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Key figures at a glance

  • ¥1,304B – IT modernization services market size, 2025
  • 10.2% – Projected average annual growth rate, 2025–2030
  • ¥2,123B – Forecast market size by 2030
  • ~80% – Large and mid-sized enterprises still running legacy systems

Why Japan is outpacing the world

Japan’s IT services market is forecast to grow at a CAGR of 6.6% from 2024 to 2029, nearly double the global average of 3.6%. The answer is structural. Japan carries a uniquely heavy legacy burden, decades of investment in proprietary mainframe environments, complex bespoke systems, and a workforce that has long maintained them. Now, three forces are converging to make modernization unavoidable:

  • Fujitsu Mainframe Sunset – In 2022, Fujitsu announced the end of sales and support for its mainframe and UNIX server products around 2030. This single announcement put more than 1,000 enterprises on an irreversible countdown, accelerating timelines across the entire Japanese market.
  • AI Readiness Imperative – AI adoption presupposes tightly integrated data pipelines and modern business process architectures, exactly what legacy systems make impossible. Modernization is no longer optional for companies that want to remain AI-competitive.
  • Demographic Pressure – The generation of engineers who built and maintained Japan’s legacy systems is retiring. Organizations face a narrowing window to migrate knowledge and infrastructure before institutional memory disappears entirely.

Three paths to modernization

IDC segments IT modernization services into three execution types, each with distinct implications for services firms:

  • Rehost – Lift-and-shift to non-legacy platforms. Preserves existing application assets. The near-term entry point for enterprises constrained by budget or migration timelines.
  • Rewrite – Convert legacy source code to modern languages without changing business logic. A middle path for controlled transformation.
  • Rebuild – Redefine processes, data models, and architecture from the ground up. The highest-value, highest-complexity path.

Near-term, rehost is the second-largest segment after rebuild, driven by enterprises responding urgently to mainframe end-of-life deadlines — though it has already reached maturity and is forecast to decline. The mid-to-long-term growth opportunity lies in application modernization, rewriting, refactoring, and the adoption of microservices and cloud-native architectures.

What enterprises need from services firms

IDC surveyed large and mid-sized Japanese enterprises and found that organizations with significant legacy exposure do not simply want technical execution, they want transformation partners. Security remains a baseline expectation, but top-ranked needs now include business process redesign and cloud architecture strategy.

Demand signals also diverge meaningfully by sector:

  • Financial Services – Prioritizes cloud-native application development capabilities, the ability to innovate rapidly on modern infrastructure.
  • Manufacturing and Distribution – Prioritizes business process transformation, embedding efficiency and intelligence into operations, not just upgrading the underlying technology.

Across all sectors, IDC observes a consistent shift in enterprise expectations: business outcomes are becoming the primary purchase criterion. Technical competence is assumed; value creation is the differentiator.

How to build a winning position now

For services firms, the competitive imperative is clear. The service providers best positioned to win this market will do three things:

1. Codify your legacy modernization track record

Past engagements are an underutilized asset. Service providers should build structured libraries of business outcomes achieved, cost reductions, cycle time improvements, AI readiness unlocked and make these the core of their go-to-market narrative.

2. Develop industry-specific reference architectures for the AI era

Generic modernization pitches are losing traction. Enterprises want system architectures and implementation roadmaps calibrated to their sector, their regulatory environment, and their AI ambitions.

3. Invest in application modernization capabilities ahead of demand

The rehost wave is already approaching its peak. The high-margin opportunity –  rewrite, refactor, rebuild – is building behind it. Service providers who develop deep cloud-native and microservices capabilities now will be the ones enterprises turn to in the second half of this decade.

About the IDC Report

IDC has published a comprehensive analysis of Japan’s IT modernization market: 2026 Japan IT Modernization Market Analysis. The report provides a medium-term market forecast for IT modernization of legacy systems — a primary growth driver in IDC’s Japan IT services market outlook. Legacy systems are characterized by aging and obsolescence, excessive complexity and scale, and a lack of transparency. It covers enterprises’ IT modernization trends and an analysis of the service vendors’ services trend. Market forecasts are segmented by service type, execution type (rehost, rewrite, rebuild), system type, and industry vertical. Together, these analyses offer a comprehensive view of shifting enterprise needs, emerging market opportunities, and the strategies and service offerings of leading vendors in Japan’s IT modernization landscape.

For more detailed insights and market trends, please contact our analysts by completing this form IDC | Identifying Market Opportunities – Contact Us.

Masaru Muramatsu - Senior Research Analyst, Software, Services, and IT Spending, IDC Japan - IDC Japan

Analyst Profile
Masaru Muramatsu is a senior research analyst, responsible for research and analysis of the Japanese IT services market, including IT consulting, systems integration, business services.

