Logo
Become a Client

Resource Regions: Asia Pacific

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

Imagine you’re the account executive on a procurement software deal, and leadership on the buyer’s side just reached out: they want twenty minutes before the broader vendor review starts. Real conversation. No marketing slide deck, no long product tour. They’ll expect you to know their IT spending trajectory, where the competitive field is moving, and why now is the moment to act.

A fast reply won’t cut it here. You need an actual plan, reports pulled, trackers cross-referenced, findings stitched together. You could do that by hand, or you could open the AI tool sitting in front of you and ask it to build the whole thing.

One tool, every surface you’re already in

Most people assume IDC Quanta is a portal you visit. It’s actually a fabric: the same IDC intelligence, methodology, and research base, running everywhere you work. That’s Claude Chat, Cowork, and Code. It’s Claude for Chrome and the Microsoft Office add-ins, too. The deepest workflow integration lives in Cowork, which is where both scenarios below happen. One connection, live through Claude’s MCP integration (the open standard that lets Claude connect directly to outside data sources), with an IDC skill built in to handle the jobs sellers and buyers actually run.

You don’t switch tools to get IDC data into your work. You just ask.

Ask for the plan

Open Cowork and type it straight to Claude:

Build an account plan for a call with <your target account’s> procurement team, grounded in IDC’s data on their IT spending trajectory, the competitive landscape for procurement software, and any buying signals worth flagging.

Claude Cowork meets IDC Quanta MCP connector 1

Claude comes back with a couple of quick multiple-choice questions, like which vertical cut or which time horizon. Pick your answers, and it gets to work. A few minutes later, a fully structured account plan is sitting in a Word document. Spending trends are mapped out. Competitive positioning gets a clear read. Buying signals surface where they matter. The kind of document that used to take an analyst half a day now takes the length of your coffee.

Claude Cowork results from IDC Quanta Query

Watch it show its work

Here’s the part that actually matters. Open the document and you’ll notice something most AI tools never bother to do: it tells you which parts came from where. IDC-sourced data sits clearly apart from what Claude reasoned on top of it. Nothing blends together into an unlabeled wall of confident-sounding text.

That distinction is the whole point. When your VP asks where a number came from, you’re not guessing. You can point to the line and say exactly what it is: IDC research, or Claude’s synthesis. The document already told you.

IDC’s 2025 Worldwide C-Suite Tech Survey found that excessive time spent on forecasting and reporting ranks among sales leaders’ top three operational pain points (IDC #US54377226, March 2026). A sourced, labeled account plan is built to cut exactly that kind of drag.

Same intelligence, the other side of the table

Now flip seats. You’re the buyer, evaluating procurement software vendors, and you need an independent shortlist, not a vendor’s pitch deck dressed up as analysis.

Same tool, same Cowork window. Ask Claude:

Give me a shortlist of the top three options based on IDC’s worldwide procurement applications research, and generate an Excel evaluation scorecard I can share with my team.

Claude builds the scorecard from the same shortlist of the three vendors actually leading the field, sourced from IDC’s Worldwide Procurement Applications Market Shares, 2025 (IDC #US53723426, June 2026). The IDC-grounded data and Claude’s analysis are labeled separately again. Your team sees exactly what’s evidence and what’s reasoning, no matter which side of the deal they’re sitting on.

Everything You Need, in the Tab You’re Already In

Our first blog showed you a portal built for hard questions with real citations. Our second blog put that same intelligence in your inbox. This closes the series on the biggest move yet: IDC Quanta living inside the tools you use, Claude included, where the work happens. A seller and a buyer can each walk into the same negotiation better prepared, neither one leaving the screen in front of them.

If you’re already using Claude, the IDC Quanta connector is one prompt away. If you’re not yet a customer, book a demo and bring the account you’re working right now.

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.

Governance is no longer an afterthought for agentic AI. According to IDC’s Future Enterprise Resiliency and Spending Survey, Wave 10, enterprises now allocate an average of 16.7% of their total planned AI spending to AI and Agent security and governance, a share on par with investment in other core layers of the AI tech stack. The takeaway for CIOs and vendors: governance has moved from a compliance checkbox to a strategic budget priority.

That institutional shift was visible at WAIC 2026 in Shanghai. On July 16, representatives from 29 countries signed the Agreement on Establishing the World Artificial Intelligence Cooperation Organization and became its founding members. The following day, the conference issued a chair’s statement and two governance action plans, while also proposing a global initiative on trusted Agent connectivity and interoperability. A year after China proposed the creation of WAICO at WAIC 2025, the signing of the agreement represents institutional follow-through worth watching. Its practical impact will depend on how these commitments are translated into common standards, coordination mechanisms and implementation programs.

