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Key Highlights
  • 75% of Asia/Pacific care providers say agentic AI delivers greater productivity gains than GenAI
  • 50% of providers will use advanced risk stratification for population health by 2028
  • Asia/Pacific accounts for nearly 60% of the global aging population
  • Agentic AI’s share of GenAI budgets will grow from 18% (2025) to 29% (2026)
  • Multimodal AI will predict 50% of chronic and rare diseases before symptoms by 2030
  • AI agents will be used by 33% of top-tier hospitals for real-time clinical decision support
  • Quantum platforms will enable 100x faster diagnostics for 20% of leading institutions by 2030
  • Singapore General Hospital’s AI chatbot saves ~660 clinician hours annually

Asia/Pacific healthcare provider organizations are at a critical inflection point. Generative AI (GenAI) is no longer an experimental initiative — it has become a strategic investment imperative. To navigate this shift, care providers need a clear roadmap that aligns AI priorities with emerging models of care delivery. IDC’s recently published FutureScape report for healthcare provides this roadmap and outlines how providers can move from experimentation to measurable impact.

A key highlight of this year’s FutureScape is agentic AI, which marks a new milestone in the region’s AI maturity. According to IDC’s Agentic AI Survey, 75% of Asia/Pacific care providers report that agentic AI outperforms GenAI in delivering measurable productivity gains. However, this transition requires robust regulatory frameworks and strong human-in-the-loop mechanisms to ensure ethical, transparent, and accountable deployment.

Asia/Pacific care providers must first address care productivity, enabling clinicians and operations teams to do more with constrained resources. This begins with building a resilient and trusted data foundation that can unlock the full value of agentic AI. With productivity gains as the anchor, providers can then reimagine care delivery by integrating advanced analytics, seamless workflows, and explainability to support personalized, secure, and transparent care.

This blog highlights five of the ten key predictions from the recently published IDC report: IDC FutureScape: Worldwide Healthcare Industry 2026 Predictions — Asia/Pacific (Excluding Japan) Implications

The Next Phase of Population Health Management: Toward Data-Driven, Proactive Intervention

By 2028, 50% of healthcare organizations in Asia/Pacific will leverage advanced risk stratification tools to tackle population health, specifically focusing on the chronic disease burden and aging population.

Asia/Pacific is home to nearly 60% of the world’s aging population, and the growing burden of noncommunicable diseases continues to place sustained pressure on healthcare systems. IDC expects population health management to become more data driven and proactive, as advanced risk stratification enables earlier identification of at-risk cohorts and more effective interventions.

This shift highlights the need for interoperable data platforms that unify clinical, demographic, and social determinants of health data. A strong example is Taiwan’s AI-on-DM (Diabetes Management) initiative — the country’s first large-scale healthcare AI program. Led by the Ministry of Health and the National Health Insurance Administration (NHIA), the initiative integrates long-term type 2 diabetes data with a medical large language model (LLM) to predict and manage complication risks for more than 2 million patients, years in advance. The program is expected to expand to other chronic and complex conditions, including hypertension and cancer.

Despite this progress, fragmented data environments and digital inequity remain major barriers across the region. Providers that invest in real-time analytics, standardized data exchange, and secure data sharing will be better positioned to shift from reactive care to preventive, population-scale interventions.

Agentic AI and the Future of Patient Experience: Advancing Digital Equity and Trust

By 2028, 45% of healthcare organizations in Asia/Pacific will advance agentic AI-enabled engagement by prioritizing digital equity, cultural alignment, and trust to produce personalized and empathetic communication.

Agentic AI introduces a new model of patient engagement. Unlike traditional automation, agentic systems adapt interactions in real time by drawing on clinical context, patient-reported outcomes, and social factors. In a region defined by linguistic, cultural, and socioeconomic diversity, this capability is critical.

IDC research shows that Asia/Pacific providers are rapidly increasing investment in agentic AI for patient engagement and care coordination. IDC’s 2025 FERS Survey indicates that agentic AI’s share of GenAI budgets will rise from 18% in 2025 to 29% in 2026. A practical example can be seen in Synapxe’s efforts to modernize Singapore’s national health digital infrastructure. By embedding AI-driven decision support and intelligent automation across patient-facing and care coordination platforms, Synapxe is enabling more proactive, personalized, and culturally aligned engagement.

Trust remains foundational. Transparent AI behavior, explainable recommendations, and clear escalation paths to clinicians are essential for adoption by both patients and care teams. Providers that embed governance, cultural sensitivity, and digital equity into agentic engagement strategies will build stronger patient relationships and improve outcomes.

Multimodal AI and the Shift to Predictive, Preventive Care

By 2030, multimodal AI will predict 50% of chronic and rare diseases before symptoms, making predictive care a reality with broader health data, including wearables and multiomics in Asia/Pacific.

This marks a decisive shift from reactive to predictive and preventive healthcare. Advances in multimodal AI — spanning clinical records, medical imaging, genomics, proteomics, and real-time wearable data — are enabling earlier and more accurate disease risk identification, often years before symptoms appear.

For a region facing rapid population aging and rising chronic disease prevalence, the impact is significant. Multimodal AI models can continuously analyze longitudinal health data to detect subtle patterns invisible to traditional diagnostics. In China, multimodal AI systems combining medical imaging, clinical records, and laboratory data have demonstrated approximately 98% accuracy in detecting biliary atresia.

Earlier detection supports targeted prevention, personalized care pathways, and reduced downstream costs. For rare but life-threatening pediatric conditions, such as biliary atresia, earlier diagnosis can dramatically improve outcomes.

Real-Time Decision Support: Governed AI Agents in Clinical Care

By 2030, 33% of top-tier hospitals in Asia/Pacific will deploy AI agents to deliver real-time decision support and autonomous workflows with greater than 80% accuracy while escalating exceptions to clinical staff.