中国公有云市场正在经历一场由AI驱动的结构性转折。IDC最新发布的《中国公有云服务市场跟踪报告，2025 下半年》数据显示，2025年下半年公有云IaaS市场人民币同比增速恢复至20.0%，整体公有云市场(IaaS+PaaS+SaaS)半年总值首次突破2000亿元。这一增长并非简单的市场回暖，其背后是AI需求对云计算产业底层逻辑的重塑：市场认可度的提升正在从“资源规模”转向“全栈AI能力”，市场份额正加速向“算力+大模型”双强厂商收敛，出海成为增长第二曲线，而行业间的需求分化也将进一步拉大。IDC认为，未来三到五年，公有云市场的竞争将不再是价格战与规模战，而是算力、模型、行业方案、生态与全球化能力的综合较量。谁能在这一轮AI红利中真正从“云厂商”升级为“AI服务商”，谁就将主导下一轮市场格局。

AI 能力将重构市场份额 ，大模型为云厂夺回定价权

在传统IaaS市场中，云主机租赁长期占据主导，价格战也因此成为竞争的主旋律。然而，随着生成式AI和大模型需求的爆发，客户在选择云服务商时的核心依据正在发生根本性变化。算力、大模型、芯片与平台的全栈能力，正取代过去的资源规模与价格优势，成为新的竞争焦点。相应地，定价逻辑也在从资源计费转向价值付费。

这一转变最直接的体现，是市场增长动力从存量博弈转向增量创新。传统IaaS价格战逐步退潮，取而代之的是智算需求驱动的新一轮扩张。大模型产品的定价模式正向按Token计费倾斜，云基础设施产品，尤其是高性能算力的需求大幅提升。在这一新竞争环境中，云服务商是否具备AI原生能力与高效的算力调度能力，将直接决定其能否占据有利位置。

全栈能力成为竞争壁垒，市场向“算力+大模型”双强厂商收敛

智算集群、液冷数据中心、自研芯片、大模型训练等领域均属于高投入赛道，只有具备完整闭环能力的厂商，才能实现算力规模化变现并穿越投入周期。根据IDC MarketScape评估，阿里云、百度智能云等具备全栈能力的云厂商，其市场份额正持续提升。这些厂商凭借硬件芯片、异构算力兼容、集群调度、MaaS平台及行业生态等综合优势，已在政企、互联网等多个行业中积累起丰富的落地案例。

相比之下，单纯依赖资源出租的厂商，由于缺乏技术与生态支撑，增速已开始放缓。未来市场将进一步向具备全栈AI能力、生态协同能力和行业深度适配能力的头部厂商集中。

行业分化加剧，高适配行业领跑

不同行业在公有云需求上的差异正在拉大。自动驾驶、电商、游戏、互联网金融、协同办公等与AI结合度高且资本红利充足的领域，公有云需求保持高速增长。IDC预测，2025年泛科技行业的AI公有云渗透率将持续提升，行业间的市场增速差距将进一步扩大。而政务、制造、传统金融等行业，则受合规要求和系统改造周期等因素限制，上云节奏相对缓慢。这些行业客户在选型时，更加关注模型的训练与推理性能、行业精调落地情况、数据主权与合规、成本治理等多维度能力，这也在推动云服务商加快行业化与场景化产品的布局。

海外资源加速布局，出海成为增长第二曲线

海外背景的云厂商，如AWS和微软Azure，在云算力出海领域依然保持强劲竞争力，持续服务于中资企业的全球化部署与跨境AI业务。与此同时，阿里云、腾讯云、华为云以及运营商阵营也在积极瞄准中资企业出海需求，在跨境电商、游戏发行、AI应用出海等领域加速落子。根据IDC跟踪数据显示，中过企业出海用云市场规模五年复合增长率超过30%，远高于国内市场增速。出海正成为中国云厂商寻找增长“第二曲线”的重要方向。

AI红利花落谁家：三类厂商各显其能

在这一轮AI驱动的市场重构中，不同类型的云服务商正走出截然不同的增长路径。

以阿里云和百度智能云为代表的全栈型厂商，凭借“模型+芯片+平台+公有云基础设施”形成的产品线闭环，构建起公有云竞争的护城河。其公有云IaaS同比增速已从2024年的个位数增长提升至2025年的25%以上，市场份额持续提升，并在大模型平台、MaaS、行业精调等领域不断加码。

以腾讯云和火山引擎为代表的场景驱动型厂商，则更加聚焦AI的商业化落地，推动将AI真正“用起来”。腾讯云在2025年首次实现规模化盈利，而火山引擎则凭借高性价比的智算方案与灵活计费模式，在2025年再次实现超100%的同比增速，市场份额快速提升。

以中国电信天翼云、移动云和华为云为代表的算力运营型厂商，依靠自建与跨平台调度能力的结合，灵活适配第三方方案，为政企、金融等高安全性行业的AI应用提供保障。凭借多年积累的机房资源、属地化服务和央企背景，这些厂商的企业级服务优势逐步显现，市场排名稳居前五。

从算力到模型：未来四阶段演进路径正在形成

展望未来，中国公有云市场的竞争将沿着清晰的阶段路径演进。

在2026年的第一阶段，算力投入依然是AI发展的核心方向。智算集群、自研芯片、数据中心建设推动资本开支持续攀升。IDC预测，到2027年，超过85%的中国组织将把传统云环境转型为适配AI工作负载的新型平台。

到2027年前的第二阶段，商业化将迎来突破。云服务商的营收模式将从单纯算力出租，转向“Token+算力”的双营收结构，收费模式从资源侧向场景侧迁移，云厂商的盈利格局将随之重塑。大模型驱动的AI云服务市场格局正在形成，MaaS、行业精调和Agent平台等新型商业模式加速落地。

