erin holds a master's degree in Leadership and Management in Health and Social Care from the University of Southampton, and a bachelor's degree in Clinical Medicine from Capital Medical University.

从生成式 AI 到智能体，真正的变化是什么？

过去两年，生成式 AI 在企业中的普及速度远超预期。但 IDC 指出，生成式 AI并不是终点。当 AI 只能生成内容时，它仍然是工具；而当 AI能够 感知环境、调用工具、执行任务并持续反馈结果，它才真正开始参与企业运行。

智能体正是在这一背景下出现。它不再局限于单点问答或流程辅助，而是以数字劳动力、流程协调者和决策顾问的形式深度嵌入业务流程。企业竞争的分水岭，也随之从是否部署 AI，转向“是否具备规模化、安全化、可治理地运行智能体的能力”。

在《IDC FutureScape：全球 Agentic AI 2026 年预测——中国启示》（Doc#CHC54084526，2026年1月）中，IDC 系统刻画了未来五年中国企业在智能体发展过程中将面临的十个关键转折点。

十大预测：智能体如何重塑企业的运行方式

预测 1｜数据就绪度

到2027年，如果企业没有优先构建高质量的AI就绪数据，在扩展AI解决方案时将面临幻觉频发、错误率高的问题，导致生产力下降15%。

数据质量不再只是IT部门的KPI，而是企业的生存红线。如果投喂给智能体的数据是脏的、乱的、没有经过治理的，那么企业得到的将不是效率提升，而是需要耗费更多人力去修正错误的负生产力。

预测 2｜定价

到2028年，传统的按席位收费模式将被淘汰。随着智能体作为数字劳动力接管大量重复性工作，70%的软件供应商将不得不重构其商业模式，转向按业务结果、交易量或自动化成果计费的新模式。

在智能体时代，传统的按席位收费模式将越来越难以匹配价值创造的实际形态。当一个智能体在典型场景下一天可以完成过去多个人工岗位累计才能完成的工作量时，按人头收费的定价逻辑将难以为继。

预测 3｜智能体项目失败

到2028年，69%的企业自建智能体项目将因未能实现投资回报率目标（ROI）而被放弃，因为企业难以充分认识到项目实施的实际成本和价值。

企业往往会受市场热度裹挟而仓促启动智能体项目。然而，由于未能对潜在应用场景进行深度研判，开发团队被迫仓促推进的项目，往往陷入落地即闲置的窘境。在此背景下，选择能够打通数据、应用、治理全链路，且深度契合业务场景的合作伙伴，无疑是更具可行性的路径。

预测 4｜客户体验智能体编排

到 2027 年，45% 的企业将管理跨多个渠道、应用程序和供应商的多智能体（Multi-Agent），从而实现更无缝、上下文更丰富的体验。

这里的编排并非指的是单纯的工作流配置编排，而是指构建支持多智能体动态协作的系统架构。未来的竞争不在于拥有一个超级智能体，而在于编排能力。企业建立智能体系统架构应避免过于刚性的流程，拥抱灵活的协同框架，让智能体与智能体、人类与智能体能够无缝协同工作。

预测 5｜智能体服务体验

到2029年，30%的中国500强企业将运用AI客户服务智能体，主动且个性化地联系客户，在客户尚未意识到问题时就解决问题。

服务模式将发生根本性逆转，从被动响应投诉升级为主动解决问题。这种预判式的服务能力，将在存量市场中建立起全新的差异化体验。

预测 6｜人工监督作为战略职能

到2027年，50%的AI驱动型企业应用部署将设立新的专业职位，负责监督智能体，作为合规核心，确保自主工作流中的结果可追溯。

智能体的自主性不等于无人值守。随着智能体权力的扩大，人类的角色必须从操作者转变为监督者，以确保在合规与伦理的安全边界内释放AI的能力与价值。

预测 7｜AI 卓越中心

到2027年，那些建立了成熟AI或智能体卓越中心（CoE）的企业，其创新、速度和服务质量将比竞争对手高出20%。

零散的烟囱式试点难以支撑AI的真正落地和组织的规模化创新，建立AI CoE卓越中心是弥合技术与业务鸿沟、实现跨职能规模化治理的关键组织保障。

预测 8｜岗位角色转型

到2026年，中国500强企业中40%的岗位将涉及与智能体的深度协作，重新定义传统的初级、中级和高级岗位。

人才的定义正在被改写。未来的核心竞争力不再单纯是个人执行力，而是智能体的管理协同能力，即构建、指挥、评估和优化数字劳动力工作的能力。

预测 9｜Agent 战略顾问

到2031年，60%的中国500强CEO将利用智能体进行战略决策，这一趋势由市场波动性、创新速度要求，以及董事会层面对更快决策和智能驱动决策的多重需求推动。

智能体正在从业务一线的手脚进化为董事会的外脑。通过实时处理海量数据并进行情景模拟，它能为高层决策提供人类难以企及的数据广度与速度支撑。

预测 10｜AI 对业务的颠覆性影响

到2030年，多达20%的中国500强企业将因智能体管控不力引发的高关注度事件，面临诉讼、巨额罚款，甚至导致CIO被问责。

随着智能体掌握更多自主权，缺乏透明框架和审计机制的企业将面临巨大的法律与声誉风险。

这些预测共同揭示的本质变化

IDC FutureScape 2026 反复强调一个核心结论：

智能体改变的不是某个流程，而是企业如何运行、如何决策、如何承担责任。当智能体能够自主执行任务、协调流程并影响结果，企业必须重新思考数据、架构、治理、组织和领导力的边界。

IDC 中国研究经理 孙振亚表示，中国企业正在从生成式AI 阶段迈入智能体阶段的关键窗口期，但这个过程并非是简单的技术升级，而是一项系统化的工程。FutureScape 2026 显示，智能体的规模化落地必须要有AI 就绪的数据底座、多智能体的编排平台以及完善的治理机制。对于缺乏这些关键要素的企业而言，智能体带来的可能不是机遇，而是效率与合规层面的重大风险源。

一个面向企业领导层的扩展性建议

IDC 认为，智能体并不是一项可以逐步叠加的技术能力，而是一种会持续放大组织既有优势与短板的系统性力量。当智能体开始承担决策、执行与协调角色，企业原有的数据质量、流程设计、治理成熟度以及组织协同能力，都会被迅速放大并体现在结果层面。

