近日,国家网信办、国家发展改革委、工业和信息化部联合印发《智能体规范应用与创新发展实施意见》(以下简称《实施意见》)。这是国家层面首次针对智能体这一人工智能产品形态,给出覆盖技术底座、标准协议、场景应用、安全治理和产业生态的系统性政策框架。这意味着企业级智能体从过去的小规模技术试点阶段,正式进入以治理、协同和规模化为核心的新竞争周期。

从IDC的视角看,《实施意见》把技术底座、安全治理、应用牵引和创新生态构建成了一张全局路线图。技术与标准构成发展基础,分类分级与权限管控划定行为边界,五大类典型场景提供规模化落地入口,开源与供需平台则负责连接研发与市场。

在IDC预测中,2026年和2027年将是是中国企业场景中的活跃智能体数量增速最快的两年,单年同比增长超过200%,并将在2031年达到3.5亿个活跃智能体,2026-2031年CAGR达135.3%(《中国智能体规模预测,2026-2031》)。而《实施意见》这样的智能体国家顶层政策,也将进一步加速智能体走向规模化的应用。

活跃智能体数量增长反映的是企业部署广度,也会直接放大运行、权限、审计和治理压力。一个企业内部只有几个智能体时,治理方法更接近单个项目的管理;当Agent数量进入快速增长周期,治理就会变成IT架构问题。身份、权限、审计、工具调用、异常回退和跨系统协同,都会从项目交付细节变成平台能力要求。

《实施意见》放在这个阶段变化中看,正是把这些问题推到了产业议程前台,也让企业级Agent从小规模试点建设进入更正式的IT治理和产业协同框架。对企业来说,Agent会更快进入正式IT治理体系;对技术供应商来说,产品竞争也会从功能构建,延伸到运行、治理、评测和生态分发能力。

场景牵引推动Agent规模化复制

《实施意见》明确提出,要围绕科学研究、产业发展、提振消费、民生福祉和社会治理五大方向,打造19个典型应用场景。政策并没有停留在技术倡导层面,而是把智能体放进具体的任务环境里,通过典型场景牵引技术验证、产品迭代和应用落地。

智能体与上一阶段生成式AI应用的差别,就在于它需要进入业务环节端到端的完成任务。生成式AI更多围绕内容生成、知识问答和辅助分析展开,智能体则需要调用工具、访问系统、执行动作,并对结果产生影响。只有进入真实业务场景,任务边界、数据条件、系统接口、权限控制和结果责任才会被定义出来,才能真正落地出好用可用的智能体。

当前智能体在企业业务中的渗透率依然较低,仅有18%(IDC Syndicated Survey 2026: China AI Agents Market)。智能体没有在企业业务中真正规模化应用起来,核心在于缺少清晰的切入场景和可复用的落地路径。政策在这个角度提供了场景牵引,通过典型应用场景帮助智能体走向业务落地,并进一步形成规模化应用。

开发平台将会从构建工具延伸为运行和治理底座

《实施意见》在夯实发展基础部分提出,要提升智能体任务理解、任务规划、工具使用、长期记忆等核心能力,推动智能体互认互通和群体协同,同时完善底层框架、功能组件以及研发、测试、部署、运维工具链。

这与IDC观察到的,智能体平台厂商产品迭代的趋势高度吻合。2025年头部平台的产品形态已经从单纯的开发工具演进为涵盖规划、开发、测试、发布、观测、优化、治理的完整系统,企业也越来越关注智能体深入业务后,智能体是否是否可观测可治理,以及如何确保智能体执行任务的稳定性安全性(具体见IDC即将发布的《中国智能体开发平台市场份额,2025》中的重大市场变化章节);同时,智能体规模化进入企业之后,企业内部多种来源的智能体(应用内智能体、低无代码配置智能体、独立智能体、定制智能体)如何统一管理和协同也会成为企业下一个阶段的关键需求。

《实施意见》中关于工具链、互认互通、群体协同的部署,将把这些工程化能力进一步推向产品标配。这些要求会转化为智能体开发、测试、运行、观测和治理的一整套工程能力,并逐步沉淀为智能体开发平台的核心竞争力。

安全治理成为Agent进入业务流程的准入条件

《实施意见》用相当篇幅讨论产品准则、决策权限、行为管控、内生安全、供应链安全、应用衍生风险、分类分级治理与合规服务体系。安全合规既是政策关注的重点,也是企业实际落地中的关键门槛。IDC在2026年最新智能体调研中发现,62%的企业把数据权限与安全合规列为智能体跨系统执行的首要障碍;而在智能体平台引入目标上,58.7%的企业把治理与合规放在首位(IDC Syndicated Survey 2026)。

智能体只有安全可控,才能正在被企业放心的纳入业务流程中。因此智能体的权限治理、行为审计、决策追溯、安全护栏等能力需逐渐的沉淀为智能体或智能体平台的标准能力。在金融、医疗、政务、公共安全等高合规领域,这种能力更是智能体能否落地的前提条件。

标准化分发和生态将重塑智能体产业

《实施意见》在标准协议和创新生态部分提出,要建立智能体标准体系,加强智能体互联协议(AIP)等关键标准推广应用,探索建设智能体注册平台,提供数字身份管理、检索发现、能力声明等服务,并推动智能体软件商店和行业供需信息发布平台建设。

智能体标准体系的成熟会明显推进智能体,尤其是独立智能体的规模化进程。在IDC 2026年初的预测中,独立智能体是增长弹性最高的智能体形态之一,其爆发主要依赖标准协议和智能体生态的成熟(《中国智能体规模预测,2026-2031》)。AIP、注册平台、软件商店和供需平台逐步成型后,独立智能体的构建、分发和使用门槛都会随之下降,独立智能体尤其是像Xclaw这类智能体产品有机会更快进入企业规模化应用场景中。

行动建议

面向企业用户

  • 把安全治理能力列为智能体规模化前置条件。 企业应尽早建立场景分级、任务边界和权限管理机制。在金融、医疗、政务等高合规领域,应参考政策要求提前规划备案、检测、安全评估以及第三方权威评测,低风险场景也应建立合规自测和风险报告机制。
  • 建设智能体统一运营和协同体系随着智能体的规模化应用,企业内部将同时存在多类智能体,因此企业在平台选型和架构规划中,需要提前考虑智能体注册、统一身份、统一权限、统一审计、统一成本管理和跨智能体协同能力。

面向技术供应商

  • 安全治理需成为产品基础能力。智能体安全治理相关的决策权限边界、行为管控、最小权限、全链路审计、异常检测和回滚机制等能力,会逐步成为企业采购时的核心指标。
  • 建立智能体全生命周期能力。智能体全生命周期涉及到的开发、测试、部署、运行、评测、优化和治理等均需建立起相应的平台级能力,为未来企业级智能体规模化运营提供支撑。
  • 构建异构智能体的统一管理能力。智能体规模化时代,企业智能体的来源多样,平台需要建立统一的智能体身份、权限、审计、成本和调度能力,帮助企业把分散的智能体管起来,建设企业级的智能体运行和治理入口。
  • 关注智能体标准协议和平台商店的建设。AIP、智能体注册平台、软件商店和供需平台会推动智能体的标准化分发。建议厂商提前规划产品路线,并参考政策要求开展第三方功能、性能、质量与合规评测。

分析师观点

孙振亚,IDC中国分析师:《IDC中国研究经理孙振亚表示,《实施意见》的出台,标志着中国智能体产业从技术和市场自发探索,进入政策框架下的规模化扩张阶段。政策一方面通过分类分级、决策权限、行为管控和合规服务体系,为企业部署智能体提供可预期的合规路径;另一方面通过标准协议、注册平台、软件商店、第三方评测和供需对接机制,为智能体走向跨系统协作和产品化分发铺设了基础设施。