Clinical environments demand speed, accuracy, and accountability. IDC expects AI agents to increasingly augment clinicians by synthesizing multimodal data and delivering context-aware insights at the point of care. These agents will not replace clinicians, but they will automate routine tasks and support faster, more consistent decision-making.

A real-world example is Singapore General Hospital’s AI chatbot, Peach (Perioperative AI Chatbot), which supports pre-operative assessments and saves approximately 660 doctor hours per year.

Success depends on data quality, interoperability, and governance. AI agents must operate within clearly defined boundaries, with continuous monitoring and escalation mechanisms. Hospitals that invest early in AI-ready infrastructure will improve clinician efficiency while preserving clinical oversight.

From Classical Limits to Quantum Leap: Preparing for Precision-Driven Care

By 2030, 20% of top-tier healthcare institutions in Asia/Pacific will harness quantum platforms for 100x faster diagnostics, simulations, and digital twins in precision-driven complex care.

Quantum computing remains emerging, but it represents a long-term inflection point for healthcare. IDC expects early adoption in complex diagnostics, precision medicine, and advanced simulations. Governments and institutions across Asia/Pacific are already investing in quantum ecosystems.

In Australia, the University of Wollongong’s quantum-enhanced imaging research demonstrates how hybrid quantum-classical techniques can accelerate genomics, biomarker discovery, and precision radiotherapy. For healthcare leaders, this reinforces the importance of future-ready data architectures and skills development.

Moving Forward: From Insight to Action

Together, these predictions highlight a clear message for Asia/Pacific healthcare providers. Agentic AI, advanced analytics, and emerging technologies can deliver measurable gains in productivity, patient experience, and clinical outcomes. However, success depends on trusted data foundations, interoperability, explainability, and strong human oversight.

IDC FutureScape provides a practical roadmap for navigating this transition. Providers that act now to align data, governance, and workforce strategies will be best positioned to lead in the next era of AI-driven, patient-centric care.

Manoj Vallikkat - Senior Research Manager - IDC

Analyst Profile
Manoj Vallikkat currently works as a senior research manager for Healthcare Insights in IDC Asia/Pacific. His research covers digital transformation (DX) across care delivery systems in the region, focusing on areas such as evolving healthtech ecosystem, patient-centric care, and predictive care management. He also covers the life sciences segment, with special interest in artificial intelligence (AI)-based drug discovery and remote clinical trial practices. Manoj has led key consulting engagements across the country markets in the Asia/Pacific region. He has also handled various GMS engagements for tech providers, which include tailored reports, round-tables, and speaking gigs.

国内ITインフラ市場（2025年の振り返り）

AIインフラ
2025年もAIインフラ投資が主役となりました。2024年に引き続きハイパースケーラーやサービスプロバイダーによる投資が中心ですが、研究機関や一般企業によるAIインフラ投資も徐々に拡大しています。

AIインフラは国内外の動向が一般ニュースに取り上げられるほど注目度が高く、2025年は講演でもAIインフラ関連のテーマが多くなりました。エージェンティックAIなどAI活用が進むにつれてAIインフラ投資への関心が高まってきますので、2026年も引き続き国内ITインフラ市場の注目テーマになるとみています。

仮想化＆HCI
2024年から市場が流動的になっていますが、利用企業ごとに方向性が定まりつつあります。仮想化環境の移行を決めた企業では検証や移行作業が本格化してきました。2026年はこうした動きがさらに顕著になりそうです。

マネージドサービス
2025年はマネージドサービスやITインフラ運用の調査に注力しました。複数のレポートを発行しましたが、特にマネージドクラウドサービス領域でベンダー評価レポートを発行できたことは大きな取り組みでした。

IDC MarketScape: Japan Managed Hybrid and Multi-public Cloud Services 2025 Vendor Assessment

クラウド移行は、ユーザー企業・ベンダー双方にとって関係性を見直す機会になっています。
調査にご協力いただいた皆様にはこの場を借りて御礼申し上げます。

FutureScape（今後5年間の予測）

IDCでは毎年、各調査領域でFutureScape（今後5年間の10項目の予測）を発行しています。デジタルインフラ戦略では、AIインフラ関連と、ITインフラへのエージェンティックAI適用に関する項目が中心です。

AIの影響は、コンピュート、データセンター、データロジスティクス、ネットワーク、コンテナ化、エッジ、プライベートデジタルインフラへの再投資など幅広い領域で強まります。

IDC FutureScape: Worldwide Digital Infrastructure 2026 Predictions – Japan Implications

2026年に向けて

デジタルインフラ戦略の調査を主導して3年が経ちました。複数の領域を担当するアナリストと連携しながら調査を拡充してきましたが、2025年はさらに幅が広がった年でした。

2026年は新たな挑戦が始まる1年になりそうです。引き続き、AIインフラ、仮想化、インフラモダナイゼーションなどITインフラ分野の調査に取り組みます。そして、GPUクラウドなどを含む、Accelerated Compute as a Serviceの分野もMarketScapeを実施予定です。


2025年もご支援ありがとうございました。2026年もどうぞよろしくお願いいたします。

関連する調査やご相談について

より詳細なインサイトや市場動向については、当社アナリストへお気軽にご相談ください。

Yukihisa Hode - Research Manager, Infrastructure & Devices, Research, IDC Japan - IDC Japan

Analyst Profile
Yukihisa Hode is a research manager covering digital infrastructure strategies as well as AI infrastructure, IT infrastructure services, IT operations, hybrid/multicloud and hyperconverged infrastructure (HCI). He leads the research program on digital infrastructure strategies, providing insight and advice on the digital infrastructure through research reports, marketing content, and presentations to support IT and digital decision-making.

With AI-generated content saturating every channel, every vendor can sound confident and strategic. That’s why in today’s B2B landscape, buyers are not overwhelmed by a lack of information anymore, they are overwhelmed by too much of it. The real challenge for buyers is not finding options, but quickly identifying which solutions are truly worth their attention.

