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Agentic AI is changing what employees do all day. As agents take on much of the work, employees are left to scope the task, write the instructions, watch the run and check the output. That is AI orchestration.

Problem is, almost no one was hired or trained for this.

So can employees do it? The early numbers aren’t encouraging. In the IDC 2026 Global IT Skills Survey (April 2026), only 31% of IT leaders said their organizations are ready for AI. The biggest blocker they named was a lack of AI skills.

The gap reaches past technical skills. In IDC’s Future Enterprise Resiliency and Spending (FERS) Survey Wave 1 (January 2026), enterprise leaders called analytic judgment the single most essential skill for working with AI. They also placed it among the skills most lacking in their organizations. Agents make that gap expensive. They work fast, and their output reads with confidence regardless of whether it is correct.

An employee who cannot spot a hallucination or push back on AI when it is wrong or biased will just make confident-sounding mistakes at the speed of AI.

Maturity, unfortunately, doesn’t close the gap. In IDC’s Worldwide IT Practitioner Survey (June 2026), IT leaders reported that as AI maturity rises, specialized skills get harder to find. Examples include telemetry interpretation, operational governance and orchestration itself. Scaling automation does not produce the people needed to govern it.

Moreover, training budgets may be pointing at the wrong target. Half of global enterprises now invest in agentic AI technology (FERS Survey Wave 2, March 2026). Orchestration training has pooled around prompting because prompting is the easiest piece to buy. That is dangerously shortsighted. Yes, instruction writing matters. But it is the piece least likely to cause a public failure. The costly gaps sit in framing, supervision, oversight, verification and governance.

Five limbs of orchestration

IDC’s AI Orchestration Skills Framework splits the human work into five limbs:

  • Frame: decide what the workflow is and what stays human
  • Delegate: turn intent into instruction
  • Supervise: run the workflow and know when to step in
  • Verify: check the output and the outcome
  • Govern: own the workflow over time

Each limb draws on four kinds of skills: technical, business, leadership and human. The mix shifts around the framework. Technical skills concentrate in Delegate and Verify. Business skills carry Frame. Human and leadership skills support Supervise and Govern. That unevenness is useful. It tells learning leaders which groups need which limb, so they can drop the single course for everyone.

The limbs do not run in sequence, and the framework is no maturity ladder. Every workflow in production passes through all five, which is why employees need a mix of orchestration skills to thrive in the age of AI.

Two layers of workers

The first layer is the specialists. Enterprises are staffing new hybrid roles such as workflow orchestrator, agent operations lead and AI risk monitor. These roles are visible and funded.

The second layer is larger. It includes every employee who now runs agents as part of ordinary work and has no title for it. Nobody handed these people an orchestration role. The work arrived inside the job they already had. They run production workflows with no plan at all. That is how an enterprise ends up with a solid AI training program and a serious oversight problem at the same time.

“

Every enterprise putting agents into production is quietly asking employees to orchestrate. But very few organizations I speak to have explicitly described to employees what orchestration involves. Even fewer tell me they have trained them for it.

— Gina Smith, PhD, Senior Research Director, IDC

Where to start

  • Name one owner for every agentic workflow in production.
  • Do not count prompt engineering as orchestration training.
  • Measure the process before an agent touches it. Without a baseline, any savings claim is unprovable.
  • Get verification working before scaling. Fluent, wrong output is the failure most likely to reach a customer.
  • Make it safe to halt a run. Teams punished for a false alarm go quiet.

The full report breaks each limb into skills, what those skills look like in real work and how to train them. Enterprises will deploy agents whether or not the skills are ready.

Read the full report: IDC PlanScape: An AI Orchestration Skills Framework

Gina Smith, Ph.D. - Senior Research Director, IT Skills for Digital Business - IDC

Analyst Profile LinkedIn
As a Senior Research Director at IDC, Gina Smith produces research in the IT education and skills sector. Her responsibilities include primary research, analysis, and the production of market insights worldwide. The New York Times bestselling author of Apple cofounder Steve Wozniak’s memoir, iWoz: How I Invented the Personal Computer (2006/2011) and The Genomics Age (2005), a Barron’s Book of the Year, Gina has more than 25 years of working experience in technology journalism, publishing, and tech startup management. She was CEO of Oracle founder Larry Ellison’s network computer startup in 2000, served ABC News as its first on-air technology correspondent, and was editor-in-chief of BYTE magazine.

If your most critical systems went down tomorrow because of a cyber attack, how long would it take to get running again? And when did you last test that?

Michelle Abraham, Research Vice President on IDC’s Security and Trust team, says leadership should know both answers. In a recent conversation, she explains what that takes: cybersecurity fundamentals that rarely make headlines, such as knowing what you own and who owns it, and tracking whether risk is going down.

Watch the full interview below, then read on for the points security leaders and business leaders can act on.

What cybersecurity fundamentals do organizations still get wrong?

Abraham’s answer starts with what hasn’t changed. “Foundational items like asset visibility and making sure you have the data collected are as important as they always have been,” she says. Knowing what you have comes first. Identity and access controls follow: least privilege, strong authentication such as MFA, credential hygiene, and regular reviews of who has access. Then come the logs and the right detections, plus tested processes for when an incident does pop up.

She notes these topics get less airtime than newer technology, and she doubts that a tool for every threat category makes an organization secure.

Does buying more security tools make an organization safer?

