Physical AI时代开启:具身智能机器人重构新一代生产力

7月20日,2026世界人工智能大会迎来收官之日。值此大会尾声,IDC同期举办了“Physical AI时代开启:具身智能机器人重构新一代生产力”线上专场会议,IDC中国分析师团队基于全球追踪数据及工业用户调研,系统分享了具身智能机器人的技术演进、市场格局与商业化路径。本文为会议核心内容纪要。

本届WAIC特设具身智能专属展区,汇聚众多机器人整机、核心零部件及具身智能大模型企业,集中展示量产人形机器人、四足机器人、轮臂复合机器人以及具身智能大模型的最新进展。展馆内,人形机器人在展台前挥手致意、四足机器人在人群中灵活穿行、机械臂在模拟产线上精准操作——围观的人群里,既有举起手机拍照的普通观众,也有拿着笔记本仔细记录参数的工程师和采购决策者。

热闹背后,一个更值得关注的信号正在浮现:今年的展品不再只是“能动的演示”,而是围绕3C电子、汽车制造、仓储物流、商用服务、家庭陪伴等真实场景展开应用展示,反映出中国具身智能机器人正由技术验证迈向商业化落地,产业实用价值持续释放。

这一变化背后,是AI产业正由生成式AI迈向Physical AI。生成式AI赋予机器认知能力,Physical AI进一步赋予机器在物理世界中自主感知、规划和行动的能力。国际数据(IDC)最新用户调研结果显示,继生成式AI之后,Physical AI将成为未来两年企业AI布局的重点方向。

具身智能机器人是Physical AI最重要的产业载体。IDC预计,未来五年中国具身智能支出将由14亿美元增长至770亿美元,年复合增长率达94%,中国有望持续引领全球具身智能机器人产业发展。

机器人原生智能的三大技术底座

围绕机器人原生智能的持续提升,正在推动模型、数据和算力三大技术底座加速演进。这三者环环相扣,共同决定了机器人的智能化上限。

模型层面,世界模型与VLA(视觉-语言-行动)的协同正成为核心架构。世界模型负责物理世界建模、任务规划及仿真数据生成,VLA模型负责感知、理解与动作生成,两者协同推动具身智能机器人迈向理解、预测、规划、执行的全面能力。

数据层面,“仿真优先、虚实融合”已成为行业共识。互联网视频、第一视角数据、仿真数据和真实机器人运行数据共同构建数据飞轮,持续驱动机器人能力迭代。数据飞轮转得越快,机器人的进化效率就越高。

算力层面,AI原生计算正驱动机器人架构全面升级。随着模型规模持续扩大,推理算力需求快速增长——IDC预测,到2027年推理将占智能算力需求的70%以上。云边协同带动计算平台、感知系统、运动控制及机器人操作系统全面升级,为规模化部署提供支撑。

从关注热度走向落地探索,多形态具身机器人需求形成

技术突破之外,市场的反应同样关键。IDC用户调研显示,超过80%的企业已开始关注并评估具身智能技术,部分企业已进入试点验证阶段。不同应用需求正在推动机器人向多形态发展,各品类的商业化节奏也呈现出明显差异。

人形机器人是增长最快的品类。2025年全球出货量接近1.8万台,同比增长800%,正由展示验证走向工业应用。中国厂商保持领先,中尺寸机型贡献主要出货量,全尺寸机型支撑高价值市场。IDC预计,2030年全球出货量将突破51万台。

四足机器人形成“消费驱动规模、商用驱动价值”的发展格局。2025年全球出货量预计约6万台,工业巡检、应急救援等行业应用持续扩大,情绪陪伴与专业作业需求共同推动市场增长。

商用服务机器人进入全球化扩张阶段。2025年全球出货量超过15.5万台,配送、清洁等应用持续向室外、多功能及多机器人协同方向发展,中国厂商继续保持全球领先。

外骨骼机器人加速向医疗康复、消费助力和工业辅助等多场景拓展。2025年中国市场出货量约2.6万台,多模态感知、轻量化设计及AI算法持续提升产品能力,医疗康复场景已形成成熟商业模式,消费与工业场景正处在从0到1的突破期。

家庭清洁机器人:家庭具身智能发展的最佳入口

相比通用家庭机器人仍处于早期探索阶段,家庭清洁机器人已率先实现规模化商业落地,成为家庭具身智能发展的最佳入口。

2025年全球家庭清洁机器人出货量达到3273万台,同比增长20.1%,市场正由单一室内清洁迈向覆盖扫地、割草、擦窗、泳池等场景的全场景智能清洁。其中,扫地机器人仍是最大的细分市场,2025年全球出货量达到2412万台;割草机器人和擦窗机器人保持高速增长,增速分别达到64%和70.4%,无线化、智能化成为产品升级的重要方向。

随着AI能力持续提升,行业竞争正由清洁能力转向智能能力。空间理解、自主导航、多模态感知、智能决策以及家庭IoT生态协同,正在成为产品差异化的核心竞争力。依托AI算法、供应链和全球化布局优势,中国厂商持续扩大在全球家庭清洁机器人市场的领先地位。

展望未来,家庭清洁机器人将逐步由单一功能设备演进为家庭具身智能体,并进一步融合家庭大模型、智能家居和IoT生态,成为未来家庭服务的重要智能终端。

商业化进入规模复制阶段

当技术逐步成熟,产业竞争的核心正由单点技术突破转向规模化落地能力。谁能在真实场景中跑通闭环、实现复制,谁就能在下一阶段占据主动。

应用场景来看,分层演进的节奏已逐步清晰。服务场景率先完成市场培育;工业场景进入规模化导入阶段,码垛、搬运、拾取、检测等应用持续落地;家庭场景则处于技术迭代和应用培育阶段,蓄势待发。

工程化能力来看,落地周期持续缩短。机器人正从单一产品走向工程化交付,模块化软硬件提升了场景适配能力,全栈解决方案与云边端协同架构加快应用部署,多品类机器人协同作业持续拓展应用边界,推动具身智能从单机智能迈向系统智能。

商业模式来看,行业正由卖产品走向卖服务。“硬件+软件+服务+AI”的融合模式正在成为主流,一次性销售、RaaS租赁/订阅、服务增值、本体与模型协同等模式并行发展。其中RaaS有效降低了用户使用门槛,加速了机器人的普及推广。

IDC四个判断:未来五年的竞争焦点

基于持续的全球市场跟踪,IDC对具身智能机器人产业提出四点判断:

  • 技术竞争进入系统能力竞争阶段。 模型、数据和算力将持续协同演进,世界模型、VLA和数据飞轮将成为机器人智能能力提升的核心驱动力。单点技术的领先不再足以构建壁垒,系统能力才是决胜关键。
  • 商业价值将由场景验证走向规模复制。 未来竞争重点将由技术突破转向场景适配、工程化交付和商业模式创新,机器人将进入规模化部署阶段。谁能率先跑通场景闭环,谁就能占据先机。
  • 生态能力将成为产业竞争的分水岭。 平台能力、开源生态、数据闭环和产业协同,将决定企业能否在五年后依然留在牌桌上。生态建设不再是可选项,而是必答题。
  • 中国厂商有望持续引领全球产业化。 依托完善的制造体系、丰富的应用场景、完整的供应链和数据规模优势,中国企业有望继续引领全球具身智能机器人产业的发展。