进入2028年的第三阶段，竞争焦点将从算力底座转向大模型应用场景的的训练、微调和推理优化。“云上模型好用度”将成为企业客户选择云服务商的决定性因素。企业买家将优先评估多模态模型覆盖能力、推理准确性、行业适配能力以及生态工具链的完备性。

而纵观未来五年，行业分化将成为长期特征。泛科技行业将持续领跑，传统行业则在政策引导与国产化适配推动下逐步而坚定的推进上云进程。市场将向具备全球化能力、行业方案能力和生态协同能力的综合AI服务商集中，头部厂商将通过全栈能力与行业深耕构建起长期壁垒。

分析师结语：从“云厂商”到“AI服务商”的升级之战

2025年下半年中国公有云市场重回高增长，其本质是AI产业爆发所带来的公有云基础设施红利。IDC中国研究经理崔婷婷表示，IaaS增速重回20%以上，标志着中国公有云行业已从存量博弈转向增量创新。未来三到五年，AI能力的竞争将进入白热化阶段，市场不再是简单的价格与规模比拼，而是算力、模型、行业方案、生态与全球化能力的综合较量。云计算，尤其是公有云服务，作为AI竞争的核心载体，其资源铺设广度、能力韧性、安全性、营收增长与利润转换率的动态提升，也将直接反映出AI阵营的发展状态，成为AI竞争态势的晴雨表。在这场升级之战中，谁能更快将AI能力转化为客户可感知的业务价值，谁就有望在下一轮格局洗牌中占据先机，真正完成从“云厂商”到“AI服务商”的跃迁。

如需进一步了解IDC相关研究，或就中国公有云市场发展趋势进行深入交流，欢迎与IDC联系，获取更多洞察与数据支持。

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The global semiconductor market is undergoing a seismic transformation. IDC’s latest forecast projects the industry will surge past the $1 trillion revenue threshold in 2026, significantly ahead of prior expectations. The growth will be driven overwhelmingly by AI infrastructure investment, which is reshaping the entire market.

Total semiconductor revenues are forecast to reach $1.29 trillion in 2026, up 52.8% year over year from $842.8 billion in 2025. The memory segment is at the epicenter of this shift: DRAM revenues alone are projected to nearly triple in 2026 to $418.6 billion, driven by demand for high-bandwidth memory (HBM) and DDR from hyperscalers and AI infrastructure providers. Meanwhile, non-memory semiconductors are growing at a robust but more measured pace, reaching $693.5 billion in 2026.

In this post, we break down three forces reshaping semiconductors right now: why AI infrastructure has become the industry’s new center of gravity, what’s happening in memory markets and why it matters beyond the data center, and how other markets from automotive and IoT to mobile and PCs are navigating a market increasingly defined by AI.

Global Semiconductor Market: Selected Forecast (USD Billions)

Source: IDC Semiconductor & Semiconductor Applications Forecast, April 2026. A = Actual, E = Estimate, F = Forecast.

AI infrastructure: The engine of the supercycle

The single most consequential shift in the semiconductor market is the emergence of AI infrastructure as a structurally dominant end market. What began as a cyclical uplift in data center spending has evolved into a self-reinforcing investment cycle that is reshaping demand patterns across the semiconductor value chain.

Hyperscale capital expenditure exceeded $100 billion for the first time in Q3 2025, and the i4 are expected to increase capex by 70% year over year to approximately $600 billion in 2026. IDC forecasts data center semiconductor revenues to reach $477.1 billion in 2026. By 2030, data center semiconductors will account for $843.2 billion, nearly half the total semiconductor market.

“The semiconductor industry has crossed a structural threshold. AI is no longer a demand catalyst — it is the demand foundation. The race to build out AI infrastructure is consuming silicon at a pace the industry has never seen, and the implications for memory, logic, and packaging are profound.” — Jeff Janukowicz, Research VP, Semiconductors & Semiconductor Manufacturing, IDC

Datacenter Semiconductor Revenue Decomposition

Source: IDC Semiconductor Applications Forecast, April 2026.

The $281 billion “intelligent” datacenter segment, encompassing CPUs, AI accelerators, GPUs, custom ASICs, and networking silicon, now constitutes the largest identifiable category within non-memory semiconductors. Spending is heavily concentrated among top-tier hyperscalers and a growing set of sovereign AI infrastructure programs, many of which have secured long-term supply agreements with leading chip manufacturers.

Three factors are keeping this growth self-sustaining rather than cyclical:

  • Compute intensity continues to rise. Generative AI and agentic workloads require far more compute density per rack than prior architectures, increasing the overall silicon footprint
  • Inference demand compounds on itself. Each new model generation increases the volume of inference, requiring ongoing hardware upgrades
  • AI is spreading beyond the data center. As enterprises, edge deployments, and client devices begin running AI workloads locally, demand becomes more distributed

Memory: From cyclical commodity to strategic constraint

If you want to understand what’s really happening in semiconductors right now, start with memory.

Total memory revenues rise from $226 billion in 2025 to $594.7 billion in 2026, and then to $790.4 billion in 2027. This is not simply a recovery cycle, it reflects a market that is being structurally repriced.

DRAM is where the shift is most visible. IDC forecasts $418.6 billion in DRAM revenues for 2026, up 177% year over year. This is not primarily a volume story driven by consumer devices. Hyperscalers are buying a fundamentally different, more expensive class of memory and are willing to pay a premium to secure supply. Each HBM chip also requires significantly more silicon real estate, further tightening the availability of other types of DRAM.