Not on its own. “More tools can just mean more noise,” Abraham says. If a team can’t act on the alerts from the tools it already has, another one adds to the pile. Fragmentation adds complexity, and “complexity can be the enemy of security.” Each tool tends to see only part of the attack surface, so the data has to be connected or attackers will find the gaps between tools.

Abraham ranks backup and recovery systems above most detection tools. Before the next purchase, ask what your team can’t act on today.

How should leaders measure whether security is improving?

If tools don’t show whether security is working, something else has to. Reports that count scans run or issues found measure effort. Counting activity, in her words, “just tells you how busy your security team is, but not whether you are any harder for attackers to breach.” She adds that a count doesn’t say whether critical issues were fixed or how long they sat open.

Her alternative starts with the business: which threats matter most, how much risk the organization will accept, and whether current risk sits within that level. Useful measures include how complete your asset inventory is and how much of it controls like MFA and endpoint protection cover. Third parties belong on the list: she sees organizations attacked through vendor systems, including data warehouse and backup systems. The goal is “looking at the trends more than the counts,” with risk moving down over time.

Why do security fixes stall between teams?

In the handoffs, Abraham says. Discovery tools can count the same asset more than once, so teams need a way to deduplicate. The bigger sticking point is ownership. When a vulnerable asset turns up, the security team usually isn’t the one who fixes it. One person may own the asset and someone else the application running on it. Without a clear record, the issue ping-pongs around and the fix waits. Automated asset tagging helps, along with ownership information that matches across every system.

Prioritization matters here too. A lower-severity issue that is reachable, sits on a critical asset, and is being exploited should move to the front of the queue. A high CVSS score can send a team after an exposure that doesn’t matter in its environment. Even a well-ranked list stalls, though, if the people who own the fix don’t treat it as their job.

How do you get the whole organization to own security?

The people who fix a problem often sit outside the security team, so Abraham recommends making the work easy for them. Give fixing teams a ranked list of validated exposures, with patch information or recommended mitigations attached so nobody starts from scratch. Set deadlines by asset criticality, such as three days for one class of asset and a week or two for another, and define an exception process. “If the remediation process isn’t tracked, oftentimes it doesn’t happen.”

Then make risk visible by department in front of the management team. Organizations have told her this creates enough competition that departments want to look better in the dashboards. She has also heard of it applied to asset tagging: who has the most assets with complete tags.

What one question should every leader ask?

Abraham leaves leaders with one question to ask their security teams:

“

If our most critical systems went down tomorrow due to a cyber attack, how long is it going to be till we’re operating again?

— Michelle Abraham, Research Vice President, Security and Trust, IDC

Compare the answer with how long the business can survive without that system. A mismatch puts decisions in leadership’s hands: a change to process or IT architecture, or more funding.

Abraham also urges testing, and mapping the dependencies between systems. If your identity system is down, you may not be able to bring up the others. Start there: find out what has to be running before anything else can.

Christina Cardoza - Content Marketing Manager - IDC

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

AI spending is arriving before the business case. In IDC’s 2026 Worldwide AI Economics and Cost of Ownership Survey, 69% of organizations said there is a significant gap between AI spending and measurable financial outcomes. Only 15% had realized an AI ROI above 100% to date.

That gap is why enterprise AI deals now turn on three proofs: value that holds up with finance, trust on governance and security, and support that reaches beyond IT. Underneath sits a fourth gap, culture and operating model, because agentic AI changes how work gets done and buyers haven’t caught up. IDC’s analysts say vendors who leave any of the four open will see their deals slow down.

At IDC’s October 7 webinar, The AI Execution Gap: A Playbook for Selling AI to the Enterprise, Laurie Buczek, GVP of Market and Business Intelligence, and Tony Olvet, GVP of Worldwide C-Suite and Buyer Insights Research, laid out what vendors now have to prove.

Agentic AI changes how work gets done, and buyers are still catching up

The gap underneath the other three shows up in the adoption numbers: IDC research found that 80% of organizations have started deploying agentic AI, yet only 17% use it enterprise-wide. Business teams find it hard to picture a way of working that doesn’t exist yet, and many business leaders can’t tell their IT counterparts how agentic AI will change their function. Buczek said IDC’s surveys and conversations show CEOs seeing agentic AI as a way to transform their business models. For vendors, that means a pitch about the tool lands on a buyer who is still working out the redesign. Olvet’s closing advice was to lead with business transformation.

Prove the value in terms finance will accept

That redesign has a price tag, and cost has moved from a worry to a risk. In an IDC survey from May 2026, 73% of respondents named excessive AI cost a significant risk. Nearly half of organizations can’t prove out AI value, in part because they’re applying digital transformation-era frameworks to agentic AI, which keeps learning and changing. Baselines are hard to set when the operating model itself is being reinvented.

The vendors who get through anchor the pitch in outcomes the customer can measure. Olvet’s test: can your customer report the result on an earnings call? Some large banks already point to net new revenue and product launches that came out of their AI investments. IDC expects infrastructure build-out to catch up with demand around 2029, which should bring costs down. Until then, the guidance is to articulate long-term value.

Governance is a sales issue, not just a compliance one

Go back to the 17% figure. The webinar’s explanation starts with governance: agentic AI is hard to govern, and trust takes time to build. Governance and security also topped the webinar’s opening poll on the gap that slows AI deals the most. Security is often the number one roadblock to implementation and to realizing value, so raise it before the buyer does and make the reduced risk easy to document in the buyer’s own governance platform.