与IDC同行,抢占智能体规模化的战略先机

《实施意见》的发布,标志着企业级智能体正式进入治理驱动、生态协同的新阶段。但对企业来说,仍然存在诸如“如何在自己的行业与业务中,识别高价值的智能体场景”、“如何在平台选型、安全治理、标准化对接等关键决策上少走弯路”等现实挑战。

IDC长期追踪全球与中国智能体市场的发展脉动,拥有覆盖技术供应商、企业用户、投资机构的多维研究体系。已发布《中国Agent基础设施平台/执行平台技术评估,2026》、《中国智能体规模预测,2026—2031》、《IDC Market Glance:中国AI Agent市场概览,1Q26》等智能体报告,即将发布《Agent企业最佳实践与场景精选(ROI视角)》、《中国智能体开发平台市场份额,2025》、以及针对Xclaw产品进行评估的《企业级通用Agent助手评估》等一系列重磅报告,并可为企业提供定制化的场景评估、平台选型与治理成熟度诊断服务。

欢迎您联系IDC中国智能体研究团队,获取最新研究成果,或与分析师进行交流。让我们助您在智能体规模化的浪潮中,走得更稳、更快、更远。

请点击此处与我们联系。

Zhenya Sun

Zhenya Sun - Research Manager

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…

视角决定格局。​若仍将公有云安全视作单一的技术模块,便无法窥见其真正的战略全貌。

IDC 2025全球数据指明:公有云安全已跨越单纯的合规门槛,跃升为企业的数字生存底座。​ 它不再是隐匿于防火墙之后的附属插件,而是支撑AI规模化落地、护航业务出海、乃至抵御周期波动的核心底层资产。

三个信号值得认真对待:

  • 信号一:全球公有云安全支出超1100亿美元,增速超20%,是IT支出中最坚硬的赛道
  • 信号二:中国市场云安全收入增速普遍高于云基础设施本身增速,客户正在从为资源付费向为安全付费跨越
  • 信号三:随着AI业务场景的深化,云原生安全已成为大模型落地的隐性门槛,若缺乏深度集成的原生安全体系,企业很难安心地将高价值数据与应用托管其上

一、全球浪潮:安全正在重新定义云的价值

站在2026年的时间节点回看,公有云早已不再仅仅是企业的外挂资源,而是数字化生存的电力系统。

2025年,全球企业正在混合云和多云架构中疯狂奔跑,全球公有云安全市场连续多年保持每年20%以上增速,2025年支出飙升至1100亿美元。

在全球范围内,AWS、Microsoft Azure和Google Cloud等头部玩家正经历着前所未有的技术迭代。他们不再仅仅提供安全插件,而是通过硬件级隔离、身份边界重塑以及AI驱动的情报分析,将安全深度嵌入云原生底座。

与此同时,以Palo Alto Networks、CrowdStrike、Wiz和Zscaler为代表的安全巨头也在快速进化。他们正推动从单一工具向CNAPP云原生应用保护平台和代码到云(Code-to-Cloud)的统一平台转型,试图在多云环境中建立一道跨厂商、自动化的全栈防御屏障。

在这场技术长跑中,谁能率先解决复杂环境下的可见性真空,谁就将定义下一个十年的云安全标准。

二、中国实践:快速跟进与创新,但仍有差距

2025年,中国云计算市场规模持续扩大,云安全产品的营收增速普遍高于云基础设施本身的增速,显示出客户从买资源向买安全的意识跨越。

作为国内市场的领头羊,阿里云已将其安全产品线全面推向智能体时代,通过升级Agentic SOC,利用大模型实现了超过80%的安全事件自动响应。华为云演进为AI原生安全架构,通过其安全大模型实现对AI算力集群的实时监测。腾讯云充分发挥攻防领域的深厚沉淀,在内容风控、反欺诈、DDoS高防等领域形成了极高的市场占有率。

传统网安巨头正通过云地协同战略,在云安全管理平台C-SOC市场占据核心位置。深信服、奇安信等厂商通过与公有云厂商的深度技术绑定,成功实现了从卖防火墙硬件到卖云安全服务的转型。

但不可忽视的是,相比于全球安全厂商,中国安全厂商目前在公有云安全市场仍需发力。虽然在私有云和特定业务场景中展现了卓越的实战能力,但在全球化的SaaS安全标准、跨云的Agentless治理精度、以及安全左移的代码级闭环上,国内厂商仍有巨大的进阶空间。

三、商业启示:为什么公有云安全是好生意

海外巨头的成功路径为中国厂商提供了极具诱惑力的商业模板:

极致的用户粘性

安全业务之所以具备IT板块中最高的粘性,是因为它正逐渐从外部挂载的锁具演变为深度嵌入企业架构的神经系统。一旦部署,更换安全供应商不再是简单的卸载与重装,而是一场涉及底层策略重写、合规记录迁移以及全员操作习惯重塑的伤筋动骨式手术。

抵御周期的现金流

与传统网络安全硬件的订单式生存不同,Wiz和CrowdStrike的成功路径证明了SaaS化订阅模式在资本市场中的统治力。这种模式具有极强的抗周期属性,即便在经济下行期,企业可能会削减新员工入职或新业务线扩张,但绝不敢轻易关掉云端的安全防护。

掌握AI与全球化的通行证

在当前的全球技术格局下,拥有顶级的云安全能力已不再仅仅是为了防御黑客,它更是企业进军AI云和全球化市场的入场券。只有具备原生云安全能力的厂商,才能解决大模型在云端训练、推理过程中的数据投毒和提示词注入风险。中国厂商若能构建起符合国际主流标准的云安全体系,本质上就是为中国企业的全球化交付提供了一套合规标准化的底座。

四、IDC:不止于数据,更是决策指南

IDC已发布 IDC中国半年度安全软件数据跟踪报告——公有云,2025H2,其中包含公有云部署模式下的数据安全软件、终端安全软件、身份和访问控制软件、软件安全网关、安全分析和运营软件、漏洞管理软件等子市场的厂商数据跟踪。

上述商业逻辑的兑现,最终需要落地到真实的市场数据中加以验证。化繁为简,洞见未来。 本次报告透过公有云安全市场不同赛道中的厂商营收数据,为您剥离噪音、还原真相,助您在复杂的市场环境中精准锚定业务增长点与投资机遇。无论您是寻求赛道突围的企业,还是布局未来的投资者,皆能在此获取专属的行动指南。

具体而言,可以为不同角色的参与者提供以下支持——

云厂商:帮助您精准识别自身安全产品的市场位次,明确自身赛道定位与竞争位置。

安全厂商:帮助您应找准生态位,重点锚定高增长赛道,避免无效内卷;同时积极融入头部云厂商生态,通过技术集成与联运合作,借助云市场流量触达客户。

企业客户:为您提供了客观的供应商评价维度,帮助 CIO 识别哪些厂商在特定公有云安全领域具备长期投入和领先地位。

投资机构:报告是评估公有云安全景气度的核心参考,能够清晰展现公有云安全市场的天花板及各子市场的集中度,利用数据降低因信息不对称导致的投资偏差。

结语

公有云安全已经完成了从附加品到必需品的身份蜕变。在客户从买资源向买安全的意识跨越中,IDC 将持续通过系统性的研究,助力每一位参与者:看清坐标、捕捉先机、定义未来。

欢迎广大云厂商和安全厂商关注IDC公有云安全系列研究,如需进一步沟通或获取深度数据与战略洞察,请与IDC联系。

请点击此处与我们联系。

Joe Zhao

Joe Zhao - Senior Research Manager

Joe Zhao is a senior research manager of Enterprise Research for IDC China. He focuses on research and analysis of the China security market. Joe has more than 12 years of domestic and international work experience in ICT. Prior to…

What Happened in India’s Smartphone Market in Q1 2026?