Why Governance Is the Gatekeeper, Not the Brake

IDC’s October 2025 FutureScape: Worldwide Agentic Artificial Intelligence 2026 Predictions forecasts that by 2030, up to 20% of G1000 organizations will have faced lawsuits, substantial fines, and CIO dismissals due to high-profile disruptions stemming from inadequate controls and governance of AI agents. Far from restricting what Agents can do, governance is what makes deeper integration possible in the first place. Without clear identity and authorization rules, companies won’t grant Agents access to core systems. Without full observability and immutable logs, teams can’t reconstruct what happened after an incident. Without built-in suspension, rollback and human-override controls, businesses can’t contain the damage when an Agent misbehaves. Trust scales with control and not the other way around.

Three Layers of Trust

Enterprise confidence in Agents builds across three interconnected layers:

1. Internal controls — Agent identity, permissions and audit trails

2. Cross-platform interoperability — standardized verification mechanisms

3. Cross-border alignment — regulatory frameworks and capacity-building

As Agents increasingly need to work across organizational and platform boundaries, the second layer matters as much as the first. Shared identity credentials, common connectivity standards, and audit-ready activity trails keep accountability clear even across company lines. Without them, every cross-platform integration means redundant security validation and custom risk assessment — a tax on the whole Agent economy.

Translating Global Governance Rules into Enterprise Agent Design

The three trust layers above aren’t abstract, they dictate real architecture choices. Identity and permissions become credential and account governance. Audit-ready activity trails become tool-invocation logs and accountability records. Risk tiers become access scopes and human-review checkpoints for high-stakes actions — the same logic enterprises already apply to a routine quotation-approval workflow, now extended to Agents.

What This Means for Enterprises and Vendors

This reshapes how enterprises should design and evaluate Agent systems in practice:

  • Assign every Agent a unique identity — with defined authorizing stakeholders and bounded permissions, calibrated to risk. Routine tasks can run autonomously; anything touching sensitive data, capital transactions or production systems needs human-override capability.
  • Build fully observable, rollback-enabled pipelines — with complete audit trails covering every input, tool call and state change.
  • Push vendors for verifiable evaluation results — beyond task completion rates, including how well an Agent respects privilege boundaries, recovers from failure and escalates to humans.
  • Architect for cross-organization collaboration — Agents should present verifiable credentials, and receiving platforms should enforce granular, risk-based access policies.

Need to know how AI governance will impact your business? Explore the latest research, AI Governance: The Trust Layer — Governance Is Not Glamorous, But It Is About AI Trust and 中国智能体开发平台市场份额, 2025 to learn more. Fill out this form to Contact Us.

This blog is an extract of the original blog WAIC2026现场观察| 聚焦可行动AI，探索适配产业的治理规范 by Zhenya Sun, published in WeChat.

Zhenya Sun - Research Manager - IDC

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

At the IDC Quanta launch webinar, Joe Bradley, CTO at IDC, made the case for why trusting an AI-powered answer shouldn’t require faith; it should require an architecture you can actually inspect. You’ve heard “AI-powered” enough times this year that a healthy dose of skepticism is the right response. Fair. So instead of asking you to trust that IDC Quanta gets its answers right, here’s what Bradley says is actually happening under the hood when it does.

Bradley breaks it down into two layers. The first is an MCP server: essentially a pipe that gives Claude direct access to IDC’s data, the trackers, the forecasts, the market figures. It also carries instructions for how that data is structured and how to use it. The second is IDC’s own Claude plugin, which goes further, Bradley explains. It shapes how the AI reasons about that data, tells it what’s relevant for what purpose, and requires it to surface a source before handing over any answer. Put together, when someone asks a question, Claude isn’t searching a phrase in a database, Bradley says. It’s reasoning with IDC’s own methodology built into the process.

That’s the theory. Here’s what it looks like in practice.

The Moment It Earned Trust

In the product demo, Bradley walked through a fictional scenario: Marcus Chen, a Senior FP&A analyst at Vantix Security, is building a market forecast ahead of a CFO review. He is projecting 16% growth in a segment his company competes in. He asked IDC Quanta to check that number against an external benchmark, right inside Excel.