“The memory market is at an unprecedented inflection point, with demand materially outpacing supply. For an industry long characterized by boom-and-bust cycles, this time is different. The rapid expansion of AI infrastructure and workloads is placing significant pressure on the memory ecosystem. As a result, the market is shifting from a cyclical recovery following the 2023 downturn to a more structurally constrained environment, with clear implications for end markets.” — Jeff Janukowicz, Research VP, Semiconductors & Semiconductor Manufacturing, IDC

The HBM bottleneck

High-bandwidth memory has become the primary constraint in the AI accelerator supply chain. Most capacity is already pre-committed through 2026, with forward allocations extending into 2027. That capacity is concentrated in NVIDIA and AMD GPU platforms, along with a growing set of hyperscaler custom silicon programs.

The production economics are also very different. HBM relies on advanced packaging and stacking technologies, resulting in per-bit costs that are several times higher than standard DRAM.

Suppliers are investing aggressively to expand capacity, but the technical complexity and capital intensity mean meaningful new supply will not reach the market until late 2026 at the earliest.

NAND: AI drives storage demand

NAND Flash revenues are forecast to reach $174.1 billion in 2026, up 138.5% from 2025. AI infrastructure is again the dominant driver, with demand coming from training datasets, checkpoint storage, and high-performance inference environments.

Unlike DRAM, the NAND market is seeing broader repricing. Enterprise SSD prices have surged as hyperscalers secure supply, which is tightening availability across consumer and OEM channels.

Other markets: Navigating the shadow of the AI supercycle

While AI infrastructure dominates the headlines, the broader semiconductor market is facing a more nuanced environment.

Non-memory, non-datacenter revenues are projected at $406.3 billion in 2026. Several end markets are dealing with margin pressure, supply allocation challenges, and macroeconomic headwinds.

In mobile, semiconductor revenues are forecast to decline to $89.8 billion in 2026. The issue is not consumer demand, particularly for AI-capable devices, but cost pressure. Memory now represents a larger portion of the bill of materials, forcing OEMs to make difficult tradeoffs between margin, pricing, and product specifications.

Automotive is being shaped more by macro factors than AI. Tariffs, interest rates, and energy prices are weighing on demand. While the long-term outlook remains strong, 2026 reflects a period of near-term softness.

IoT shows a similar pattern. The segment is projected at $136.6 billion in 2026, with near-term pressure from inventory digestion and cautious spending. However, edge AI is beginning to create a new, higher-value demand category that will become more meaningful over time.

Source: IDC Semiconductor Forecast, April 2026.

Outlook: Path to $1.75 trillion

IDC’s base case projects semiconductor revenues reaching $1.75 trillion by 2030.

Several dynamics will shape that trajectory:

  • Memory pricing will normalize, but remain structurally higher than pre-AI levels
  • Non-memory semiconductors will continue steady growth, driven by AI adoption across devices and industries
  • Macro and geopolitical risks will remain important variables

What is clear is that the semiconductor market has undergone a fundamental shift.

“What the data makes clear is that the semiconductor market has undergone a permanent expansion of its addressable opportunity. AI infrastructure has reset the demand baseline, memory has repriced as a strategic asset, and the industry’s growth trajectory through 2030 is no longer contingent on a consumer refresh cycle.” — Nina Turner, Research Director, Semiconductors, IDC

IDC will be tracking how AI infrastructure investment continues to reshape semiconductor demand at Computex 2026.

Jeff Janukowicz

Jeff Janukowicz - Research Vice President, Global Lead, Semiconductors and Enabling Technologies

Analyst Profile
Jeff Janukowicz is Research VP within IDC’s enterprise infrastructure global research domain. He is the global subdomain lead for Semiconductor and Enabling Technologies. Jeff and his team deliver data-driven analysis, technology insights, market trends, and strategic guidance across compute, memory,…
Nina Turner

Nina Turner - Research Director, Semiconductors and Enabling Technologies

Analyst Profile
Nina Turner is Research Director within IDC’s enterprise infrastructure global research domain. She focuses on silicon technologies and packaging as part of the Enabling Technologies subdomain. Nina and her team cover the breadth of processors and architectures, from datacenters to…

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

AI Adoption in EMEA: High Investment, Limited Business Value

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

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

Why AI Projects Fail: The Execution Gap

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

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

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

AI ROI: Why Proving Business Value Remains So Difficult

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

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

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

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

Scaling Enterprise AI: Why Moving Beyond Pilots Is So Hard

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

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

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

AI Governance and Regulation in EMEA: Barrier or Opportunity?

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

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

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

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

AI and Workforce Transformation: Why the Human Factor Matters

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

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

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

The Evolving Role of the CIO in AI-Driven Organizations

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

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

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

From AI Strategy to Execution: What Differentiates Leading Organizations

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

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

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

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

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

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

Join the Discussion

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

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

Martina Longo - Research Manager, Digital Business - IDC

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

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

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

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

The user attention battle is quietly beginning

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

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

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

MES stands for “Must Evolve Soon”

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

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

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

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

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

Right-size AI now or face the potential consequences

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

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

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

So what?