India’s smartphone market shipments declined 4.1% year over year to 31.0 million units in Q1 2026, according to IDC’s Worldwide Quarterly Mobile Phone Tracker. Rising memory prices drove brands to front-load channel inventory ahead of anticipated cost escalations, pushing shipment volumes above initial expectations. However, underlying consumer demand remained subdued — weighed down by a typical post-festive slowdown, elevated device prices, and cautious spending sentiment. Despite falling volume, the market grew 5.8% in value terms, underscoring India’s ongoing shift from volume-led to value-driven growth.

Why It Matters

The Q1 2026 data signals a structural turning point for one of the world’s largest smartphone markets. Brands, retailers, and investors should take note:

  • Device makers relying on entry-level volume face shrinking margins and reduced market viability as memory costs continue to rise.
  • Consumers in sub-US$100 brackets are being pushed upmarket by necessity rather than aspiration — a trend that reshapes demand forecasting for 2026 and beyond.
  • The gap between channel inventory and actual consumer demand points to a near-term correction risk, particularly in affordable segments.

Market Dynamics: What Drove the Outcome?

The market’s performance was shaped by three converging forces:

  • Memory cost inflation → entry-level collapse: A global memory shortage drove up component prices across newly launched and existing models alike. Brands could no longer sustain profitability in the sub-US$100 tier, leading to reduced model availability and weaker channel participation. Shipments in this entry-level segment fell 59% YoY, with segment share collapsing from 18% to just 8%.
  • Forced premiumization → mass-budget gains: Consumers who could no longer find affordable sub-US$100 options migrated upward. The mass-budget segment (US$100–200) grew 10% YoY, expanding its share from 39% to 45% — driven more by eroding entry-level value than deliberate upgrade intent.
  • Promotional pullback → constrained demand recovery: Rising input costs limited brands’ ability to deploy the aggressive discounting and channel-led promotions that historically fuel mass-market growth. With fewer price interventions, underlying consumer demand stayed soft, particularly online, where shipments fell 14% YoY and share declined from 42% to 38%.

Price Band Performance

  • Entry-level (sub-US$100): −59% YoY; share fell from 18% to 8%
  • Mass-budget (US$100–200): +10% YoY; share rose from 39% to 45%
  • Entry-premium (US$200–400): −3% YoY; share edged up from 26% to 27%
  • Mid-premium (US$400–600): +29% YoY; share rose from 6% to 8%
  • Premium (US$600–800): +32% YoY; share rose from 4% to 6%
  • Super-premium (US$800+): −1% YoY; 7% share maintained

India Smartphone Market at a Glance – Q1 2026

  • Total shipments: 31.0 million units (−4.1% YoY)
  • Average selling price (ASP): US$302 (+10.4% YoY) — a record high
  • Market value growth: +5.8% YoY despite volume decline
  • Offline channel: 62% share (up from 58%); +3% YoY
  • Online channel: 38% share (down from 42%); −14% YoY
  • Top five brands: vivo (#1), Samsung (#2), OPPO (#3), Apple (#4), Motorola (#5, new entrant)

Analyst Insight

“Average selling prices increased 10.4% YoY to a record US$302 in Q1 2026, driven by persistent memory cost inflation across both newly launched devices and existing models. Unlike previous quarters, aggressive discounting and channel-led promotional schemes remained limited, as rising input costs constrained brands’ ability to stimulate demand through pricing interventions. The current environment signals a broader structural shift in the market, where brands may increasingly need to rely on product differentiation, financing offers, and premiumization strategies rather than price-led promotions to drive demand through the remainder of 2026.” said Aditya Rampal, senior research analyst, Devices Research, IDC Asia Pacific.

Note: This chart/table shows data by IDC’s Brand field. Company ranking may differ where Companies own more than one Brand.

*Figures in tables/charts rounded to the first decimal point.

IDC Outlook: What’s Next?

The first half of 2026 is expected to remain relatively resilient as brands draw on existing component inventories to partially offset rising memory costs. However, brands are increasingly revising annual shipment targets downward, with channel inventory managed cautiously — especially in entry-level segments.

Recovery in the second half will depend on how effectively brands balance product innovation, pricing strategy, and cost management against sustained component inflation and uneven consumer demand.

  • What could accelerate growth? Stabilization of memory prices, new financing models, and festive-season promotional activity.
  • What could slow it down? A prolonged memory shortage, further rupee depreciation, and continued weakness in mass-market consumer confidence.
  • What should readers watch next quarter? Whether brands can close the gap between channel inventory and end user sales, and how second-half pricing strategies unfold.

“In a value-conscious market like India, consumers have traditionally delayed purchases in anticipation of festive discounts and promotional offers. However, that pattern is unlikely to hold in the current cycle. With the global memory shortage expected to continue into 2027 and rupee depreciation adding further cost pressure, smartphone prices are set to rise further across segments. Consumers considering an upgrade may find better value in purchasing sooner, as pricing pressures are expected to intensify over the coming quarters,” said Upasana Joshi, senior research manager, Devices Research, IDC Asia/Pacific.

Frequently Asked Questions

Why did the market decline despite strong premium demand?

Growth in higher price bands could not offset the sharp collapse of the entry-level segment. While brands pushed inventory ahead of anticipated price hikes, weak consumer demand and limited promotional activity constrained actual market absorption, leaving supply-side momentum ahead of end-user demand.

Which brands benefited most in Q1 2026?

Motorola and OPPO were the only top-five brands to register YoY growth, with Motorola entering the top five for the first time. Apple held fourth place with a 9% shipment share while leading the market by value with a dominant 28% share. Notably, despite a 5% YoY decline in Apple shipments, the iPhone 17 alone contributed 4% of total smartphone volumes — underscoring Apple’s sustained premium strength amid broader demand softness.

What risks could impact the market in 2026?

A prolonged memory shortage, rupee depreciation, and weakening mass-market viability are the key near-term headwinds. Recovery will increasingly depend on brands’ ability to drive demand through financing and affordability-led models, rather than the aggressive price-led promotions that have historically fueled market growth.

-Ends-

About IDC

International Data Corporation (IDC) is the premier global provider of trusted technology intelligence, advisory services, and events. With more than 1,000 analysts worldwide, IDC offers global, regional, and local expertise on technology, IT benchmarking and sourcing, and industry opportunities and trends in over 100 countries. IDC’s analysis and insights help IT professionals, business executives, and the investment community to make fact-based technology decisions and to achieve their key business objectives. To learn more about IDC, please visit  www.idc.com. Follow IDC on Twitter at @IDC and LinkedIn. Subscribe to the IDC Blog for industry news and insights.

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Upasana Joshi

Upasana Joshi - Research Manager

Upasana Joshi is an Research Manager for Channel Research at IDC India. Based in Delhi, Upasana is primarily responsible for City Level Smartphone Tracker in India. The research involves analyzing the trends within Smart Phone domain, market sizing, brand performance…
Aditya Rampal

Aditya Rampal - Senior Research Analyst

Aditya Rampal is a senior research analyst for the India mobile market at IDC. He is responsible for end-to end mobile devices market research involving both primary and secondary research for smartphone and feature phones. Aditya analyzes trends within the…

Digital sovereignty is becoming a strategic priority across EMEA, reshaping how governments and enterprises choose infrastructure and network partners. This blog explores what the shift means for telcos, sovereign cloud, and AI infrastructure. 

Around the early 2010s, data residency was already part of the policy debate, but the infrastructure landscape was still more fragmented, and the issue had not yet become as central to cloud, AI, and national digital strategy as it is today. Telcos, regional ISPs, and a long tail of independent providers ran most of the hosting. The policy debates of the time already touched on carrier-neutral internet exchange points, peering, net neutrality, and data residency – how traffic moved between networks, where data was hosted, and who had jurisdiction over it. 

That picture has since changed twice over. 