The new buyer mindset: Filtering for fit, not just information

Buyers are not asking for proof because they are curious. They are asking because they need to filter aggressively. Their goal is not to find the most impressive story, but to find the vendor who can meaningfully address their current needs. In this environment, proof isn’t just about demonstrating value. It’s about signaling fit. Proof tells the buyer: “This applies to your situation, right now.”

Agentic AI and the changing discovery process

This shift is being accelerated by agentic AI, autonomous systems that retrieve, coordinate, and even act on behalf of buyers.

IDC research shows that discovery is no longer a linear, one-time phase. It’s a continuous, adaptive process, increasingly mediated by AI agents that surface and prioritize solutions based on relevance, applicability, and trustworthiness.

Buyers now rely on these systems to narrow the field before human evaluation even begins.

As a result, brands that succeed aren’t just visible, they’re findable in context. Their knowledge assets are:

  • machine-readable
  • verifiable
  • and referenced by trusted third parties

Why proof is the new standard

Buyers have learned that every vendor can claim impact, and every solution can be positioned as innovative. As a result, they filter differently. Not by who sounds the smartest, but by who can demonstrate situational fit. Proof becomes the buyer’s way of answering three critical questions:

  1. Does this solution address the pressure I’m dealing with now?
  2. Is this applicable to my specific context, not just a generic scenario?
  3. Can I use this information to make sense of my situation internally?

If the answer to any of these is unclear, buyers move on, even if they continue to “engage” with content. This disconnect explains why engagement metrics may look strong, while leads fail to advance through the funnel.

Trust, ethics, and the new table stakes

Relevance isn’t just operational, it’s ethical.

IDC’s 2025 Tech Buyer Survey reveals that 81% of buyers and 88% of C-level executives now prioritize ethical AI use when selecting technology partners.

Buyers are looking for signals of responsible, transparent, and ethical practices.

As a result, proof now extends beyond outcomes alone. It includes visible evidence of governance, data provenance, and compliance.

While AI systems, including search, copilots, and agentic discovery tools, do not judge ethics or morally evaluate vendors, they do prioritize solutions and content that are:

  • well structured and machine-readable
  • frequently referenced by trusted third parties
  • associated with authoritative sources
  • consistent across credible channels

In an AI-mediated buying journey, relevance is reinforced by what can be verified, referenced, and trusted, not simply by what is claimed.

What it takes to be credible in the answer engine economy

As AI agents mediate more of the discovery and evaluation process, the structure and validation of your content matter more than ever.

IDC’s analysis shows that knowledge management, ensuring your assets are machine-readable, well-structured, and validated by third parties, is now the foundation for being surfaced as a credible option. Brands that invest in distributed authority, such as analyst partnerships and independent benchmarks, are more likely to be recommended by AI agents, trusted by buyers and shortlisted earlier.

When engagement looks healthy, but pipeline doesn’t

Many marketing teams see healthy engagement metrics, downloads, clicks, lead volume —yet still struggle to convert that activity into pipeline contribution and bookings impact. This is not a sign of buyer disinterest. It is a sign that proof of fit and credibility has not been established early enough in the journey. Buyers will engage with content that sounds promising, but they’ll only advance when they see clear evidence that it applies to them. Late-stage proof doesn’t create pipeline momentum. It only explains why it never materialized.

Closing the gap

The most common breakdown in messaging happens here:

Vendors lead with transformation and innovation.
Buyers are asking, “What does this fix first?” and “Is this actually for me?”

Forcing buyers to translate vision into applicability is a losing strategy. Instead, marketers must lead with proof that is:

  • immediately recognizable
  • contextually relevant
  • and actionable without interpretation

That’s how relevance is established, and momentum begins.

The new standard: Proof over promises

Leading marketing teams are shifting their approach. They are no longer focused on making messages stronger or louder. Instead, they are asking, “How do we prove fit and credibility earlier?” and “What signals help buyers self-qualify faster?” This shift is the foundation of a proof-over-promises strategy. It is not about abandoning storytelling, but about earning the right to be considered before asking for belief. This happens through clear, early evidence.

Pressure-test your messaging

To help marketers align with how buyers actually decide, we created What Triggers B2B Buyers in 2026: A Practical Playbook for B2B Marketers. This practical diagnostic is grounded in IDC buyer research and outlines five tests that every message must pass to be considered credible and actionable by today’s buyers. If a message fails two or more tests, it is not necessarily wrong; it is simply misaligned with the buyer’s current needs.

For more:

  1. Explore the Proof Playbook to see whether your messaging proves relevance early or asks buyers to trust it too soon.
  2. Move beyond claims and into evidence buyers recognize as relevant to their situation. Explore how IDC Business Value helps turn relevance into momentum.
  3. Reach out to a market expert at IDC to explore analyst-led validation solutions, market data and b2b buyer research

IDC - -

LinkedIn
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.

There is an old adage that you may have heard of: Those who fail to plan, plan to fail. While we are not in the mode of advocating for not being prepared to face off against cyber attackers, we do live in an age where AI is being used by cyber-criminal groups and nation-state actors. Their use of AI is causing the time metrics of how quickly cybersecurity defenders have to response to be measured in smaller doses like minutes, rather than the months, weeks, days and hours of a not-so long-ago time frame.

Cybersecurity, like many other technology domains, does not exist in a static environment. It’s mission statement, after all, is to protect digital assets. Assets that are constantly changing in who and what they are, as well as the human and increasingly not-so human identities that are associated with them. Unfortunately, the tactics, techniques and procedures that attackers use to deploy their craft has evolved with the outcome being faster attacks that are harder to detect and respond to.

Cybersecurity playbooks—the concise, predefined sets of technical and procedural steps that guide teams through detecting, responding to, and recovering from security incidents with speed and consistency—are one of the foundation pillars of security operation centers (SOCs). In the good old days prior to AI enhanced attacks, organizations that had a good threat intelligence capability could often map out how an attack could play out and use it to apply the appropriate responses. This traditional approach—relying on static, manually updated playbooks—can no longer keep pace with the speed and sophistication of modern attacks.