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

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

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

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

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

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

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

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

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

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

中国PC市场进入调整与转型的交汇阶段

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

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

细分市场分化加剧，增长动能出现结构性转移

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

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

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

AI笔记本加速渗透，推动产品结构升级

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

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

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

高端细分市场表现稳健，成为对冲周期波动的重要支撑

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

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

市场展望：结构升级与供应链因素将持续影响行业走势

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

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

IDC观点

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

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

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

IDC Directions 2026 brought together more than 700 technology and business leaders for a single day of focused, analyst-led intelligence on where enterprise AI is heading and what to do about it.

The scale tells part of the story: 82 IDC analysts, 56 speakers, and 29 sessions across marketing, data, emerging technology, and AI-ready infrastructure. The attendee response tells the rest. In IDC’s post-event attendee survey, 98% said the day was worth their time and 96% left with insights they could act on.

Catch up on what you missed at IDC Directions 2026.

IDC built this year’s Directions around a question most technology executives are wrestling with right now: AI ambition is everywhere. How do you turn it into enterprise results? Every session pointed toward an answer.

Three Conversations That Set the Agenda

Chief Product & Research Officer Meredith Whalen opened with her keynote on the AI Supercycle, IDC’s term for the once-in-three-decades technology expansion cycle now underway, driven by AI infrastructure investment and the enterprise adoption wave that follows. The infrastructure buildout is already underway. The enterprise adoption wave is next. Whether your organization captures value as it shifts to new layers of the stack depends on decisions being made right now.

IDC CEO Lorenzo Larini brought the broader context into sharp relief. The volume of information is now growing at 17 petabytes per second. That’s not a backdrop — it’s the challenge. Making confident decisions in that environment requires a different kind of intelligence infrastructure, one built for speed and clarity rather than volume alone.

Lorenzo Larini speaking about the volume of data growth
Alessandro Perilli looks to the future in his Directions presentation

Vice President of Enterprise AI Strategies Alessandro Perilli put a number on what’s coming: by 2029, IDC forecasts that enterprises will collectively be running more than one billion AI agents. The organizations now designing cross-functional, multi-agent environments for orchestration and resiliency will have a structural edge over those that aren’t.

IDC Quanta: A New Platform for the AI Era

Directions was also where we shared more about IDC Quanta, our AI platform that puts IDC’s research and market intelligence directly into the tools enterprise teams already use. Built on 60+ years of IDC data and developed with input from more than 65 customers, Quanta is contextual, secure, and built to surface the signals that matter to your business before you think to ask.

Joe Bradley encourages the audience to join the waitlist for IDC Quanta, IDC's new AI platform

Early access is now full. The next window is coming. Reserve your spot now to be first in line when it opens, and get exclusive updates as the platform evolves.

 Visit our AI platform page to stay in the loop on IDC Quanta.

Everything Is Now Available on Demand

Whether you attended and want to revisit what you saw, or couldn’t make it and want to see what you missed: it’s all there. Sessions available include:

  • General sessions and mainstage keynotes
  • Breakouts across the Marketing, Data, Emerging Technology, and AI-Ready Infrastructure tracks
  • Analyst perspectives from across IDC’s research practice

The sessions were designed to give you something to take back to your team, your planning process, your next conversation about where to invest. They still will.

Don’t wait. See IDC Directions on Demand.

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.

IT has never been an easy job for CIOs and their teams. There’s the day-to-day reality of running the business, keeping systems available, managing risk, supporting customers, and delivering new capabilities. At the same time, CIOs are expected to keep pace with both the evolution of existing technologies and the steady stream of new ones entering the market. Some of the toughest challenges aren’t technical. They come from friction across roles, overlap, turf, and unclear ownership across IT and the business.

AI accelerates both progress and pressure

AI can help on the technology front. It can accelerate development, improve productivity, and make it easier to keep up with change. But it’s also introducing a new set of challenges — ones that can quickly turn into operational issues if CIOs don’t get ahead of them. Most CIOs are already feeling pressure from multiple directions. Boards and executive teams have invested heavily in AI and expect results. Business units are under the same pressure — and they don’t always wait for IT. Sometimes they partner. Other times, they move ahead on their own. That dynamic isn’t new. What’s changing is how easy it has become for the business to build technology without IT.

From IT-led development to business-led build

A recent article in The Wall Street Journal noted that OpenAI is working with firms like Accenture, Capgemini, and PwC to expand adoption of its AI coding tool, Codex — which generates and updates code from plain-English prompts — across enterprises, with millions of weekly active users and growing enterprise uptake. OpenAI Chief Revenue Officer Denise Dresser was quoted as saying that OpenAI’s consulting partners will help bring Codex “into every single line of business.”

Tools like Codex allow users to generate and update code using natural language. But that framing understates what’s really happening. These tools can be used to build applications, automate workflows, and create AI-driven agents that execute tasks across the business. Adoption is moving quickly. Finance teams can build finance applications. Sales teams can build sales tools. Increasingly, they’re doing it using plain-English prompts.

How boundaries blur

For CIOs, the challenge isn’t the technology; it’s the breakdown of traditional boundaries. Business units have often stepped in when they felt IT couldn’t move fast enough. Most CIOs have heard some version of: “I’m trying to run a business here. Either get it done or get out of the way.” What’s different now is that the barrier to entry is gone. With tools like Codex, and the support of external partners, business units don’t just have ideas. They can build and deploy. That’s where turf skirmishes emerge.