因此，企业不应将智能体视为单一技术投资，而应将其纳入企业运行模式的长期演进路径来规划。这意味着：

  • 在技术层面，必须优先夯实 AI 就绪数据、智能体编排与可观测性能力，而非堆叠模型或工具；
  • 在治理层面，需将人工监督、责任边界和可追溯性制度化，而不是事后补救；
  • 在组织层面，需同步重构岗位角色、能力模型与决策流程，使人机协作成为默认工作方式；
  • 在管理层面，高管团队需要形成对智能体的共同认知，平衡效率和安全，把如何有效治理也纳入战略考量，而非单纯追求速度。

那些能够在规模化之前就完成这些准备的企业，更有可能把智能体转化为持续生产力；反之，智能体的能力越强，潜在风险也会被放大得越快。

行动指南：企业推进智能体的现实起点

结合 FutureScape 2026 的十大预测，IDC 建议企业在未来 12–24 个月内，优先从以下几个方面入手，逐步构建可持续的智能体能力：

第一，先解决基础数据问题
在引入或扩展智能体之前，对关键业务场景开展 AI 就绪数据评估，重点关注数据的完整性、语义一致性、上下文关联能力以及可追溯性。没有高质量数据，智能体带来的将更多是返工与人工干预，而非自动化红利。

第二，从高价值、低歧义的流程切入
优先选择目标清晰、结果可衡量、决策歧义较小的流程作为智能体的落地点，例如客户服务分流、内部运营协调或标准化审批支持，而非一开始就覆盖高度复杂或高风险场景。

第三，把治理与监督嵌入设计之初
在智能体架构设计阶段即明确人工介入点、升级路径与审计机制，确保所有自主决策都具备可解释性与回溯能力，而不是等到智能体进入关键流程后再补治理。

第四，建立跨职能的智能体管理机制
将 IT、数据、业务、合规与人力资源纳入同一治理框架，避免智能体成为某个部门的工具。在多智能体(Multi-Agent)场景下，统一编排、权限与责任归属尤为关键。

第五，为岗位与能力转型预留空间
提前识别哪些岗位将与 智能体深度协作，哪些能力需要被重塑，并通过培训、试点和角色演进，帮助员工适应新的工作方式。智能体的成功，很大程度上取决于组织是否准备好与智能体共事。

通过以上路径，企业可以在控制风险的前提下，逐步释放智能体的规模化价值，避免陷入技术领先但组织滞后的常见陷阱。

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Zhenya Sun - Research Manager - IDC

Zhenya Sun is a research manager for the IDC team focused on exploring the application of technology and industrial development of AI and AI agents. He is also responsible for providing clients with consulting services on technologies, products, and markets related to large language models (LLMs) and AI agents, as well as delivering speeches at industry conferences and internal seminars. Before joining IDC, Zhenya served as a project management officer (PMO), responsible for internal and external strategic consulting, AI application research and advisory services, AI project framework standardization, management system construction, and technical training on AI applications. Prior to that, he also led initiatives in product development process optimization and user market analysis. Zhenya holds a Master's Degree in Engineering Management with a specialization in Information Systems Engineering from the University of the Chinese Academy of Sciences.

Government organizations across Asia/Pacific are entering a defining phase in their digital evolution. Economic constraints, heightened citizen expectations, talent shortages, and tightening regulatory mandates are converging just as digital systems shift from automation to autonomous orchestration. For government technology leaders, this is no longer about adopting another digital tool. It is about preparing institutions for agentic AI and the operating models required to use it responsibly.

What is Agentic AI and Why it Matters for the Government

Agentic AI represents a step beyond analytical or recommendation-based systems. These systems can interpret intent, plan tasks, and execute actions within policy-defined boundaries. They navigate across systems, channels, and agencies, coordinating activities that previously relied on manual intervention, casework, or administrative adjudication. In a climate where governments are expected to deliver more with fewer resources, agentic AI offers a path to fundamentally reshape how public services are delivered and managed.

Why Data Readiness is the Real Barrier to Agentic AI

This shift is already influencing investment priorities. According to IDC FutureScape: Worldwide National Government 2026 Predictions: Asia/Pacific (Excluding Japan) Implications research, in 2026, 40% of national governments in Asia/Pacific excluding Japan (APeJ) will invest 10% of their IT budget in data architecture and governance solutions to address gaps that are preventing them from realizing the benefits of agentic AI. This signals a clear recognition that data readiness —not algorithms—is now the primary barrier to scaling autonomy.

IDC survey data reinforces this outlook. While many government agencies are exploring agent-driven workflows, relatively few have moved beyond pilots. The primary barriers are not technical ambition but gaps in data quality, system integration, and oversight models. As a result, national administrations across Asia/Pacific are increasing allocations toward data management, interoperability, and governance, acknowledging that agentic AI readiness depends more on institutional foundations than on model sophistication.

Agentic AI systems require structured, traceable, and interoperable data to reason and act responsibly across high-stakes domains such as benefits administration, taxation, compliance, emergency response, and infrastructure operations. Without strong data foundations and clear policy constructs, autonomy introduces operational, regulatory, and trust risks rather than value. For government leaders, data architecture and governance are becoming strategic prerequisites for agentic AI, not supporting functions.

Strategic Forces Shaping Government Agentic Adoption

Several macro-level forces are shaping the pace and direction of agentic AI adoption in government:

  • Budgetary pressure: Fiscal constraints persist even as demand for digital service continues to expand.
  • Sovereignty and compliance: Requirements around data residency, algorithmic transparency, and accountability are tightening.
  • Workforce disruption: Structural skill gaps in cybersecurity, data engineering, compliance engineering, and MLOps remain unsolved.
  • Citizen Expectations: Citizens increasingly expect faster, more personalized, and more equitable services, influenced by private-sector experiences.

IDC data shows these forces converging as agentic AI moves from conceptual exploration toward early operational pilots. Government leaders increasingly see agentic capabilities as tools for accelerating workflows, improve decision support, and enhance service quality. However, integration, governance, and compliance remain the primary obstacles to scaling beyond pilots. Without deliberate management, these crosscurrents risk fragmented investments and new digital silos. Addressed strategically, they can accelerate modernization while reinforcing public trust.