Recent IDC research predicts that by the first half of 2027, 85% of detection and response playbooks will be generated dynamically at the time a SOC alert is triggered. This marks a fundamental shift in how organizations approach incident response, risk management, and operational resilience.

Why dynamic playbooks matter

Dynamic playbooks, powered by AI, offer several advantages over static guides:

  • Real-time adaptation: Playbooks are tailored to the specific context of each alert, factoring in the latest threat intelligence, asset configurations, and business priorities.
  • Reduced analyst burden: SOC teams, especially those with limited resources or complex infrastructures, are freed from the time-consuming task of manually updating playbooks every six months or year.
  • Consistent, effective response: Automated playbook generation ensures that responses are both standardized and contextually relevant, reducing the risk of outdated procedures and missed steps.

The risks of relying on static playbooks

Organizations that rely on static or infrequently updated playbooks face several challenges:

  • Stale procedures: As environments change, playbooks can quickly become obsolete, leaving gaps in coverage and increasing risk.
  • Resource drain: Maintaining playbooks is a significant overhead, particularly for smaller SOC teams already stretched thin.
  • Inconsistent response: Outdated playbooks can lead to inconsistent handling of threats, undermining both security and compliance.

IT impact: Automation, integration, and innovation

The move to dynamic playbooks will reshape SOC operations:

  • Automation: AI-driven systems will automate the creation and updating of playbooks, integrating seamlessly with detection tools and incident response platforms.
  • Integration: Dynamic playbooks will pull data from across the IT ecosystem—threat intelligence feeds, asset inventories, and business process maps—to ensure responses are both comprehensive and relevant.
  • Continuous improvement: Machine learning models will refine playbook logic over time, learning from past incidents to improve future responses.

The continuous improvement cycle for AI generated paybooks

As your organization transitions to dynamic, AI-driven playbooks, validating their efficacy becomes a critical operational and strategic priority. Ensuring that automated playbooks deliver reliable, context-aware responses is essential for maintaining security posture and regulatory compliance.

Key approaches to validation

  • Simulated incident testing: Regularly run tabletop exercises and red team simulations using AI-generated playbooks. Evaluate how the playbooks guide response actions, adapt to evolving scenarios, and support decision-making under pressure.
  • Continuous feedback loops: Integrate analyst feedback directly into the playbook generation process. Encourage SOC teams to annotate playbook steps, flag ambiguities, and suggest refinements, enabling the AI to learn and improve over time.
  • Compliance and audit reviews: Ensure that AI-generated playbooks align with regulatory requirements and industry standards. Conduct periodic audits to verify that automated responses meet documentation, reporting, and escalation protocols.
  • Peer benchmarking: Collaborate with industry peers and professional services firms or your managed security services provider (MSSP) to benchmark playbook performance. Leveraging external expertise to validate logic, coverage, and adaptability against best-in-class practices is a prudent path forward by utilizing expertise beyond your physical or virtual 4 walls of your organization.

Download the full IDC FutureScape: Worldwide Security and Trust 2026 Predictions report to understand the predictions shaping cyber resilience, AI governance, and enterprise trust over the next five years.

Craig Robinson - Research Vice President , Security Services - IDC

Analyst Profile LinkedIn
Craig Robinson is a Research Vice President within IDC’s Security Services research practice, focusing on managed services, consulting, and integration. Coverage areas include Managed Detection and Response services, Cyber Resilience, and Incident Readiness & Response services.

国务院新闻办公室于2026年1月19日上午10时举行了新闻发布会，国家统计局局长康义，国家统计局新闻发言人、总经济师、国民经济综合统计司司长付凌晖介绍了2025年国民经济运行情况。本文基于此次会议发布的数据给出分析。

核心观点

2025 年中国经济运行并非一次周期性下行，而是一次关键性的结构性切换。尽管 2024 年与 2025 年的 GDP 增速均保持在 5.0% 左右，但驱动增长的内在动能已发生显著变化：固定资产投资由正转负，制造业投资大幅放缓，通缩压力仍然存在；与此同时，信息服务业投资快速增长，高技术出口表现突出，消费延续结构性修复，人口与劳动力约束明显加剧。

IDC 认为，2025 年实际上完成了中国经济从“投资拉动型增长”向“结构再平衡、效率驱动型增长”的关键过渡。这一变化将深刻影响 2026 年的市场环境，使其呈现出总体温和增长、结构性高度分化的特征。

对 ICT 市场而言，这意味着一个明确转向：从以资本开支和规模扩张为核心，转向以可量化 ROI 为导向、以软件和 AI 为核心的价值创造模式。能够顺应这一结构性转变的厂商与企业将持续跑赢市场，而仍停留在传统扩张逻辑中的参与者将面临更大压力。

一、宏观背景：增速保持稳定，结构分化加剧

2024 年与 2025 年，中国经济均实现了约 5.0% 的 GDP 增长目标，但增长质量与结构已明显不同。

2024 年的经济增长在四季度明显回升，反映出政策支持、基础设施投资以及制造业阶段性复苏的拉动效应；而 2025 年则呈现出“前高后低”的增长节奏，季度 GDP 增速逐步回落，反映出企业投资信心与项目预期趋于谨慎，尤其是在大型资本性项目方面。

从产业结构看，2025 年第三产业占比继续提升，第二产业贡献趋于稳定，表明中国经济正进一步迈向以服务业和知识密集型产业为主导的发展阶段。这一趋势对技术需求结构具有深远影响。