IDC analysts built a five-pillar model for agentic AI governance. It covers areas such as explainability and auditability, and identity and access, including non-human identities. The full set is in the webinar replay. In a special IDC study, 96% of early agentic adopters governed it centrally.

A single IT champion won’t carry the deal

Governance and finance reviews bring more people into the deal. IDC’s September 2026 C-Suite Tech Survey asked who is involved in AI funding decisions. The CIO or CTO still leads at 63%, but C-level business function leaders follow at 62% and CFOs at 61%. In most organizations, business leaders now lead or share agentic AI investment decisions. A chief AI officer is joining the room too: in an IDC CEO survey, 42% of CEOs said they plan to hire one in the next 12 months.

Sellers should build contacts across IT, the business and finance, and use their champion and sponsor to open doors.

What to do next

Buyers have grown wary of AI theatre. Their top frustrations with the AI vendor community, from IDC’s C-Suite Tech Survey, are limited integration with existing systems, lack of model and data transparency, and vendor lock-in concerns (each at 34%), followed by overpromising and underdelivering and unclear pricing models (each at 31%).

Against that backdrop, sell how your product changes the way a team works, and bring a business case a CFO can defend, built on outcomes the customer can measure. Put governance and security on the table early, with a way to document the reduced risk. Plan the deal around a committee that spans IT, the business and finance.

Where does your go-to-market stand?

If these four gaps sound familiar, start with the one that slows your own deals the most. The AI Execution Gap Benchmark lets you grade your go-to-market readiness against all four, using what enterprise buyers are asking right now.

Get the Benchmark | Watch the webinar replay

And if you want independent proof behind your AI story, explore IDC Multiplier.

Christina Cardoza - Content Marketing Manager - IDC

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

影像云、AI医疗云成为市场的新机会。国际数据公司（IDC）最新研究显示，2025年中国医学影像云解决方案市场规模达到14.5亿元，前五名厂商合计份额为30.8%。与此同时，大模型与智能体技术落地医疗场景，推动医疗云定位发生转变，从传统业务运行底座，逐步升级为 AI 应用的承载底座。两大变化共同驱动医疗云厂商的增长逻辑发生切换，竞争重心由基础设施资源比拼，转向场景落地、AI 能力建设与生态协同。

IDC结合近期发布的《中国医疗IaaS+PaaS市场份额，2025》、《Market Share：中国医学影像云解决方案市场份额，2025》及《IDC MarketScape：中国AI医疗云2026年厂商评估》等研究报告，解答三个问题：影像云市场的现实增长机遇在哪里？AI 医疗云的核心竞争门槛如何界定？不同类型厂商应当如何调整自身竞争策略。

影像云市场短期将迎来快速增长，平台及应用层是厂商是关键切入点

医保影像云索引、云胶片、跨区域影像调阅等政策持续落地，影像云正在从医院内部辅助工具，升级为区域医疗互联互通的核心基础设施。市场需求也从单纯存储备份，延伸至云 PACS、远程协同诊断、影像质控、AI 辅助诊断等多元应用。

IDC 数据显示，2025 年国内医学影像云解决方案市场规模为 14.5 亿元，市场规模有限。然而，伴随医保影像云项目陆续投入运营，未来 1-3 年行业将进入高速扩张阶段。长期来看，影像云的建设重点将转向数据价值挖掘，AI 辅助诊断、智能报告生成、全流程影像质控，会成为市场第二增长曲线。

影像云解决方案市场前五名厂商合计市场份额为30.8%，市场呈现出“底层集中、应用分散”的竞争特征。三大运营商依托网络、数据中心和政企合作资源，在底层基础设施中占据重要位置，新厂商很难在这一细分市场实现突破；而平台与应用层格局未定，各类厂商仍可凭借专业产品、医疗行业理解和本地服务能力进入市场，并建立差异化优势，从而取得更高份额。

AI医疗云需求增长在即，AI技术适配与行业属性成为核心竞争力

医疗业务持续迭代，叠加大模型、智能体技术普及，医院对医疗云平台提出新的要求。AI 应用不再局限单点辅助工具，逐步进化为可以调用业务系统、处理复杂业务任务的智能体，医院对于异构算力、模型推理、数据治理、智能体运行环境的需求持续提升。医疗云也将从传统业务系统的运行底座，逐步演进为AI应用和智能体的运行底座。

现阶段不同等级医院数字化基础、预算投入、数据治理能力差距明显，多数 AI 应用仍处于试点局部落地阶段，整体新增算力需求还未完全释放。但随着医疗智能体应用数量与覆盖场景扩张，云上运行 Agent 会成为医疗云重要增量来源。

基于市场需求的变化，医疗云厂商的解决方案不再局限于服务器、存储和云资源价格，而应该逐步转向AI算力能力、数据平台、AI模型及智能体平台能力，以及医疗业务适配能力和医疗专业技术服务能力的综合能力建设。基于这一趋势，IDC发布了《中国医疗IaaS+PaaS市场份额，2025》，并推出《IDC MarketScape：中国AI医疗云2026年厂商评估》，从市场份额和厂商能力两个层面观察行业发展。

从市场格局来看，中国移动、中国电信、中国联通凭借云网融合和政企渠道优势快速崛起，占据了市场领先梯队。同时，中国移动、中国电信、中国联通、浪潮集团能够将基础设施优势转化为面向医疗AI的综合服务能力，同时深耕医疗行业，适配医院业务和法规的需求，并提供持续稳定的运营服务，从而在厂商评估中位于领导者类别。