How Agentic AI Transforms  Government Functions

Agentic AI opens up new opportunities across three core government domains:

1. Operational orchestration – Agent-driven systems can coordinate multi-step workflows that span multiple agencies or departments, reducing handoffs and administrative lag. This is particularly valuable in benefits processing, regulatory inspections, tax compliance, procurement, licensing, and infrastructure operations, where complexity and interdependence are high. IDC surveys show that a growing share of Asia/Pacific government agencies are prioritizing orchestration capabilities over standalone task automation, marking a shift in architectural strategy.

2. Citizen service delivery – Agentic AI capabilities enable proactive, context-aware, and personalized interactions. Agents can identify citizen needs, trigger workflows, prompt follow-ups, and escalate cases to human supervisors when required. This directly supports government priorities around service equity, responsiveness, and improved case resolution outcomes.

3. Decision support for policy and planning – Agentic systems can synthesize data, model scenarios, and present options for policymakers during crises, planning cycles, or resource allocation exercises. This does not replace human authority; it expands the analytical capacity available to decision-makers when time and complexity are constraints. Across all three domains, trust is the central requirement. Agentic systems deliver sustainable value only when paired with auditability, human oversight, and transparent accountability structures. Without these safeguards, autonomy becomes a liability —especially in regulated or politically sensitive environments.

 What Government Technology Buyers Must Do Now

For CIOs, CTOs, Chief Digital Officers, and procurement leaders, the transition to agentic AI raises several practical considerations: Institutional readiness is the first barrier.

Many agencies continue to rely on siloed legacy systems, inconsistent data definitions, and limited interoperability. Agentic AI amplifies these weaknesses. Without mature integration, clean data, and consistent metadata, autonomy is either unsafe or impractical.

Governance must be built into the workflow.

Because agentic systems act rather than merely recommend, governments must design for traceability, audit trails, human-in-loop controls, and clear escalation paths from the outset. Policy and sovereignty alignment cannot be retrofitted after deployment.

Operating models and workforce must evolve.

Agentic AI reshapes work patterns rather than simply reducing labor. While agencies currently rely heavily on external system integrators and cloud providers, new internal roles in agent orchestration, compliance engineering, and lifecycle management will become essential over time.

The message for technology buyers is clear: agentic AI is not merely a technology decision. It is an institutional capability decision.

Procurement and Vendor Evaluation for Agentic AI

As governments move beyond proofs of concept, procurement teams must distinguish between true agentic platforms and offerings that simulate autonomy through scripted automation or interfaces. IDC recommends evaluating vendors against criteria such as:

  • Orchestration of multi-step, cross-system workflows
  • Integration and interoperability across legacy and multi-cloud environments
  • Auditability, explainability and documentation
  • Alignment with sovereignty and policy mandates
  • Support for open standards and architectural portability
  • Clear responsibility models across the autonomy lifecycle.

Governments that structure RFx around interoperability, auditability, and policy alignment will be better positioned to deploy agentic capabilities responsibly without increasing regulatory or operational risk.

The Leadership Mandate for Agentic AI in Government

Agentic AI is no longer distant. It is a leadership mandate. As economic pressure, regulatory expectations, workforce disruption, and citizen demands intersect, government leaders must move beyond isolated pilots toward responsible orchestration at scale.

That mandate requires alignment across strategy, data foundations, governance, and operating models. Agencies that establish these foundations will translate agentic AI into resilience, accountability, and measurable public value. Those that do not will remain locked in pilot mode—unable to scale autonomy without unacceptable risk.

Register now for the live webinar on 24 February 2025 at 1:30 pm SGT to join IDC in charting the agentic future with confidence.

Ravikant Sharma - Research Director, Government Insights for Asia/Pacific - IDC

Analyst Profile LinkedIn
Ravi Kant Sharma serves as the Research Director for Government Insights for Asia/Pacific (excluding Japan and China) (APEJC) at IDC’s office in Bangalore, India. He is tasked with guiding public sector agencies and the tech industry by collaborating with them in strategic planning. He has led numerous complex consulting engagements, represented IDC at industry events, and hosted workshops focusing on understanding how digital investments impact key economies in Asia/Pacific within the context of global trends — all while collaborating with IDC’s global Government Insights teams.

Travelers and diners are no longer navigating journeys on their own. Increasingly, AI agents are doing it for them.

By 2026, hospitality, dining, and travel brands will operate in an environment where discovery, comparison, booking, and service are mediated by intelligent agents acting on behalf of guests. These agents will not just search, but they will evaluate options and apply preferences to find the best value and most appropriate offering. Ultimately agents will eventually even be able to complete bookings and orders to transact in real-time.  That shift fundamentally changes how guests will come to discover and interact with hospitality and travel brands, and if those guests will even need – or want – to interact directly with a brand.

IDC’s FutureScape: Worldwide Hospitality, Dining, and Travel 2026 Predictions show that agentic AI is fundamentally changing the distribution “funnel.” This will reshape how brands compete, forcing a rethinking of data strategies, personalization, and even how brands are “found” in the first place.

From channels to agents: The new front door to travel and hospitality

Historically, hospitality and travel brands optimized for channels. Search engines, online travel agencies, loyalty apps, third-party delivery platforms, and physical locations defined how guests found and engaged with them.

Agentic AI changes that model.

IDC predicts that by 2030, 30% of travel bookings will be executed by AI agents, accelerating investment in LLM optimization and increasing direct bookings and profitability

In this environment, the first interaction may never involve a human browsing a website. Instead, an AI agent will query multiple sources, assess availability and pricing, weigh preferences, and complete the booking autonomously.

For hotels, airlines, and restaurants, this means one thing: if your data is incomplete, outdated, or fragmented, you effectively disappear from the agent’s decision set.

Agent-led search requires brands to rethink discoverability. It is no longer enough to rank well for keywords. Brands must ensure that large language models and agents can accurately understand what they offer, when it is available, and why it is relevant to a specific traveler or diner at any given moment.

Guest-centricity starts with a 360-degree data foundation

At the heart of this transformation is data. Not necessarily more data, but easier access to connected insights, so brands can quickly and easily analyze and take action to provide truly memorable and meaningful experiences.

IDC forecasts that by 2030, 50% of AI budgets in hospitality and travel will be allocated to personalization efforts, powering ambient intelligence and preference anticipation to increase guest satisfaction by 25%.

Achieving that level of personalization requires a unified, real-time view of the organization and the guest.

