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BYD surpassing Tesla in global battery electric vehicle (BEV) sales in 2025 marks a pivotal shift in the global new energy vehicle landscape. This milestone reflects not only divergent market strategies, but also differences in the stage of EV adoption across price segments.

BYD’s global sales momentum was driven primarily by its entry-level models—such as the Dolphin and Atto 3—which have made a substantial contribution to volume growth. This performance underscores BYD’s product competitiveness and signals an acceleration of electrification within the global mass‑market passenger vehicle segment.

According to IDC, global BEV sales are expected to exceed 12.1 million units in 2025, sustaining double‑digit year‑on‑year growth. Growth is being driven largely by the affordable EV segment. Following the rapid electrification of the premium market prior to 2020 and the mid‑range market between 2021 and 2023, entry‑level EVs have now emerged as the next growth engine, supported by declining battery costs and expanding charging infrastructure.

Based on long‑term market tracking, IDC observes that technological innovation typically penetrates the premium segment first before cascading into the mass market. The evolution of EV adoption globally is consistent with this pattern.

Divergent growth paths have shaped different adoption timelines for BYD and Tesla:

BYD: A Representative of a Full‑Scenario Green Energy Ecosystem

BYD’s business extends well beyond passenger vehicles, spanning public transportation, commercial vehicles, rail transit, and energy storage systems—together forming a comprehensive green energy ecosystem.

In the passenger vehicle market, BYD has rapidly expanded in emerging and mass markets through highly cost‑competitive models, while gradually introducing higher‑end product lines in mature markets.

Tesla: A Technology‑Centric Narrative as a Tech Company

From a brand positioning perspective, Tesla has consistently focused on attracting technology‑savvy, high‑end consumers, positioning itself as a technology‑driven company rather than a traditional automaker. Its AI‑related research extends beyond vehicles into areas such as robotics and energy management.

IDC believes that the growth logic of the entry‑level EV market differs materially from that of early‑stage markets. At this stage, cost efficiency, channel coverage, and operational execution become significantly more critical.

Does Sales Leadership Signal a “Return” to the Dealer Model?

In overseas markets in 2025, BYD’s most notable growth has occurred in Europe, driven by the rapid expansion of its dealer and service networks, localized manufacturing, strong product competitiveness, and differentiated technologies.

In Europe, BYD has partnered with established local automotive retailers to deliver sales and after‑sales services. In contrast, Tesla—which has maintained a direct‑to‑consumer (DTC) sales model—has been overtaken by BYD in unit sales.

This has led some observers to conclude that the traditional dealer model is regaining dominance. IDC does not believe this interpretation is accurate.

In the premium segment, the DTC model continues to be adopted not only by NIO and Li Auto, but also by high‑end EV brands incubated by traditional OEMs, such as Zeekr and WEY.

Premium EV customers place a high value on pricing transparency, seamless omnichannel experiences, and post‑purchase engagement. Digitalization and vehicle intelligence have connected historically fragmented offline touchpoints—such as vehicle purchase and maintenance—into a cohesive customer journey, creating a solid foundation for the DTC model.

At the same time, mass‑market EV manufacturers are increasingly adopting hybrid models that combine traditional dealerships with direct sales, or agency‑style structures. These approaches provide OEMs with greater control over pricing strategy and brand consistency, particularly when entering new markets.

For example, BYD has adopted a hybrid agency model in the UK: pricing is set centrally by headquarters, inventory is allocated through a European distribution hub, and local retail partners act as agents responsible for sales, delivery, and after‑sales support.

IDC believes that DTC and agency models remain a critical long‑term trend—not only in the premium segment, but increasingly in the mass‑market EV space as well.

The Long‑Term Rationale for DTC and Agency Models Remains Intact

Electrification is a cornerstone of sustainable development in the automotive industry, yet its market‑side adoption continues to be constrained by multiple factors, including battery technology.

Battery degradation remains highly uncertain. Charging behavior, ambient temperature, and usage intensity all have a direct impact on battery health, making value depreciation difficult to predict. This uncertainty fuels consumer anxiety around long‑term ownership costs and complicates residual value forecasting at a fleet level.

While leasing is often viewed in overseas markets as an effective way to mitigate EV depreciation risk, it carries inherent limitations. Fundamentally, leasing shifts risk rather than eliminating it. If actual battery degradation exceeds forecasts, residual values can still decline sharply, leading to asset impairments and margin pressure.

By restructuring the value chain, DTC and agency models centralize data ownership, enabling transformational solutions to a range of risks—including battery degradation. Under these models, dealers evolve into service partners. OEMs also gain access to full-lifecycle vehicle data, including mileage, battery health, charging patterns, and vehicle condition.

This data‑driven approach has the potential to fundamentally reshape residual value forecasting and risk management.

When further integrated with leasing models:

  • OEMs can retain vehicle ownership and repurpose or refurbish batteries at the end of the lease term, enabling a circular economy that offsets degradation costs and reduces environmental impact.
  • High‑frequency technology updates—from sensor upgrades to algorithm iteration—become feasible, accelerating the commercialization of autonomous driving services and improving the economic viability of autonomous fleets.

Key Takeaways from IDC’s Perspective

IDC recommends that EV manufacturers collaborate closely with dealers that possess strong regional advantages, adopting a “hub‑and‑spoke” hybrid strategy:

  • Hubs serve as brand flagships and data collection centers, ensuring brand consistency in high‑visibility markets.
  • Spokes focus on delivery and service, with dealers’ core competitiveness shifting toward localized customer support.

Under this model, true returns on investment do not primarily stem from higher gross margins, but from data ownership. This data enable the precise deployment of high‑margin services, transforming revenue models from one‑time vehicle sales into recurring income streams.

Direct sales depend on persistent digital connectivity with vehicles. OEMs must work with partners capable of delivering seamless and secure upgrades to ensure long‑term customer engagement and vehicle usability. Technology providers, in turn, must adapt to the demand volatility inherent in direct sales models by developing:

  • Modular, on‑demand manufacturing processes
  • OTA‑ready architectures
  • End‑to‑end cybersecurity protocols

For more information and related research, please click here to contact IDC.  

Bull Wang - Research Manager - IDC

As a research manager for client systems research in IDC China, Bull Wang has his research focused on topics of autonomous vehicle, connected vehicle, new energy vehicle, next-generation mobility service, and other automotive-relevant topics. Bull is responsible for conducting research and analysis for China and the global market, providing services for tech buyers, tech vendors, and tech watchers. Prior to joining IDC, Bull had experience in conducting market research projects, such as brand health tracking, campaign evaluation, car clinic, and consumer portrait. His other experiences include social media monitoring for acquainting public opinion on brand and product. Bull has long served the leading OEMs in automotive industry. Bull graduated from China Foreign Affair University, majoring in diplomacy, and obtained a Law bachelor's degree.