短期内，厂商应立足自身禀赋，以合作共赢抢占横、纵向市场机会

行业竞争从底层基础设施的比拼，转向应用场景与生态层面的综合较量。各类厂商应该根据自身禀赋动态调整自身策略：

首先，横向拓展影像云等业务场景。 对具备云基础设施、网络资源和区域交付能力的云技术厂商，应关注医保影像云、县域医共体和区域医疗协同等业务机会，并通过平台运营和生态合作拓展服务范围；对具备医疗软件及专业影像产品积累的厂商，则应加强跨机构协同、数据利用和AI应用能力，提升专业产品的持续服务价值。

其次，纵向在业务中融合AI能力。云厂商需要加强异构算力、模型服务、数据处理和智能体运行环境的协同，但不应仅以算力规模或模型部署能力作为产品竞争力的衡量标准。医疗业务的高度专业性，医疗AI技术厂商可与云厂商形成互补，将自身在临床知识、影像分析、病历理解等领域的积累转化为可复用的AI服务能力，从而更好适配用户对于一体化解决方案的需求，提升整体交付效率与长期运营价值。

最后，开放生态补齐能力短板。 医疗云建设涉及基础设施、医疗软件和AI应用等多个环节，单一厂商难以覆盖全部能力。云厂商需要明确自研与合作的边界，与医疗信息化厂商、设备厂商及专业AI企业建立协同机制，综合开发解决方案，将本土化、安全、稳定性和持续运维纳入整体方案设计，逐步形成可复制、可持续的医疗云技术和服务能力。

IDC分析师林红认为，医疗云市场正在经历一次结构性切换：过去十年厂商比拼的是资源覆盖和交付能力，未来三到五年比拼的将是场景理解与AI落地能力。影像云是政策驱动下确定性最强的短期机会，但真正的长期壁垒在于能否把AI能力嵌入临床业务流，形成可持续运营的服务模式。对厂商而言，基础设施优势只是入场券，能否与医疗信息化厂商、AI企业形成互补生态，将决定其在新一轮竞争中的位置。市场格局远未定型，现在正是布局窗口期。

IDC相关研究报告：

本文核心观点来源于 IDC 报告《Market Share：中国医学影像云解决方案市场份额，2025》（Doc# CHC52955525，2026年9月）、《中国医疗IaaS+PaaS市场份额，2025》，（Doc# CHC54685326，2026年9月）、《IDC MarketScape：中国AI医疗云2026年厂商评估》（Doc# CHC54110126，2026年9月）

进一步交流

医疗云市场正处于关键转折期，影像云与AI医疗云的机会窗口正在打开。如果您的企业希望深入了解市场格局、厂商竞争态势，或需要针对自身业务进行定制化研究，欢迎与IDC中国医疗行业团队联系。我们期待与您进一步交流。请点击此处与我们联系。

Erin Lin

Erin Lin - Senior Market Analyst

Analyst Profile
Erin Lin serves as a senior market analyst of the IDC China Health Insights group. She is responsible for conducting research and analysis about the health industry for both domestic and regional markets. She is also involved in consulting and…

国际数据公司（IDC）在持续跟踪中国PC 市场与端侧AI 发展过程中观察到，在过去两年，AI PC 从概念逐步走向落地。随着端侧算力持续提升、本地大模型能力不断成熟，以及Agent开始具备理解任务、规划步骤并执行任务的能力，PC 正进入一个新的发展阶段。

在这一背景下，“智能体电脑（Agentic Computer）”正在成为PC 产业新的发展方向。它以本地AI算力为基础，以自主Agent 为核心，通过模型、工具与应用的协同，让AI 从“辅助用户操作”进一步走向“理解目标、自主规划并执行任务”。

在 IDC 看来，智能体电脑带来的变化，并不是简单地“给PC增加AI 功能”，而是PC 价值逻辑的重构：PC 正从AI 辅助工具，逐步转变为AI任务执行和生产力入口。

从AI辅助到自主执行，智能体电脑改变了什么？

从 AI 辅助到自主执行，智能体电脑正在改变 PC 的交互方式

传统PC的交互逻辑相对明确：用户发出指令，PC根据指令完成操作。AI PC 在此基础上增加了自然语言交互、生成式AI 等能力，但用户仍然需要参与任务拆解和具体操作。

智能体电脑则进一步改变了这一模式。简单来看，两者的交互逻辑可以概括为：

IDC认为，这种变化的关键，并不只是增加了一个“执行”环节，而是改变了用户与 PC交互的基本方式。过去，用户需要告诉电脑“怎么做”；未来，用户可以更多地告诉电脑“我要完成什么”。

这意味着，PC 的使用方式将逐渐从围绕“点击、命令和操作”，转向围绕“目标、任务和结果”。相应地，AI 在PC 上的应用范围也将从单点功能进一步扩展到办公、研发、创作、知识管理和个人助理等连续任务场景。与此同时，PC的评价标准也会随之发生变化。

过去消费者选择PC，关注的是处理器、显卡、内存和屏幕等传统硬件参数。当Agent 成为PC 的重要工作负载之后，用户可能更关注另外一组问题：本地模型能不能稳定运行？响应速度够不够快？Agent 能不能真正把一项连续任务完成？

IDC建议，PC厂商下一步需要把产品重心从“提供算力与功能”转向“保证任务被真正完成”——本地模型的稳定运行、Agent的连续执行能力与响应效率，将取代部分传统参数，成为产品定义的新起点。