For hotels, this means connecting property management systems, loyalty programs, guest profiles, and on-property interactions into a single, actionable data fabric. For airlines, it means aligning inventory, pricing, operations, and customer history to anticipate needs from seat selection to disruption recovery. For restaurants, it means synchronizing menus, pricing, availability, and customer preferences across in-store, delivery, and digital channels.

Without this 360-degree view, agentic AI cannot deliver on its promise. Fragmented data leads to generic offers, broken experiences, and missed revenue opportunities. Unified data enables brands to move from reactive service to proactive engagement, recognizing guests and diners across every touchpoint.

Personalization becomes continuous, not campaign-based

In an agentic world, personalization is no longer a marketing tactic. It becomes an operating model and first-party data will be a secret sauce to ensure that brands don’t lose control of their guests to LLMs.

Agents will continuously interpret intent, context, and constraints. A traveler’s preferences, budget sensitivity, loyalty status, dietary needs, and timing constraints will all factor into decisions made in seconds. Brands that rely on static segments or periodic campaigns will struggle to keep up.

This is where ambient intelligence emerges. Personalization shifts from “what offer should we send?” to “how should the experience adapt right now?” Hotel rooms that can be adjusted to known preferences upon arrival. A restaurant that surfaces menu recommendations aligned to past behavior and real-time inventory. An airline that proactively rebooks a passenger before disruption becomes frustration.

These experiences depend on data that is current, trusted, and interoperable across systems.

Superapps, wallets, and the expansion of the digital guest journey

IDC also predicts that data modernization, security, and AI investments will increase by 65% as hospitality and travel organizations respond to demand for digital wallet–driven superapps, with 25% of major brands deploying one by 2030.

Superapps extend the customer relationship beyond booking into payments, identity, loyalty, and in-trip engagement. When combined with agentic AI, they become powerful orchestration layers. An agent can manage payments, redeem loyalty benefits, confirm availability, and coordinate experiences across partners, all on behalf of the guest.

But again, this only works if the underlying data is accurate and synchronized. Out-of-date room availability, inconsistent menu information, or disconnected loyalty data can yield undesired results. Hotels, restaurants, or travel options may not be surfaced by agents at all or if they are based on erroneous information can erode guest experience and undermine trust when guests don’t receive the specific service they were expecting.

What This Means for Hospitality Leaders in 2026

The shift to agentic AI is not incremental, and therefore should not be viewed as one-off projects, but rather as an infrastructure on which to build strategy.

Hospitality, dining, and travel brands must act now to:

  • Modernize data architectures to support real-time, enterprise-wide visibility.
  • Ensure offerings, availability, and pricing are machine-readable and continuously updated.
  • Invest in personalization capabilities that operate across the full guest journey, not isolated moments.
  • Rethink discoverability for an agent-led search environment, where accuracy and context determine inclusion.

Guest-centricity in 2026 will go beyond loyalty points and SEO. It will be defined by how well a brand leans into its first-party data to enable intelligent agents to represent their brand in a light that aligns with guests’ interests seamlessly, accurately, and at scale.

The brands that succeed will be those that treat data not as a back-office asset, but as the foundation of trust, personalization, and growth in an agent-driven economy.

Dorothy Creamer - Sr. Research Manager - IDC

Analyst Profile
Dorothy Creamer is Senior Research Manager for IDC Research, Hospitality & Travel Digital Transformation Strategies, providing research and advisory services for hotels, casinos, restaurants and travel organizations. Ms. Creamer's research will focus on how these business segments are transforming and leveraging technology to increase efficiencies, deliver operational benefits and identify new revenue streams. Ms. Creamer's research will report on effective digital strategies to empower both guests and employees and analysis of areas of opportunity in a fast-evolving and highly competitive segment.

For most CMOs, Q1 begins with a familiar reassurance: We’ll move fast. We’ll see what works. We can adjust.

That logic used to hold. But in 2026, it does not.

Q1 is no longer a learning quarter. It is the point where go-to-market direction begins to harden financially, operationally, and organizationally. By the time early results appear, budgets have already been deployed, teams aligned, and market narratives established. What feels flexible in January often becomes difficult to change by March.

That makes Q1 the most dangerous time of the year to be directionally wrong.

Commitment now happens earlier than most teams realize

In Q1, commitment accelerates faster than most teams expect.

Budgets move from plan to spend early in the quarter. Teams align around specific markets, messages, and motions. Enablement, content, and tooling go live. Leadership begins to form a point of view on what is working and where to double down.

Once that momentum builds, reversal becomes slow, expensive, and difficult to execute, even when later evidence points in a different direction. Direction often locks before performance data can meaningfully challenge it.

In previous years, this transition happened more gradually. Today, compressed buying cycles and pressure to show early momentum have shortened the window for reconsideration.

Directional risk outweighs execution risk in Q1

When Q1 underperforms, reviews often focus on execution. Messaging missed the mark. Campaigns ramped too slowly. Sales conversion lagged.

Those explanations miss the deeper issue.

The most damaging Q1 failures start with direction. Once a market is prioritized early in the year, everything organizes around that choice. Budget, headcount, content strategy, enablement, and leadership attention align behind it. When signs later point to underperformance, the cost of changing course extends beyond spend and into organizational friction.

Directional GTM risk occurs when early investment is committed to markets that lack buyer urgency at a time when internal alignment is hardest to unwind. In that environment, strong execution can still reinforce the wrong decision.

Pipeline arrives too late to protect early decisions

Many GTM plans still rely on pipeline as the primary validation mechanism. Teams launch, monitor early indicators, and plan to optimize based on what they see.

The limitation is timing.

Pipeline reflects reality only after investment, alignment, and momentum are already in motion. Early Q1 bets rarely fail loudly. They show up as steady but unremarkable performance that consumes budget while quietly limiting upside.

By the time pipeline confirms a mistake, recovery is no longer quick. The cost of change has already compounded.

Buyer behavior accelerates the cost of early mistakes

Buyer behavior has shifted in ways that further compress the margin for error.

B2B buyers now signal urgency digitally, often before vendors engage directly. Markets with active demand surface quickly. Markets that require education or reframing absorb spend without producing early signal.

This dynamic changes how Q1 investment behaves. Markets where buyer urgency already exists convert earlier. Markets without urgency require sustained spend before traction appears.

Early-year investment into the latter carries outsized risk. When urgency is absent, activity increases, but momentum does not.

Strong CMOs reduce risk by narrowing earlier

High-performing CMOs face the same uncertainty as everyone else. The difference is how they manage it.