In under a minute, Quanta surfaced IDC’s actual forecast for that market: 12.1% growth through 2029, with the category decelerating to single digits in the later years. His model hadn’t caught up to where the market was actually headed. That’s the payoff of the architecture above. The answer arrived with its source attached.

Why Even Build an App

Technical buyers reasonably ask why IDC needs its own app when Claude and ChatGPT already exist. IDC isn’t positioning itself as a competitor to the assistants people already use daily. It’s building something with a narrower job.

The case for IDC Quanta comes down to control over how IDC’s own data gets handled and delivered. It exists because of what only a dedicated app can guarantee: a single place that collects everything relevant across an IDC relationship, a direct line to a live analyst when the automated answer isn’t enough, and data handling built on tenant isolation, enforced access controls, and audit logs that capture every user action. Which raises the next question technical buyers ask first.

Provenance You Can Check Yourself

Bradley’s last test for anyone skeptical of AI-generated answers is provenance. Can you verify where it actually came from? In a second demo, he showed IDC Quanta processing a strategy document sent over email, then breaking its answer down into cited data cuts, each one tied explicitly to the filters and definitions behind it.

Nothing here is asserted without a source attached, and nothing requires trusting the AI’s summary over the underlying data itself. That’s the actual answer to “how do you know it’s not confidently wrong”: you don’t have to take Quanta’s word for it. You can check.

See It Yourself

The architecture, the demo, and the sourcing are easier to evaluate firsthand than to take on faith. Request a demo, or talk to your IDC account team if you already have one.

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.

过去几年，大语言模型和生成式AI率先改变知识处理、内容生产和软件流程。随着多模态模型、世界模型、VLA模型和边缘AI持续发展，AI正在进入车辆、机器人、无人机、工业设备和城市设施，能力范围也从生成内容、调用软件，扩展到感知环境、规划任务和控制设备。

物理AI面对持续变化的真实环境，需要处理设备状态、空间关系、环境变化、物理规律和安全要求。系统既要形成“感知—决策—执行—反馈”的实时运行闭环，也要形成“真实运行—数据回流—模型训练—仿真验证—重新部署”的研发迭代闭环。前一类闭环影响任务能否稳定完成，后一类闭环影响产品能否持续改进并扩大应用范围。

IDC 2026年全球CEO调查显示，35.2%的受访CEO将物理AI列为未来12—24个月重点关注的新技术投资方向。资源行业的比例达到50%，制造和零售均为46%，医疗为38%。当前市场已经进入投入和生产验证并行阶段，企业普遍从任务清晰、数据可获得、收益可衡量的场景切入。

IDC认为，硬件决定系统能否稳定进入现场，软件影响学习速度、验证效率和复制能力，数据闭环负责把应用需求、软件工具和硬件执行连接起来。物理AI未来的差距，会越来越多地体现在运行数据能否沉淀为场景资产、验证用例和模型更新。

数据闭环贯穿三层产业架构

为了更系统地拆解数据闭环如何落地，IDC将中国物理AI产业划分为三层相互咬合的结构。 应用和业务层定义真实任务、运行流程和商业价值；软件基础设施层组织模型与策略、仿真验证和场景数据；硬件基础设施层提供计算、连接、供能、感知和物理执行条件。

三层围绕真实运行形成循环：应用产生任务和现场问题，硬件完成感知与执行，软件把运行数据转化为训练数据、仿真场景和验证用例，更新后的模型再进入设备和业务系统。下面逐层展开，看每一层在闭环中具体承担什么角色、当前进展到哪一步。

应用和业务层：闭环质量影响商业化节奏

任务边界、数据反馈、安全责任和投入产出，是判断场景成熟度的四项重要条件。运行问题能够被记录、复现和验证的场景，更容易进入规模部署。

智能驾驶已经形成较完整的数据采集、场景挖掘、仿真测试和版本回归流程，是当前数据闭环较成熟的物理AI场景。

具身智能终端正在从动作演示进入连续任务执行阶段。任务成功率、人工接管频率、异常恢复能力和跨环境适应能力，将逐步成为主要评价指标。

无人机和空天系统需要把飞行状态、空间环境、通信链路、任务调度、运行监管和异常处置纳入统一的运行与验证体系。

工业现场与工程装备拥有清晰的工艺、安全和成本目标。质量检测、预测维护、物料搬运、巡检、精密操作和无人作业等任务，已经开始产生可量化的效率、质量和安全收益。制造业具备流程明确、数据基础较好和指标体系成熟等条件，将继续成为物理AI的重要验证场。