过去一年，生成式AI迅速从“前沿技术”演变为企业讨论中的常规议题。从董事会到业务一线，关注点已经不再是“要不要用AI”，而是企业在不同发展阶段，应该如何选择落地路径、如何判断投入节奏，以及如何尽量降低不必要的试错成本。

从市场实践来看，企业的AI探索并不存在统一范式：有的企业从具体应用场景切入，有的优先推动流程自动化，也有企业选择先夯实数据和平台基础。这些选择背后，往往与行业属性、组织能力、数字化成熟度和管理目标密切相关，并不存在绝对正确的先后顺序。

在这一过程中，企业级应用、企业级服务以及数据库与数据管理，往往以不同形式、不同权重出现在企业的AI实践中。IDC开展相关研究，并非试图将这些因素“硬性绑定”为成功前提，而是希望更真实地反映市场的复杂性，帮助企业理解不同路径下可能面临的机会与约束。

从“加AI功能”到“用AI做事”：企业级应用的重构正在发生

AI Agent正在改变企业应用的基本形态

在很多企业中，生成式AI最初的落地方式是“功能叠加”：写文案、生成报表、自动摘要。这类能力提升了效率，但并没有改变应用的本质。

IDC的研究发现，真正具有颠覆意义的变化来自AI Agent（智能体）的引入。企业级应用正在经历从“被动工具”到“主动参与业务执行”的转变：

  • 应用不再只是被人操作，而是能够理解目标、拆解任务并自动执行
  • 用户界面逐渐从复杂菜单，转向自然语言和流程驱动
  • 应用之间开始通过Agent进行协同，而非人工串联

IDC将这一变化总结为企业级应用的“Agentic演进路径”，并指出未来几年内，Agent将从辅助角色逐步走向主导角色。

哪些业务场景最先受益？

从企业实际落地情况来看，生成式AI和智能体的应用并未集中在单一部门，而是优先出现在高频交互、高度标准化或知识密集型的业务与技术场景中，包括：

  • 客户服务与智能联络中心：AI被广泛用于自动应答、坐席辅助、工单分流与服务质量监控，在不完全替代人工的前提下，提高响应效率和服务一致性。
  • 办公自动化与知识管理：会议纪要、文档整理、企业知识问答等场景逐步由AI承担基础工作，降低员工获取信息和跨部门协作的成本。
  • 内容生成与市场营销：从内容和素材生成，延伸至客户洞察、活动优化和线索管理，营销决策开始更多依赖数据与模型驱动。
  • 职能流程自动化：在财务、供应链、HR、采购、法务等职能领域，AI被用于规则明确、重复性高的流程自动化、合规检查和风险识别。
  • 研发与IT运维：代码生成、测试、故障定位和运维自动化成为AI落地的重要方向，直接影响研发效率、系统稳定性和运维成本。

IDC之所以持续追踪这些细分场景，是因为企业在做AI投资决策时，往往需要回答一个现实问题：哪些应用场景已经具备规模化条件，哪些仍处在早期探索阶段。这类研究的价值，在于帮助企业避免“平均用力”，而是将有限资源投入到最有可能产生业务回报的方向。

没有服务能力，AI很难真正跑起来

一个在客户中反复出现的共识是：AI Agent的成败，不仅仅是模型本身，很大程度还取决于项目实施过程中对于数据治理，安全合规，流程重塑，平台整合等环节的设计和把控，以及后期的运营和维护。

企业在推进过程中普遍会遇到：

  • 业务流程是否适合被Agent接管
  • 多个Agent如何协同、治理和监控
  • 如何持续评估ROI，而不是一次性交付

这也是为什么企业级服务在AI时代的重要性被显著放大。IDC在软件与服务研究中，将AI咨询、Agent设计与开发、系统集成、运维与持续优化视为一个完整闭环，而非单一项目 。

对企业而言，这类研究的价值并不仅在于“推荐某一家供应商”，而在于帮助管理层理解能力建设的先后顺序：哪些能力需要长期内生，哪些可以借助生态伙伴补齐，从而避免“试点成功、规模失败”的常见陷阱。

AI走得多远，取决于数据和数据库走得多稳

数据库正在从“后台系统”走向“AI基础设施”

如果说企业级应用决定了AI“做什么”，那么数据库和数据管理决定的则是AI“能不能做、做得好不好”。

在生成式AI快速演进的同时，中国数据库市场也正在经历深刻变化：一方面，AI对数据实时性、多模态和向量能力提出更高要求；另一方面，国产化进程推动本土数据库厂商在功能和市场份额上持续提升 。

AI for Data：让数据库更智能

IDC在数据库研究中发现，AI正在反向赋能数据库自身：

  • 自动调优与容量预测
  • 基于AI的异常检测和安全防护
  • 更智能的运维和资源调度

这些能力直接降低了数据库复杂度，使企业能够用更少的人力支撑更复杂的业务和AI负载。

Data for AI：让AI真正可用

更关键的是，数据库正在成为AI应用的“能力上限”：

  • 向量引擎和多模数据管理决定了Agent是否具备“长期记忆”和上下文理解能力
  • 数据治理和权限体系决定了AI是否可信、可控
  • 实时数据能力决定了AI是否能够参与业务决策，而不仅是事后分析

IDC在数据库管理系统市场的研究中强调：未来企业AI竞争的本质，是数据架构和数据能力的竞争。

为什么IDC要持续开展这些研究？

IDC之所以持续在企业级应用、企业级服务以及数据库与数据管理领域投入研究，一方面，这些领域是企业AI价值真正发生的位置：应用决定AI是否进入业务流程，服务决定AI能否规模化运行，数据库管理和数据治理决定AI是否长期可持续。任何一环缺失，AI都很难从“亮点项目”走向“稳定能力”。

从企业决策者视角看，这些研究真正解决了什么问题？

通过持续的市场数据、趋势判断和实践洞察，IDC希望帮助客户：

  • 看清AI技术和应用的成熟节奏
  • 了解行业发展的最新趋势和最佳案例
  • 对于热点领域和技术的评估和参考实践

在生成式AI引领的新一轮技术升级中，真正具备长期优势的企业，往往不是最早“尝鲜”的企业，而是那些能够构建高质量数据资产、完善AI治理体系、深度重塑业务流程并持续融合行业Know-how，实现数据驱动的敏捷创新与可持续落地的企业。这正是IDC持续开展相关研究的出发点，也是客户能够从这些研究中获得的长期价值。

IDC 2026年软件和服务领域研究计划：

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。

Lizzie Li - Associate Research Director - IDC

Lizzie Li is Associate Research Director of IDC China's Enterprise System and Software Research that focuses on research and analysis of the China Datacenter, Cloud Computing, and IT infrastructure markets. She also provides intelligence and consulting services in customized projects for local and multinational corporation (MNCs) IT vendors. Lizzie’s research domain covers Datacenters, Cloud Computing, Virtualization, and her duties include providing consulting proposals to IT vendors on sales, marketing, and research fields. Lizzie Li has seven years of experience in the IT industry, including Internet datacenters, cloud computing services, mobile telecommunication systems, and enterprise markets. Prior to joining IDC, Lizzie Li worked for 21vianet, Nokia Siemens Networks, and Huawei, and was responsible for sales analysis, project management, and technical support. Lizzie graduated from Huazhong University of Science and Technology with a Master’s degree in Pattern Recognition and Intelligent Systems.

过去三年，边缘计算云服务一直是云计算市场中最具确定性的增长引擎之一。2021—2024 年，中国边缘公有云服务市场保持了接近 38% 的年均复合增长率，在内容分发、实时交互、数据合规与用户体验提升等场景中，边缘云的价值已被行业客户广泛验证。

但进入 2024 年之后，市场环境正在发生明显变化。随着企业 IT 预算与资源快速向 AI 相关投入倾斜，边缘计算云服务的增长节奏出现波动：一方面，边缘 AI 场景在短期内尚未形成清晰、可复制的商业闭环；另一方面，部分传统需求增速放缓，使得市场在 2024 年下半年一度出现明显回落。

在这样的背景下，一个更具根本性的问题摆在行业客户和服务商面前：在 AI 成为核心业务能力的时代，边缘云究竟应该扮演什么角色？它是否仍然只是成本优化与资源下沉的工具，还是正在演变为承载新一代 AI 应用、Agent 形态与分布式网络能力的关键基础设施？本文将围绕这一问题，对 AI 时代边缘云的价值演进路径进行分析。