进一步交流

具身智能机器人产业正处在从技术验证走向规模化落地的关键转折期。IDC持续追踪全球机器人市场动态,覆盖人形、四足、商用服务、家庭清洁、外骨骼等全品类,为企业提供数据驱动的市场洞察与战略决策支持。如需获取完整报告、行业数据或与分析师团队深入交流,欢迎联系IDC中国

Lily Li

Lily Li - Research Manager

Lily is the Research Manager for China Robotics and Embodied Intelligence, specializing in market research on embodied intelligent robots. She has long focused on the development trends of China’s embodied intelligence robotics industry, systematically studying the evolution of robot hardware,…

2026年7月17日,上海世界人工智能大会(WAIC)现场,全球首款 AI 智能体手机、23 克轻量化 AI 眼镜集中首发,人形机器人走出演示场景落地工业、服务一线。

这些现场实景与IDC最新终端市场数据形成双向印证:全球智能终端产业已正式告别概念化技术叙事,AI从附加功能升级为重构硬件、交互与产业格局的底层核心。对于不同角色的参与者而言,这场变化的含义与应对路径各不相同。

数据层面:AI终端全线增长,分化格局已现

IDC研究数据显示,在国内整体终端市场承压背景下,AI硬件成为唯一增长主线,各细分赛道呈现差异化扩张态势:

  • 手机:2026年国内生成式AI手机渗透率突破50%,正式进入普及周期。端边云协同算力、原生智能体系统、全场景生态是行业突围核心方向。
  • 具身智能机器人:中国持续引领全球发展,预计2026年市场规模接近50亿美元。其中人形机器人作为高阶载体,全球出货同比增长超过150%,工业场景为重点突破领域。
  • 家用大清洁机器人:家用机器人仍是出货量最大的机器人品类,2026年第一季度全球出货893万台,同比增长36.7%。扫地机器人出货656.3万台,同比增长29.4%,头部厂商在欧洲市场布局力度持续加大。
  • PC/平板:2026年中国Gen AI PC出货587万台,市场占比11.9%,销量同比提升166.4%;Gen AI平板出货112万台,占比35.6%,销量同比提升68.4%。AI已成为平板摆脱低价同质化竞争的核心抓手之一。
  • 可穿戴/智能眼镜:轻量化硬件与AI大模型融合已成新品标配,1500-3000元价位为核心竞争区间,AI集成与多元场景生态是增长核心驱动力。

产业增长驱动力源于大模型成熟、国产零部件配套升级、实景需求持续释放。同时,跨应用权限互通、智能体长效记忆、数据隐私合规仍是当前三大核心瓶颈。

趋势层面:WAIC实景揭示的四重变革

本届WAIC集中呈现了四个方向的结构性变化,这些变化正在重新定义终端产品的竞争逻辑:

趋势一:AI原生软硬件闭环成为行业硬性标准

阶跃星辰STEPX Neo搭载自研Step AOS智能体原生系统,在安卓底层增设智能体调度层,搭建”大模型-原生系统-硬件”一体化架构;努比亚量产AI手机依托MCP/A2A协议实现跨应用自主执行;Rokid原生操作系统YodaOS落地消费级智能眼镜。行业竞争重心已从硬件参数比拼转向软硬一体生态建设。

趋势二:交互逻辑从“人操控设备”向”机器理解意图”跃迁

传统触控、语音交互正向意图感知交互迭代:STEPX Neo依靠双域长效记忆简化重复指令;智能眼镜打造”被动感知、主动服务”模式,依托视听感知自动识别需求并后台响应,实现无操作主动服务。交互效率的提升,直接决定了终端设备在真实场景中的可用性。

趋势三:端云协同架构标准化,具身智能搭建数据闭环

主流智能眼镜统一采用”端侧轻量化模型+云端大模型”分层算力架构,空间计算芯片与自研光学引擎成为头部厂商高端产品趋势标配。人形机器人赛道重点布局世界模型、数字孪生、合成数据体系,具备”模型训练-数据采集-落地反馈”完整闭环的企业,竞争优势已显著拉开。

趋势四:单品智能向多设备场景协同升级

大会首发的”HGR人·镜·犬混合智能协同系统”,依托智能眼镜联动机器狗完成现实服务任务;工业、仓储、商用、家庭多场景同步落地多机器人联动方案。行业发展方向已从单一设备智能化,转向全域场景协同智能。

综合以上趋势,IDC得出三个核心判断:

判断一行业已跨越尝鲜期,进入普及阶段。AI手机渗透率过半、机器人市场增速翻倍,叠加量产机型集中发布,表明消费者需求已从猎奇尝鲜转向实用刚需,市场增长由厂商单向教育驱动转为用户主动采购拉动。

判断二场景落地能力正在成为企业核心分水岭。厂商宣传逻辑从”硬件功能堆砌”转向”垂直场景解决方案”——人形机器人深耕制造、物流商用场景;智能眼镜差异化布局本地生活、通用办公、个性化服务。真实场景的落地深度,是拉开企业差距的关键指标。

判断三国产产业链完成从跟跑到自主协同的转型。本届WAIC集中亮相的国产底层技术底座——自研智能体操作系统、本土端侧芯片加速渗透、机器人核心零部件国产化持续提升——表明硬件、系统、大模型、生态四位一体已成为AI终端规模化落地的必要条件。

未来12-18个月产业核心变量预判

基于当前数据趋势与技术实景,以下变量值得持续关注:

  • 技术端:终端专用NPU、轻量化端侧大模型、智能体原生OS将成为旗舰标配;无底层重构的浅层AI设备将逐步被市场淘汰;国产眼镜芯片加速渗透以高通AR1为主的市场格局。
  • 市场端:AI智能体手机价格将持续下探覆盖全价位;1500-3000元智能眼镜将开启价格战;内容电商线上种草加速眼镜消费转化,线下专业验配渠道潜力待释放。
  • 政策生态端:AI终端安全、隐私合规规范持续收紧,合规能力不足的中小品牌将加速出清;政企、办公、出行垂直场景成为智能体商业化核心赛道,产业扶持政策持续降低消费门槛。

不同角色的行动参考

以上数据与判断,对不同类型参与者的含义各有侧重:

终端厂商及产业链企业:竞争逻辑已从参数比拼转向软硬一体生态建设。需重点关注:是否具备完整的”模型训练-数据采集-落地反馈”迭代闭环;在垂直场景中是否形成了可验证的解决方案;端侧芯片、操作系统等底层能力是否自主可控。