为什么是现在？端侧算力、模型与Agent开始同时成熟

智能体电脑并不是单纯由硬件升级推动出来的产品概念。它之所以在当前阶段受到关注，是因为端侧算力、模型能力和Agent 软件生态正在逐渐具备落地条件。

首先是端侧算力。新一代 PC 平台的竞争重点正在从传统 CPU性能逐步扩展到AI 计算能力。Intel Panther Lake、AMD Strix Halo/Gorgon Halo、NVIDIA GB10/N1X 和Qualcomm X2 Elite/Plus等具备端侧AI 能力的平台陆续出现，为本地大模型和Agent 的运行提供了更强的硬件基础。

其次是模型。随着模型效率不断提升，本地模型的规模开始与PC的硬件能力逐渐匹配。对于智能体电脑而言，30B级模型正在成为重要的参考基准。但在实际推理过程中，单纯提升AI算力并不足够。模型运行效率还受到内存容量和内存带宽的影响。当前智能体电脑的基础门槛需至少实32GB内存和135GB/s 内存带宽；面向更高阶的本地AI工作负载，则需要进一步向64GB以上内存和250GB/s以上带宽提升。

最后是Agent 软件。AI 的交互方式正在从“对话”向“任务执行”演进。本地RAG、代码助手、自动化办公、个人助理和本地多模态应用，都可能成为智能体电脑较为明确的落地场景。同时，Agent对本地文件、网络资源及外设的调用，也对权限管理、数据安全和使用边界提出了更高要求。

因此，智能体电脑真正需要解决的问题，已经从过去的“PC 能不能运行模型”，进一步变成了“模型能不能在 PC 上持续、稳定地完成任务”。随着算力、模型和 Agent 应用逐渐形成闭环，智能体电脑才真正具备从概念走向产品的基础。

IDC认为，厂商需要把端侧算力、本地模型与Agent 软件作为一个整体来定义产品，只有三者形成闭环，智能体电脑才真正具备可持续的用户价值。

从 TOPS 到实际 AI 工作负载效率，智能体电脑正在改写硬件标准

智能体电脑带来的变化，首先会反映在硬件设计上。与单纯强调某一种芯片不同，智能体电脑更强调适配级异构算力、充足的内存带宽以及大容量统一内存之间的协同。

IDC基于对本地模型运行效率和 Agent任务负载的跟踪，提出以下入门级参考门槛：

从基础配置来看，入门级智能体电脑建议具备80TOPS 以上AI 算力、32GB 以上内存、135GB/s 以上内存带宽，并配置至少1TB NVMe SSD。异构算力将成为智能体电脑重要的底座，CPU、NPU和GPU 将承担不同类型的 AI 任务，其中，NPU更适合持续、高吞吐、低功耗的推理任务，GPU则更适合大规模矩阵计算和复杂推理。

对于本地Agent 而言，模型加载和Token生成速度同样直接影响用户体验。当前阶段，Token生成速度不应低于10 Token/s。

IDC建议，PC厂商应将上述门槛视为产品定义的起点而非终点——在按场景分层规划配置的同时，把从算力、内存带宽到Token 生成速度的整链路效率作为验证标准，确保规格最终能转化为用户可感知的Agent使用体验。

硬件只是入场券，Agent 应用决定智能体电脑的市场空间

如果说硬件能力决定智能体电脑“能不能进入市场”，那么真正决定用户是否愿意持续使用和购买的，则是Agent能否解决真实、高频的问题。

目前来看，智能体电脑已经呈现出几条较为明确的应用路径，包括办公自动化、代码开发、本地知识库、个人助手以及多模态处理等。这意味着，PC 产业的商业逻辑也可能从过去的“卖AI 能力”，逐步转向“卖AI 生产力”。

对于个人用户而言，Agent可以围绕本地文件、资料和应用完成信息整理、内容生成和任务协同，让用户从逐项操作转向以目标驱动任务。

对于企业用户而言，本地Agent 则有机会成为员工的个人 AI 助手。当Agent 能够调用企业知识库、内部文件以及部分业务流程时，PC 不再只是访问企业软件和云服务的终端，也可能成为企业AI 能力真正进入员工工作流的重要载体。

因此，从产业链角度来看，智能体电脑并不只是一次硬件升级，而更接近于PC 软硬件协同模式的一次变化：硬件提供本地AI工作负载的基础，模型提供智能能力，Agent 应用则决定这些能力能否真正转化为生产力。

IDC认为，PC厂商需要从“卖AI 能力”转向“卖AI 生产力”：以办公自动化、代码开发、本地知识库等高频场景为切入点定义产品，并与模型及软件伙伴共建Agent 生态，把可感知的效率提升转化为用户持续使用与复购的理由。

IDC观察：智能体电脑还在早期，市场已进入验证阶段

从出货规模来看，未来几年中国智能体电脑市场有望进入加速增长阶段。IDC预测，中国智能体电脑出货量将从2026年的77万台增长至2030年的2,080万台，5年 CAGR 预计达到127%。

这一增长并不会呈现一条平滑的曲线。随着本地模型能力、硬件算力和 Agent 应用逐步成熟，市场规模化进程有望进一步加速。尤其是2028年之后，出货量增速明显提升，意味着智能体电脑有望逐渐从先锋产品成为更主流的产品品类。