经典边缘云服务价值完成了从“成本优化”到“业务创新”的跃迁

不同时代背景、不同发展阶段、不同应用场景，为了满足企业自身业务发展诉求和回应资本诉求，行业客户对IT架构建设诉求可能存在巨大差异。

中国边缘计算云服务市场过去三年高速增长的背后，是不同影响因素交织形成的一轮分布式IT架构建设浪潮。其最初驱动力，是中国互联网行业各品类头部客户在用户规模见顶后对IT架构效率的机制追求，与边缘计算节点在运营成本、业务合规、用户体验等多维度原生优势的一次精准匹配。

行业客户广泛认可基础产品商业价值后，供需双方开始赋予边缘计算节点更丰富的内涵，随之而来的，是多样化的云服务产品下沉，以及据此完成的IT架构变革与业务创新。

AI时代边缘云服务价值实现顺序可能完全相反

2024年开始，部分服务商开始尝试复制经典边缘云过去三年的增长路径，希望从基础产品开始，完成边缘云市场的增长驱动力转换，但结果却是，传统增长需求增速放缓，但新边缘AI基础产品的商业价值并未获得广泛认可。回顾过去一年的市场变化，我们认为，AI时代边缘计算云服务的演进路径可能存在差异，价值演进逻辑甚至完全相反。

在当前时间节点下，主要行业客户的核心诉求，是探索扩大AI应用场景，客户迫切需要在更短的时间内探索出可以大规模商业化落地的应用场景。这一阶段，客户普遍对于初期运营的成本关注度有限，因而在本轮AI浪潮中，除了离线专属单点机柜满足了客户在私有化环境中的AI尝鲜诉求，在更广泛的边缘计算云服务体系中，最先受益的反而是基于边缘云“分布式”架构的更高阶创新方案，诸如大模型网关、AI搜索配套服务等。

例如基于边缘计算节点的AI搜索，2025下半年开始已经获得市场认可，“分布式”的边缘计算节点帮助模型克服了通用搜索引擎仅能返回摘要和元数据的局限性，并在大规模内容返回、抓取速度、动态内容解析、反爬对抗等维度展示出独特商业价值。

同时，以C端硬件形态和开源项目形态的AI Agent个人助理正在引起市场的广泛兴趣，甚至有望成为对话式AI、AI Coding之后的下一个顶流热点。在这一场景下，不考虑既有互联网内容与广告生态对上述应用场景的限制，“分布式”的边缘结算节点能够在用户响应时间、敏感数据保护、与庞大的互联网生态对接等方面同样提供了差异化价值。

IDC对于边缘计算服务/边缘云服务商的建议：

  1. 避免将注意力局限在边缘推理算力提供。虽然在边缘计算节点部署异构算力裸机是实现边缘云商业化变现的最快路径，但如上述推论所示，在各种类型的AI应用大规模铺开后，或者AI场景经过市场洗礼其商业价值被广泛验证后，行业客户才会将注意力转移至长期运营所需的更合理、经济的产品与服务。尤其是没有内部业务兜底的中小服务商，应当避免在获得明确的商业需求前，在边缘大规模部署异构算力。
  2. 保持对AI应用新场景的敏感，尤其是可落地的Agent形态应用。建立AI应用流程的全局视角，注并拆解其中边缘计算节点可承担的负载与业务流程，关与边缘云分布式体系带来的原生价值相匹配。
  3. 关注与多媒体场景相关的AI应用。图片、音视频等多媒体内容需要消耗更多的网络与算力资源，因而在大规模商业化推广后，边缘云“分布式”的生产体系、更靠近用户的网络环境优势将被进一步放大。
  4. 坚持既有优秀实践。更先进的大模型与多模态模型确实会改变我们的工作、生活与娱乐方式，进而对IT架构带来深远影响，包括后端软硬件架构和资源比例，但边缘云产品服务在既有场景中的价值逻辑依然长期成立，甚至在用户将预算向AI场景倾斜后，其优势有被进一步放大的趋势。

IDC 相关研究

围绕 AI 时代边缘云与边缘 AI 的发展趋势、技术能力与市场格局，IDC 将持续开展系统性研究，包括但不限于：

  • 《IDC Market Glance：中国边缘 AI 基础设施和平台，2026Q1》（即将发布）
    —— 从市场结构与关键能力维度，解析边缘 AI 产业链与主要参与者。
  • 《IDC Tech Assessment：中国边缘云智能服务技术能力评估，2026》（即将发布）
    —— 评估主流边缘云服务商在 AI 服务、分布式架构与应用支撑能力方面的技术成熟度。
  • 《IDC China Semiannual Edge Cloud Tracker，2025H1》
    —— 持续跟踪中国边缘公有云市场规模、竞争格局及行业应用变化。

如需进一步了解 AI 时代边缘云与边缘 AI 相关研究内容，或咨询 IDC 在云计算、AI 基础设施及数字化转型领域的其他研究成果，请点击此处，欢迎与我们保持联系。

Eric Wei - Research Manager - IDC

Eric Wei is primarily served as the role of Research Manager in Enterprise Research sector for IDC China, and he is responsible for conducting analysis and research for the China Industry Cloud Solution market and China Cloud Market Ecosystem, especially in Industrial Cloud Solution area. He is also involved in regional and global consulting and business development in related markets. Prior to joining IDC, Eric has over 3 years of working experiences in China Academy of Information and Communications Technology (CAICT). His research area there included next generation technology and its market tendency, Industrial Internet and Intelligent manufacturing. Eric holds Bachelor of Mechanical Engineering and Automation and Master of Instrument Science and Technology form Tsinghua University.

过去二十多年，中国政府信息化从“信息化—电子政务—数字政府”持续演进，有效支撑了治理能力现代化。当前，在大模型等 AI 技术突破与数据要素制度逐步完善的背景下，数智政府正在成为新阶段与新目标。IDC 认为，数智政府并非在数字政府之上简单“叠加AI”，而是围绕数据驱动（数据要素）、智能决策（政务大模型）与人机协同（智能体），对治理链条进行系统重构。

IDC认为，每一次政府数字化的跃迁，都源于技术成熟、治理需求与制度环境三者的同频变化。

一是技术条件发生质变。大模型带来“内容生成与推理”叠加“行业知识整合”的能力范式，结合数据、云与智能算力的规模化供给，以及更自然的人机交互方式，使政务系统从“流程线上化”走向“理解政策与业务、辅助决策与执行”的新阶段。

二是治理需求结构性变化。财政约束趋紧要求降本增效与集约建设；公共服务需求持续增长且更个性化；城市治理与风险管理复杂度上升，仅依赖增人增系统的传统路径难以对冲复杂度的上升，必须通过数智化手段重构治理方式与服务范式。

三是制度环境为规模化建设“铺路”。《国家数据基础设施建设指引》、《可信数据空间发展行动计划》与《政务领域人工智能大模型部署应用指引》等政策文件持续明确方向：数智化是数字政府建设的主攻方向，且需在安全、合规、可控前提下推进。这为数智政府由试点探索走向规模化落地奠定了制度基础。

未来五年“数智政府”市场的机遇与重构

根据 IDC《中国数字政府市场预测，2025—2030》，未来五年中国数字政府市场将呈现“总量稳增、结构重构”的特征。到 2030 年，市场规模预计达到 1857 亿元人民币，2025—2030 年 CAGR 为 4.1%。数字政府建设模式也由一次性项目投资转向政企合作，注重长期运营、服务化和效果导向。主要体现在三层：

基础设施：从云到智。基础设施仍是最大支出项，但增长重心正向智算中心与异构算力迁移。当前政府行业大模型应用及基础架构渗透率仅 1.5%，智能算力将成为数智政府的“隐性生产力”，具备显著成长空间。

平台层：成为数智中枢。以政务大模型平台、智能体平台、数据中台为核心，形成可持续复用的智能服务能力。IDC 预测平台层将保持接近 9% 的较高复合增速，是长期战略价值最突出的细分领域。