First, market gravity shifted. A handful of hyperscalers and major social platforms absorbed most of the workloads, the storage, and the user attention. Hosting that used to sit inside national operators’ data centers consolidated into a few global clouds. 

Then policy caught up. Across EMEA, governments and regulators have moved data privacy, residency, and infrastructure control out of the compliance file and into national and regional strategy, with AI sovereignty layered on top, and geopolitical sovereignty sitting above all of it. 

For telcos, this is no longer a niche or optional conversation. It is actively shaping how enterprises and governments choose their network and infrastructure partners. 

How digital sovereignty is influencing buying decisions in EMEA 

The signal is clear. IDC’s EMEA Enterprise Communications and Collaboration Survey 2025 shows that, in response to geopolitical uncertainty, 28% of organizations are now more likely to use network service providers based in their own region, 27% are increasing their use of sovereign network services, and 26% are diversifying their network providers. Network infrastructure sits at the center of this shift, 70% of organizations cite sovereign controls over network infrastructure software as the most important component of technical sovereignty.  

The shift is showing up in budgets too. IDC’s 2025 Future Enterprise Resiliency & Spending Survey shows that nearly 30% of telcos plan to migrate applications from public cloud to country sovereign cloud infrastructure in 2026, with cybersecurity, regulatory compliance, and operational resilience among the top drivers of increased telco spending. 

Where AI and digital sovereignty converge 

AI has become a central thread in sovereignty conversations. The connection between cloud and AI needs has tightened, and sovereignty is now a recurring factor in both. IDC’s Worldwide AI and Generative AI Spending Guide Forecast (August 2025) projects AI and GenAI spending in EMEA telecommunications growing at a CAGR of 32% between 2024 and 2029, with telco AI spending set to treble by 2028. On the demand side, 53% of EMEA governments plan to increase their use of sovereign cloud for AI solutions, putting telcos squarely in the frame as infrastructure partners in sovereign AI ecosystems. 

The infrastructure shift is concrete. Among telcos, expanding data center capacity, AI inferencing (58%) and LLM training (54%) are the workloads driving most of the new build. Where these facilities sit, who certifies them, and who governs them is becoming a first-order strategic question. 

The role of telcos in sovereign cloud and AI ecosystems 

Governments across EMEA are pushing sovereign cloud and AI initiatives to take greater control of digital infrastructure and compute, and that is sharpening what buyers expect from their providers. 

Enterprise and public sector buyers are increasingly evaluating providers based on capabilities such as: 

  • In-country or in-region data centers  
  • Country-level certifications  
  • Freedom from lock-in 
  • Solutions to support operational resilience 
  • Sovereign controls of infrastructure  

Telcos are well placed to answer this list. National operators already own most of the underlying assets: in-country and in-region data centers, a regulatory and certification footprint, established government and enterprise relationships, and the connectivity layer itself. This is where telcos hold something cloud providers don’t; sovereign control over data in transit and the network layer itself. The bigger opportunity isn’t supplying pieces of someone else’s sovereign build. It’s pairing with sovereign cloud providers to deliver an end-to-end sovereign stack, data at rest and in motion, that neither side can credibly offer alone. 

What sovereignty looks like across Europe, the Middle East, and Africa 

Sovereignty is not a single play. The operator playbook looks different by sub-region. 

In Europe, regulation matters; and most of it now sits at EU level rather than national, but the top driver has shifted to protecting against extra-territorial data requests. Operators with strong domestic positions and certified infrastructure are best placed for both. 

In the Middle East, sovereignty is being driven top-down as national strategy. Governments are pairing sovereign cloud and AI ambition with serious capital, often through national champions, with major operators positioned as primary infrastructure partners. Established data localization regimes in some markets give operators a head start on dedicated capacity. 

In Africa, the story is data localization meeting infrastructure scale. National data protection frameworks are increasingly pushing data inside national borders, and pan-regional operators are expanding capacity that doubles as a sovereign-cloud foundation. 

Across all three, the playbook converges on regional infrastructure, certifications, simpler portfolios, and active ecosystem participation. Operators are also working with fewer, more strategic partners, ones that can take end-to-end accountability. 

Why sovereignty is becoming a default expectation 

Sovereignty has moved from a compliance topic to procurement criterion. It now sits alongside performance, cost, and scalability. AI and platform-based models only sharpen the demand for control, transparency, and resilience. 

For telcos, this isn’t a niche compliance discussion. It’s a strategic play, redefining what credible infrastructure looks like across EMEA, and where operators sit in the sovereign cloud and AI stack. 

Explore the broader telecom trends shaping 2026 
 
Sovereignty is one of several trends shaping the telecom market. In the IDC eBook State of the Telco Market 2026, you’ll find detailed data, forecasts, and analysis on topics including sovereign AI, infrastructure investment, and evolving business models. 

Download the eBook to explore the data behind these developments and better understand how the telco landscape is evolving. 

If you’re currently evaluating how sovereignty requirements will impact your network, infrastructure, or partner strategy, our experts are happy to exchange perspectives. Whether you’re at an early stage or already executing, we welcome the conversation. Get in touch with our team to continue the discussion. 

Tolga Yalcin

Tolga Yalcin - Research Director, Telecoms and ICT Regulations, IDC Middle East, Turkey, and Africa (META)

Tolga oversees the telecommunications and ICT policy and regulations side of IDC’s syndicated research, custom consulting, and advisory services in the Middle East, Turkey, and Africa region. Tolga plays an integral role in all IDC telecommunications-related initiatives in the Middle…

For years, digital accessibility, the practice of ensuring that digital products and services can be perceived, understood, and used by everyone regardless of ability, was treated as a compliance checkbox. That framing is no longer adequate. AI is reshaping accessibility into a strategic capability, one that is adaptive, continuous, and embedded in how people work, interact, and innovate.

As AI-enabled work becomes the norm, accessibility is no longer about supporting a small subset of users. It is about ensuring that everyone, across physical, sensory, cognitive, and neurodiverse dimensions, can fully participate in increasingly digital and AI-mediated environments. In this context, accessibility becomes foundational to productivity, inclusion, and ultimately business performance. Accessibility is also part of company culture: involving disabled and neurodiverse individuals in co-design, not just testing, creates more robust and adaptable systems. Sustaining long-term impact also requires investment in skills and culture, training employees, fostering inclusive design practices, and making accessibility a shared responsibility across teams.

The opportunity: AI as a scaler of inclusion and innovation

AI introduces a powerful opportunity to rethink accessibility at scale.

First, it enables real-time content adaptation. Capabilities such as automatic captioning, transcription, translation, and alternative text generation allow organizations to dynamically tailor content to different user needs. AI can also adjust reading levels, restructure complex information, and personalize interaction styles, supporting a broader range of cognitive and sensory preferences.

Second, AI supports continuous accessibility operations. Traditionally, accessibility has relied on periodic audits and remediation efforts. AI-driven testing tools now allow organizations to embed accessibility checks directly into development pipelines, transforming accessibility into a continuous, iterative process aligned with DevOps cycles.

Third, AI helps democratize innovation. By making tools and workflows more accessible, organizations can engage a wider and more diverse talent pool, including neurodiverse individuals and those historically underserved by traditional work environments. This expands creative input, improves problem-solving, and strengthens organizational resilience.

Finally, AI enables data-driven accessibility insights. Organizations can use AI to analyze accessibility barriers, monitor usage patterns, and measure outcomes, linking accessibility directly to business metrics such as productivity, employee engagement, and customer satisfaction.

The pitfalls: Bias, complexity, and the risk of scaling barriers

Despite its promise, AI also introduces significant risks that organizations must actively manage.