行业投资者:赛道分化趋势已明确。需关注具身智能机器人、智能眼镜等增速领先品类的结构性机会;同时留意合规收紧带来的行业洗牌,具备生态壁垒的头部企业抗风险能力更优。

应用开发者与场景方案商:多设备协同与场景智能方向正在打开增量空间。HGR人·镜·犬等混合协同方案的落地,预示着设备联动与服务闭环的想象空间远大于单品智能化。

与IDC进一步交流

以上洞察基于IDC覆盖全球终端市场的持续数据追踪与WAIC现场实景调研。当前时间窗口的关键特征在于:当前时间窗口的特殊性在于:数据转向已经发生,但市场共识尚未完全形成。对于正在进行AI终端战略规划、产品定义或市场进入决策的机构而言,这意味着一个关键的时间窗口。

如您希望就具体赛道数据进行深度探讨,或针对您的业务场景获取定制化的数据与分析支持,欢迎联系我们(请点击此处)进一步交流。

Fiona Wu

Fiona Wu - Associate Vice President, Client System Research and IPDS, IDC China

Fiona Wu, associate vice president for IDC China's Client System Research, has 10 years of experience working in the IT industry and is focused on research and analysis of the China IT hardware market. She leads the team to provide…

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IDC continues to provide extensive coverage of hundreds of detailed technology markets and industries. However, we redesigned how we package our coverage around two core improvements: 

  1. From individual SKUs to complete packages: Coverage areas built around core technology markets and business decisions, from AI infrastructure to cybersecurity, now offer broader, outcome-oriented intelligence sets. 
  2. Research and data, together: Clients no longer need to purchase research and data products separately. Each coverage area now includes both qualitative (e.g. written analysis, MarketScapes, expert points of view) and quantitative intelligence (e.g. forecasts, market share). The evidence and narrative sit side by side for a complete view of the market. 

We also made sure every package includes the expert engagement opportunities clients have told us they love:  

  1. Analyst access: Direct advisory from IDC’s leading subject matter experts. 
  2. Event access: Invitations to IDC events for select packages and tiers. 

IDC Quanta: Included in every subscription  

Intelligence is only useful if you use it.    

IDC Quanta, our AI-powered research platform, synthesizes insights across coverage areas and against each client’s own data, providing a more personalized and useful intelligence experience. IDC Quanta also brings IDC content directly into the tools clients are already using, providing the option to bypass portals of the past. 

The power of IDC Quanta is a primary reason why many of our 175+ beta customers told us they wanted IDC Quanta bundled with their core intelligence services, citing it as an enhancement to their current service rather than a separate product. 

That’s why IDC Quanta is included in every subscription. It’s not sold as a standalone product or an add-on. It’s built into our packaging because every IDC client deserves to move as fast as the market demands.   

Why this matters: Built for how our clients work 

Fewer, broader coverage areas are simpler to use and buy. When paired with IDC Quanta’s powerful AI capabilities, clients benefit from fast, well-rounded responses that support how their business actually works.  

Take an example of building a business case for entering a new market. Having worked with our clients over the years, we know they need the evidence – market forecast, share, channel partners, competitive positioning and pricing, and timeline –  as well as the narrative for how they will execute the plan – product roadmaps, go-to-market strategy, risks, and success metrics. IDC’s new packaging model provides the data and research clients need so they spend less time understanding and more time executing.  

Let’s also consider a specific market situation for further illustration:  

Example: Data & Storage Infrastructure

Imagine a company that provides cloud storage and enterprise arrays is looking to capitalize on the rapidly expanding opportunities driven by AI.

BEFORE

Client purchases a data Tracker and separate research bundles, each of which includes dozens of PDF research reports.

TODAY

 Client subscribes to their core coverage areas for Data & Storage Infrastructure which includes a 10x increase in research intelligence and multiple data Trackers, plus IDC Quanta to access, synthesize, and activate that intelligence.  

The new packaging also recognizes that one size doesn’t fit anyone. We created distinct packages mapped by breadth of coverage and depth of engagement. Whether a client is a startup focused on a single market or a large enterprise spanning multiple geographies and technology domains, there’s an option to support their needs. 

What our clients are saying 

As an intelligence firm, we did our research before making this shift. We surveyed existing clients and used their feedback to shape the final approach. Clients shared comments like: 

“Our needs change over time. The research we need in Q1 might be different than Q3. Broader access would prevent us from having to go through a new contract or purchase process for every shift.”  

“As we embed AI in our workflows, these capabilities allow us to remain relevant. We see value in the new model, including more data, MCP integrations, and the ability to upload our own data.” 

The data backed this up. When asked to quantify the value addition, the average response from clients interviewed was that they saw more than 50% increase in value in the new packaging compared to their current service mix.  

What happens next 

This is what listening looks like in practice. Clients asked for broader access to our coverage to help them make better decisions, not coverage that mirrors our org chart. Clients asked for a more complete view of the market and the speed to act on it. We built it. Now it is ready for you. 

Ready to explore which package fits your strategy? The IDC sales team is here to help create the best IDC experience for your team.   

Interested in exploring how IDC Quanta can accelerate your workflow?
Book a demo today.

Kate Bae - Head of Product and Partnerships - IDC

Kate Bae is Head of Product and Partnerships at IDC, where she leads the shift to embedded, AI-fueled experiences that turn IDC's intelligence into a real-time advantage for customers. She brings over a decade of product leadership from NCR Voyix, NielsenIQ, and Nielsen to the role.

Anand Singhania - Vice President, Pricing Strategy - IDC

Anand Singhania is VP, Pricing & Monetization Strategy at IDC, where he develops and executes pricing strategies to drive commercial growth. He brings 15+ years of pricing and commercial strategy leadership across Life Sciences, Industrials, Technology, and Healthcare, including senior roles at EY and Simon-Kucher & Partners.

At the IDC Quanta launch webinar, Jamie Fiorda, SVP of Product Marketing at IDC, laid out why the gap between how fast markets move and how fast organizations understand them is becoming the defining challenge for companies trying to act before conditions shift again. He also laid out where AI-powered intelligence tools fit into closing it.

Fiorda’s starting point was access: who actually gets to use the intelligence a company already has. In most organizations, the market intelligence a company already invests in tends to serve a narrow slice of the business (a research team, maybe a strategy analyst or two), while the insights that could sharpen decisions elsewhere in the company never reach the people making them.

That’s the pattern Fiorda described: intelligence that exists, but sits in a portal most of the organization has no reason to open. The problem isn’t the quality of the research. It’s that the value of it is being rationed by who has access, right as every function in the business is under pressure to move faster on planning that used to have more runway.

Positioning AI as an Intelligence Layer, Not Another Tool

Fiorda’s case for IDC Quanta wasn’t framed as a new subscription or a new dashboard to learn. It was framed as infrastructure: a layer that sits beneath the tools an organization already uses, making the intelligence within them actionable and defensible.

Organizations already have plenty of AI tools. What most of them can’t do is prove their answers are trustworthy. That gap shows up largest in finance: nearly a third of global enterprises still run their finance analytics function with no formal structure at all, department by department, which is exactly where an M&A assumption or a market-sizing input gets challenged first by a board, an investor, or an auditor. Fiorda’s argument centers on embedding intelligence directly into the workflows where strategy is developed, backed by a source a leadership team can point to when that challenge arises.