Development is no longer confined to IT. It’s happening across marketing, finance, and operations — anywhere there’s a problem to solve and the motivation to act. Without clear alignment within the organization, that quickly turns into overlapping solutions, duplicated effort, and ambiguous responsibilities.

Left unmanaged, this leads to familiar issues: fragmented architectures, integration challenges, security gaps, and increased operational risk. IDC’s 2025 assessment of low-code and no-code developer technologies confirms this shift is already underway at scale. But handled well, it can unlock a level of innovation most organizations struggle to achieve through centralized IT alone.

What CIOs should do next

The CIOs who get ahead of this won’t try to shut it down. They’ll get in front of it and shape it. In IDC’s conversations with enterprise CIOs, this is one of the most common inflection points we’re tracking right now. A few practical steps can make a meaningful difference.

Build fluency inside IT

Your teams need to understand tools like Codex well enough to guide the organization. That may mean partnering with firms like Accenture or PwC to accelerate the learning curve. The objective is straightforward: be the team the business turns to — not the one it works around.

Enable fast experimentation — but define the path to scale

Business units should be able to prototype quickly. In many cases, they’re closer to the problem and can move faster. But prototypes need a clear path into production. IT’s role is to take what works and integrate it, secure it, and operationalize it so it can scale across the enterprise.

Define roles early, before they get defined for you

If you don’t establish clear roles across IT and the business, they will emerge on their own — and usually through friction. Align early on:

  • Who builds demos and proofs of concept versus test and production
  • Who owns ideation versus scaling out and running
  • How solutions move from idea to production

Without that clarity, turf issues don’t go away — they grow.

Balancing speed and control

This isn’t just about Codex. It’s about what happens when the ability to build technology moves beyond IT. The CIOs who succeed won’t focus solely on control. They’ll focus on enablement — creating the conditions for the business to move faster while ensuring the right guardrails are in place. That balance — between speed and structure, autonomy and accountability — will determine how effectively organizations turn AI investment into real business outcomes.

For more on tools that help simplify software development, see IDC MarketScape: Worldwide Low-Code and No-Code Developer Technologies 2025 Vendor Assessment.

Gerald Johnston

Gerald Johnston - Adjunct Research Advisor

Analyst Profile
Jerry Johnston, an adjunct research advisor with IDC’s IT Executive Programs (IEP), founded GJ Technology Consulting, LLC, where he assisted global financial institutions and helped launch a UK startup bank. Johnston is an experienced financial services and consulting executive who…

An AI system flags a high-value customer for fraud and blocks a transaction.
The customer churns within days. The business cannot explain why the decision was made.

A regulator asks for an audit trail of an autonomous workflow.
The organization cannot trace how the outcome was generated.

These are not edge cases. They are early signals of a broader shift.

As AI systems move into core operations, decisions are faster, workflows are more autonomous, and consequences are more visible. What changes is not just scale. It is accountability.

The challenge is no longer whether AI works.
The challenge is whether it can be trusted to work reliably, transparently, and at scale.

The new reality: scale without trust creates instability

Enterprise AI is entering high-stakes environments.

  • Decisions are automated
  • Workflows are autonomous
  • Data moves across systems and partners

This creates new pressure points:

  • Limited visibility into AI-driven decisions
  • Increasing regulatory and compliance exposure
  • Vulnerabilities across data, models, and agents
  • Erosion of customer and stakeholder confidence

Expectations are rising at the same time. Customers, regulators, and employees demand accountability, explainability, and control.

Without trust, scale introduces instability.

The shift: from AI adoption to trusted AI systems

IDC’s FutureScape 2026 predictions highlight a critical transition.

Organizations are moving from deploying AI systems to embedding trust into those systems.

This requires a new operating model:

  • Trust is built into workflows, not added after deployment
  • Governance operates continuously, not periodically
  • Security spans the full AI ecosystem, not isolated components

In practice, this means an AI-driven decision is no longer a black box.

A financial services firm deploying agentic AI for credit decisions can trace how a decision was made, validate the data used, demonstrate compliance, and apply human oversight where needed. That level of visibility allows AI to operate in regulated environments with confidence.

Trust, in this context, is operational.

To get there, organizations must move from principle to execution.

Charting the path: four moves to build trust and resilience

To succeed in this environment, leaders must take a deliberate approach to governance, transparency, security, and organizational readiness.

1. Embed governance into everyday operations

AI governance must move beyond policy frameworks.

Leading organizations are integrating governance directly into workflows through automated compliance checks, continuous monitoring, and embedded controls.

Without this:
Governance becomes reactive. Issues surface after failure, increasing regulatory risk and slowing adoption.

2. Establish transparency and accountability at scale

Autonomous systems require visibility.

Organizations must ensure that AI decisions can be traced, audited, and explained, with clear ownership for outcomes.

Without this: Decisions cannot be defended to regulators, customers, or internal stakeholders, limiting the use of AI in critical operations.

3. Strengthen security across the AI ecosystem

AI expands the attack surface across data, models, and agent interactions.

Organizations are adopting unified approaches to security, risk, and compliance that operate continuously across the AI lifecycle.

Without this: Vulnerabilities scale with adoption, exposing organizations to breaches, manipulation, and operational disruption.

4. Build a resilient, AI-ready organization

Resilience extends beyond systems to people and processes.