They narrow focus earlier in the quarter. They pressure-test direction before scaling. They recognize that optionality decreases rapidly once execution is underway.

Rather than spreading investment across multiple attractive opportunities, they concentrate resources where buyer urgency is already visible and defensible. Early success is measured by signal quality, not volume. Clarity about what to delay is as important as clarity about what to accelerate.

In Q1, focus functions as a form of risk management.

The decision that shapes the rest of the year

As pressure builds to show early momentum, the most dangerous assumption leaders make is that there will be time to fix a wrong bet.

Q1 does not determine the entire year, but it sets the constraints under which the rest of the year operates. Early market choices shape where credibility accumulates, where teams invest effort, and where recovery remains possible.

In 2026, the most damaging Q1 GTM failure is committing to the wrong direction before market signals arrive. Once momentum builds, correction becomes increasingly costly.

The question is not whether teams can execute.

It is whether they are pointed in the right direction before execution begins.

Validate direction before momentum locks in

In a quarter where speed matters, confidence matters more.

IDC’s GTM Validation Brief helps marketing leaders pressure-test where to commit early in Q1 and where to wait. It uses buyer-side evidence and market signals to validate direction before budgets, teams, and narratives harden.

This approach is not about caution. It is about capital discipline. Early momentum should compound growth, not trap it in the wrong places.

Download the GTM Validation Brief to validate your Q1 GTM strategy before direction locks in.

Christina Cardoza - Content Marketing Manager - IDC

LinkedIn
Christina Cardoza is a Content Marketing Manager at IDC, where she specializes in brand content and social media strategy. With a background in journalism and editorial leadership, she has a proven ability to transform complex technology topics into clear, actionable insights.

Sustainability is no longer a side program or a reporting exercise. It is becoming an operational mandate.

Economic uncertainty, regulatory pressure, and accelerating AI adoption are converging to force a fundamental shift in how organizations approach environmental, social, and governance (ESG) goals. In IDC’s FutureScape: Worldwide Sustainability/ESG 2026 Predictions, we see sustainability moving decisively from strategy decks into day-to-day execution—powered by AI, embedded into core operations, and owned across the enterprise, not just by sustainability teams.

For business and technology leaders, the message is clear: ESG success over the next three to five years will depend less on ambition and more on operationalization.

Sustainability is becoming an execution discipline

For years, many organizations treated sustainability as a long-term aspiration. That is changing fast.

IDC predicts that by 2027, 80% of sustainability services engagements will focus primarily on operationalizing sustainability strategy, not defining it. This shift will drive demand for a new wave of IT and OT services that connect sustainability goals directly to systems, processes, and outcomes.

What this means in practice:

  • Sustainability targets will increasingly be translated into system requirements.
  • ESG initiatives will be measured by execution velocity and measurable impact.
  • Technology leaders will play a significant role in making sustainability real.

Sustainability is no longer about whether the strategy is sound. It is about whether the organization can execute it at scale.

AI turns ESG from reporting to real-time management

One of the most important changes in IDC’s Sustainability/ESG FutureScape is the role of AI.

By 2030, more than 65% of global enterprises will use agentic AI–driven ESG software to support sustainable sourcing, lowering Scope 3 emissions while improving efficiency and resilience across supply networks.

This signals a shift from periodic ESG reporting to continuous ESG management:

  • AI will ingest supplier, logistics, and operational data in near real time.
  • Sustainability risk will be modeled, predicted, and mitigated before it escalates.
  • ESG performance will increasingly influence procurement and sourcing decisions automatically.

In parallel, IDC expects that by 2027, at least 30% of enterprise sustainability-related AI use cases will focus on sustainability risk analytics and risk management, reinforcing ESG as a core risk discipline rather than a compliance afterthought.

For leaders, AI investments are becoming sustainability investments.

Manufacturing and data centers move to the front line

Operational sustainability will be most visible where energy and resource intensity are highest.

IDC predicts that by 2027, 40% of manufacturers will use AI-driven analytics and automation to optimize energy efficiency, reducing carbon emissions by as much as 30%. These gains will not come from incremental efficiency programs but from deeply integrated analytics tied to production systems.

Data centers are undergoing a similar reckoning. By 2028, 50% of data center decision-makers will prioritize investments in modular facilities, edge locations, efficient server and storage systems, and renewable energy infrastructure to meet rising demand sustainably.

Just as important is transparency. IDC forecasts that by late 2027, 80% of AI data centers will report resource consumption metrics such as water use and pollution, setting new expectations for environmental accountability and community impact.

Sustainability performance is becoming inseparable from infrastructure strategy.

Circular IT becomes a cost and trust advantage

Sustainability is also reshaping how organizations think about technology assets.

By 2028, 75% of enterprises will set formal IT asset circularity goals, with 90% of assets returned to the circular economy and 20% sourced as renewed equipment. IDC expects this to drive both cost savings and stronger vendor relationships.

This reflects a broader shift:

  • Sustainability initiatives are being justified through financial discipline.
  • Procurement teams are aligning ESG goals with cost optimization.
  • Vendors will increasingly be evaluated on their circularity capabilities.

Circularity is moving from a sustainability pledge to a commercial differentiator.

The role of the CSO expands—and connects to AI

As sustainability becomes operational, leadership models are evolving.

IDC predicts that by 2026, 60% of large organizations’ Chief Sustainability Officers (CSOs) will help drive AI deployment in procurement, scrutinizing supply chains end to end for social, environmental, and governance criteria.

This reflects a broader organizational change:

  • CSOs are becoming integrators across business, technology, and risk functions.
  • Sustainability leadership is increasingly data-driven and AI-enabled.
  • ESG accountability is moving closer to core decision-making.

The CSO of the future is not just a policy leader but a technology-enabled change agent.

What leaders should do now

IDC’s Sustainability/ESG FutureScape 2026 points to a narrow window for action. Organizations that wait for perfect clarity will fall behind those that start operationalizing now.

Key priorities for the next 12–24 months include:

  • Align sustainability goals with core operational and technology road maps.
  • Invest in AI-enabled ESG platforms that support continuous insight and action.
  • Embed sustainability metrics into procurement, manufacturing, and infrastructure decisions.
  • Treat ESG risk management as a strategic capability, not a compliance function.

The organizations that succeed will be those that move sustainability out of reports and into systems.