One of the most critical challenges is bias in AI models. Many AI systems are trained on data and designed by teams that lack diversity. This can result in outputs that unintentionally exclude or disadvantage certain groups, particularly people with disabilities or non-standard interaction patterns. Without deliberate inclusion in design and testing, AI can reinforce existing barriers or create entirely new ones. Feedback loops that combine AI-driven insights with real user experiences are essential to countering this risk.

Another risk lies in inaccessible AI-generated content. While generative AI can produce fluent and polished outputs, these may still fail accessibility standards through improper structure, missing semantic cues, or formats that are difficult for assistive technologies to interpret. Auto-generated captions, for example, are often not accurate enough for compliance purposes.

The rise of agentic AI systems (autonomous AI that acts across workflows and applications without direct human instruction at each step) adds further complexity. If poorly designed, they can propagate inaccessible processes at scale, embedding friction into core operations rather than eliminating it.

There is also a governance challenge. As AI becomes embedded across systems, organizations must ensure clear accountability, transparency, and control over how accessibility preferences are handled, how decisions are made, and how user data is used.

Recommendations: Turning intent into impact

Organizations that want to lead in AI-enabled accessibility should focus on four key actions:

  • Prioritize accessibility as a design principle. Move from reactive compliance to proactive, accessible-by-design systems embedded in AI-enabled platforms and services.
  • Establish proactive AI accessibility governance. Integrate accessibility into AI governance frameworks early, ensuring inclusive workflows and avoiding costly retrofits.
  • Design for workforce adaptability and inclusion. Extend accessibility strategies beyond compliance to support diverse employee needs, including neurodiversity, aging workforces, and varying cognitive styles.
  • Act early to mitigate risk and maximize value. Early investment reduces remediation costs, strengthens trust, and positions accessibility as a strategic differentiator rather than a regulatory burden.

AI is redefining digital accessibility as a core element of how organizations operate, innovate, and compete. Those that embrace accessibility as a strategic priority will not only meet regulatory requirements but also unlock broader talent, improve user experiences, and build more resilient AI systems.

Erica Spinoni

Erica Spinoni - Senior Research Analyst, WW AI-Enabled Future of Work & EMEA Practice Lead

As member of the global FoW team, Erica also leads the group’s EMEA-focused research, exploring how new workplace models and technologies such as AI and automation are transforming employee experience and productivity across Europe, the Middle East, and Africa. She…
Amy Loomis, Ph.D.

Amy Loomis, Ph.D. - Group Vice President, Workplace Solutions

Amy Loomis is Group Vice President for IDC’s worldwide Workplace Solutions.  Amy leads a team of analysts focused on the evolving nature of human resources, skills development, collaboration, and leadership across the employee lifecycle. Her research into the Future of…
Melinda-Carol Ballou

Melinda-Carol Ballou - Research Director, AI Assurance, ALM, Quality & Portfolio Strategies

Melinda Ballou delivers insights into the future of AI assurance, the impact of AI, ML and agentic adoption on agile and digital work, resilience, quality, product and software engineering, the role of technology in business and culture, and the evolution…

If you’ve spent the last few years talking to enterprise IT buyers about cost efficiency, you weren’t wrong. That was the conversation. But over the past few months, things have clearly shifted.

The outbreak of war in the Middle East, with its direct impact on people and organizations in the region, as well as broader effects on energy costs and IT manufacturing supply chains, is a primary driver. At the same time, early AI buildout pressures on memory supply were already raising concerns.

Today, when CIOs and their teams make technology decisions, the question is no longer, “How do we optimize spend?” It’s, “How do we keep the business running when things break?”

This shift shows up clearly in data from two major surveys on IT priorities and spending plans conducted in February and again in March. Concerns about hardware supply constraints have increased by more than 15%, and geopolitical risk is rising quickly. Meanwhile, traditional cost pressures, while still present, are starting to take a back seat.

This is not because cost no longer matters. It is because cost is now seen as downstream. If systems go down, supply chains stall, or cyber incidents escalate, cost becomes secondary very quickly.

What are IT buyers most concerned about in 2026?

When you talk to IT leaders today, the tone is different. There is more urgency, more realism, and more skepticism. They are thinking about exposure:

  • Where are we too dependent on a single cloud region?
  • What happens if a supplier cannot deliver?
  • How quickly can we recover from a cyber event?

Increasingly, they recognize that these risks are interconnected. A geopolitical event can disrupt supply chains, which impacts infrastructure, which affects applications, and ultimately hits revenue.

That is why IDC is seeing a clear pivot toward resilience.

Cybersecurity has moved to the top of the investment list globally, not just as a defensive measure but as a core part of keeping operations running. At the same time, organizations are accelerating investments in multi-region cloud architectures and backup strategies. Cloud security and multi-region resilience are now leading priorities across every major region.

IDC is also hearing from CIOs about a growing push to reduce dependency. CEOs are placing more focus on diversifying suppliers across all parts of the business. CIOs are responding by exploring sovereign cloud options and rethinking how and where infrastructure is deployed.

AI has not disappeared from the agenda, but it is being reframed. It is no longer just about innovation. It is about using automation and intelligence to keep systems stable under pressure.

Put simply, IT buyers are trying to build systems that can bend without breaking.

What does this shift mean for IT suppliers?

For suppliers, this shift creates both risk and opportunity.

The biggest risk is continuing to sell the way you did before. Leading with performance benchmarks, cost savings, or incremental features will not resonate the same way.

The opportunity is much bigger. Buyers are actively looking for partners who can help them navigate uncertainty. They are asking tougher questions:

  • What happens if this service goes down in one region?
  • How quickly can workloads move?
  • Where are the hidden dependencies?
  • How exposed am I if conditions worsen?

If you can answer these questions clearly and credibly, you move from being a vendor to becoming a strategic partner.

How should IT suppliers respond to rising resilience demands?

The challenge is that resilience means something different depending on where you sit in the ecosystem. The common thread is this: you must show how your offering performs under stress, not just under ideal conditions.

Cloud providers: How to prove resilience beyond scale

For cloud providers, this is a moment to rethink the narrative.

Scale and efficiency still matter, but they are no longer enough. CIOs want to know how your platform behaves when a region is disrupted, connectivity is constrained, or workloads need to move quickly.

This means making multi-region resilience the default, not an add-on. It also requires transparency about risk exposure and greater flexibility around sovereignty and localization.

In short, you are not just selling capacity anymore. You are selling survivability.

SaaS providers: Why continuity is now a core differentiator

SaaS providers are increasingly part of the critical path of operations. If your application goes down, the business feels it immediately.

Buyers want reassurance. They want to understand your disaster recovery posture, regional architecture, and dependencies. They want to know how their data is protected and how quickly services can be restored.

The vendors that stand out will clearly articulate how they maintain continuity, not just deliver functionality.

IT and professional services firms: From transformation to readiness

For services firms, the conversation has shifted from long-term transformation to immediate readiness.

Clients still care about transformation, but right now they need help answering urgent questions: Where are we exposed? What should we fix first? How do we prepare for multiple scenarios?

There is a real opportunity to lead with practical, actionable support. Rapid assessments, scenario planning, and resilience design are where clients need help now.

Speed matters. Clarity matters even more.

Communications providers: Why network resilience is now critical infrastructure

Connectivity has always been important. Now it is critical infrastructure in the truest sense.

Organizations are looking for redundancy, alternative routing, and, in some cases, entirely new connectivity models, including satellite and hybrid networks.

The differentiator is reliability under pressure. If you can demonstrate that your network keeps people and systems connected when other options fail, that becomes a powerful advantage.

Infrastructure vendors: Delivering certainty in uncertain supply chains

Hardware vendors are facing a different kind of scrutiny.

Availability and certainty in delivery are becoming as important as performance. Buyers want to know not just what the system can do, but whether they can actually get it, deploy it, and rely on it.

Transparency into supply chains, flexibility in configurations, and the ability to adapt to constraints are becoming key differentiators. In this environment, certainty is value.