Organizations must prepare for workforce shifts, system disruptions, and evolving regulatory requirements.

Without this: AI-driven operations become fragile, with disruptions cascading across workflows and slowing response to change.

The payoff: trust as a foundation for scale

When trust is embedded into AI systems, organizations unlock consistent and measurable impact.

They gain:

  • Confidence in scaling AI initiatives
  • Stronger relationships with customers and stakeholders
  • Faster adoption of new capabilities
  • Greater resilience in uncertain environments

Trust enables organizations to move forward with clarity and control.

From control to confidence

The agentic future introduces new forms of risk alongside new opportunity.

Organizations that cannot explain, govern, or secure their AI systems will encounter increasing friction as they scale. Those that embed trust into their operations will move with greater confidence, expand into higher-value use cases, and sustain performance over time.

FutureScape 2026 makes the trajectory clear.

AI adoption is accelerating.
Trust will determine who can sustain it.

Those who operationalize trust will define the next phase of competitive advantage in the agentic economy.

Explore the FutureScape 2026 predictions behind trusted AI systems

FutureScape 2026 includes detailed research, analyst perspectives, and events that expand on building trust, resilience, and prosperity in the agentic future

Core Research

Analyst perspectives

On-demand webinars

Explore the full FutureScape 2026 predictions.

IDC - -

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

企业看到了智能体但不知道如何落地

2026年初，OpenClaw（龙虾）这类开源智能体产品很快成为市场焦点，端到端任务执行能力对企业很有吸引力。但智能体（智能体）要在企业里真正用起来，在什么场景落地成了主要的卡点。企业认知到了智能体的能力，但回到自己的业务流中，不清楚哪些环节可以交给智能体去做，哪些场景最值得优先投入。IDC 2026年智能体企业用户的调研数据显示，仍有60%的中国企业处于了解评估和试点智能体的阶段，仅有18%的企业把智能体纳入了核心业务流（IDC Syndicated Survey 2026: China AI Agents Market 2026），企业仍然难以跨越从Copilot助理向Agentic AI转型的阶段。为了解决这一问题，IDC提出了一个从业务约束出发的智能体落地框架——COMPASS模型。

业务卡点是关键：从企业业务的三层效率约束入手

到底应该如何选择智能体在业务中的落地场景呢？我们可以参考企业价值链流程，通过观察业务场景中的效率约束，来寻找切入点。一项业务从外部信息进入企业、到内部处理完成、再到能力沉淀放大，效率约束主要集中在三个层面。

第一层，信息输入的约束。企业每天接收大量外部输入，包括邮件、扫描件、图片、语音、聊天记录和表单。很多信息不能直接进入业务系统，要先经过识别、提取、转换和归类，才能变成后续流程可用的数据。

第二层，信息处理的约束。信息进入业务流之后，要在多个系统、多个角色之间流转、分析、协调。数据需要跨系统汇总，状态需要同步，异常需要及时发现，决策需要结合多方信息判断，责任需要在不同角色之间衔接。

第三层，信息资产化的约束。这是容易被管理者忽略的深层约束，企业高价值的业务经验和决策逻辑通常固化在少数员工手里。而人的并发处理能力也存在刚性上限，业务波峰时很难敏捷弹性的应对，也很难规模化增长。这一环节的关键在于让知识经验沉淀和复用，确保企业规模化扩张时不再出现效率瓶颈。

COMPASS模型：企业智能体落地的七维度指南

基于这三层约束，我们可以进一步拆解出企业智能体落地的七个关键能力维度，即IDC提出的COMPASS模型。

1.      感知与理解（Perception）

外部输入格式各异，进入核心业务系统前往往要依赖人工识别文档类型、提取字段、比对票据，效率瓶颈集中在非标准信息怎样被理解提取、怎样转化为系统可用的结构化数据。

智能体借助多模态大模型能力可以直接读取手写单据、非标合同、会议录音和视频素材，提取关键实体，按要求输出JSON或其它结构化数据，让海量外部信息直接成为系统可用的结构化信息。

2.      分析与决策（Analysis）

企业数据和业务规则分散在不同系统中，人工做综合分析效率往往很低，关联节点越多越容易疏漏，依赖经验的判断也缺乏清晰的决策记录，组织很难做系统性复盘。

智能体可以通过工具调用从多个系统实时拉取数据，结合预设业务规则和历史模式，输出结构化分析结论和决策建议。低风险或标准场景可以直接调用执行，非标和高风险场景则带着完整分析升级给人工处理。

3.      流程编排（Orchestration）

单个系统内部流转通常比较顺畅，跨系统连通往往比较脆弱。过去依赖API解决跨系统流转，建立的是确定性规则，一旦出现条件分支、异常状态，或者需要根据实时反馈在ERP、CRM和财务系统之间切换判断，硬编码连接容易中断退回人工，构建这些自动化流程本身也要占用较多IT开发资源。

智能体在任务执行环节能够做到系统调用的动态规划，接收到像”给大客户安排加急发货”的指令后，会根据上下文自主决策，调用RPA或基于API接口依次调用ERP核对库存、向WMS确认排期、在CRM中更新状态；流程生成环节，智能体可以基于已有规则库和流程模板，自动组装出RPA或自动化流程并部署上线，构建和迭代周期都会明显缩短。