Turning insight into impact

Sustainability is entering a new phase—one defined by execution, accountability, and technology-enabled scale.

IDC’s FutureScape: Worldwide Sustainability/ESG 2026 Predictions show that ESG leaders will not win by talking louder about sustainability. They will win by building it into how their organizations operate, decide, and invest.

In an era of economic and regulatory crosscurrents, sustainability is no longer about signaling values. It is about delivering outcomes—with confidence.

Learn more: Explore the full IDC FutureScape: Worldwide Sustainability/ESG 2026 Predictions to understand how sustainability, AI, and operational transformation are converging—and what it means for your organization’s next move.

Bjoern Stengel - Sr. Manager, Data & Analytics - IDC

Analyst Profile
Bjoern Stengel is IDC's global sustainability research lead. His research focuses on how environmental, social, and governance (ESG) topics impact and shape business strategies and technology usage. He provides insights into market opportunities, adoption strategies, and use cases for sustainability-related technologies and services. Bjoern helps IDC's clients understand the impact of technology-enabled, sustainable transformation processes in the context of sustainable business strategies, operations, and products and services through research reports, news publications, and speaking engagements at industry events such as Climate Week NYC.

Asia/Pacific enterprises are entering a new era of cybersecurity defined by the convergence of human expertise, autonomous AI agents, and trust frameworks. IDC calls this the Cyber Trinity, a security model that integrates human judgment, autonomous AI agents, and embedded trust frameworks. Drawing on IDC FutureScape: Worldwide Security and Trust 2026 Predictions – Asia/Pacific Excluding Japan (Implications), this analysis examines how AI-driven SOCs, embedded AI governance, synthetic identity threats, sovereign AI requirements, and quantum-era risks are reshaping security strategies across the region.

As organizations accelerate toward AI-first operating models, security and trust are no longer reactive controls. They are now engineered, governed, and continuously validated capabilities that determine enterprise resilience, regulatory compliance, and long-term competitiveness.

Why security and trust are being redefined in Asia/Pacific

The security landscape in Asia Pacific excluding Japan (APeJ) is undergoing rapid change. IDC forecasts that total security spending in the region will reach US$39.5 Billion in 2026, growing at a 10% CAGR to US$52.4 billion by 2029. This growth reflects more than rising threat volumes. It signals a structural shift in how organizations must build and govern trust in an AI-driven world.

As enterprises adopt agentic AI, face fragmented regulatory requirements, and contend with sophisticated adversaries using AI-powered techniques, traditional security models are proving insufficient. Trust, once implicit, must now be engineered, governed, and continuously validated.

Five security and trust shifts shaping 2026

IDC’s analysis points to five major shifts that will define security and trust strategies across Asia/Pacific over the next 18–24 months.

1. Autonomous, AI-driven security operations

Security Operations Centers (SOCs) are evolving from human-centric environments to AI-augmented and increasingly autonomous operations. AI agents are deployed to triage alerts, reduce false positives, normalize incident response, and orchestrate remediation at machine speed. IDC’s Asia Pacific Security Study 2025 states that 39% of enterprises plan to apply AI/GenAI solutions in the next 12 months to optimize threat detection and analysis capabilities. This shift is essential as skills shortages and exploding telemetry volumes overwhelm traditional SOC models.

2. Embedded AI governance and sovereign AI requirements

Governments across Asia/Pacific are tightening controls on data usage and AI systems. Only 7% of enterprises are highly prepared in terms of GRC skills to support these new requirements, driving demand for privacy-by-design, compliance-by-design, and sovereign AI architectures Enterprises are reassessing cloud strategies, adopting retrieval-augmented generation (RAG), and exploring private compute environments to meet data residency and regulatory requirements while scaling AI responsibly.

3. Synthetic identity as a core trust threat

According to IDC’s 2025 Future Enterprise Resiliency & Spending (FERS) study, 49% of APeJ enterprises have paid at least US$10,000 in ransom due to ransomware breaches. Adversaries are using AI to create synthetic identities that blend real and fabricated data, undermining authentication systems across financial services, e-commerce, and government platforms. These attacks erode digital trust at scale, forcing organizations to modernize identity protection and adopt AI-powered anomaly detection to distinguish legitimate users from synthetic fraud.

4. Quantum readiness and cyber risk quantification

As quantum computing advances, enterprises are beginning to assess the long-term viability of existing cryptographic systems. Crypto-agility and quantum readiness are emerging as strategic imperatives. By 2028, IDC predicts that 20% of Asia’s top 2000 enterprises will engage cybersecurity professional services firms to conduct quantum risk assessments. The ability to quantify cyber risk in financial terms is also becoming a board-level requirement, shaping budgets, insurance strategies, and M&A decisions.

5. Dynamic playbooks and endpoint-level trust

Static security playbooks are giving way to dynamic, AI-generated response models that adapt in real time to evolving threats. 30% of enterprises will be prioritizing the expansion of its MDR capabilities across assets, endpoints and applications. The rise of deepfakes and AI-enabled deception is also accelerating demand for endpoint detection capabilities that balance privacy, performance, and resilience.

What Cyber Trinity means for enterprise leaders

Together, these shifts signal a fundamental change: security and trust are no longer reactive controls. They are strategic foundations for innovation. Organizations that succeed will be those that can:

  • Balance human oversight with autonomous AI decision-making
  • Embed governance directly into AI and security architectures
  • Treat trust as a measurable, managed asset
  • Anticipate regulatory and technological disruption rather than respond after the fact

From Insight to Action

These themes form the foundation of IDC’s FutureScape 2026 Security & Trust Predictions for Asia/Pacific, which will be explored in depth by IDC analysts Sakshi Grover and Yih Khai Wong in an upcoming webinar. The discussion will focus on how organizations can architect, govern, and operationalize the Cyber Trinity to strengthen resilience and lead with confidence in an autonomous security landscape. Register now.

About the Authors

Sakshi Grover - Senior Research Manager - IDC

Analyst Profile
Sakshi Grover is a senior research manager for IDC Asia/Pacific Cybersecurity Services, supporting its research and client engagement activities across Asia/Pacific markets. Additionally, she serves as the lead security analyst for IDC India. Sakshi is responsible for delivering syndicated custom research and consulting engagements on next-generation emerging and disruptive technologies. Her tasks include developing and socializing IDC's point of view within security services, covering both legacy and modern cybersecurity technologies. Her role involves close collaboration with technology vendors and buyers, developing market insights, and providing research, consulting, and advisory services in the fields of security software and services. This includes partnering on research efforts with relevant country analysts in the local IDC offices. Sakshi's views on security have been quoted in numerous publications, such as the Economic Times, Business Standard, Data Quest, CRN, and others.