二、2024–2025 年的六大关键结构性变化

IDC 从宏观与产业数据中识别出六项对未来市场具有决定性影响的变化。

1、固定资产投资由“正增长”转为“整体收缩”。2024 年固定资产投资同比增长 3.2%，而 2025 年则下降 3.8%，出现明确拐点。基础设施投资回落，制造业投资几乎停滞，房地产投资进一步下探。这既是周期性谨慎的结果，也是结构性调整的体现：地方财政约束趋紧，企业资本纪律增强，传统投资项目的回报率持续下降。

市场含义：以大规模资本开支为主要驱动的市场空间正在系统性收缩。依赖“重资产、重工程、重建设”的行业与技术领域，在 2026 年仍将面临持续压力。

2、制造业投资急剧放缓，但并未系统性下滑。制造业投资增速从 2024 年的 9.2% 大幅回落至 2025 年的 0.6%，反映出企业在需求不确定和利润承压背景下，对扩张计划进行重新评估。但与此同时，高技术制造业和装备制造业的工业增加值仍保持相对较快增长，显示制造业投资并非全面退潮，而是向高技术、高附加值领域高度集中。

市场含义：2026 年制造业相关 ICT 需求将呈现“客户数量减少、单客户深度提升”的特征，重点集中于具有明确效率与质量目标的先进制造企业。

3、信息服务业投资成为逆周期结构性亮点。与整体投资趋势形成鲜明对比的是，2025 年信息服务业投资同比增长超过 28%。这表明在资本整体趋紧的背景下，政府与企业仍在持续加大对数字基础能力的投入。这类投资并非追求规模扩张，而是围绕效率提升、韧性增强与长期竞争力构建展开。

市场含义：云服务、数据平台、AI 能力、软件基础设施将成为 2026 年最具确定性的增长板块之一。

4、消费温和修复，但升级特征显著。社会消费品零售总额增速从 2024 年的 3.5% 小幅提升至 2025 年的 3.7%。尽管整体恢复节奏仍然温和，但消费结构发生明显变化。通讯器材、文化办公用品、体育娱乐用品及智能家电等品类实现两位数增长，表明消费者并非简单扩大支出，而是向数字化、智能化和体验型消费升级。

市场含义：2026 年与消费相关的 ICT 机会将更多集中在零售数字化、全渠道运营、智能供应链以及 AI 驱动的客户运营领域。

5、出口保持韧性，增长质量持续改善。2025 年出口增速虽略低于 2024 年，但仍保持较高水平，高技术产品出口同比增长超过 13%。对“一带一路”国家出口以及民营企业出口占比持续提升。这表明中国外向型竞争力正在由规模优势向技术与价值优势转变。

市场含义：2026 年，企业“走出去”将继续成为 ICT 市场的重要增长来源，带动跨境云架构、数据合规、全球供应链系统与安全能力的需求。

6、人口下降明显加速，劳动力约束显性化。2025 年人口减少规模明显扩大，而城镇化率持续提升。劳动力供给约束已从长期趋势转变为现实挑战。这一变化强化了企业对自动化、数字化与智能决策的迫切需求。

市场含义：到 2026 年，“用技术替代人力、用智能提升效率”将从选择项转变为企业经营的核心议题。

三、对 2026 年 ICT 市场整体影响的基本判断

IDC 预计，2026 年中国 ICT 市场将进入一个总量温和增长、结构高度分化的新阶段。

从“扩张型建设”走向“价值型优化”。市场需求的核心将从“新系统建设”转向对既有数字资产的深度利用与价值释放。项目审批将更加关注成本节约、效率提升和回报周期。

从硬件导向转向软件与 AI 主导。硬件密集型项目面临更严格的预算约束，而软件、云服务、数据与 AI 应用将在企业 ICT 支出中占据更高比重。

从一次性交付转向持续价值交付。订阅制、按量付费和结果导向型（Outcome-based）模式将更受欢迎。企业将优先选择能够持续创造业务价值的技术伙伴。

四、战略建议

IDC 认为，2026 年不是高速反弹之年，而是结构清晰之年。中国 ICT 市场的赢家，将是那些真正理解并顺应新经济逻辑的参与者：效率优先于规模，智能优先于扩张，韧性优先于速度。这一转型也将为 2026 年之后更具可持续性的创新增长周期奠定基础。

对 ICT 厂商，从“技术能力叙事”转向“业务结果叙事”，突出可量化 ROI；加大对行业 AI、数据与场景化解决方案的投入；强化对企业出海和全球化运营的支持能力。

对行业用户，将 ICT 投资评估标准从功能完整性转向业务影响与效率改善；优先布局 AI 与数据平台，提升自动化与决策能力；将数字化转型与组织、人才和流程重构协同推进。

IDC相关研究

• 中国智能经济演进趋势与数智化商机5大洞察, Jan 2026, Doc# CHC53833826

• IDC FutureScape: 全球AI驱动的业务战略2026年预测：中国启示,  Dec 2025, Doc # CHC53834026

• IDC FutureScape: 全球CEO议程2026年预测（中文版），Jan 2026, Doc# CHC53833926

• IDC FutureScape: 2025年全球I行业预测（中文版），Jan 2026，Doc# CHC53858725

• 2026中国两会政府工作报告对AI大转型和ICT市场的影响, 即将发布

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Lianfeng Wu - Vice President - IDC