4.      监控与主动响应（Monitoring）

供应链延误、财务偏差、客户行为异常、系统运维故障，任何一环的响应滞后都可能带来连锁影响。人无法7×24小时保持稳定判断，传统做法依靠阈值报警、定时巡检和轮班值守，覆盖范围有限。

智能体可以在持续监测的基础上结合上下文做智能判断，一笔异常大额退货可以同时关联该客户的近期订单模式、产品批次的质量数据、物流环节的异常记录，在告警的同时给出初步的归因分析和处理建议。

5.      协调沟通（Collaboration）

跨部门协作中信息天然不对称，产品设计的背后逻辑、法务建议的依据、仓库的实时库存，散落在不同的人、系统和沟通记录里，需要时很难立刻拼出全貌。澄清、追踪、同步消耗的时间往往占据业务人员相当大的比例。

智能体可以作为跨部门协作的中枢，持续追踪任务各环节进展，在关键节点主动通知相关方，出现延误或阻塞时自动升级；需要多方对齐时，能够快速汇总散落在各系统和沟通记录中的上下文，生成结构化的决策议题。

6.      知识沉淀（Sedimentation）

企业大量执行能力和决策能力绑定在个体员工身上，关键人员离职、调岗或休假，流程质量和决策水平都会受到影响，新人入职要经过较长周期才能积累足够的隐性知识。

智能体的运行过程本身就可以是知识显性化的过程，任务执行路径、分析框架、决策规则和异常处理方式均可以便捷的结构化地记录下来，并随着运行时间持续积累，可以复用到新的业务场景、新的分支机构和新的团队，同时也能反过来优化智能体自身的响应策略，形成持续迭代的数据飞轮。

7.      规模弹性（Scalability）

人的产能是线性的，一个人一天处理100笔订单就是极限，高峰期临时加人未必能带来同比效率提升。《人月神话》里有一个经典观察，项目每增加一个人，沟通链路会延长，交接成本也会上升，整体效率的提升往往被初始化和协调的开销抵消。

智能体的处理能力则可以随算力弹性扩展，业务高峰期不需要临时招人培训，订单量翻倍时调度更多计算资源就能跟上，背后是运行沙箱、记忆存储、调度管理等基础能力模块在支撑智能体在生产环境中稳定可控的弹性伸缩。

这七个维度就是COMPASS（指南针）模型，由上面提到的Collaboration、Orchestration、Monitoring、Perception、Analysis、Sedimentation、Scalability七个首字母构成。COMPASS对应三层约束视角，Perception对应的是信息输入层，解决外部信息能否准确进入企业系统的问题；Analysis、Orchestration、Monitoring、Collaboration对应的是信息处理层，覆盖分析决策、跨系统流转推进、监控响应和跨角色协同；Sedimentation和Scalability对应的是信息资产化层，把个人经验转化为企业可复用的数字资产，同时支撑企业规模化增长。

分析师观点：

COMPASS模型可以作为企业落地智能体的诊断和指导工具，企业可以对照七个维度盘点自身业务流的关键瓶颈，识别出需要优先交给智能体的环节。从业务瓶颈倒推切入点，更贴近企业实际的业务需求，也能够减少企业因为智能体技术或产品高速迭代带来的选择困难。

从做什么到用什么：IDC MarketGlance解决选型问题

COMPASS模型回答的是智能体怎么切入的问题，但企业在落地时仍然会面临怎么在市场上找到合适的产品和供应商这一问题。当下市场中宣称具备智能体产品和服务能力的厂商数量已经相当可观，但能力层次和落地经验的差异很大，企业难以抉择。IDC通过大量深度的厂商调研和观察，为企业提供了中国智能体市场概览这一报告(包含中国智能体行业应用与开发平台市场概览和企业级智能体应用市场概览)，预先筛选出了覆盖不同行业和业务场景中，满足评估要求的可靠智能体产品及解决方案供应商，供企业参考选择。

配套工具与实践：从方法论走向落地（限时资料免费获取）

另外基于COMPASS模型，我们准备了两份配套的智能体落地指南材料，一份是面向人的，一份是面向智能体的skill。这个skill我们已经上传了Openclaw的官方skill商店clawhub中，可以直接把skill链接( https://clawhub.ai/seanlandtop/idc-enterprise-agent-compass )给智能体安装使用，让智能体可以基于IDC的COPASS模型帮助企业进行智能体落地指引。

限时资料免费下载：可扫描二维码下载详情:

案例征集：寻找可量化ROI的Agent实践

为了进一步沉淀实践经验，IDC计划于2026年4月启动Agent最佳实践案例（ROI视角）研究，面向已经在真实业务场景中部署Agent并取得可量化效果的企业与技术供应商开展案例征集，以期为更多企业提供可参考的落地路径。

与IDC进一步交流

如果您的企业正在评估或推进Agent在业务中的落地，或希望基于COMPASS模型进一步梳理业务切入点与优先级，IDC可以提供相应的研究支持与分析服务，包括基于行业与业务场景的落地路径分析，以及结合IDC MarketGlance的供应商方向参考。

同时，对于已经进入实践阶段的企业，IDC也可以围绕Agent应用效果与ROI进行评估与复盘，帮助企业更系统地推进从试点到规模化落地的过程。

如需进一步了解相关研究内容或开展交流，欢迎与IDC联系。请点击此处与我们联系。

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.