Why IT buying decisions are shifting from optimization to assurance

Stepping back, what we are seeing is a shift in how technology decisions are made.

It is less about optimization and more about assurance. Less about peak performance and more about consistent operation.

The suppliers that win over the next six months will be the ones that can answer a simple but critical question:

What happens when things do not go according to plan?

From an enterprise IT leader’s perspective, that is no longer a hypothetical. It is the reality they are planning for every day. Resilience is no longer just a capability. It is the basis for trust.

What should IT suppliers do next?

If you are an IT supplier, now is the time to recalibrate how you engage with customers.

Start by pressure-testing your value proposition:

  • Can you clearly articulate how your offering performs under disruption?
  • Can you quantify how you improve resilience, not just efficiency?
  • Can you help customers understand and reduce their exposure?

Just as importantly, ground your strategy in real buyer insight.

IDC’s latest Future Enterprise Resiliency & Spending Survey (March 2026, Wave 2) provides a detailed view into how enterprise IT leaders across regions are reprioritizing risk, resilience, and investment decisions in response to geopolitical and supply chain disruption.

We encourage you to explore the survey findings to better understand:

Suppliers that align early with these shifts will be better positioned to engage, differentiate, and win. Because in this market, insight isn’t just helpful.

It’s your competitive edge.

Rick Villars

Rick Villars - Group Vice President Worldwide Research

Rick is IDC's leading analyst guiding research on the future of the IT Industry. He coordinates all IDC research related to the impact of Cloud and the shift to digital business models across infrastructure, platforms, software, and services. He helps…

AI is not just changing job descriptions; it is actively rewiring how work is coordinated, controlled, and created, and it is doing so on multiple fronts at once, inside the same organization.

AI Is Transforming Work on Multiple Fronts Simultaneously

Some of our IDC Future of Work predictions bring this into sharp focus: by 2027, 40% of current job roles in large organizations will be redefined or eliminated, accelerated by GenAI adoption. At the same time, by 2030, around 70% of new job roles in Europe are expected to be directly enabled by AI technology. This is not a neat “old jobs out, new jobs in” swap. It is a systemic reconfiguration of how value flows through the enterprise. Yet most leadership frameworks still present AI scenarios as if they were mutually exclusive: automate to cut headcount, augment to boost productivity, redesign work for agility, or push toward autonomous operations.

When Automation, Augmentation, and Autonomy Collide

On the ground, those dynamics do not arrive one by one; they collide. In the same business unit, you may be cutting FTEs as routine tasks are automated and taken over by “digital colleagues,” while simultaneously hiring AI orchestrators, prompt engineers, and automation product owners to keep up with demand for AI-adjacent skills. You may be tearing up long-standing workflows as agentic systems reshape a significant share of knowledge work, at the same time as parts of your operation drift toward near-autonomous execution, powered by employees building personal agents and conversational workflows that quietly absorb whole segments of the process. These are not options on a slide; they are concurrent forces acting on the same organizational fabric. Treating them like menu choices is not workforce planning. It is misdiagnosing an organizational phase transition, a fundamental shift in the underlying architecture of how work happens.

From Role-Based Models to Capability-Based Architectures

The uncomfortable truth is that many leaders are still planning for roles, new and “to be eliminated,” while AI is reshaping the landscape at the level of capabilities and architecture. You can see the tension in three simple signals. A clear majority of European organizations have already deployed or are piloting automation to offset chronic labor shortages. A growing share of executives openly discusses replacing positions with automation, and many plan to substitute a measurable portion of their workforce with “digital colleagues.” Meanwhile, by the end of this year, a meaningful slice of frustrated knowledge workers with no formal development background will be building their own agentic workflows to change how they work, regardless of what HR’s role catalog says. When people can spin up an agent in a week, any static role taxonomy you publish today is out of date tomorrow. The center of gravity moves from “what roles do we have?” to “what capabilities can we compose, and how fluidly can we recombine them as AI matures?”

Why Traditional Role Models No Longer Hold

Role-centric models allow for some seriously wrong assumptions: that tasks are stable enough to bundle into jobs, that jobs are stable enough to plan around for three to five years, and that hierarchies are stable enough to govern how value flows. Agentic AI quietly breaks all three. Tasks fragment, recombine, and migrate between humans and machines in near real time. Work starts to look less like a tidy org chart and more like a living graph of capabilities, human, machine, and hybrid. In that context, planning headcount against static job descriptions is like trying to architect a cloud-native platform using only server rack diagrams.

Architecture Determines the ROI of AI

However, IDC’s Future of Work research also shows that when enterprises invest in digital adoption and automated learning technologies, they can unlock substantial productivity gains. The pattern across these findings is consistent: it is the architecture that determines the yield of AI, not just the tools themselves. If your workflows are fragmented, AI struggles to “see” the end-to-end journey it needs to transform. When critical data is locked in legacy systems, it cannot provide the rich, contextual recommendations you were promised. When governance is tuned for stability rather than experimentation, it throttles the learning cycles AI needs to be useful. Layer on top the reality that many organizations openly acknowledge they lack the capability support to implement automation effectively, and a clear picture emerges.

AI Amplifies Existing Organizational Weaknesses

In that environment, throwing more AI at the problem does not fix anything. It amplifies what is already there. Bad processes simply run faster. Poor decisions scale further. Shadow automation blooms in the gaps, as frustrated employees script around the constraints of the operating model. AI becomes an accelerant, not a cure.

Reframing the Strategic Question for Leaders

This is why the strategic question has to change. Instead of asking, “Which jobs will we automate?”, leaders need to ask, “Is our organization structurally able to absorb intelligence at scale?” Answering that requires moving from headcount planning to capability mapping, designing work around the interplay between human strengths, judgment, domain expertise, relationship-building, and machine strengths such as pattern recognition, generation, and orchestration. It means treating architecture as a product: standardizing interfaces, workflows, and data contracts so AI can plug into work without bespoke integration every single time. It means tracking how many workflows, decisions, and customer journeys are genuinely enhanced by AI, not just how many licenses have been bought. And it means steering reduction, augmentation, redesign, and autonomy as one coherent portfolio of change, not four disconnected projects.

Conclusion: The Real Stress Test Is Your Operating Model

AI is already changing jobs. The real test is whether your operating model can evolve quickly enough to harness that change, or whether AI will simply accelerate you toward the limits of the system you already have.

If you would like more information, drop your details in here.

Meike Escherich - Associate Research Director, European Future of Work - IDC

Meike Escherich is an associate research director with IDC's European Future of Work practice, based in the UK. In this role, she provides coverage of key technology trends across the Future of Work, specializing in how to enable and foster teamwork in a flexible work environment. Her research looks at how technologies influence workers' skills and behaviors, organizational culture, worker experience and how the workspace itself is enabling the future enterprise.

AI adoption is accelerating across EMEA, yet many organizations struggle to translate investment into measurable business value. This blog explores the structural challenges behind stalled AI initiatives and what differentiates organizations that successfully scale.

AI Adoption in EMEA: High Investment, Limited Business Value

AI adoption across EMEA has progressed significantly over the past 12–18 months, with organizations moving beyond experimentation into broader deployment phases. However, progress remains uneven.

IDC research shows that a substantial share of organizations are slowing down, scaling back, or refocusing their AI initiatives. This reflects a shift in priorities rather than a decline in interest. As macroeconomic pressures, regulatory complexity, and competing IT investments intensify, organizations are increasingly challenged to execute AI initiatives while demonstrating measurable business outcomes.

Why AI Projects Fail: The Execution Gap

The challenges that limit AI impact are consistent across industries, but particularly pronounced in EMEA.