应用层：从项目建设走向能力运营。政务服务、政务办公、社会治理等领域的应用形态由一体化建设转向持续运营与生态化建设。对供应商而言，竞争焦点将从“交付规模”转向“可复用能力+运营成效”。

IDC 数智政府百强榜：行业领先实践

基于市场规模、技术成熟度、实践效果与可复制性等多维评估，IDC 发布了 “2025年中国数智政府百强榜 ”，提炼可复制的领先实践与产业共识，集中体现五大趋势：

第一，政务服务从“数字窗口”升级为“智能体入口”。AI助手、数字人不再只是展示，而是承担咨询、引导、办理、评价等多角色协同，成为统一入口的雏形。典型实践包括“贵人智办”AI助手、爱山东济南分厅服务AI能力提升、扬州市智慧门户”三好”小助手项目等项目。

第二，政务办公进入“AI初审 + 人工决策”的常态。公文处理、审批、督办等场景，正在从“全人工”走向“机器先筛、人工把关”，效率提升来自流程与决策链条的重构。典型案例实践包括烟台市智慧公文大模型项目、智慧政务协同办公平台AI应用项目等。

第三，数据要素从“政府内部资产”走向“治理与产业共同资源”。公共数据授权运营、城市数据空间等实践，推动数据在安全可控前提下释放外部价值，形成“数据—场景—产业”的闭环。典型实践包括数字福建释放公共数据赋能产业发展，治理数字优化惠及民生福祉、全球数源中心数据流通利用基础设施先行先试项目、上海数据集团城市数据空间新范式等项目。

第四，数智化开始直接提升城市体验与民生幸福感。养老、社区、园区、新城治理等场景，越来越强调“体验指标”和“结果导向”，这将倒逼产品从功能堆砌转向可衡量的成效。典型实践包括”盛情康养”沈阳基本养老服务综合平台、福田率先落地政务大模型发布首个城市智能体、广东某市管委会以AI引领构建智慧新城等项目。

第五，基础设施走向高度集约与智能调度。多云纳管、一云多芯、统一算力平台正在成为“标配”，这对厂商的体系化能力提出更高要求：能不能管得住、调得动、算得稳、控得牢。典型实践包括湖南省级数字政府、甘肃数字政府、长三角枢纽芜湖集群算力公共服务平台、中原智算中心等项目。

数智政府是“十五五”治理现代化的新起点

IDC 认为，数智政府将成为未来一个五年乃至更长周期内中国政府数字化建设的主线，深刻影响公共服务供给方式、城市运行治理模式、数据要素价值释放路径以及数字技术的社会价值实现。在“十五五”全面展开之际，数智政府的规模化建设成效，将成为衡量治理现代化水平的重要维度，也将重塑政府数字化市场的竞争格局与产业机会。

相关研究报告

《IDC PeerScape: 中国数智政府领导者实践，2025》(Doc#CHC53833026，2026年1月)。

更多推荐：

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。

The use of Vibe programming platforms, in which software developers use natural language prompts to guide large language models (LLMs) to generate, refine, and debug code, continues to expand. In parallel, the Vibe technology itself continues to evolve. While it is unclear how this will play out in the future, Vibe programming has already disrupted existing methodologies for developing software. And those impacts are significant. It is like agile on steroids, or “hyper-agile.”

Methods for software development, whether more akin to waterfall or agile development, are built around what has been a steadfast bottleneck: the time and effort required to write code.

How traditional development methods work around the coding bottleneck

In order to navigate around this bottleneck, organizations do things like:

• Spend time, even in agile workflows, defining desired outcomes and software requirements.
• Spend time identifying exhaustive lists of software requirements. Given the time and effort to build software, stakeholders treat each development project as their one and only opportunity to get everything on their wish list, and so they define and want it all.
• Build in feedback loops. One of the truths about software development is that no one knows what they want until they see it. As a result, particularly in agile workflows, stakeholders are asked to review work to reduce the likelihood of the project going off track. In the agile method of Scrum, this feedback comes in product, release, and sprint planning, as well as during end-of-sprint demonstrations.
• Employ project and portfolio management. Because of the time and effort required to build software, the cost of getting things wrong is high. In response, organizations build ancillary processes like project and portfolio management, which are designed to reduce project risk.
• Treat software projects like capital projects, requiring an approved business or value case that generates a return on what could be a large investment in time and effort.

What changes when code is no longer the constraint

How does the use of Vibe programming platforms change this? It significantly reduces the time and effort required to write code. With Vibe programming, the AI-powered platform writes the software, while human software engineers refactor it. Even better, Vibe programming frees software engineers to focus on defining and designing solutions.

The bottleneck moves: A theory of constraints moment

What does this mean? The bottleneck around which development methodologies have traditionally been defined shifts to another part of the process. This is a perfect example of the theory of constraints.

If the historically massive bottleneck around writing code is gone, how do our methods need to change?

Early lessons from Vibe programming in practice

My early experience with Vibe programming indicates the following.

• Requirements can evolve instead of being exhaustively defined upfront.
The need for a detailed or exhaustive definition of requirements goes away. Because the time from identifying a need to the delivery of working software is short, requirements can evolve through a series of expanding versions. The initial requirements might be as simple as a concept that can then be iterated.

With Vibe programming, it takes far less time to create a version of the software. Given that, how much does the quality of the initial definition of requirements really matter? If the delivery team does not grasp the requirements, a corrected version is only hours or a few days away.

The requirements should focus on the desired outcome of the software product. What is its purpose? How will its impact be measured? What are its success and acceptance criteria? These outcome-based requirements become the North Star for the development team.

• Scope no longer needs to be tightly constrained.
There is no longer a need to control scope by saying no to some ideas or needs. Stakeholders can provide their wish lists.

• Stakeholder review becomes the new bottleneck.
If it takes 50%, 60%, or 75% less time to build a version of the software, stakeholder reviews and decision-making must occur on a much faster cycle. It is likely that the team will have a review-ready version of the software in a day or two, or even more frequently early in the project.

Stakeholders and decision-makers are often busy people. Do they have time to meet once a day or every other day to review the most recent version of the software? Perhaps not.

One option is to replace frequent reviews and decision-making with a more empowered development and product team. As the team defines desired outcomes, it should also identify which types of decisions require stakeholder involvement. These should be limited to decisions that affect outcomes.

Should the team consult with the CEO about how many pixels from the edge of the page an image should appear? Hopefully not. Only a small subset of decisions should require stakeholder review so the team can move quickly.

• The business case should be treated as an experiment.
Because the time and effort required to build a product are dramatically reduced, organizations should rethink how they approach the business or value case for projects.

Is it still necessary to build, review, and approve a business or value case before starting the work? Or can the project be treated as an experiment, where an initial version is delivered based on expected value and its impact is measured?

If the impact aligns with expectations, functionality can be expanded and the next version released. If it does not align, the team can pivot and move to the next concept. The cost of getting it wrong is now low.

• Deployment only becomes a bottleneck if teams let it.
There could still be a bottleneck in the deployment process, but only if the organization is not already adept at DevOps, DevSecOps, and CI/CD. The AI can be informed of the deployment target and perform much of the work.

Accelerating time to value requires process change, not just tools

In scenarios where a team is building something designed to generate value, Vibe programming allows organizations to realize that value much more quickly. However, to truly shorten the time to value, the entire process, from inception to reviews and development cycles, must change so it does not become the next set of bottlenecks.

It would be a shame for an organization to inject Vibe capabilities without overhauling the broader software development process.