智慧城市相关应用需要打通感知、分析、调度和执行。AI进入交通、设施、低空和应急等物理系统的实际运行后，才会形成更完整的物理AI能力。

无论哪个场景，应用层产生的海量运行反馈，都需要经由软件层转化为可复用的能力——这正是下一层的核心使命。

软件基础设施层：把运行反馈转化为可复用能力

软件基础设施层包括模型与策略、仿真与验证、场景数据与合成数据。三类能力通过数据闭环持续协同，决定物理AI的进化速度和规模复制能力。

模型与策略方面，世界模型仍处于多条技术路线并行发展阶段，可用于交互环境生成、状态变化预测和行动结果推演；VLA模型连接视觉、语言和动作，为机器人及自主设备生成任务策略。未来系统将更多采用分层架构，由高层模型理解任务与环境，中间层完成预测和规划，底层控制系统负责实时、稳定和安全执行。

仿真与验证方面，数字孪生、空间智能和仿真平台之间的协同正在加强。数字孪生提供设备结构、空间关系、物理参数、工艺规则和实时状态；空间智能帮助模型理解三维环境和对象关系；世界模型扩展场景生成、状态预测和策略探索；仿真平台承担测试、回归和安全验证。虚拟环境由此可以覆盖训练、方案推演、系统测试和运行优化。

场景数据与合成数据方面，物理AI需要的数据已经从单帧图像和单点记录，扩展到包含时间、空间、设备状态、动作过程和执行结果的连续场景数据。真实运行中的故障、接管、任务失败和高风险事件，需要经过筛选、标注和结构化处理，沉淀为可检索、可复现和可重复使用的场景资产。

数据闭环将分散的模型、数据和仿真工具连接成持续研发体系。任务覆盖度、异常覆盖度、问题复现率、回归关闭率和跨版本一致性，将逐渐成为物理AI软件平台的重要评价指标。物理AI软件平台商业模式也会从单次工具采购，延伸到场景资产管理、模型生命周期管理、持续验证和安全证据链服务。

软件层的一切优化最终都要回到物理世界中验证，而硬件层正是这个闭环的起止点。

硬件基础设施层：连接真实数据与物理执行

硬件层包括能源电力、算力和IoT/OT连接基础设施。云端承担模型训练、批量仿真和数据处理，边缘节点负责现场协同与模型管理，设备端完成实时感知、推理和控制。

传感器、摄像头、雷达和设备运行系统提供真实环境数据，控制器和执行器负责把模型判断转化为物理动作。硬件层既是数据闭环的起点，也是系统执行结果的出口。

涉及车辆制动、机器人避碰和工业安全的关键任务，还需要保留本地执行、故障降级、冗余控制和人工接管能力。

中国在设备制造、通信网络、能源系统和工程实施方面具备较好基础，高端AI芯片、工业级传感器、核心零部件和复杂环境可靠性仍需持续提升。

三层架构完整就位之后，真正的挑战不在于单点突破，而在于如何让闭环从技术层面延伸到行业应用层面。

展望：从技术闭环走向行业闭环

未来几年，世界模型仍将保持多条技术路线并行，数字孪生、空间智能、仿真平台、合成数据和真实运行数据之间的连接会继续加强。模型能力的提升将扩大系统可以完成的任务范围，验证体系则负责控制进入真实环境的速度和风险。

中国拥有丰富的制造、城市运行和机器人应用场景。下一阶段需要提高软件平台的产品化水平，推动场景数据跨项目复用，加强设备接口协同，并建立覆盖模型、硬件和运行过程的评测与验证体系。

物理AI的市场差距将更多体现在系统学习效率、验证可信度、长期运行能力和跨场景复制能力。企业能否把现场问题快速转化为场景资产、验证用例和模型更新，将影响其产品迭代速度和规模化能力。

IDC将持续跟踪物理AI技术、市场和生态变化，并通过“物理AI+行业场景”的研究方式，重点关注制造业、城市物理系统和具身智能等方向，进一步分析技术架构、市场机会、厂商格局和产业化路径，为技术供应商和行业用户提供持续参考。

进一步交流

如需了解物理AI架构评估、场景落地路径或数据闭环能力诊断等研究方向，欢迎联系IDC中国物理AI与行业智能化研究团队。我们将安排对应行业分析师与您深入沟通，提供定制化决策参考。

Lily Li

Lily Li - Research Manager

Analyst Profile
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,…

This is the second 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.