Analyst Profile
Mr. Wu Lianfeng, the Vice President and Chief Research Analyst of IDC China, has more than 25 years of experience working in the IT industry. Since joining IDC in 2000, Mr. Wu has extensive research and consulting experience in the areas of overall ICT market, vertical industry market, Internet and new media, smart connected devices, software and service outsourcing, digital transformation, digital economy, and emerging technology, among others. In recent years, Mr. Wu has been leading IDC China's digital transformation research and event. In 2017, he started to build the CXO circle excellence club, the vision of which is to help industry CXOs transform from good to excellent. Mr. Wu holds monthly offline activities and publishes daily articles that focus on digital transformation: business trends, technology trends, industry trends, organizations, and people role trends. Mr. Wu also worked with IDC global analysts to lead China's annual ICT direction forum and Top 10 Predictions (IDC FutureScapes) forum, providing industry forecasts of the latest development directions and business opportunities. At the same time, Mr. Wu works with a team of analysts to explore and discover new research topics and build thought leadership in the ICT market. Recent research areas he has delved in include Future of Work (FoW), Future Industry, Smart City, and DevOps, among others. Mr. Wu is also a guest speaker in all kinds of top ICT summit, CIO summit, and industry digital transformation summit. He gives nearly 50 speeches every year, which greatly promotes the application and development of digital technology in the industry. Prior to joining IDC, Mr. Wu worked with China Academe Launch-vehicle Technology (CALT), China Hewlett-Packard Co. Ltd., Jardine Pacific (JOS) Information Technology Co. Ltd., accumulating 9 years of working experience in the field of IT and telecommunications. Mr. Wu holds an MBA from the University of International Business and Economics in Beijing, a Master's degree in Engineering from China Academy of Launch Vehicle Technology, and a bachelor's degree in Engineering from the University of Electronic Science and Technology.

一、比亚迪超越特斯拉：全球新能源车市场的关键拐点

比亚迪在2025年全球纯电动（BEV）车销量上超越特斯拉，标志着全球新能源汽车格局的关键转折，反映出不同市场策略以及各价格区间电动车普及阶段的差异。

比亚迪全球销量的强劲增长主要贡献于其入门级电动车型，如海豚、Atto 3等车型对销量扩张贡献显著。这一表现不仅证明了比亚迪产品的竞争力，也预示着电动化转型在经济型乘用车全球市场的加速。

IDC数据显示，2025年全球纯电动汽车销量将超过1210万辆，保持两位数的同比增长。

经济型电动车价格区间对这一增长贡献巨大，继2020年前高端市场电动化加速、2021-2023年中端市场跟进之后，如今入门级市场因电池成本下降和充电基础设施完善，成为新的增长核心。

IDC基于过往市场追踪发现，创新技术通常优先占领高端市场，后向大众市场下沉，全球汽车市场电动化的发展符合这一演进规律。

二、不同的增长路径，决定增长周期起点不同

比亚迪：全场景绿色能源生态的代表。

  • 比亚迪的业务布局并不局限于乘用车，而是覆盖了公共交通、商用车、轨道交通及储能系统，构建起完整的绿色能源生态体系。
  • 在乘用车领域，比亚迪通过高性价比车型快速打开新兴市场与大众市场；在成熟市场，则逐步引入更高端的产品线。

特斯拉：以技术为核心叙事的科技公司路径。

  • 就品牌叙事而言，特斯拉聚焦于吸引科技敏感型高端消费者，始终将自身定位为一家以技术驱动的科技公司，其相关的AI研究也延伸至机器人和能源管理等领域。
  • IDC认为，入门级市场的增长逻辑与市场发展的早期阶段有所不同，成本效率、渠道覆盖、运营能力的重要性有所增强。

三、销量上的超越是否意味着经销商模式“回归”？

2025年的海外市场中，比亚迪在欧洲的增长最为显著，得益于经销与服务网络的快速扩张，以及产品的竞争力、生产的本地化、技术的差异化。

针对欧洲市场，比亚迪与当地成熟汽车零售商合作，提供销售与服务。相比之下，坚持直销（DTC）模式的特斯拉在销量上被比亚迪超越。

这导致许多人直观上认为经销商模式重新占据上风，然而事实并非如此。

对于高端市场，DTC模式不仅被蔚来、理想采用，也被极氪、魏牌等传统车企孵化的高端电动车品牌采纳。

高端电动车用户重视透明度、无缝全渠道体验及购后互动。数字化与智能化使原先购车、维保等零散的线下触点通过线上连结为整体，为DTC模式奠定了良好的基础。

经济型电动车厂商亦逐渐采用“传统及直营混合”或类agency模式。尤其对于进入新市场时，这种模式可使厂商对定价策略与品牌的一致性具备更大的主动。

如比亚迪在英国即采用混合agency模式，价格由总部统一制定，库存由欧洲中心分配，本地零售伙伴作为代理负责销售、交付、售后支持。

IDC认为，DTC与agency模式依然是市场发展的重要趋势，不仅高端市场如此，经济型电动车市场亦在向这一模式靠拢。

四、DTC与agency模式的长期逻辑依然成立

电动化是汽车行业可持续发展的重要基石，但其在市场端的普及受限于包括电池技术在内的诸多因素。

电池衰减存在高度的不确定性，诸如充电习惯、环境温度、使用强度之类的因素均可能对电池健康形成直接影响，导致价值损耗难以预测。这不仅使消费者对长期持有成本产生焦虑，也使机构预测对整批车辆的残值预测形成较大障碍。

尽管租赁模式在海外常被视为应对电动车贬值的有效方案，但其自身亦存在固有风险。这一模式本质上仅实现了风险的转移，如果实际电池衰减快于预测，租赁车辆残值依然会大幅下滑，造成资产减值，压缩利润空间。

DTC与agency模式通过对价值链的重构使数据得以集中，为包含电池衰减在内的多种风险提供了变革性的解决方案。该模式下：

  • 经销商转型为服务伙伴。
  • 主机厂能在车辆全生命周期内获取使用数据，包括行驶里程、电池健康、充电模式和车辆状况。

这种数据赋能将彻底革新残值预测与风险管理。

并且，在进一步与租赁模式相融合的情形下：

  • 通过保留车辆所有权，在租期结束后对电池进行再利用或翻新，构建循环经济，抵消电池衰减成本并降低环境影响。
  • 可以支持从传感器升级到算法迭代的高频技术更新，快速迭代自动驾驶服务场景，提升自动驾驶车队在商业上的可行性。