IDC总结与展望

随着 AI Agent 能力不断成熟，PC 的竞争逻辑也将逐步发生变化。硬件性能仍是基础，但 AI 工作负载效率、本地模型运行能力与 Agent 生产力体验，将共同构成新的竞争维度。智能体电脑的长期发展，不仅取决于算力的提升，更取决于硬件、模型、Agent 软件与安全体系能否协同发展，形成成熟的本地 AI 生态。

IDC 认为，对于 PC 厂商而言，下一阶段的关键在于推动竞争从“参数领先”走向“价值交付”：通过优化算力调度、完善 Agent 与工具生态，并将安全与隐私融入底层架构，让 AI 能力真正转化为用户可感知的效率提升。

当用户与 PC 的交互方式从“操作电脑”转向“定义任务、获取结果”，智能体电脑所带来的将不只是产品形态的演进，更是 PC 价值体系的重塑。谁能率先打通“硬件—模型—Agent—安全”的协同链路，并将技术能力转化为可持续的用户价值，谁就更有可能在智能体电脑的发展进程中建立差异化优势。

本文相关参考报告：

IDC Quarterly Personal Computing Device Tracker

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In October 2024, IDC’s FutureScape: Worldwide Cloud 2025 Predictions forecast that by 2026, 75% of organizations would use specialized accelerated cloud computing services to speed up deployment and scale their AI and other accelerated applications. Two years later, Dave McCarthy, Group Vice President of Cloud and Data Center Infrastructure at IDC, says the number still holds and may run a little low.

He reads the 75% as organizations that are somewhere on their AI journey. Two things shifted along the way: some AI work went to private cloud, and the field of providers widened beyond the three hyperscalers.

Watch the full conversation with IDC’s Dave McCarthy below, or keep reading for the highlights.

Why did IDC put a 75% number on it?

McCarthy points to three forces that gave the team confidence.

The first was application modernization. After years in the cloud, companies are revisiting decisions they made inside it, and that now counts as modernization alongside moving a legacy app out of a datacenter.

The second was AI. As McCarthy puts it, “the type of services that you would use for an AI application look very different than the ones that you would use for a more traditional general purpose workload.”

The third was who organizations trusted. When IDC asked which partners were most strategic for AI services, cloud providers typically ranked first.

What played out differently than expected?

Because IDC’s prediction did not specify a type of cloud, it left room for a shift McCarthy describes. Public cloud still appears to be a primary destination because providers make it easy to start and carry the latest hardware and software. Concerns about data privacy and corporate information also drove some AI work into private cloud.

More companies have entered the market, and McCarthy says, “It’s not just the three hyperscalers anymore.” IDC already knew a new class of specialized cloud provider was arriving, he says, but two years ago many of them were bare-bones, handing over hardware to customers with the expertise to use it. IDC did not expect much enterprise adoption outside the big AI labs. Since then, those providers have been building the tooling and support that enterprise workloads need. Capacity pressure adds to the pull: some customers who prefer their usual hyperscaler struggle to get the capacity they want when they want it, and that gives other providers room to fill the gap.

Recent IDC research on specialized cloud services, often called neoclouds, covers one slice of the prediction, since the major hyperscalers also sell GPU-enabled cloud servers. It shows both the pull and the limits:

  • In IDC’s AI Tech Trends Survey (July 2026, n=493 in Europe), high performance for specialized workloads was the top reason for using or considering neocloud services, cited by 42% of respondents
  • In IDC’s Cloud Pulse Survey, neocloud providers such as CoreWeave, Nebius, Vultr, Lambda, and Crusoe each landed in the middle single digits when organizations named providers important to their cloud strategy over the next two years. The established hyperscalers dominated those road maps.
  • IDC’s June 2026 guidance on the neocloud decision advises weighing more than price. Neoclouds generally offer lower GPU pricing, but they typically have fewer datacenter locations and are a poor fit for generic IT workloads.

Does 75% still hold?

McCarthy stands by the number: “I still feel the number is fairly confident… maybe it’s a little bit low in some ways.”

The figure does not mean companies have moved 75% of their workloads to AI. Many organizations reach accelerated compute indirectly, through managed services and tools that run on it behind the scenes, rather than by buying GPU services directly.

IDC’s public cloud IaaS market share research (September 2026) found that the overall market grew 24.6% to $172 billion in 2025, as AI workloads reached production and drove demand for scalable and specialized infrastructure.

What should enterprises watch next?

McCarthy sees the first wave of AI as focused on training. That is changing. “When you hear about AI agents, that’s all inference,” he says, meaning the work of putting existing models into operation.

Inference follows a different deployment pattern than training: it favors infrastructure distributed closer to users and to where data is created. McCarthy notes that some companies once seen as content delivery networks are now retooling themselves as AI inference clouds. The same IDC market share research identifies inference as a growing infrastructure investment area. As enterprises weigh latency, utilization, and cost per inference, that is driving spending on inference-optimized chips and distributed infrastructure.

Whether driven by regulation or corporate governance, McCarthy says, organizations want more control and more specificity about where their AI runs. Two other predictions in the same FutureScape point the same way. IDC expected 50% of organizations to look for network infrastructure providers with sovereign controls by 2026, and 80% of CIOs to rely on cloud providers’ edge services for GenAI inferencing by 2027.

The takeaway: keep your options open

Things are moving fast, McCarthy says, and the cloud choices companies made five or six years ago may look very different today. His advice is to “design for flexibility” and review the options available, because what works best depends on each company’s needs.