What This Looks Like Across a Business

Fiorda walked through what that shift means function by function, not as a feature list, but as a picture of how fast an organization can move when intelligence isn’t gated to one team.

A market intelligence team doesn’t change what it does, just how quickly it does it: sizing markets and benchmarking with intelligence flowing directly into the tools they already use.

Finance picks up a use case that’s often entirely new: market sizing inside their own models, risk analysis, revenue validation, all backed by third-party data at the point of decision. IDC research points to real upside here: cognitive technologies applied across due diligence and predictive analytics are projected to drive a 25% increase in M&A returns by 2027, largely because AI-enabled screening surfaces viable targets faster than traditional methods can.

Product gains competitive benchmarking and roadmap validation the same way. Marketing builds segmentation and messaging grounded in actual demand trends instead of assumptions a real shift for CMOs navigating today’s pressure to justify every dollar: IDC research found 52% are already leaning harder into scenario-based planning and 41% report increased pressure to justify marketing ROI, both signs that assumption-driven messaging is no longer good enough. And sales walks into a room with validated, current talking points instead of a battlecard built last quarter.

The strategic point is that all five are pulling from the same source of truth, at the same time, without waiting on each other.

The Question Leaders Must Consider

Fiorda’s close reframed the pitch as a single question for leadership to sit with: if the intelligence an organization already values could reach five functions instead of one, what would that be worth? It’s worth sitting with the scale of that gap: IDC research shows nearly 60% of Chief Data Officers say their organizations need to rethink how they use analytics in decision-making, and only about a third of executives are active users of the intelligence tools already in place. That’s not just a research quality problem. It’s strategic value sitting untouched, simply because of where information lives.

In a landscape changing this quickly, the organizations that move fastest will be the ones where that research actually reaches every team that needs it.

Learn More

Organizations already working with IDC can talk to their account team about which teams could get access next. Those exploring IDC Quanta for the first time can request a demo at idc.com/quanta to see where this fits for your team.

Ryan Smith - Content Marketing Director - IDC

Ryan Smith is the Director of Content Marketing at IDC, where he leads brand-level content and social media strategy, aligning research insights with compelling storytelling to engage technology decision-makers. With a background in both IT and marketing, Ryan brings a unique blend of technical understanding and creative strategy to his work. He’s also a seasoned storyteller, speaker, and podcast host who believes the right message, told the right way, can drive both trust and transformation.

The word “cloud” carries an aura of weightlessness and a borderless ether that is beyond geography or gravity. However, in the world of technology, cloud runs on physical concrete, undersea fiber-optic cables, complex power grids, and real people who keep it running.

How the Middle East War has redefined enterprise resilience

The geopolitical conflict across the Middle East in early 2026 has stress-tested cloud adoption and digital transformation road maps like never before. However, the defining narrative is not collapse; it is resilience. What has emerged is a region doubling down on its cloud and AI ambitions, even as the ground beneath it shifts.

For years, CIOs managed risk through a digital-first lens: ransomware defenses, data breach protocols, and distributed denial-of-service (DDoS) mitigation. This baseline changed with the Middle East War. What makes this conflict unusually disruptive to enterprise IT is that datacenters and cloud regions are not collateral damage; they are direct targets.

The issue is bigger than any single provider, facility, or location. Even when infrastructure remains physically intact, enterprises face fragile connectivity, dangerous concentration risk, vendor lock-in, and energy price volatility. Critical workloads still depend on a narrow set of providers, transit paths, and subsea and cross-border cable routes.

The real risk, then, is not whether a single site goes dark. It is whether a geographic cluster, supply route, carrier path, or a set of interdependent providers is simultaneously disrupted. For enterprise leaders, this was a stark, irreversible realization: the risk profile has shifted from cyber resilience to infrastructure resilience.

Round-the-clock continuity

IDC spoke with senior leaders from end-user organizations, hyperscalers, regional cloud providers, and partners across the Middle East. In the first weeks of the disruptions, one goal dominated everything: keeping the lights on for financial institutions, public sector bodies, and private enterprises.

The work happening behind the scenes was extraordinary. Engineering, operations, and management teams ran 24/7 shifts with a mandate to prevent data loss, stabilize systems, and migrate business-critical workloads to secure alternative regions as quickly as possible. Cloud providers and partners collaborated directly with key customers to execute rapid migrations, moving workloads out of conflict zones to global landing zones in Europe and Asia.

This all unfolded under severe physical constraints, such as closed airspace and a supply chain that halted the movement of critical tech talent and replacement hardware. Teams operated under intense mental strain and operational uncertainty, making high-stakes decisions with incomplete information.

Despite these conditions, the results were striking. Enterprises with structured multiregional failover architectures recovered rapidly, preserving database integrity and minimizing service interruptions. The organizations that fared the worst were those still operating inside single-region designs, adding a vulnerability that will take years to fully address across the region.

A resilient region, despite challenges

The severity of the conflict has not derailed broader cloud adoption. IDC’s ICT Spending Black Book (May 2026) reveals that the Middle East and Africa’s (MEA) digital economy has hit a speed bump, but it is not stopping:

  • Sustained growth: MEA IT spending is projected to grow by approximately 5% year over year (YoY).
  • Resilient floor: Even in an extended downside scenario, in which prolonged regional conflict dampens consumer and enterprise confidence, MEA IT spending is expected to hold at 3–4% growth.

These are not figures from a region in retreat. They are figures from a region that is recalibrating.

The AI-cloud mandate

The structural demand for cloud and AI infrastructure remains fundamentally intact. As per IDC’s March 2026 Tech Buyer Survey, infrastructure and IT operations, alongside AI, are the two investment areas most shielded from budget cuts in the current environment.

Enterprises are not abandoning cloud migrations; they are redesigning their architectures to build greater resilience. Four strategic shifts now define how regional technology buyers approach cloud:

  • Multiregional and distributed architectures are the new baseline. Cloud security and multiregional backup (56%) is the fastest-accelerating strategy across the Middle East, Turkey, and Africa (META). Multipath connectivity (38%) and multiregional, multi-availability zone design (30%) are close behind.
  • Energy resilience has moved to the core of cloud decisions. Energy-resilient infrastructure (44%) now ranks among the highest-priority investments, a direct response to the power grid volatility exposed during the conflict.
  • Workload portability and provider diversification are nonnegotiable. Enterprises are actively designing architectures that span multiple providers and environments to reduce lock-in (37%).
  • Sovereign cloud has become a boardroom priority. Deploying workloads in local sovereign datacenters has risen to a top-tier spending priority (31%), driven by both regulatory pressure and a deeper understanding of geopolitical risk.

The long-term play: Strategic investments in the Gulf

The turbulence of 2026 has not altered the long-term economic transformation ambitions of Gulf nations. UAE Vision 2031, Saudi Vision 2030, Qatar National Vision 2030, and similar national agendas remain firmly on course. Global and regional technology providers are matching this resolve with capital at scale.