Niel Nickolaisen - Adjunct Research Advisor, IT Executive Program - IDC

Analyst Profile LinkedIn
Niel Nickolaisen is an adjunct research advisor for IDC’s IT Executive Programs (IEP). He is considered a thought leader in the use of Agile principles to improve IT delivery. And he has a passion for helping others deliver on what he considers to be the three roles of IT leadership: enabling strategy, achieving operational excellence, and creating a culture of trust and ownership.
Key Highlights
  • 75% of Asia/Pacific care providers say agentic AI delivers greater productivity gains than GenAI
  • 50% of providers will use advanced risk stratification for population health by 2028
  • Asia/Pacific accounts for nearly 60% of the global aging population
  • Agentic AI’s share of GenAI budgets will grow from 18% (2025) to 29% (2026)
  • Multimodal AI will predict 50% of chronic and rare diseases before symptoms by 2030
  • AI agents will be used by 33% of top-tier hospitals for real-time clinical decision support
  • Quantum platforms will enable 100x faster diagnostics for 20% of leading institutions by 2030
  • Singapore General Hospital’s AI chatbot saves ~660 clinician hours annually

Asia/Pacific healthcare provider organizations are at a critical inflection point. Generative AI (GenAI) is no longer an experimental initiative — it has become a strategic investment imperative. To navigate this shift, care providers need a clear roadmap that aligns AI priorities with emerging models of care delivery. IDC’s recently published FutureScape report for healthcare provides this roadmap and outlines how providers can move from experimentation to measurable impact.

A key highlight of this year’s FutureScape is agentic AI, which marks a new milestone in the region’s AI maturity. According to IDC’s Agentic AI Survey, 75% of Asia/Pacific care providers report that agentic AI outperforms GenAI in delivering measurable productivity gains. However, this transition requires robust regulatory frameworks and strong human-in-the-loop mechanisms to ensure ethical, transparent, and accountable deployment.

Asia/Pacific care providers must first address care productivity, enabling clinicians and operations teams to do more with constrained resources. This begins with building a resilient and trusted data foundation that can unlock the full value of agentic AI. With productivity gains as the anchor, providers can then reimagine care delivery by integrating advanced analytics, seamless workflows, and explainability to support personalized, secure, and transparent care.

This blog highlights five of the ten key predictions from the recently published IDC report: IDC FutureScape: Worldwide Healthcare Industry 2026 Predictions — Asia/Pacific (Excluding Japan) Implications

The Next Phase of Population Health Management: Toward Data-Driven, Proactive Intervention

By 2028, 50% of healthcare organizations in Asia/Pacific will leverage advanced risk stratification tools to tackle population health, specifically focusing on the chronic disease burden and aging population.

Asia/Pacific is home to nearly 60% of the world’s aging population, and the growing burden of noncommunicable diseases continues to place sustained pressure on healthcare systems. IDC expects population health management to become more data driven and proactive, as advanced risk stratification enables earlier identification of at-risk cohorts and more effective interventions.

This shift highlights the need for interoperable data platforms that unify clinical, demographic, and social determinants of health data. A strong example is Taiwan’s AI-on-DM (Diabetes Management) initiative — the country’s first large-scale healthcare AI program. Led by the Ministry of Health and the National Health Insurance Administration (NHIA), the initiative integrates long-term type 2 diabetes data with a medical large language model (LLM) to predict and manage complication risks for more than 2 million patients, years in advance. The program is expected to expand to other chronic and complex conditions, including hypertension and cancer.

Despite this progress, fragmented data environments and digital inequity remain major barriers across the region. Providers that invest in real-time analytics, standardized data exchange, and secure data sharing will be better positioned to shift from reactive care to preventive, population-scale interventions.

Agentic AI and the Future of Patient Experience: Advancing Digital Equity and Trust

By 2028, 45% of healthcare organizations in Asia/Pacific will advance agentic AI-enabled engagement by prioritizing digital equity, cultural alignment, and trust to produce personalized and empathetic communication.

Agentic AI introduces a new model of patient engagement. Unlike traditional automation, agentic systems adapt interactions in real time by drawing on clinical context, patient-reported outcomes, and social factors. In a region defined by linguistic, cultural, and socioeconomic diversity, this capability is critical.

IDC research shows that Asia/Pacific providers are rapidly increasing investment in agentic AI for patient engagement and care coordination. IDC’s 2025 FERS Survey indicates that agentic AI’s share of GenAI budgets will rise from 18% in 2025 to 29% in 2026. A practical example can be seen in Synapxe’s efforts to modernize Singapore’s national health digital infrastructure. By embedding AI-driven decision support and intelligent automation across patient-facing and care coordination platforms, Synapxe is enabling more proactive, personalized, and culturally aligned engagement.

Trust remains foundational. Transparent AI behavior, explainable recommendations, and clear escalation paths to clinicians are essential for adoption by both patients and care teams. Providers that embed governance, cultural sensitivity, and digital equity into agentic engagement strategies will build stronger patient relationships and improve outcomes.

Multimodal AI and the Shift to Predictive, Preventive Care

By 2030, multimodal AI will predict 50% of chronic and rare diseases before symptoms, making predictive care a reality with broader health data, including wearables and multiomics in Asia/Pacific.

This marks a decisive shift from reactive to predictive and preventive healthcare. Advances in multimodal AI — spanning clinical records, medical imaging, genomics, proteomics, and real-time wearable data — are enabling earlier and more accurate disease risk identification, often years before symptoms appear.

For a region facing rapid population aging and rising chronic disease prevalence, the impact is significant. Multimodal AI models can continuously analyze longitudinal health data to detect subtle patterns invisible to traditional diagnostics. In China, multimodal AI systems combining medical imaging, clinical records, and laboratory data have demonstrated approximately 98% accuracy in detecting biliary atresia.

Earlier detection supports targeted prevention, personalized care pathways, and reduced downstream costs. For rare but life-threatening pediatric conditions, such as biliary atresia, earlier diagnosis can dramatically improve outcomes.

Real-Time Decision Support: Governed AI Agents in Clinical Care

By 2030, 33% of top-tier hospitals in Asia/Pacific will deploy AI agents to deliver real-time decision support and autonomous workflows with greater than 80% accuracy while escalating exceptions to clinical staff.

Clinical environments demand speed, accuracy, and accountability. IDC expects AI agents to increasingly augment clinicians by synthesizing multimodal data and delivering context-aware insights at the point of care. These agents will not replace clinicians, but they will automate routine tasks and support faster, more consistent decision-making.

A real-world example is Singapore General Hospital’s AI chatbot, Peach (Perioperative AI Chatbot), which supports pre-operative assessments and saves approximately 660 doctor hours per year.

Success depends on data quality, interoperability, and governance. AI agents must operate within clearly defined boundaries, with continuous monitoring and escalation mechanisms. Hospitals that invest early in AI-ready infrastructure will improve clinician efficiency while preserving clinical oversight.

From Classical Limits to Quantum Leap: Preparing for Precision-Driven Care

By 2030, 20% of top-tier healthcare institutions in Asia/Pacific will harness quantum platforms for 100x faster diagnostics, simulations, and digital twins in precision-driven complex care.

Quantum computing remains emerging, but it represents a long-term inflection point for healthcare. IDC expects early adoption in complex diagnostics, precision medicine, and advanced simulations. Governments and institutions across Asia/Pacific are already investing in quantum ecosystems.

In Australia, the University of Wollongong’s quantum-enhanced imaging research demonstrates how hybrid quantum-classical techniques can accelerate genomics, biomarker discovery, and precision radiotherapy. For healthcare leaders, this reinforces the importance of future-ready data architectures and skills development.

Moving Forward: From Insight to Action

Together, these predictions highlight a clear message for Asia/Pacific healthcare providers. Agentic AI, advanced analytics, and emerging technologies can deliver measurable gains in productivity, patient experience, and clinical outcomes. However, success depends on trusted data foundations, interoperability, explainability, and strong human oversight.