Christina Cardoza - Content Marketing Manager - IDC

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

The robot arm putting parts together on a factory floor is the easy image. It’s not the whole story, and according to Sarah Lee, IDC’s Senior Research Director of Manufacturing IT Strategies, thinking it is the whole story is exactly what’s slowing companies down.

Physical AI has become one of those terms that gets stretched to cover almost anything with a sensor attached to it. Lee’s definition is narrower and more useful: It’s the convergence of AI algorithms (machine learning, computer vision, now multimodal agentic systems) with physical hardware, built into a closed loop. Sense the environment. Interpret the data. Decide. Act in the physical world. Then feed what happened back into the system so it gets better next time.

That loop is the whole point: physical AI senses the problem, decides what to do about it, and takes the action itself, inside the same system, something a traditional dashboard that just flags an issue for a person to fix was never built to do.

Watch the full conversation with Sarah Lee below, or keep reading for the highlights.

The pressure isn’t coming from the tech side

Knowing what counts as physical AI doesn’t explain why it’s suddenly everywhere at once. Ask most people, and they’ll point to better vision models or cheaper sensors. Lee points somewhere else first: labor.

Mining and construction companies are sitting on expensive equipment that’s idle because there’s nobody to operate it. Manufacturing floors are losing decades of tribal knowledge to retirement faster than new workers can absorb it. Add tariffs and supply chain volatility on top, and automation stops being a nice-to-have. “The urgency really isn’t coming from the tech side,” Lee said. “It’s coming from a workforce and operating environment that needs to be more resilient when hiring alone isn’t enough to solve the problem.”

Most of the equipment this has to run on wasn’t built for it. IDC’s research puts a finer point on the brownfield problem: the majority of manufacturing facilities in operation today are more than two decades old, running legacy PLCs with no native connectivity. That means the hardest bottleneck usually isn’t compute power or model quality. It’s getting clean, usable data out of machines that were never designed to share it.

The technology caught up at a useful moment. Better vision-language models, better simulation and digital twins, and edge AI that can make real-time decisions without a round trip to the cloud. All of it converged to make the closed loop actually workable at the pace a shop floor or a mine site needs.

One architecture, very different floors

The underlying loop stays consistent, but what fills it changes by industry. Discrete manufacturing is furthest along, with robots and cobots doing precision work, inspection, and material movement. Warehouses and hospitals are leaning on autonomous mobile robots and forklifts for material handling. Construction, mining, and agriculture are running autonomous drill rigs and haul trucks in harsh, disconnected environments where the failure tolerance and regulatory bar look nothing like a factory floor.

Lee frames adoption as a maturity curve rather than a single leap, because almost no operation is starting from zero:

  1. Instrumented operations: sensors feed data back, but a person still makes the call
  2. Closed-loop automation: AI starts interpreting and triggering action within bounded limits
  3. Embodied autonomy: machines perceive, plan, and act on their own
  4. Coordinated systems (still emerging): multiple machines, people, and systems sharing one context and safety state

Most companies are somewhere in the middle of that curve, and Lee’s advice for anyone earlier on it is blunt: check whether your data is actually clean and ready before you pick a pilot. “Where do you first deploy? Where do you first do the POCs? It’s where your data’s clean, your data’s ready, and it’s relevant, good data.”

Where ROI shows up (and where it stalls)

The gap between a successful pilot and real ROI is where most projects die. The gains (productivity, throughput, safety, faster commissioning) show up at fleet scale, not pilot scale. Getting there means solving for orchestration across multiple machines and sites, not just proving one robot works in one corner of the plant.

What IDC’s research is picking up right now

That orchestration problem is exactly what IDC’s own research has been tracking, and the numbers show how fast the ground is shifting underneath it. In a May 2026 survey, physical AI ranked as the second-highest AI investment priority for the next 24 months, behind only GenAI assistants and copilots. IDC’s 2026 Worldwide CEO Survey found something similar from the top of the house: 45% of CEOs named physical AI among their top three emerging technologies for investment over the next 12 to 24 months, trailing only agentic and generative AI at scale, and sustainability technology.

Digital twins are moving past visualization, and IDC’s research describes exactly how: the workflow is inverting from sim-to-real toward real-to-sim, where a plant’s own production data trains and validates the AI before it ever touches the physical environment. That turns site-specific plant data into a durable competitive asset. And the share of manufacturers running ten or more agentic AI solutions is expected to double by the end of the year.

Lee pushed back hardest on one framing: “lights out” factories. What she’s actually seeing is closer to lights transformed, with workers moving out of manual execution and into supervision, exception handling, and governance.

“

The most important automation decisions manufacturers will make over the next few years are not about which arm to buy, but about which control plane to trust, whose simulation to build on, and who owns the data their robots generate.

— Sarah Lee, Senior Research Director of Manufacturing IT Strategies, IDC

That’s the shift underneath everything else here: the arm was never the hard part. The foundation around it is.

Christina Cardoza - Content Marketing Manager - IDC

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

Application, platform, infrastructure and services each follow their own dynamics. IDC analysts Ewa Zborowska and Carla Arend explain where European demand is heading and how vendors can position for it.

Europe’s AI market is worth $105 billion, and customers are busy building their own AI stack, layer by layer, reshaping each one as they go. For vendors, the interesting question is not whether this market will grow, but where exactly the money is heading in the AI stack and how to position for it.