Table 1: Strategic Investments by Cloud Providers in the GCC

ProviderInvestments
Amazon Web Services (AWS)US$5.3 billion for a Saudi Arabia region; US$5 billion for its United Arab Emirates (UAE) region through 2036; US$5 billion for the AI Zone with HUMAIN in Saudi Arabia
MicrosoftUS$15 billion in the UAE across 2023–2029, including the US$1.5 billion G42 investment; new Saudi Arabia datacenter region
GoogleA US$10 billion AI hub with Saudi Arabia’s Public Investment Fund (PIF), connected to the Dammam region; a US$2 billion, 10-year commitment in Turkey
OracleUS$14 billion in Saudi Arabia over 10 years, building on existing regions in Riyadh and Jeddah, with further expansion plans, including with NEOM and Dammam.

Note: Selected investments; not a comprehensive list. Source: IDC, 2026

These investments focus on common themes such as cloud regions, AI infrastructure, sovereign cloud, partner enablement, and skills development. These commitments carry a clear signal. Providers do not make multibillion-dollar, decade-long bets on unstable ground. The Gulf is being built as a tier-1 pillar of global digital infrastructure, not a regional afterthought.

Building the skill pipeline

Physical datacenters are only half the equation. A genuinely resilient digital economy requires a skilled local workforce, one that can build, maintain, and protect critical systems without depending on international talent flows that a closed airspace can instantly sever.

Major cloud providers have responded by investing heavily in grassroots workforce development across the UAE, Saudi Arabia, Qatar, and other Gulf states. Rather than waiting for talent to emerge through traditional pathways, they are reaching university students and young graduates early, putting them through intensive cloud and AI bootcamps and ensuring they hold recognized certifications before entering the workforce.

The urgency is real. The skills gap ranks as the region’s third-largest technology risk, cited by 56% of tech buyers, according to IDC’s study. Closing this gap is not only a workforce development challenge but also a national security imperative.

What comes next for regional cloud strategy

The Middle East War has fundamentally changed how enterprise leaders think about cloud architecture. High-availability models confined to a single region are no longer sufficient. Going forward, cloud strategy will demand cross-border diversification, automated cross-continental failover, and a genuine commitment to shared regional resilience.

What the past months have demonstrated is that the region’s digital infrastructure, when built with a multiregional design, local talent, and committed provider partnerships, can withstand extraordinary pressure and continue operating. The regional cloud is not going anywhere. It is simply learning how to survive the sandstorm.

Harish Dunakhe

Harish Dunakhe - Research Director, Software and Cloud, META IDC

Harish is part of IDC’s AI-Fueled Business Strategies Global research team. This team’s mandate is to assess the impact of AI and digital technologies on an organization’s growth strategies, adoption of digital business models, digital maturity, and study the impact…
Jebin George

Jebin George - Senior Research Manager, Cloud and Datacenters, Enterprise Infrastructure

Jebin is a Senior Research Manager within IDC’s enterprise infrastructure global research domain and part of the cloud and datacenters subdomain. He focuses on cloud infrastructure and software adoption trends across the Middle East, Türkiye, and Africa, with particular emphasis…

The retail industry is at a turning point with automation and AI. For many retailers, the goal is simple: use technology to improve efficiency and effectiveness, whether for store associates, developers, customers, or suppliers. While industry giants like Walmart are setting the pace, the core principles of successful retail innovation apply to stores of all sizes.

“When everyone has AI, competitive advantage vanishes. True potency lies in how you secure, differentiate, and trust your data, processes, and people.” — Ananda Chakravarty, VP Research, IDC Retail Insights

The scale of innovation

Walmart currently holds a unique position as a “blue chip” benchmark for retail innovation. Its success is driven by a massive physical presence—providing abundant testing grounds for new technologies—and an executive team that combines deep technical expertise with retail operations know-how. Furthermore, their worldwide network and influence over supplier relationships give them capabilities that typical retailers cannot easily match. They aren’t just doing retail; they are also expanding into adjacent areas like retail media and advertising.

The automation imperative

For every retailer today, automation is top-of-mind. It is no longer a question of if but how to implement technology to streamline store operations. The challenge for most is moving from historical, anecdotal data to predictive, real-time insights.

Retailers are grappling with a “data problem”, one that includes ensuring collection and use of the data appropriately to make decisions. They must collect and make sense of large amounts of scattered information—from product and demographic data to sales metrics and demand forecasting. Updating these systems to support better, long-term decision-making is the groundwork for moving forward. While cost savings are a common promise of AI, the reality for retailers is a mix of ongoing investment and changing expenses as they bring in newer, more powerful tools.

AI has proven measurable ROI in retail—but the gains it brings are just table stakes. Winning in the next wave requires building true competitive potency through differentiated data, proprietary processes, and trusted people.

Key technologies: Vision and labels

Several technologies are moving from pilot programs into everyday use:

  • Computer Vision: Widely used by point-of-sale (POS) and fraud avoidance vendors, this technology is increasingly common for product identification, age verification, and fraud detection. While Walmart’s implementation of self-checkouts for fresh produce is notable, the capability itself is well-established in the market, just not mainstream.
  • Digital Shelf Labels (DSLs): These allow for flexible pricing, significantly reducing the manual labor required to update store tags and category resets. While they offer real advantages in speed and labor savings, they represent a significant financial and logistical challenge regarding hardware, maintenance, and the complexity of rolling out across thousands of stores with tens of thousands of individual products.

Strategic advice for smaller retailers

Smaller retailers should avoid the trap of simply trying to copy the giants. Walmart often runs experimental programs that they may discontinue, such as their 5-year experiment with Bossa Nova for tracking on-shelf facings and inventory using gliding robots and cameras in the store. For a smaller retailer, the margin for error is much tighter and programs must translate into real ROI.

Instead, prioritize:

  • Foundational Operations: Keep your data clean and your inventory counts accurate. If you don’t know where your stock is, or your inventory records are unreliable, advanced AI won’t solve the core issue.
  • Strategic Selection: Identify which technologies deliver the greatest, most lasting financial impact for your specific business. Don’t chase trends; focus on fixing real problems in your day-to-day operations.
  • Partnering: Smaller retailers don’t need to build everything in-house. Work with software vendors or technology partners to put proven solutions in place that fit your budget and store size.

THREE PILLARS FOR RETAIL AI SUCCESS

1. FOUNDATION Clean data, accurate inventory, reliable systems
2. SELECTION Pick tools that solve your problems, not the market’s
3. PARTNERSHIP Leverage proven vendor solutions, scale smart

Ultimately, the most successful retailers will be those who master the basics—clean, orderly, engaging stores—and use technology to enhance, rather than replace, that human experience. Technology is a powerful tool, but it cannot fix a fundamentally broken retail operation. Success lies in identifying the unique value of your store and applying the right tools to build on that strength.

Want to go deeper?