It’s 4:47 PM. You’re prepping for tomorrow’s board meeting, and a question just landed that wasn’t on your list: how does your cloud infrastructure position compare to what’s shifted in the market over the last two quarters. You could open a portal, log in, run a search, filter by date, read three reports, and synthesize an answer. Or you could pull up your phone in the elevator.

Most executives don’t have a research workflow. They have an inbox. IDC Quanta was built on that premise: the fastest tool for a time-pressed leader is the one they never have to open, because it’s already open.

What it actually does

IDC Quanta in email lets you ask a question and get a sourced IDC answer back in about 60 seconds. It works the way you’d already expect email to work, which is exactly the point.

Emailing Quanta
  1. Compose an email to brief@quanta.idc.com, from your phone, your laptop, whatever’s in front of you.

  2. Ask the question in plain language. No query syntax, no keyword tricks. “Which cloud infrastructure vendors gained share in EMEA in the past 12 months?” is a complete request.

  3. Send it, and keep moving. A structured, sourced answer arrives in about 60 seconds, fast enough that you can send the question walking into a meeting and have the answer before you sit down.

  4. The reply carries its own receipts. Every claim in the answer traces to a specific IDC source. If you want to go deeper, “View in IDC Quanta” drops you straight into the full portal conversation, context intact.
A sample email response from IDC Quanta

No login screen to remember, no new app for IT to vet. And no tab-switching mid-meeting. The intelligence comes to the inbox. The inbox doesn’t change to accommodate it.

Where this earns its keep

The mechanic is simple. What makes it valuable is what it replaces across a week of an executive’s actual work:

  • Before the call.
    A prospect meeting is in twenty minutes and you need a current read on their competitive positioning. Email the question on your way to the conference room. The answer’s there before you sit down, cited and ready to use.

  • Benchmarking your own thinking.
    Attach a competitive deck or account plan to your email along with your question. IDC Quanta reads it alongside its own research base and flags where your internal view and IDC’s tracked data disagree. You’re getting your own analysis checked against the numbers.

  • The follow-up nobody has time to chase.
    Someone asks a sharp question in a meeting and the honest answer is “let me get back to you.” Now that follow-up takes one email and about a minute. Who else on your team wishes they had that?

  • Board and investor prep, compressed.
    The night-before scramble for one more data point doesn’t need the whole deck reopened. One email, one sourced answer, dropped straight into the slide.

Why this isn’t just a fast AI reply

A lot of tools will answer an email question with confidence. Confidence isn’t the same as being right, and it isn’t the same as being defensible in a room full of people who will ask where the number came from. Every IDC Quanta answer draws on IDC’s research base: 1,000+ analysts across 100+ countries, tracking 15B+ data points and 800K+ companies annually. That’s the citation trail behind every answer. It’s what makes an answer dropped in your inbox worth repeating in the room.

Get it in your inbox

If you’re already an IDC Quanta customer, brief@quanta.idc.com is live. Send the question you didn’t have time to research properly and see how fast a sourced answer actually moves.

If you’re not yet a customer, request a demo, and we’ll show you what it looks like when your inbox starts acting like a research 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.

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。

在模型层面，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。

在数据层面，“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。

在算力层面，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。

从应用场景来看，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。

从工程化能力来看，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。

从商业模式来看，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

Analyst Profile
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,…
Predictions Jul 20, 2026 Erin Lin

IDC最新报告：医院核心业务系统市场规模破100亿大关，智能体与医共体成为“十五五”时期新增长极

2025年国内医院核心业务系统市场的智能体、医共体、本土替代的改造需求将稳步释放，成为新的增长极。头部厂商相继完成云原生底座迭代与医疗大模型原生融合布局，AI 智能体、全域数据治理、全栈信创持续迭代，未来市场的竞争将更加聚焦于技术深度与、服务运营与生态协同能力的综合较量。

Read full release
Marketing Jul 20, 2026 Siri Si

IDC最新报告：揭秘金融生成式AI规模化卡点，端到端落地能力决定胜负

未来市场竞争不再比拼单一产品功能，而是由分层产品体系、金融专属工程落地能力、多智能体协同架构、长期持续运营服务四大维度共同决定。数据治理、模型全生命周期管控、智能体治理等核心议题将贯穿行业发展全程，能够深度融合金融业务规则、解决规模化落地工程难题、绑定业务长期价值的解决方案，将主导下一阶段金融行业大模型市场格局。

Read full release