Yih Khai Wong - Senior Research Manager - IDC

Analyst Profile
Yih Khai Wong is a senior research manager for IDC Asia/Pacific's Cybersecurity practice, supporting cybersecurity research and client engagements through the Asia/Pacific Security Opportunities: Trust and Resilience program. Yih Khai's area of focus is on security technologies, including cloud-native application protection, identity, endpoint and network security. He works closely with technology vendors and buyers, delivering actionable market insights and advice within the cybersecurity ecosystem. Before rejoining IDC, Yih Khai was a principal analyst covering the cloud, datacenter, and edge computing market with ABI Research. Prior to that, Yih Khai was in EY, in his capacity as an assistant director at EY's research and insights group. Yih Khai started his analyst career with IDC Malaysia as an analyst covering the enterprise applications market.

一个正在发生的变化：软件不再只是“人写的”

在过去二十年里，软件工程的核心始终围绕“人如何写代码、交付系统”展开。即便进入 DevOps 时代，自动化更多也只是加快了既有流程。但 IDC 指出，随着 Agentic AI 的成熟，软件开发正在发生一次结构性转变：开发不再完全由人主导执行，而是由人类开发者与自主 AI 智能体协作完成。

在《IDC FutureScape：全球开发者和 DevOps 2026 年预测——中国启示》（Doc# CHC54059126

，2026年1月）中，IDC 明确提出：未来五年，Agentic AI 将深度嵌入从开发、测试到运维和安全的整个生命周期，迫使 DevOps 从“工具链升级”走向“运行模式重构”。

IDC 的核心洞察：DevOps 的问题，已经不只是效率

在中国市场，许多企业仍将 DevOps 视为提升交付速度、降低沟通成本的方法。但 IDC 认为，这种理解正在失效。
当 AI 智能体开始自动生成代码、执行测试、修复缺陷并参与决策，真正的挑战不再是“怎么用工具”，而是：

  • 谁来管理和监督智能体？
  • 如何保证 Agent 的行为可解释、可审计？
  • 人类开发者的角色将如何转型？
  • 企业是否具备规模化运行智能体的治理与平台能力？

这些问题，正是 FutureScape 2026 十大预测试图回答的核心。

十大预测：Agentic AI 将如何重塑开发者与 DevOps 生态

预测 1｜智能体开发采用

到 2028 年，面对智能体部署量增长 10 倍的局面，50% 的中国 1000 强企业将采用智能体开发生命周期，以实现企业级智能体 AI 的有效规模化落地。

这意味着，传统 SDLC 已不足以支撑智能体开发，企业必须引入专门面向 Agent 的开发与治理方法论。

预测 2｜多智能体编排

到 2029 年，多智能体编排的风险与复杂性将促使企业强化战略布局、扩充卓越中心（COE）资源，并将 AI 治理与监控工具的支出增加 30%。

当单一 Agent 变成 Agent 集群，治理与可见性将成为规模化落地的前提。

预测 3｜自主式智能体 AI 工作单元

到 2030 年，80% 的开发者将与自主 AI 智能体展开协作，推动人类开发者向规划、设计与编排角色转型，并重塑开发者工具生态系统。

开发者将不再只是“写代码的人”，而是“引导和监督智能体的人”。

预测 4｜氛围编程采用

到 2027 年，随着企业级能力的成熟，35% 的专业开发者将采用氛围编程开发平台构建生产级应用。

自然语言正在成为新的开发接口，但前提是企业级治理与质量控制能力同步成熟。

预测 5｜嵌入 DevOps 的智能体应用

到 2030 年，65% 的企业将把 AI 智能体嵌入 DevOps 和 DevSecOps 流水线，用于执行开发与安全工作流。

Agent 将成为流水线中的“常驻成员”，而非外部插件。

预测 6｜前沿模型采用

到 2027 年，在开发者偏好的驱动下，70% 的 AI 用例将仅由少数几个前沿模型提供支持。

模型选择正在从“多而杂”走向“少而精”。

预测 7｜智能体 AI 项目失败

到 2028 年，70% 的“自建型”智能体 AI 项目将因未能达成投资回报率目标而被放弃。

低估治理、运维和组织成本，是失败的主要原因。

预测 8｜AI 质量保障扩展

到 2028 年，AI 质量保障将推动智能体测试和跨应用生命周期管理的采用率至少提升 30%。

没有质量保障的 Agentic DevOps，无法进入生产核心。

预测 9｜AI 加速应用开发

到 2029 年，通过使用智能体 AI 软件开发工具，企业的应用开发与现代化迭代速度将提升 400%。

速度跃迁的前提，是平台化与治理并行。

预测 10｜开发者模型微调

到 2027 年，微调将取代检索增强生成（RAG）成为大语言模型改造的主流模式，这将推动开发者对开源权重模型的使用率提升 80%。

模型工程正在走向更深度的定制化。

分析师观点

IDC 中国研究经理王彦翔认为，开发者和 DevOps 正站在从“自动化时代”迈向“智能体时代”的关键门槛。FutureScape 2026 显示，真正拉开差距的，不是是否引入 AI 编码工具，而是企业是否具备平台工程、治理能力和开发者角色转型的整体规划。那些仅在局部场景试点智能体的组织，将很难释放规模化价值；而将 Agentic AI 作为企业级能力来建设的组织，更有可能在速度、质量和创新能力上形成长期优势。

一个面向技术与业务领导者的综合建议

IDC 并不建议企业急于“全面智能体化”。更重要的是，以 DevOps 为核心，系统性重构开发流程、平台能力与治理机制：建立智能体开发生命周期（ADLC）、强化多智能体编排与监控、同步推进开发者技能转型，并将 AI 治理嵌入每一个交付环节。


只有这样，Agentic AI 才能成为持续创新的引擎，而不是新的技术债务来源。

行动指南：企业可以从哪里开始？

  • 从 高价值、低风险的开发或运维场景 切入，验证 Agent 的实际收益
  • 建立 跨职能的 AI / Agent 卓越中心（COE），统一治理与平台策略
  • 投资 平台工程与 AI 质量保障，而不仅是开发工具
  • 提前规划 开发者角色与能力转型，为人机协作做好准备

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Bryan Wang - Senior Market Analyst - IDC

Bryan Wang is a senior market analyst for Cloud Computing in the Emerging Technology sector for IDC China. He focuses on research and analysis of China's cloud computing market, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SAAS). Bryan is also responsible for providing market analysis and research in relevant fields together with IDC's regional and global research teams. Before joining IDC, Bryan worked as a cloud computing solution architect for well-known manufacturers and systems integrators. He was mainly responsible for presales consulting, project design, industry insight, project management, and other work. He has rich experience and a profound understanding of the cloud computing field. Bryan graduated with a B.A. in Inorganic Nonmetallic Materials Engineering from Central South University.