IDC FutureScape provides a practical roadmap for navigating this transition. Providers that act now to align data, governance, and workforce strategies will be best positioned to lead in the next era of AI-driven, patient-centric care.

Manoj Vallikkat - Senior Research Manager - IDC

Analyst Profile
Manoj Vallikkat currently works as a senior research manager for Healthcare Insights in IDC Asia/Pacific. His research covers digital transformation (DX) across care delivery systems in the region, focusing on areas such as evolving healthtech ecosystem, patient-centric care, and predictive care management. He also covers the life sciences segment, with special interest in artificial intelligence (AI)-based drug discovery and remote clinical trial practices. Manoj has led key consulting engagements across the country markets in the Asia/Pacific region. He has also handled various GMS engagements for tech providers, which include tailored reports, round-tables, and speaking gigs.

国内ITインフラ市場（2025年の振り返り）

AIインフラ
2025年もAIインフラ投資が主役となりました。2024年に引き続きハイパースケーラーやサービスプロバイダーによる投資が中心ですが、研究機関や一般企業によるAIインフラ投資も徐々に拡大しています。

AIインフラは国内外の動向が一般ニュースに取り上げられるほど注目度が高く、2025年は講演でもAIインフラ関連のテーマが多くなりました。エージェンティックAIなどAI活用が進むにつれてAIインフラ投資への関心が高まってきますので、2026年も引き続き国内ITインフラ市場の注目テーマになるとみています。

仮想化＆HCI
2024年から市場が流動的になっていますが、利用企業ごとに方向性が定まりつつあります。仮想化環境の移行を決めた企業では検証や移行作業が本格化してきました。2026年はこうした動きがさらに顕著になりそうです。

マネージドサービス
2025年はマネージドサービスやITインフラ運用の調査に注力しました。複数のレポートを発行しましたが、特にマネージドクラウドサービス領域でベンダー評価レポートを発行できたことは大きな取り組みでした。

IDC MarketScape: Japan Managed Hybrid and Multi-public Cloud Services 2025 Vendor Assessment

クラウド移行は、ユーザー企業・ベンダー双方にとって関係性を見直す機会になっています。
調査にご協力いただいた皆様にはこの場を借りて御礼申し上げます。

FutureScape（今後5年間の予測）

IDCでは毎年、各調査領域でFutureScape（今後5年間の10項目の予測）を発行しています。デジタルインフラ戦略では、AIインフラ関連と、ITインフラへのエージェンティックAI適用に関する項目が中心です。

AIの影響は、コンピュート、データセンター、データロジスティクス、ネットワーク、コンテナ化、エッジ、プライベートデジタルインフラへの再投資など幅広い領域で強まります。

IDC FutureScape: Worldwide Digital Infrastructure 2026 Predictions – Japan Implications

2026年に向けて

デジタルインフラ戦略の調査を主導して3年が経ちました。複数の領域を担当するアナリストと連携しながら調査を拡充してきましたが、2025年はさらに幅が広がった年でした。

2026年は新たな挑戦が始まる1年になりそうです。引き続き、AIインフラ、仮想化、インフラモダナイゼーションなどITインフラ分野の調査に取り組みます。そして、GPUクラウドなどを含む、Accelerated Compute as a Serviceの分野もMarketScapeを実施予定です。


2025年もご支援ありがとうございました。2026年もどうぞよろしくお願いいたします。

関連する調査やご相談について

より詳細なインサイトや市場動向については、当社アナリストへお気軽にご相談ください。

Yukihisa Hode - Research Manager, Infrastructure & Devices, Research, IDC Japan - IDC Japan

Analyst Profile
Yukihisa Hode is a research manager covering digital infrastructure strategies as well as AI infrastructure, IT infrastructure services, IT operations, hybrid/multicloud and hyperconverged infrastructure (HCI). He leads the research program on digital infrastructure strategies, providing insight and advice on the digital infrastructure through research reports, marketing content, and presentations to support IT and digital decision-making.

With AI-generated content saturating every channel, every vendor can sound confident and strategic. That’s why in today’s B2B landscape, buyers are not overwhelmed by a lack of information anymore, they are overwhelmed by too much of it. The real challenge for buyers is not finding options, but quickly identifying which solutions are truly worth their attention.

The new buyer mindset: Filtering for fit, not just information

Buyers are not asking for proof because they are curious. They are asking because they need to filter aggressively. Their goal is not to find the most impressive story, but to find the vendor who can meaningfully address their current needs. In this environment, proof isn’t just about demonstrating value. It’s about signaling fit. Proof tells the buyer: “This applies to your situation, right now.”

Agentic AI and the changing discovery process

This shift is being accelerated by agentic AI, autonomous systems that retrieve, coordinate, and even act on behalf of buyers.

IDC research shows that discovery is no longer a linear, one-time phase. It’s a continuous, adaptive process, increasingly mediated by AI agents that surface and prioritize solutions based on relevance, applicability, and trustworthiness.

Buyers now rely on these systems to narrow the field before human evaluation even begins.

As a result, brands that succeed aren’t just visible, they’re findable in context. Their knowledge assets are:

  • machine-readable
  • verifiable
  • and referenced by trusted third parties

Why proof is the new standard

Buyers have learned that every vendor can claim impact, and every solution can be positioned as innovative. As a result, they filter differently. Not by who sounds the smartest, but by who can demonstrate situational fit. Proof becomes the buyer’s way of answering three critical questions:

  1. Does this solution address the pressure I’m dealing with now?
  2. Is this applicable to my specific context, not just a generic scenario?
  3. Can I use this information to make sense of my situation internally?

If the answer to any of these is unclear, buyers move on, even if they continue to “engage” with content. This disconnect explains why engagement metrics may look strong, while leads fail to advance through the funnel.

Trust, ethics, and the new table stakes

Relevance isn’t just operational, it’s ethical.

IDC’s 2025 Tech Buyer Survey reveals that 81% of buyers and 88% of C-level executives now prioritize ethical AI use when selecting technology partners.

Buyers are looking for signals of responsible, transparent, and ethical practices.

As a result, proof now extends beyond outcomes alone. It includes visible evidence of governance, data provenance, and compliance.

While AI systems, including search, copilots, and agentic discovery tools, do not judge ethics or morally evaluate vendors, they do prioritize solutions and content that are:

  • well structured and machine-readable
  • frequently referenced by trusted third parties
  • associated with authoritative sources
  • consistent across credible channels

In an AI-mediated buying journey, relevance is reinforced by what can be verified, referenced, and trusted, not simply by what is claimed.

What it takes to be credible in the answer engine economy

As AI agents mediate more of the discovery and evaluation process, the structure and validation of your content matter more than ever.

IDC’s analysis shows that knowledge management, ensuring your assets are machine-readable, well-structured, and validated by third parties, is now the foundation for being surfaced as a credible option. Brands that invest in distributed authority, such as analyst partnerships and independent benchmarks, are more likely to be recommended by AI agents, trusted by buyers and shortlisted earlier.

When engagement looks healthy, but pipeline doesn’t

Many marketing teams see healthy engagement metrics, downloads, clicks, lead volume —yet still struggle to convert that activity into pipeline contribution and bookings impact. This is not a sign of buyer disinterest. It is a sign that proof of fit and credibility has not been established early enough in the journey. Buyers will engage with content that sounds promising, but they’ll only advance when they see clear evidence that it applies to them. Late-stage proof doesn’t create pipeline momentum. It only explains why it never materialized.

Closing the gap

The most common breakdown in messaging happens here:

Vendors lead with transformation and innovation.
Buyers are asking, “What does this fix first?” and “Is this actually for me?”