According to IDC research, organizations continue to face difficulty in quantifying and demonstrating AI-driven ROI, alongside competition for resources and increasing regulatory uncertainty. According to IDC research, only 9% of EMEA organizations have been able to deliver measurable business outcomes from most of their AI-related projects over the past two years (Source: IDC Future Enterprise and Resiliency Survey, Wave 1, March 2026), At the same time, resistance to process change remains a persistent barrier, especially where AI requires cross-functional alignment and new ways of working.

These factors rarely cause projects to fail outright. Instead, they contribute to a gradual loss of momentum, where initiatives remain in pilot phases or are scaled selectively without broader organizational impact.

AI ROI: Why Proving Business Value Remains So Difficult

A central issue in AI adoption is the ability to measure value consistently.

IDC research highlights that AI impact extends beyond direct cost reduction to include indirect benefits such as productivity gains, revenue enablement, and risk mitigation. This makes it difficult to capture value using traditional ROI models.

As a result, many organizations lack a standardized approach to evaluating AI initiatives. This leads to fragmented decision-making, where use cases are assessed in isolation and scaling decisions are not consistently aligned with business priorities.

Without a clear framework for value measurement, AI initiatives often struggle to move beyond experimentation.

Scaling Enterprise AI: Why Moving Beyond Pilots Is So Hard

Scaling AI requires more than successful use cases. It requires integration into core business processes and operating models.

IDC research indicates that organizations face increasing challenges when moving from pilot to scale, particularly in relation to budget allocation, operational complexity, and governance requirements. While initial projects are often funded as innovation initiatives, scaling requires sustained investment in infrastructure, data, and ongoing operations.

This transition exposes structural gaps. Organizations that lack alignment between business strategy, data architecture, and execution models often struggle to scale beyond isolated successes.

AI Governance and Regulation in EMEA: Barrier or Opportunity?

Regulation is a defining factor for AI and broader technology adoption in EMEA.

According to IDC research, regulatory requirements around data protection, AI, and cybersecurity are significantly shaping how organizations approach AI deployment. While compliance increases operational and infrastructure costs, it is also driving more structured approaches to governance.

At the same time, organizations report benefits such as improved resilience, stronger ESG performance, and increased customer trust. This suggests that regulation is not only a constraint, but also a catalyst for more sustainable and trusted AI adoption.

Organizations that integrate governance early are better positioned to scale AI effectively.

AI and Workforce Transformation: Why the Human Factor Matters

AI transformation is not purely a technology challenge. It is fundamentally an organizational one.

IDC research emphasizes the importance of aligning AI initiatives with workforce capabilities, culture, and leadership. This includes reskilling, change management, and building trust in AI-driven processes.

Organizations that fail to address these elements often encounter slower adoption and limited impact. In contrast, those that integrate the human factor into their AI strategy are better positioned to realize long-term value.

The Evolving Role of the CIO in AI-Driven Organizations

As AI becomes central to business strategy, the role of the CIO continues to expand.

IDC research shows that digital leaders are increasingly expected to drive business value, support growth, and strengthen resilience. For instance, 42% of EMEA C-Suite leaders expect their CIO role to lead digital and AI transformation with a major focus on specifically creating new revenue streams (Source: IDC Worldwide C-Suite Tech Survey, September 2025). This requires a shift from a technology-centric role to a more strategic position aligned with business outcomes.

CIOs and digital leaders are therefore playing a critical role in connecting AI initiatives with measurable impact and ensuring alignment across the organization.

From AI Strategy to Execution: What Differentiates Leading Organizations

The current phase of AI adoption in EMEA is defined by execution.

Organizations that successfully scale AI tend to take a more structured approach, linking initiatives to business objectives, embedding governance early, and aligning technology with organizational change.

However, many organizations are still in transition. Key questions remain:

  • How can AI ROI be measured consistently across different use cases?
  • Which frameworks support scaling AI at the enterprise level?
  • What changes are required to align workforce and operating models?

How should the role of digital leaders evolve to effectively support AI-fueled business transformation? These questions will be explored in more detail in the upcoming webinar.

Drawing on insights from the IDC EMEA Digital Leader Playbook, the session will provide a practical perspective on how organizations across the region are approaching AI strategy and value realization.

Join the Discussion

For organizations seeking to move from AI experimentation to measurable business impact, understanding these dynamics is critical.

Watch the recording here to gain deeper insight into how leading organizations in EMEA are turning AI into real business value.

Martina Longo - Research Manager, Digital Business - IDC

Martina Longo is a research manager in the IDC Digital Business Research Group. In her role she advises ICT players on how European organizations create business value using digital technologies. She also leads IDC European Digital Native Business research, focused on those enterprises born in a modern technological world in a mix of start-ups, scaleups, and more mature digital natives. Within the European Digital Business Research, the European Digital Native Business, Start-ups and Scale-ups theme advises technology suppliers on the market dynamics and segmentation, business priorities, tech buying patterns and go to market approaches (sell to/sell with) needed to engage digital native organizations in Europe.

Hannover Messe 2026 ran from April 20 to 24 in Hannover, Germany, and it delivered. Under the theme “Think Tech Forward”, the show brought together over 130,000 visitors from more than 150 countries, 4,000 exhibitors, and 300+ start-ups across industrial automation, software, and hardware.

Brazil was this year’s partner country, and the event itself got a makeover: a new hall layout, a revamped thematic structure, and a brand-new Defense Production Park zone, reflecting just how much the scope of industrial technology has shifted.

Here are the Top 10 things I’m taking home, and yes, I’m happy to be challenged on any of them.

The user attention battle is quietly beginning

My deepest feeling coming out from the #HMI26 floor was to be the witness of the first deployments of the armies fighting for who controls the factory of the next decade. Most demos at Hannover Messe 2026 I was exposed to started with a chat box prompting the users. The question is how many of them can co-exist in a factory setup. My answer is as little as possible. The battle for the factory UI has hence started. It can turn out this way: one system as the front-end workers actually use, the others as solid back-end.

Context is the new competitive asset. Whoever owns it, then owns the process. And physics-aware data fabrics are the competitive moat

The differentiating capability in industrial AI is not model quality, but it is contextual depth. A physics-aware industrial data fabric that connects real-life physics, process history, sensor telemetry, operational and operator knowledge provides more competitive advantage than any algorithm running on top of it. Hopefully, manufacturers will define a technology journey built around data first, then context, then impact, but I fear the need to rush the deployment of industrial AI apps may result in missed opportunities in building the critical industrial model foundation.

MES stands for “Must Evolve Soon”

This application is the spine of the plant (because it acts as both the system of engagement and the system of record). But process flexibility is now its hardest test… Why? First, top-down. Advanced Planning and Scheduling applications are seeing accelerated adoption, driven by a new generation of algorithms capable of delivering real-time, context-rich, executable plans. As APS systems push dynamic re-sequencing into execution, MES must evolve fast enough to receive and act on what APS produces, or risk being seen as the weakest link. To this, it directly follows… the bottom-up pressure. Unstructured production cells (i.e. multifunctional robots, wireless machines, AMR-driven object routing) are going to be gradually replacing fixed lines. Customer requests are shifting toward rapid configuration, faster changeovers, and multifunctional automation. MES must evolve to accommodate less deterministic workflows, or lighter tools will fill the gap.

Forget upskilling. The connected worker is all about context generation and retention

The ability to bring anybody “to speed” has been so far one of the typical selling points for connected frontline worker platforms so far. But this is barely scratching the surface. The combination of AI-first vision systems, IIoT, RFID, RTLS, and mobile or wearable devices creates an ultra-visible data substrate that makes the factory transparent. On top of it, the layer of human-process interaction managed through connected worker platforms enables unprecedented levels of visibility on how people interact with process execution steps. This is truly the best material for AI-driven process improvement. This data gold mine is not just in the machine data. It is the analysis of what happens between the worker and the process.