企业连接，正在从“基础设施”演进为战略能力

在 AI 工作负载快速增长、业务连续性要求不断提高的背景下，企业连接已不再只是网络团队的技术议题，而正在成为影响 业务韧性、运营效率与创新速度 的核心能力。
IDC 认为，企业正在进入一个全新的连接阶段：连接不再只是“管道”，而是由 AI 驱动编排、可感知并持续演进的数字底座。

为什么这份 FutureScape，对企业连接战略具有参考价值?

在《IDC FutureScape：全球企业连接2026年预测——中国启示》（Doc#CHC52329725，2025年12月）中，IDC 指出，随着 AI、Agentic AI 与边缘计算深度融入企业运营，连接能力正在被重新定义。
企业对连接的期望，已从“稳定可用”升级为 敏捷、自治、安全、面向 AI 的能力体系。这一转变，使网络与连接成为 AI 规模化落地不可或缺的前提条件。

IDC FutureScape 给出的十大关键预测：

预测1｜AI 重塑云通信

到2027年，50%的企业将部署由 Agentic 人工智能（AI）驱动的云通信 API，从而以更高水平的个性化和自动化重塑通信与协作的使用方式。
要点：个性化和智能化化重塑通信与协作。

预测2｜AI 赋能的数字全连接底座（LEO 卫星）

到2029年，50%的企业将采用低轨道（LEO）卫星连接来补充地面网络，将关键的卫星直连消费者（D2C）、直连终端（D2D）以及高速宽带纳入统一的数字全连接底座。
要点：连接韧性开始向“天地一体”扩展。

预测3｜一体化蜂窝物联网

到2027年，60%的企业将利用蜂窝物联网应用、多 SIM 卡与嵌入式SIM卡（eSIM）方案，以及窄带物联网（NB-IoT）和 5G，构建面向关键业务场景的泛在连接网络。
要点：蜂窝物联网连接正在成为业务规模化的基础。

预测4｜无线广域网（WLAN）加速扩张

到2027年，80%的企业将全网集成 AI 驱动的无线广域网（wireless WAN），以提供可扩展、安全且具备高弹性的园区和分支机构连接。
要点：AI 正在重塑无线广域网的运维与自治能力。

预测5｜超大规模与网络平台（Cloud WAN）

到2028年，50%的“云优先”企业将为其 AI 工作负载采用云广域网（cloud WAN），强化云服务提供商在网络中的角色。
要点：网络能力正加速平台化、云化。

预测6｜边缘侧推理部署

到2028年，50%的企业将把推理类用例部署在边缘侧，以驱动新增收入、改善客户体验和 / 或优化内部流程。
要点：AI 推理开始向连接边缘迁移。

预测7｜零信任网络架构

到2027年，仍有30%的企业在安全访问服务边缘（SASE）的实施上保持碎片化策略，在其 SD-WAN 部署向零信任网络演进的过程中逐步调整。
要点：零信任是方向，但路径并不一致。

预测8｜虚拟 AI 网络工程师

到2027年，Agentic AI 将在不显著扩张人力规模的前提下，使网络团队的有效人效实现近乎翻番。
要点：网络团队正在被“数字员工”增强。

预测9｜AI 重塑专业服务

到2027年，AI / 生成式 AI 以及 Agentic AI 将全面融入咨询与集成服务，使服务交付能力提升25%，并将设计与配置时间缩短60%。
要点：网络专业服务进入 AI 驱动时代。

预测10｜AI 保障数据合规与可信（ESG 连接）

到2029年，围绕 ESG 强制性要求的提升，将导致仅有40%的企业会主动投资用于遥测数据采集的网络连接。
要点：连接与 ESG 的关系更加务实与现实。

这些预测对企业意味着什么？

IDC FutureScape 2026 表明，企业连接正在经历一次角色升级：从支撑 IT 运行的基础设施，转向 支撑 AI、业务连续性与组织敏捷性的战略平台。企业若仍以传统网络视角规划连接，将难以支撑 AI 推理、边缘自治与实时决策的需求。

IDC中国助理研究总监崔凯表示，未来五年，领先企业将把连接视为“AI 原生能力”的组成部分，通过 AI 驱动的云通信、无线 WAN、边缘推理与多网络融合，构建可持续演进的数字连接底座。FutureScape 2026 显示，只有将连接、AI 与安全统一规划的企业，才能在复杂环境中保持韧性与创新速度。

给企业管理层的近期行动建议

  • 将 AI 工作负载需求系统性映射到连接与网络规划中
  • 评估无线 WAN、Cloud WAN 与卫星连接的组合策略
  • 在网络运维中引入 Agentic AI 与 AIOps
  • 将边缘推理与数据主权纳入连接架构设计
  • 与服务提供商建立长期、平台化合作关系

未来 12–24 个月值得关注的信号

  • AI 驱动连接在园区与分支机构的规模化落地
  • 边缘推理对带宽与时延需求的重塑
  • 网络团队角色从“运维者”向“平台工程师”转变

进一步推荐

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Kai Cui - Associate Research Director - IDC

Kai Cui is an associate research director for IDC China's Telecommunications and Internet of Things (IoT) Group. His research covers telecommunications, enterprise communication, and IoT industries. He is responsible for tracking and analyzing relevant areas as well as providing research and consulting services based on customized requests. Kai has more than 15 years of experience in the communications and telecom industry. Prior to joining IDC, Kai worked at Polycom, Huawei, and other communication enterprises, where he engaged in technical support, project management, and solution planning. He has an in-depth knowledge of deployment and application of communications solutions in vertical industries. Kai graduated from the Beijing Union University in 2001, with a bachelor's degree in Computer Science.