Forcing buyers to translate vision into applicability is a losing strategy. Instead, marketers must lead with proof that is:

  • immediately recognizable
  • contextually relevant
  • and actionable without interpretation

That’s how relevance is established, and momentum begins.

The new standard: Proof over promises

Leading marketing teams are shifting their approach. They are no longer focused on making messages stronger or louder. Instead, they are asking, “How do we prove fit and credibility earlier?” and “What signals help buyers self-qualify faster?” This shift is the foundation of a proof-over-promises strategy. It is not about abandoning storytelling, but about earning the right to be considered before asking for belief. This happens through clear, early evidence.

Pressure-test your messaging

To help marketers align with how buyers actually decide, we created What Triggers B2B Buyers in 2026: A Practical Playbook for B2B Marketers. This practical diagnostic is grounded in IDC buyer research and outlines five tests that every message must pass to be considered credible and actionable by today’s buyers. If a message fails two or more tests, it is not necessarily wrong; it is simply misaligned with the buyer’s current needs.

For more:

  1. Explore the Proof Playbook to see whether your messaging proves relevance early or asks buyers to trust it too soon.
  2. Move beyond claims and into evidence buyers recognize as relevant to their situation. Explore how IDC Business Value helps turn relevance into momentum.
  3. Reach out to a market expert at IDC to explore analyst-led validation solutions, market data and b2b buyer research

IDC - -

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International Data Corporation (IDC) is the premier global market intelligence, data, and events provider for the information technology, telecommunications, and consumer technology markets. With more than 1,300 analysts worldwide, IDC offers global, regional, and local expertise on technology and industry opportunities and trends in over 110 countries. IDC’s analysis and insight help IT professionals, business executives, and the investment community make fact-based technology decisions and achieve their key business objectives.

There is an old adage that you may have heard of: Those who fail to plan, plan to fail. While we are not in the mode of advocating for not being prepared to face off against cyber attackers, we do live in an age where AI is being used by cyber-criminal groups and nation-state actors. Their use of AI is causing the time metrics of how quickly cybersecurity defenders have to response to be measured in smaller doses like minutes, rather than the months, weeks, days and hours of a not-so long-ago time frame.

Cybersecurity, like many other technology domains, does not exist in a static environment. It’s mission statement, after all, is to protect digital assets. Assets that are constantly changing in who and what they are, as well as the human and increasingly not-so human identities that are associated with them. Unfortunately, the tactics, techniques and procedures that attackers use to deploy their craft has evolved with the outcome being faster attacks that are harder to detect and respond to.

Cybersecurity playbooks—the concise, predefined sets of technical and procedural steps that guide teams through detecting, responding to, and recovering from security incidents with speed and consistency—are one of the foundation pillars of security operation centers (SOCs). In the good old days prior to AI enhanced attacks, organizations that had a good threat intelligence capability could often map out how an attack could play out and use it to apply the appropriate responses. This traditional approach—relying on static, manually updated playbooks—can no longer keep pace with the speed and sophistication of modern attacks.

Recent IDC research predicts that by the first half of 2027, 85% of detection and response playbooks will be generated dynamically at the time a SOC alert is triggered. This marks a fundamental shift in how organizations approach incident response, risk management, and operational resilience.

Why dynamic playbooks matter

Dynamic playbooks, powered by AI, offer several advantages over static guides:

  • Real-time adaptation: Playbooks are tailored to the specific context of each alert, factoring in the latest threat intelligence, asset configurations, and business priorities.
  • Reduced analyst burden: SOC teams, especially those with limited resources or complex infrastructures, are freed from the time-consuming task of manually updating playbooks every six months or year.
  • Consistent, effective response: Automated playbook generation ensures that responses are both standardized and contextually relevant, reducing the risk of outdated procedures and missed steps.

The risks of relying on static playbooks

Organizations that rely on static or infrequently updated playbooks face several challenges:

  • Stale procedures: As environments change, playbooks can quickly become obsolete, leaving gaps in coverage and increasing risk.
  • Resource drain: Maintaining playbooks is a significant overhead, particularly for smaller SOC teams already stretched thin.
  • Inconsistent response: Outdated playbooks can lead to inconsistent handling of threats, undermining both security and compliance.

IT impact: Automation, integration, and innovation

The move to dynamic playbooks will reshape SOC operations:

  • Automation: AI-driven systems will automate the creation and updating of playbooks, integrating seamlessly with detection tools and incident response platforms.
  • Integration: Dynamic playbooks will pull data from across the IT ecosystem—threat intelligence feeds, asset inventories, and business process maps—to ensure responses are both comprehensive and relevant.
  • Continuous improvement: Machine learning models will refine playbook logic over time, learning from past incidents to improve future responses.

The continuous improvement cycle for AI generated paybooks

As your organization transitions to dynamic, AI-driven playbooks, validating their efficacy becomes a critical operational and strategic priority. Ensuring that automated playbooks deliver reliable, context-aware responses is essential for maintaining security posture and regulatory compliance.

Key approaches to validation

  • Simulated incident testing: Regularly run tabletop exercises and red team simulations using AI-generated playbooks. Evaluate how the playbooks guide response actions, adapt to evolving scenarios, and support decision-making under pressure.
  • Continuous feedback loops: Integrate analyst feedback directly into the playbook generation process. Encourage SOC teams to annotate playbook steps, flag ambiguities, and suggest refinements, enabling the AI to learn and improve over time.
  • Compliance and audit reviews: Ensure that AI-generated playbooks align with regulatory requirements and industry standards. Conduct periodic audits to verify that automated responses meet documentation, reporting, and escalation protocols.
  • Peer benchmarking: Collaborate with industry peers and professional services firms or your managed security services provider (MSSP) to benchmark playbook performance. Leveraging external expertise to validate logic, coverage, and adaptability against best-in-class practices is a prudent path forward by utilizing expertise beyond your physical or virtual 4 walls of your organization.

Download the full IDC FutureScape: Worldwide Security and Trust 2026 Predictions report to understand the predictions shaping cyber resilience, AI governance, and enterprise trust over the next five years.

Craig Robinson - Research Vice President , Security Services - IDC

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Craig Robinson is a Research Vice President within IDC’s Security Services research practice, focusing on managed services, consulting, and integration. Coverage areas include Managed Detection and Response services, Cyber Resilience, and Incident Readiness & Response services.

国务院新闻办公室于2026年1月19日上午10时举行了新闻发布会，国家统计局局长康义，国家统计局新闻发言人、总经济师、国民经济综合统计司司长付凌晖介绍了2025年国民经济运行情况。本文基于此次会议发布的数据给出分析。

核心观点

2025 年中国经济运行并非一次周期性下行，而是一次关键性的结构性切换。尽管 2024 年与 2025 年的 GDP 增速均保持在 5.0% 左右，但驱动增长的内在动能已发生显著变化：固定资产投资由正转负，制造业投资大幅放缓，通缩压力仍然存在；与此同时，信息服务业投资快速增长，高技术出口表现突出，消费延续结构性修复，人口与劳动力约束明显加剧。

IDC 认为，2025 年实际上完成了中国经济从“投资拉动型增长”向“结构再平衡、效率驱动型增长”的关键过渡。这一变化将深刻影响 2026 年的市场环境，使其呈现出总体温和增长、结构性高度分化的特征。

对 ICT 市场而言，这意味着一个明确转向：从以资本开支和规模扩张为核心，转向以可量化 ROI 为导向、以软件和 AI 为核心的价值创造模式。能够顺应这一结构性转变的厂商与企业将持续跑赢市场，而仍停留在传统扩张逻辑中的参与者将面临更大压力。