五、IDC 视角下的关键启示

电动车制造商应与掌握地区优势的经销商充分合作，实施“Hub & Spoke”混合策略：

  • 以Hub作为品牌旗舰和数据采集点，在高曝光市场确保品牌一致性。
  • 在Spoke网点专注于交付和服务，经销商的核心竞争力转向本地化服务。

该模式下真正的投资回报并不来源于毛利的提升，而在于数据所有权。这些数据可以使高利润服务得到精准投放，将收入模式从一次性销售转变为持续性收益。

直销依赖于与车辆的数字连接，主机厂需要能够保障无缝、安全升级的合作伙伴，以确保客户的长期粘性和车辆的长期可用。科技公司需适应直销模式下需求的波动性，发展：

  • 碎片化、按需定制的生产流程。
  • 与OTA升级适配。
  • 端到端网络安全协议。

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Bull Wang - Research Manager - IDC

As a research manager for client systems research in IDC China, Bull Wang has his research focused on topics of autonomous vehicle, connected vehicle, new energy vehicle, next-generation mobility service, and other automotive-relevant topics. Bull is responsible for conducting research and analysis for China and the global market, providing services for tech buyers, tech vendors, and tech watchers. Prior to joining IDC, Bull had experience in conducting market research projects, such as brand health tracking, campaign evaluation, car clinic, and consumer portrait. His other experiences include social media monitoring for acquainting public opinion on brand and product. Bull has long served the leading OEMs in automotive industry. Bull graduated from China Foreign Affair University, majoring in diplomacy, and obtained a Law bachelor's degree.

当前，具身智能机器人已成为物理 AI 的核心落地形态，推动机器人产业由传统自动化系统向具备感知、学习、决策与行动闭环的智能体演进。产业价值重心不再仅依赖单一算法或硬件性能，而是依托模型、数据、算力、控制与本体的系统级协同能力。整体来看，具身智能机器人技术栈不再沿线性路径演进，而是逐步形成“以模型为中心、软件定义体系、硬件随之重构”的全栈式变革路径。

基于对全球机器人与具身智能产业的持续跟踪，IDC 系统总结了当前具身智能机器人的十大关键技术趋势。这些趋势构成了机器人产业能力跃迁的技术底座，也为厂商、开发者及行业用户把握未来三年的技术方向与竞争格局提供重要参考。

模型为中心——机器人认知与能力泛化的核心驱动

世界模型与具身智能大模型协同驱动认知升级

世界模型构建对环境、自身状态及物理规律的内部表征，为机器人提供预测、规划与连续决策能力，使其从被动响应向主动规划演进。结合仿真与 Sim-to-Real 训练，世界模型降低现实训练风险与成本，是复杂任务工程化与通用人形机器人落地的重要支撑。

虚实融合数据体系成为持续进化核心基础

具身智能机器人对跨场景泛化能力的需求，使训练数据从单一实采向虚实融合体系演进。仿真合成数据成为规模化训练主体，视频学习正在成为潜在扩展路径，遥操作实采数据作为高质量补充，通过闭环训练、仿真微调与在线反馈，支撑机器人在低成本条件下实现能力扩展与持续进化。

快慢系统与技能库协同，提高复杂任务工程化效率
行业普遍采用“快思考 + 慢思考”双系统架构，高层慢系统负责任务规划与世界理解，底层快系统保障高频控制与物理交互实时性。结合模块化技能库与场景专项训练，机器人可实现多任务、多步骤操作的能力复用与稳定落地。

情感理解与个性化智能基座逐步实用化

机器人在家庭、服务和医疗场景中对情感理解、个性化交互和自主决策的需求显著提升。通过情感感知、个性化用户建模、认知决策、情感表达与持续学习，机器人实现端到端闭环的感知、理解与行为生成。大模型和长期学习支撑其从单任务执行向多场景、多任务能力演进，提高陪伴、教育及健康监测价值。

软件定义体系——机器人工程化与系统化的关键支撑

具身智能机器人开发平台走向集成化与开源生态

随着技术栈复杂度提升，具身智能机器人开发平台正形成“软硬件 + 数据 + 模型 + 工具链”的一体化生态。集成化平台通过统一接口、标准化数据与开源生态，降低开发门槛，加速算法、模型与应用在不同机器人本体和场景中的迁移与复用。

机器人操作系统向高可靠分布式架构演进，支撑大规模与高并发应用

随着系统复杂性提升，传统操作系统难以满足多自由度、多传感器、多执行器的实时协作需求。硬实时分布式操作系统通过微内核、模块化服务、任务隔离与分布式调度，保障多节点协同的确定性与可靠性，为高自由度控制、复杂场景适应及自主决策提供底层支撑，加速通用机器人系统开发与产业落地。

IT–OT 融合通信体系成为机器人实时控制关键底座

具身智能机器人对低时延、高确定性通信需求持续增长，推动 IT 与 OT 通信体系加速融合。内部 OT 网络通过 EtherCAT/CAN-FD 与时间敏感网络（TSN）融合，实现高确定性控制；外部 IT 网络借助 Wi‑Fi 7、5G/6G 提供低时延、高可靠通信。分布式控制架构下，统一协议与时间同步保障多机器人协作、云边同步及高自由度运动控制，为大规模部署和系统级协同提供关键支撑。

硬件随之重构——高复杂任务的机器人物理体系升级

端侧算力持续跃升，环境复杂度与系统规模成为核心驱动

随着机器人向具身智能化发展，多模态感知、语义理解、运动控制与实时规划的计算需求大幅增加。算力需求与信息处理复杂度、电机数量及运动控制耦合度高度相关，从家用机器人的十T级跃升至商用服务、四足及人形机器人的百T至千T级，环境复杂度与系统规模成为算力演进的主要驱动因素。

多模态感知全面升级，构建内外协同统一感知体系

机器人感知能力由单一视觉向3D视觉、触觉、力觉、惯性及内部状态感知等多模态融合发展，实现对环境与自身状态的统一理解。多源感知提升空间认知、操作精度与动态稳定性，为复杂非结构化环境下的自主决策与安全执行提供基础支撑。