Explore the full IDC research behind this blog

Ananda Chakravarty

Ananda Chakravarty - Research VP, Retail Merchandising and Marketing Analytics Strategies

Ananda Chakravarty is Vice President for IDC Retail Insights, responsible for the Retail Merchandising and Marketing Analytics Strategies practice. Mr. Chakravarty’s core research covers in-store and digital retail merchandising, digital tools, artificial intelligence, intelligent store operations, retail marketing, and retail…

“Hey Alexa: add some eggs, cage-free, from my favorite brand list to my grocery order for the week.”

“Sure. I’ve added a dozen New England Cage-Free Premium Eggs to your list.  Should I complete the purchase now and have it delivered within the next day?”

Convenient for consumers. A nightmare for brands. The dialogue above outlines the personalization that agentic buying might offer the consumer. Swap in Sparky, Pixie, Siri, AskAI, or another updated agentic bot to replace an agentic experience for Alexa above, and you’ll see the expanse of options. The outcome is the same, however, when you see the $18.99 price tag discounting fees and tips for the service. Merchants feel control slipping away with the higher price tag, while their customer relationship is whittled away by big tech.

Can consumers blindly trust agents to choose?

The benefits to consumers can drive lower prices and even price negotiation by adding “at the lowest price please” in the prompt to Alexa. However, depending on the ownership of the tech, it may require some time to settle in. What is lost is consumer choice. In the West, a critical part of shopping is the consumer’s independent choice. But in the case of agentic buying, the consumer delegates part (or all) of this capability to their partner agent. They are unable to see options, the decision-making process, or even the instructions the user may have provided initially.

Current trends do not suggest automatic acceptance of the process for the consumer. IDC’s 2026 worldwide retail consumer sentiment study shows there’s support for digital assistants. 84.7% of consumers were ready to use digital assistants and chatbots when shopping. While this may sound like a promising endorsement of agentic commerce, the agentic shift is still early. Only 30.7% are using AI chat tools such as ChatGPT to discover or research products. The trust factor has not yet been fully met for the stages of the agentic lifecycle, delaying market adoption of a customer-facing agentic-driven business model. The next version of commerce will require mastering site personalization for the agentic buyer and human shopper, each with their own view of how to buy. What this means for agentic experiences is that it’s still not ready for prime time.

A sucker punch for merchants

For the merchant side (in most cases, retailers), agentic buying is ruthless. There is an implicit undermining of brand value and a race to the bottom of price. Miniscule margins for retailers would shrink even further to the point of a surefire loss. Some retailers are considering agentic buying a new channel but others consider it an extension of eCommerce.

Whether a new channel or a just the next evolutionary step in eCommerce, the silver lining for merchants is that agentic buying is primarily affecting the 15-20% of eCommerce sales rather than the entire retail business. This is a myopic view when one considers omnichannel trends, digital discovery, and social media and the potential for each to become potential conduits for agentic transactions. The merchant is impacted by revising brand and emotional influence factors with a logical intermediary that replaces the merchant-consumer relationship. Merchants are loath to support such efforts as it results in inevitable loss of control across merchants’ target markets.  At best, agentic buying is a lopsided partnership where retailers are exploited through incurring most of the risk and a new cost burden when engaging the consumer. At worst, agentic buying hollows out the retailer’s business model to the point of eliminating a merchant’s market presence.

Consumers are already in the discovery lifeboats

The movement from keyword search to agentic discovery is already underway, fueled by massive theft of merchant IP and product data and the rapidly eroding consumer trust search engines accrued. The IDC Agentic Commerce: Buyer Lifecycle Opportunity Analysis report highlights an eventual full conversion to agentic discovery within a year. Consumers will have the ability to make selections and choose better, with more informed choices, overcoming any trust issues with hallucinations or incorrect product information. Public adoption for discovery will eliminate some challenges on the merchant side. Alternatively, merchant reluctance to cooperate (or lack of understanding of how to engage) with agentic discovery tools will contribute to an uneven and rocky transition to agentic discovery.

The tech sharks are circling

The agentic buying move is like pulling the rug out from merchants. There’s a non-zero chance that agentic buying will negate countless hours of labor for the brands and merchants that spent years preparing carefully curated customer experiences, from shelf labeling to end-cap promotions. Most retailers will chalk this up to impacting just their non-store sales, but in an omnichannel world, the changed perspectives will hit the bottom line. Multiple tensions will impact the market including trust, disintermediation, control, transparency, and data ownership.

While the discovery stage of the lifecycle is an inevitability due to already existing customer adoption, maybe the next stages of purchasing, fulfillment and beyond as part of the buying lifecycle will take a different path. The merchants are certainly counting on it. The desperation persists as merchants clamor for clarity in the agentic AI world, stranded in the center with circling tech sharks peddling agentic tools and capabilities.

While merchants are trying to make sense of how agentic will affect not only their businesses, agentic tech companies are seeking out ways to divide and conquer well-known retail domains without the leg work of physically building stores, relying completely on the digital experience.

Perhaps that’s the AI answer after all.

Instead of: “Hey Alexa, how do we prevent these tech vendors from stealing our hard-earned customer brand value?”

Merchants may find themselves asking: “Hey Alexa, who’s the best lawyer that can boost monetary damages in a lawsuit against big tech? Are there any active class action lawsuits I can join that could potentially slow down the agentic commerce shift?”

The push and pull of agentic buying tech will continue to disrupt the current market norms and only those retailers who understand the forces at work will thrive.

Need to know how agentic commerce will affect your business? Explore the latest research reports, IDC Agentic Commerce: B2C Buyer Life Cycle Opportunity Analysis to learn more about the forces impacting agentic buying.

Ananda Chakravarty

Ananda Chakravarty - Research VP, Retail Merchandising and Marketing Analytics Strategies

Ananda Chakravarty is Vice President for IDC Retail Insights, responsible for the Retail Merchandising and Marketing Analytics Strategies practice. Mr. Chakravarty’s core research covers in-store and digital retail merchandising, digital tools, artificial intelligence, intelligent store operations, retail marketing, and retail…
Heather Hershey

Heather Hershey - Research Director, Worldwide Digital Commerce

Heather Hershey is Research Director for IDC Worldwide Digital Commerce practice.  Ms. Hershey’s core research coverage includes digital commerce applications targeting businesses of all sizes and industries (B2C, B2B, B2B2C); Product Information Management (PIM) and syndication applications; Commerce personalization, search,…

Token计费账单逐月攀升、实时响应需求倒逼本地推理、数据合规红线日益收紧——端云集中算力的“三座大山”已让企业不堪重负。国际数据公司(IDC)最新预测数据显示,2026年全球Gen AI PC出货量将达0.5亿台、Gen AI手机将达4.32亿台,边缘算力正从概念走向规模化落地。本文为你拆解算力下沉的底层逻辑、终端变局与新兴赛道,并提供三类角色的可执行行动清单。