一、宏观背景：增速保持稳定，结构分化加剧

2024 年与 2025 年，中国经济均实现了约 5.0% 的 GDP 增长目标，但增长质量与结构已明显不同。

2024 年的经济增长在四季度明显回升，反映出政策支持、基础设施投资以及制造业阶段性复苏的拉动效应；而 2025 年则呈现出“前高后低”的增长节奏，季度 GDP 增速逐步回落，反映出企业投资信心与项目预期趋于谨慎，尤其是在大型资本性项目方面。

从产业结构看，2025 年第三产业占比继续提升，第二产业贡献趋于稳定，表明中国经济正进一步迈向以服务业和知识密集型产业为主导的发展阶段。这一趋势对技术需求结构具有深远影响。

二、2024–2025 年的六大关键结构性变化

IDC 从宏观与产业数据中识别出六项对未来市场具有决定性影响的变化。

1、固定资产投资由“正增长”转为“整体收缩”。2024 年固定资产投资同比增长 3.2%，而 2025 年则下降 3.8%，出现明确拐点。基础设施投资回落，制造业投资几乎停滞，房地产投资进一步下探。这既是周期性谨慎的结果，也是结构性调整的体现：地方财政约束趋紧，企业资本纪律增强，传统投资项目的回报率持续下降。

市场含义：以大规模资本开支为主要驱动的市场空间正在系统性收缩。依赖“重资产、重工程、重建设”的行业与技术领域，在 2026 年仍将面临持续压力。

2、制造业投资急剧放缓，但并未系统性下滑。制造业投资增速从 2024 年的 9.2% 大幅回落至 2025 年的 0.6%，反映出企业在需求不确定和利润承压背景下，对扩张计划进行重新评估。但与此同时，高技术制造业和装备制造业的工业增加值仍保持相对较快增长，显示制造业投资并非全面退潮，而是向高技术、高附加值领域高度集中。

市场含义：2026 年制造业相关 ICT 需求将呈现“客户数量减少、单客户深度提升”的特征，重点集中于具有明确效率与质量目标的先进制造企业。

3、信息服务业投资成为逆周期结构性亮点。与整体投资趋势形成鲜明对比的是，2025 年信息服务业投资同比增长超过 28%。这表明在资本整体趋紧的背景下，政府与企业仍在持续加大对数字基础能力的投入。这类投资并非追求规模扩张，而是围绕效率提升、韧性增强与长期竞争力构建展开。

市场含义：云服务、数据平台、AI 能力、软件基础设施将成为 2026 年最具确定性的增长板块之一。

4、消费温和修复，但升级特征显著。社会消费品零售总额增速从 2024 年的 3.5% 小幅提升至 2025 年的 3.7%。尽管整体恢复节奏仍然温和，但消费结构发生明显变化。通讯器材、文化办公用品、体育娱乐用品及智能家电等品类实现两位数增长，表明消费者并非简单扩大支出，而是向数字化、智能化和体验型消费升级。

市场含义：2026 年与消费相关的 ICT 机会将更多集中在零售数字化、全渠道运营、智能供应链以及 AI 驱动的客户运营领域。

5、出口保持韧性，增长质量持续改善。2025 年出口增速虽略低于 2024 年，但仍保持较高水平，高技术产品出口同比增长超过 13%。对“一带一路”国家出口以及民营企业出口占比持续提升。这表明中国外向型竞争力正在由规模优势向技术与价值优势转变。

市场含义：2026 年，企业“走出去”将继续成为 ICT 市场的重要增长来源，带动跨境云架构、数据合规、全球供应链系统与安全能力的需求。

6、人口下降明显加速，劳动力约束显性化。2025 年人口减少规模明显扩大，而城镇化率持续提升。劳动力供给约束已从长期趋势转变为现实挑战。这一变化强化了企业对自动化、数字化与智能决策的迫切需求。

市场含义：到 2026 年，“用技术替代人力、用智能提升效率”将从选择项转变为企业经营的核心议题。

三、对 2026 年 ICT 市场整体影响的基本判断

IDC 预计，2026 年中国 ICT 市场将进入一个总量温和增长、结构高度分化的新阶段。

从“扩张型建设”走向“价值型优化”。市场需求的核心将从“新系统建设”转向对既有数字资产的深度利用与价值释放。项目审批将更加关注成本节约、效率提升和回报周期。

从硬件导向转向软件与 AI 主导。硬件密集型项目面临更严格的预算约束，而软件、云服务、数据与 AI 应用将在企业 ICT 支出中占据更高比重。

从一次性交付转向持续价值交付。订阅制、按量付费和结果导向型（Outcome-based）模式将更受欢迎。企业将优先选择能够持续创造业务价值的技术伙伴。

四、战略建议

IDC 认为，2026 年不是高速反弹之年，而是结构清晰之年。中国 ICT 市场的赢家，将是那些真正理解并顺应新经济逻辑的参与者：效率优先于规模，智能优先于扩张，韧性优先于速度。这一转型也将为 2026 年之后更具可持续性的创新增长周期奠定基础。

对 ICT 厂商，从“技术能力叙事”转向“业务结果叙事”，突出可量化 ROI；加大对行业 AI、数据与场景化解决方案的投入；强化对企业出海和全球化运营的支持能力。

对行业用户，将 ICT 投资评估标准从功能完整性转向业务影响与效率改善；优先布局 AI 与数据平台，提升自动化与决策能力；将数字化转型与组织、人才和流程重构协同推进。

IDC相关研究

• 中国智能经济演进趋势与数智化商机5大洞察, Jan 2026, Doc# CHC53833826

• IDC FutureScape: 全球AI驱动的业务战略2026年预测：中国启示,  Dec 2025, Doc # CHC53834026

• IDC FutureScape: 全球CEO议程2026年预测（中文版），Jan 2026, Doc# CHC53833926

• IDC FutureScape: 2025年全球I行业预测（中文版），Jan 2026，Doc# CHC53858725

• 2026中国两会政府工作报告对AI大转型和ICT市场的影响, 即将发布

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。

Lianfeng Wu - Vice President - IDC

Analyst Profile
Mr. Wu Lianfeng, the Vice President and Chief Research Analyst of IDC China, has more than 25 years of experience working in the IT industry. Since joining IDC in 2000, Mr. Wu has extensive research and consulting experience in the areas of overall ICT market, vertical industry market, Internet and new media, smart connected devices, software and service outsourcing, digital transformation, digital economy, and emerging technology, among others. In recent years, Mr. Wu has been leading IDC China's digital transformation research and event. In 2017, he started to build the CXO circle excellence club, the vision of which is to help industry CXOs transform from good to excellent. Mr. Wu holds monthly offline activities and publishes daily articles that focus on digital transformation: business trends, technology trends, industry trends, organizations, and people role trends. Mr. Wu also worked with IDC global analysts to lead China's annual ICT direction forum and Top 10 Predictions (IDC FutureScapes) forum, providing industry forecasts of the latest development directions and business opportunities. At the same time, Mr. Wu works with a team of analysts to explore and discover new research topics and build thought leadership in the ICT market. Recent research areas he has delved in include Future of Work (FoW), Future Industry, Smart City, and DevOps, among others. Mr. Wu is also a guest speaker in all kinds of top ICT summit, CIO summit, and industry digital transformation summit. He gives nearly 50 speeches every year, which greatly promotes the application and development of digital technology in the industry. Prior to joining IDC, Mr. Wu worked with China Academe Launch-vehicle Technology (CALT), China Hewlett-Packard Co. Ltd., Jardine Pacific (JOS) Information Technology Co. Ltd., accumulating 9 years of working experience in the field of IT and telecommunications. Mr. Wu holds an MBA from the University of International Business and Economics in Beijing, a Master's degree in Engineering from China Academy of Launch Vehicle Technology, and a bachelor's degree in Engineering from the University of Electronic Science and Technology.