That was the topic of a recent webinar, where Ewa Zborowska and Carla Arend looked at AI deployments in the cloud versus on-premises, which AI platforms are winning and why, how the application layer is being reshaped, and whether AI sovereignty is realistic.

AI Application Layer: Embedded AI and Outcome-Based Selling

AI adoption has moved well past the pilot stage, and the application layer is changing first. Customers increasingly want AI as a feature of the software they already own rather than as a standalone product, alongside targeted investments in specific use cases. As buyers struggle to prove the return on AI, vendors will need to sell the outcome rather than the model, and embed AI features in the workflows customers already run.

AI Platforms and Agentic Architectures: The Fastest-Growing Layer

The fastest-growing part of the stack sits one layer below, in AI platforms and developer tooling. This layer decides which models, data and guardrails an enterprise can use at all, and it is being transformed by agentic architectures, where multi-component builds replace single-model integrations. The vendors who land the agentic build early have the chance to own the runtime, and to hold on to it through data gravity and governance. Customers will not settle on a single platform, which makes that early position even more valuable.

AI Infrastructure and Sovereignty: Cloud, On-Premises or Both?

Infrastructure tells a different story from the one many expect. In addition to the constraint imposed by raw GPU supply shortages, locality and energy supply increasingly play a role, so the title question has a clear answer: AI will not live in the public cloud alone. Customers will choose a mix of deployments that reflects where their data must sit and what power is available. Vendors competing here will do better selling sovereign capacity and efficiency than selling price per unit of compute. Sovereignty itself is turning from a debate into a deployment, yet there is still no single definition of it. Vendors may label AI as sovereign, but it is the customers who decide what that actually requires, and we will look at what that means for cloud providers, telcos and anyone selling into sovereign requirements.

AI Services: Getting Customers from Pilot to Production

Running across every layer are services, because few buyers get from pilot to production alone, and that is exactly where adoption tends to stall. Automation is also resetting expectations, with buyers now looking for the same work faster and cheaper. Services providers who lead with fast, automated delivery, price on outcomes and SLAs, and keep customers through governance and managed AI operations will be best placed.

What This Means for Vendors in the European AI Market

Across all of this there is opportunity at every layer, and every layer has its own dynamics. Vendors who understand those dynamics will be much better placed to grow.

If you missed our recent webinar The European AI Stack, the on-demand recording takes you through every layer in detail, including what AI sovereignty means for cloud providers and telcos. Watch it at any time here.

For your go-to-market planning, our new IDC playbook tests six common assumptions about the European AI market, from market size to buyer behaviour, against European data and sets out what to do differently. Download the eBook here.

Carla Arend

Carla Arend - Associate Vice President, Cloud and Datacenters, Enterprise Infrastructure

Analyst Profile
Carla Arend is Associate Vice President within IDC’s enterprise infrastructure global research domain and part of the cloud and datacenters services subdomain. She leads IDC’s Worldwide cloud adoption research. In this capacity, she leads a team of analysts that provides…
Ewa Zborowska

Ewa Zborowska - Research Director, AI, Europe

Analyst Profile
Ewa Zborowska is an experienced technology professional with 25 years of expertise in the European IT industry. Since 2003, she has been a member of the IDC team, based in Warsaw, researching IT services markets. In 2018, she joined the…

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

Every procurement team knows the moment. The signature page is turned, and the vendor’s negotiation team quietly disappears. What’s left is a contract, a renewal date circled two or three years out, and a procurement lead who now owns every consequence of a deal someone else helped negotiate.

That handoff has always felt like the cost of doing business. In IDC’s 2025 CIO Sentiment Survey, 57% of respondents said they measure vendor performance with no consistent, enterprise-wide vendor benchmark. It doesn’t have to work this way. IDC Quanta for Tech Leaders keeps the benchmarks in reach after signature.

Negotiation is a sequence of decisions, each one needing its own proof. What should this renewal cost given usage trends over the last 18 months? Is the vendor’s proposed ramp realistic? The evidence often exists somewhere, buried across analyst reports, pricing databases, and past deal files nobody has time to cross-reference in the three days before a renewal call.

IDC Quanta for Tech Leaders puts the evidence where the question is. Ask it in plain language, from your email or Claude (the IDC web portal works too), and it answers from IDC’s own first-party research, with citations back to the underlying evidence. Upload your contract terms, RFP requirements, or vendor shortlist, and it checks them against IDC’s pricing evaluations, built on real peer deal data.

Picture a procurement lead three weeks out from a renewal on a core software platform. The vendor has proposed a 14% increase, citing “market conditions.” She uploads the renewal terms and asks IDC Quanta how they compare. It benchmarks them against peer deals of similar size and term, and flags above-market rates and auto-renewal clauses before they lock in for another cycle. In this example, comparable renewals are landing in a much tighter band than 14%. She books an analyst to stress-test the terms before the counterproposal goes out, while the evidence still has time to change the outcome.

Then she asks IDC Quanta for an export-ready brief the negotiation team can take into the room, and for an RFP template if the renewal goes to market.

Most vendors treat the signature as the finish line. IDC treats it as the point where the real work of managing a tech investment begins. IDC Quanta is there for all of it, whether the question comes up in week one of an evaluation or three years into a contract you’re trying to renegotiate on better terms. It alerts you to vendor price changes and new entrants in your sourcing categories, so you hear about them before the next negotiation. You choose the analyst who fits the question, and your whole team can join the inquiry.

Ready to see what that looks like for your next renewal? Book a demo and bring your own numbers.


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.