AI终端产业正告别云端集中算力时代。随着Token计费模式普及、端侧算力芯片性能跃升,以及企业对数据隐私和实时响应要求的持续提高,纯粹依赖云端处理AI任务的模式已触及天花板。IDC最新发布的2026年全球PC和手机市场预测显示,2026年全球Gen AI PC出货量有望达0.5亿台,占整体PC出货量的19.6%,Gen AI手机出货量预计达4.32亿台、占整体手机出货量的39.7%——AI终端已进入规模化普及期。

但终端出货量激增只是表象。更深层的变化在于:端-边-云算力替代端云扩容,正在成为终端产品差异化竞争、企业智能化降本增效的核心路径。理解这场算力架构的深层切换,才能精准抓住AI终端赛道的结构性机遇。


然而,出货量数字背后的驱动力是什么?为什么厂商和用户不约而同地将算力需求从云端“拽”回本地?答案藏在端云模式的三个深层矛盾中。

一、算力下沉的底层逻辑:端-边-云模式的三大优势

当前AI终端产业正从端云集中算力向端-边-云协同架构转型。这一转型不是技术潮流使然,而是端云模式的三大短板已触达临界点:边缘侧产生的成本节约与安全性。

第一,节约成本。 随着Token计费模式普及,企业AI算力支出持续攀升,轻量化AI任务尚可承担,高频次推理场景下云端调用成本已让多数企业难以负荷。当每月Token费用从数千元飙升至数十万元,算力成本的陡峭曲线正在倒逼企业寻找替代方案,边缘侧大大节约成本。

第二,减少延迟。 7×24小时待机、本地化智能响应已成为终端刚需,而端云模式下的网络延迟无法满足高实时性场景——从智能家居的瞬时响应到工业现场的实时决策,云端往返的毫秒级延迟正在成为生产力损耗。边缘侧本地计算减少数据延迟问题。

第三,数据安全。 企业高频次、高隐私的业务数据通过云端处理,合规风险持续放大。尤其是在金融、医疗、政务等敏感领域,数据不出域已是刚性约束。边缘侧完美的解决了以上诸多问题。

二、终端变局:AI终端正演变为边缘算力核心

算力下沉的第一波红利,落在终端设备的形态重构上。

算力下沉带动AI智能硬件迎来高速增长,AI终端市场已进入规模化爆发周期。算力下沉重塑了手机、PC等传统终端定位——它们不再是单纯的数据采集和展示工具,而是具备本地推理能力的边缘算力节点。目前海内外轻量化AI终端密集迭代,AI眼镜、智能耳机等新品持续涌现,AI终端正从边缘侧支持AI原生应用,成为算力支持中心。

这一变局的意义在于,数亿台AI PC和AI手机构成的不是零散的设备群,而是一个分布式边缘算力池。每台设备都是算力的生产者和消费者,端侧算力从“闲置资源”变为“可用产能”。

各厂商将AI PC定位为边缘算力中心,覆盖mini PC、AI手机、高性能笔记本、塔式工作站等多元形态,兼顾普通用户端-边-云算力需求与企业本地化高强度算力部署场景,搭建起分层边缘算力体系。行业竞争重心已从纯云端算力比拼,转向端-边-云算力调度、生态适配与协同优化,为产业带来全新机遇的同时,也对算力分配、硬件适配、算法迭代提出更高要求。

三、新兴赛道:AI小型算力中心激活边缘新潜力

架构切换的窗口期,往往是新赛道爆发的黄金期。

在边缘算力生态中,AI小型算力中心成为新兴优质赛道。传统NAS、服务器只是数据存储和计算设备,而AI小型算力中心彻底升级了这一品类——集成强算力、算法与本地AI处理能力后,AI小型算力中心可独立完成本地模型推理、数据分析,无需将数据上传云端即可实现智能处理。

这一变化带来三重价值:一是降低云端压力,高频本地任务无需消耗Token费用;二是提升响应效率,本地推理消除网络延迟;三是保障数据安全,敏感数据全程不出本地设备。AI小型算力中心打通了云边端一体化算力链路,在家庭场景中可支撑智能相册管理、本地知识库检索,在中小企业场景中可满足私有化文档处理、轻量级模型微调等需求,适配范围广泛且落地门槛低。
终端变了,赛道出来了——但这些趋势对三类不同角色意味着什么?答案不是统一的,而是分层分场景的。

四、行动指南:三类角色的差异化策略

端侧迁移是AI发展的必然趋势,边缘计算机遇与挑战并存。未来能否搭建成熟的边缘算力体系,将成为厂商产品突围、企业降本增效、从业者把握赛道红利的关键。以下针对三类核心角色,给出具体可执行的行动建议。

硬件厂商:用分层产品替代同质化内卷

厂商需摒弃同质化产品研发,针对三类用户群体打造分层产品:

  • 消费市场:侧重轻量化、低功耗、高适配的AI手机、mini PC、AI小型算力中心设备,适配云端订阅算力模式。
  • 中小商户:提供一体化的边缘算力解决方案,以AI小型算力中心为核心节点,降低本地AI部署的技术门槛和运维成本。
  • 大型企业:重点布局高性能AI PC、塔式工作站,强化本地算力与算法集成能力,满足高强度本地推理场景需求。

具体可落地的第一步: 盘点现有产品线中哪些具备“本地推理能力”标签,将其从“功能卖点”升级为“独立算力节点”来定义产品定位,围绕端-边-云协同重构产品路径。

企业用户:用混合算力替代盲目云端扩容

企业无需盲目采购高端算力设备,可采用“云端通用算力+边缘端专属算力”的混合模式:

  • 常规轻量化AI任务依托云端Token订阅处理。
  • 高频次、高隐私的业务数据通过本地AI设备处理。
  • 以此平衡算力成本、响应速度与数据安全三重目标。

具体可落地的第一步: 梳理企业当前所有AI应用场景,按“实时性要求(高/中/低)”和“数据敏感度(高/中/低)”两个维度画出四象限矩阵,将落在“高实时+高敏感”象限的任务优先迁移至端侧处理,直接砍掉对应的云端Token预算。

最终用户:聚焦边缘生态,把握细分赛道红利

消费者可重点关注AI终端适配、边缘算法优化、算力调度、AI小型算力中心落地等细分领域,依托终端硬件爆发红利,布局个人AI场景化应用解决方案。

IDC洞察

AI产业下半场的核心红利不在端云扩容,而在端侧边缘算力的落地与生态完善。AI PC、AI手机的规模化爆发重构了终端算力需求,AI小型算力中心等新赛道持续补齐场景短板,端-边-云协同将彻底替代纯端云模式。短期来看,行业仍需磨合端-边-云算力配比与落地标准;长期来看,边缘算力的普及将进一步降低AI应用落地门槛,释放全场景智能化升级红利。

IDC中国助理研究副总裁武止戈认为,未来随着端侧生态持续完善、算力硬件不断迭代,边缘计算将成为AI规模化落地的核心底座,重塑整个智能产业的发展格局。

进一步交流

IDC中国终端系统研究团队将持续提供AI终端研究、端-边-云行业发展洞察、为厂商、渠道、企业用户提供可落地的边缘算力布局决策方案。如果你需要最新IDC终端产品市场规模预测、赛道竞争格局分析、厂商专属咨询、或是想要精准评估自身产品客户满意度(NPS)可联系我们分析师团队(点击此处),我们可提供一对一深度咨询服务,帮你规避端云算力内卷,精准抓住端-边-云布局确定性增量红利。

Fiona Wu

Fiona Wu - Associate Vice President, Client System Research and IPDS, IDC China

Fiona Wu, associate vice president for IDC China's Client System Research, has 10 years of experience working in the IT industry and is focused on research and analysis of the China IT hardware market. She leads the team to provide…

This is the first post in Meet IDC Quanta, a short series showing what the product actually does, starting with the portal, then your inbox, then wherever you’re already working in Claude.