The industrial metaverse is developing as a hyper-contextual decision-making environment

The exponential growth in data availability, combined with falling costs of modelling and representation, is unlocking use cases that were economically impossible two years ago. Hence, we can say that the “VCR” moment has arrived. Now we have the full capability to “zoom in and zoom out” and as well as “fast forwarding” the process for continous multi-scenario process planning and simulation, as well as “rewind” or playback the process for traceability and analysis.

Right-size AI now or face the potential consequences

The differentiating capability will be the agentic continuum, i.e. the unbroken intelligent chain across production execution. But building that chain responsibly requires confronting infrastructure and cost realities that vendor marketing may be now underplaying. Right-sizing AI and matching model scale and infrastructure to actual operational demand is a business continuity decision. The question is not “what is the most powerful model?” but “ do we need AI at all for this, and if the answer is “yes”, then “what is the appropriate model for this decision/process automation, in this operating environment?”

Manufacturing runs on deterministic sequences. Agentic AI is inherently non-deterministic. Reconciling these two realities is the governance challenge

Two distinct scenarios define the governance challenge. In the first, the desired output is well understood, and users can accept or reject an AI result without a care in the world about inspecting the internal process. In the second, the correct answer is uncertain, and full transparency into how the model generated its output is required before the result can be trusted. The challenge is how to gradually hand over large bits of process control to an agentic software layer that is stochastic in nature. Most manufacturing companies today are only comfortable approving small, incremental AI-driven changes, not because AI is incapable of more, but because the accountability and auditability frameworks for automating larger decisions do not yet exist.

So what?

What does this mean in practice? Three implications stand out.

Survive to Scale: Link the technology curve to the organisation curve

Technology is advancing faster than most organisations can absorb. The strategic risk for many manufacturers is not deploying too slowly, but it is scaling before the organisational substrate is ready.

Bring in the Naysayers: Organisational buy-in requires involving sceptics early, not convincing them late

There is a very nice saying that goes more or less as “Don’t let people saying that it can’t be done disturb the people who are already doing it.” But in this new venture, bringing the contrarians will be important. Creatin forums where sceptics stress-test plans with the utmost ferocity (before the market does it!) will be key.

Complexity demands simplicity: Focus on fundamental problems, not exhaustive use-case catalogues

Technology is evolving faster than any list can stay current. Vendors and manufacturers alike should resist chasing every new capability appearing on the horizon, and rather concentrate on first principle-based, core solutions that foster data integration for autonomy and decision-making improvement.

For a deeper look into Lorenzo’s research, visit our website. If any of these perspectives challenge your thinking or connect to your priorities, we would be glad to continue the discussion via our contact form.

Lorenzo Veronesi - Associate Research Director, IDC Manufacturing Insights - IDC

Lorenzo Veronesi is an associate research director for IDC Manufacturing Insights EMEA. In this role, Veronesi leads the Worldwide Smart Manufacturing research program and supports all the IDC MI research services for EMEA, by looking at Digital Transformation drivers in multiple manufacturing industry sub-verticals. He is also often involved in consulting projects across the world for end-users, IT vendors and public authorities. During the last decade his research has focused across key processes such as manufacturing operations management, supply chain management, and product lifecycle management in multiple manufacturing verticals, including - among others - automotive, aerospace, machinery, high-tech, chemicals, CPG, and fashion. Before joining IDC, Veronesi worked as analyst in multiple projects including research in the industrial logistics sector and as advisor for public authorities in Italy. Veronesi holds an MSc Degree in Regional Science at the London School of Economics and Political Science and has graduated cum laude at the Bocconi University in Milan.

中国PC市场进入调整与转型的交汇阶段

2026年第一季度,中国PC市场整体呈现出“弱增长”与“强分化”并存的特征。根据IDC最新数据,一季度中国PC市场整体销量达到819万台,同比增长0.8%。这一增幅虽实现由负转正,但从结构上看,市场仍处于深度调整阶段,需求恢复动力不足,行业正在从传统的周期性波动转向由结构性因素主导的发展阶段。

与以往由换机周期或宏观需求驱动的增长不同,本轮市场变化更多受到政策环境、供应链成本以及技术演进等多重因素影响。在此背景下,“是否增长”已不再是核心问题,增长来自何种结构、由何种动力驱动,成为判断市场走势的关键。

细分市场分化加剧,增长动能出现结构性转移

从细分市场表现来看,一季度中国PC市场呈现出明显的分化态势。消费市场同比下滑13.6%,在核心元器件价格上涨、补贴政策收紧以及终端需求疲软等多重压力下,整体恢复仍面临较大挑战。与此同时,中小企业市场同比下降9.6%,企业在宏观不确定性背景下趋于谨慎,IT预算收紧、设备更新周期延长,进一步抑制了采购需求。

相比之下,大客户市场实现38.8%的同比增长,在国产化替代持续推进以及政府、教育、大型企业采购需求释放的带动下,成为支撑整体市场的核心力量。

这一结构变化表明,中国PC市场正逐步从以消费驱动为主,转向由政企与结构性需求主导的发展模式,市场内部的增长动能正在发生明显转移。

AI笔记本加速渗透,推动产品结构升级

在本轮市场调整过程中,AI正逐步从技术概念走向实际应用,并成为推动PC市场结构升级的重要因素。随着端侧AI应用场景的不断丰富,具备本地算力能力的AI笔记本需求快速提升,带动整体产品形态和配置标准发生变化。

IDC数据显示,2026年1至2月,不含Apple在内的高算力AI笔记本销量占比已达到33.0%。与此同时,用户对高性能配置的需求显著提升,32GB内存搭配1TB固态硬盘的组合已成为主流配置,占比达到68.5%。

这一趋势反映出,用户对PC的需求正从“满足基础使用”转向“支持复杂应用与智能化体验”。在AI应用驱动下,PC正在从传统生产力工具演进为具备智能处理能力的终端设备,带动整个行业向高性能与智能化方向升级。

高端细分市场表现稳健,成为对冲周期波动的重要支撑

尽管整体市场承压,高端细分市场依然展现出较强韧性。以高性能游戏PC为代表,该领域在一季度保持稳健运行。尽管元器件价格上涨推动终端价格上行,但相关用户群体对性能更为敏感,对价格波动的承受能力较强,厂商也能够通过产品溢价与供应链管理对冲成本压力。

从市场竞争格局来看,头部厂商凭借产品矩阵、供应链能力以及品牌优势,持续巩固市场地位,同时部分厂商通过深耕细分领域实现稳定增长。这一趋势与AI PC的发展路径形成一定呼应,即通过提升性能与差异化能力,推动产品向中高端升级,从而在整体需求波动中保持相对稳定的发展节奏。

市场展望:结构升级与供应链因素将持续影响行业走势

展望2026年,中国PC市场仍将受到多重因素影响。上半年,元器件价格预计维持高位,供应链压力依然存在,叠加需求恢复节奏较为缓慢,市场整体仍将处于调整阶段。下半年,随着成本压力逐步缓解以及政策环境的进一步明朗,市场有望迎来温和改善。

从更长期来看,行业将持续向中高端与智能化方向演进,中低端市场空间逐步收缩,厂商竞争焦点将转向产品能力、技术整合以及供应链韧性。市场集中度有望进一步提升,头部厂商优势更加明显,而中小厂商则需要通过细分市场与差异化策略寻找发展空间。

IDC观点

总体而言,当前中国PC市场并非简单意义上的“复苏”或“下行”,而是处于结构重塑的关键阶段。AI技术的持续渗透、硬件配置的升级以及需求结构的变化,正在共同推动行业进入新的发展周期。

在这一过程中,厂商需要更加关注增长质量与结构变化,通过产品创新与能力升级,构建面向未来的竞争优势。

如需进一步了解IDC相关研究,或就中国PC市场发展趋势进行深入交流,欢迎与IDC联系,获取更多洞察与数据支持。请点击此处与我们联系。