Try this with any AI tool:

What are the top three risks facing enterprise AI platform vendors in 2026, and which competitors are best positioned to capitalize on each one?

Here’s what happens when you ask IDC Quanta instead.

Type the question. Watch what loads.

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Ryan Smith - Content Marketing Director - IDC

Ryan Smith is the Director of Content Marketing at IDC, where he leads brand-level content and social media strategy, aligning research insights with compelling storytelling to engage technology decision-makers. With a background in both IT and marketing, Ryan brings a unique blend of technical understanding and creative strategy to his work. He’s also a seasoned storyteller, speaker, and podcast host who believes the right message, told the right way, can drive both trust and transformation.

What Happened in China’s Smartphone Market in Q2 2026?

China’s smartphone shipments came in at roughly 66 million units in the second quarter of 2026, down 4.3% year over year and the fifth straight quarter of decline. Rising memory and component costs pushed most Android vendors to raise prices, which cooled upgrade demand. Huawei and Apple were the exceptions, growing close to 20% and 25% respectively.

China Smartphone Market, Top 10 Companies Market Share, and YoY Growth, Q2 2026 (Preliminary results, shipments in millions of units)
Company2Q26 Market Share2Q25 Market ShareYOY Growth
1. Huawei22.6%18.1%19.4%
2. Apple18.1%13.9%24.4%
3. OPPO16.0%17.3%-9.7%
3. vivo16.0%17.0%-11.4%
5. Xiaomi12.4%15.1%-21.7%
6. Honor11.3%11.9%-9.5%
7. Wiko1.1%2.1%-49.2%
8. Lenovo0.3%0.4%-10.4%
9. ZTE0.3%0.4%-33.5%
10. Samsung0.1%0.4%-60.8%
Others1.8%3.4%-50.3%
Total100.0%100.0%-4.3%
Source: IDC Quarterly Mobile Phone Tracker, July 14, 2026
Note:
• Data are preliminary and subject to change.
• All figures are rounded off.
• IDC declares a statistical tie in the Smartphone market when there is a difference of one-tenth of one percent (0.1%) or less in the shipment shares among two or more companies.
• vivo includes vivo and iQOO; OPPO includes OPPO, OnePlus, and realme.

 What Drove the Quarter

The pressure this quarter traced back to a few causes.

  • Memory and other core component costs climbed from late March onward, and most Android vendors responded by raising prices or trimming configurations. That directly dampened consumers’ willingness to upgrade.
  • The lift from government subsidies faded, removing a prop that had supported demand in earlier quarters.
  • Both forces showed up plainly during the “618” shopping festival, where overall smartphone sales fell close to 15% year over year.

Huawei and Apple moved in the opposite direction for reasons that were just as concrete. Both held prices steady while the rest of the Android field raised them, and both layered on targeted promotions. Huawei kept widening its lineup to cover more of the market, while Apple’s early signaling of price increases on its second-half products pulled some buyers forward into the iPhone 17 series sooner than they might have bought otherwise. Strong brand pull helped as well.

The top six vendors now hold about 96% of the market, which keeps squeezing smaller and mid-sized brands and pushes concentration higher.

China Smartphone Market at a Glance, Q2 2026

  • Total shipments: about 66 million units, down 4.3% year over year
  • Fifth consecutive quarter of year-over-year decline
  • First-half 2026 shipments: about 134 million units, down 4.2% year over year
  • Huawei and Apple: each up roughly 20% year over year
  • “618” festival smartphone sales: down close to 15% year over year
  • Vendor tiers: Huawei and Apple lead, followed by OPPO and vivo, then Xiaomi and Honor close behind
  • Top six vendors: about 96% of total market share

Analyst Insight

“Huawei and Apple held their prices steady while competitors were raising theirs, and that gave hesitant buyers a reason to go ahead and purchase in a quarter when most of the market was giving them a reason to wait,” says Arthur Guo, Research Analyst, Client Devices Research at IDC China.

What Is IDC’s Outlook?

Vendors have been cushioned so far by earlier low-cost component inventory, and as that runs down, cost pressure should land more heavily in the second half of the year. On current trends, the year-over-year decline in China could widen to around 20% in the second half of 2026.

Looking into 2027, the market faces continued headwinds, since storage pricing is unlikely to correct in a big way. Even so, there is room for measured optimism. Consumers are postponing upgrades rather than walking away from smartphones, so that delayed demand should return in time. The smartphone remains the one device almost everyone carries and relies on daily, so its core position holds. A recovery looks likely around 2028–2029 as a fresh replacement cycle comes due.

One scenario worth watching is how deeply software developers and large-model AI companies get involved, and whether some enter the market directly. The “AI agent” phone could become the direction that breaks the current impasse, though real AI adoption depends on four elements working together: hardware, operating system, ecosystem, and large models. Only when those four are integrated closely does AI move from concept to daily use, and that is what could eventually spark the next wave of upgrades.

FAQs

Why did China’s smartphone market decline even with new launches and AI features? The main drag was price. Rising memory and component costs led most Android vendors to raise prices from late March, and with government subsidy support fading, hesitant buyers chose to wait rather than upgrade.

Which vendors benefited most in Q2 2026? Huawei and Apple, growing about 20% and 25% year over year respectively. Both held prices steady while competitors raised theirs, ran targeted promotions, and drew on strong brand loyalty. Apple also saw purchases pulled forward ahead of expected second-half price increases.

What could change the market’s direction after 2026? Component costs, particularly storage, will shape the near term, with pressure likely running into 2027. Longer term, capable AI agent experiences that integrate hardware, software, ecosystem, and large models could revive upgrade demand around 2028–2029.

Learn More

Read the latest press release on preliminary data from the Worldwide Quarterly Mobile Phone Tracker or for the full data set and vendor-level detail, visit here.

Kiranjeet Kaur - Associate Research Director - IDC

Kiranjeet Kaur is an associate research director for IDC Asia/Pacific. She is involved in building IDC's successful research tracker programs and producing core market data used across all of IDC's Asia/Pacific research reports and consulting projects. Based in Singapore, Kiranjeet is part of a team that manages the quarterly Mobile Device Tracker where she is responsible for sizing and forecasting the Smartphone and mobile phone markets on a quarterly basis, examining competitive trends and studying end user trends.