安全性体系从局部防护走向系统级冗余，实现全链路稳健运行

随着应用场景拓展，安全性和稳定性成为具身智能机器人大规模落地的核心要求。行业正推动从单点防护向系统化设计转型，通过感知冗余、约束控制、运行时监控和长尾风险验证构建可信赖系统，确保机器人在动态复杂环境中可安全降级、即时干预并长期稳定运行。

IDC中国研究经理李君兰表示，当前，具身智能机器人正处于技术高度复杂且潜力巨大的交汇点：一方面，大模型、世界模型、多模态感知等 AI 能力持续突破；另一方面，机器人在真实环境中面临的物理约束、实时控制及安全可靠性远比数字世界复杂。IDC 认为，产业正沿着“模型为中心、软件定义体系、硬件随之重构”的路径演进，标志着机器人产业迈入全栈竞合的新阶段。

IDC 建议产业参与者采取六大行动方向：

  • 提前布局世界模型与具身智能大模型的协同能力，
  • 构建虚实融合的数据生产与训练体系，
  • 升级端侧算力与分布式操作系统架构，
  • 应从单一产品思维转向平台与生态思维，
  • 将安全能力内生为系统级基础模块，
  • 结合自身定位选择差异化的技术或应用突破路径。

报告信息：

本文核心观点来源于 IDC 报告《具身智能机器人技术趋势与品牌推荐，2025》（Doc# CHC53183725，2025年12月），报告不仅系统梳理了具身智能机器人十大技术趋势，也对奥比中光、地瓜机器人、NVIDIA、擎朗智能、微亿智造、银河通用、智元机器人等典型厂商进行了深入分析与品牌推荐，为产业参与者提供战略参考与厂商选择指南。欲了解更多详情或进行深度交流，请联系IDC中国机器人与具身智能领域研究经理李君兰（邮箱：lyli@idc.com）

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Lily Li - Research Manager - IDC

Lily Li is a research manager for emerging technologies in IDC China. She is responsible for conducting research and analysis for Internet of Things (IoT) in the same country. She is also involved in global and regional consulting as well as business development in related markets. Prior to joining IDC, Lily has had in-depth working experiences in the urban digital transformation (DX) field and a wide range exposure to Smart City developments. She has a deep understanding of the status quo and is knowledgeable about the market's future trends. Lily holds a master's degree from the Graduate University of Chinese Academy of Sciences (GUCAS).

AI 正改变医疗服务方式与价值逻辑

长期以来，数字化已成为中国医疗健康行业持续演进的方向。从信息系统建设、影像辅助诊断到线上医疗服务，技术在不断改善效率与覆盖面，其中AI 技术的引入也给行业发展带来了更多的机遇。但 IDC 指出，真正的分水岭并不在于“是否引入 AI”，而在于 AI 是否开始改变医疗服务方式与价值逻辑。

随着大模型逐步进入医疗场景，行业正在发生一场更深层次的变化：医疗不再只是围绕单次诊疗展开，而是开始围绕 人群、流程与长期健康结果 进行重构；AI也不再只是作为辅助工具，而是逐步成为能够“协同工作、参与决策”的智能体。这一转变，标志着医疗AI正从“工具增强”走向“智能体协作”，从“以治疗为中心”迈向“以预测和预防为导向”。

为什么这份 FutureScape，对医疗决策者具有现实参考意义？

在《IDC FutureScape：全球医疗健康行业2026年预测——中国启示》（2025年12月）中，IDC 明确指出：未来五年，中国医疗健康行业将面临三重结构性变化——人口健康压力持续上升、医疗服务向价值导向转型，以及公众对可信 AI 的期望显著提高。

这三重变化相互叠加，使得医疗 AI 的成败不再取决于算法性能本身，而取决于 治理能力、信任机制与系统性整合水平。在这一背景下，FutureScape 提供的并非单点趋势，而是一张描绘未来医疗运行逻辑的路线图。

十大预测：智能体协作如何重塑医疗健康体系

预测 1｜人口健康管理策略升级

到2027年，40%的医疗机构将采用先进的风险分层工具来应对人群健康问题，重点关注慢性病负担和老龄化人口。

这一预测反映出医疗体系正在从“以患者为单位”的诊疗模式，转向“以人群为对象”的风险管理模式。风险分层将成为分级诊疗、资源配置和医保支付的重要基础能力，也为预防性医疗提供数据支撑。

预测 2｜基于 Agentic AI 的患者交互

到2028年，45%的医疗机构将优先基于对数字公平、文化一致与信任的考虑，推动基于 Agentic AI 的医患交互方式，从而实现个性化且富有同理心的交互。

IDC 强调，医疗交互的价值不只在效率，更在信任。Agentic AI 能够结合临床数据与社会健康决定因素，提供更具情境感知与同理心的沟通方式。

预测 3｜沉浸式行为治疗平台

到2028年，15%的行为健康服务提供者将配备具备 AI Agent 的 XR 平台，实现沉浸式引导治疗的自动化，减少30%的面对面就诊次数。

在行为健康资源长期短缺的背景下，XR 与 AI Agent 的结合为扩大服务覆盖、降低就诊门槛提供了新的可能性，同时也推动治疗方式从“诊室中心”走向“持续陪伴”。

预测 4｜AI 推动支付方主导模式

到2028年，“payvider”模式将在中国医疗保健领域实现20%的渗透率，将医疗服务与保险相结合，并加速基于价值的医疗和数字健康的发展，以改善患者预后。

支付方与服务方的融合，将推动医疗激励机制从“按项目付费”转向“按结果付费”，也对数据共享、跨机构协同提出更高要求。

预测 5｜AI 驱动的智慧病房

到2029年，20%的新建医院或重大改造项目将配备由人工智能驱动的智慧病房，其可根据患者病情严重程度及临床背景动态调整监测参数、工作流程、查房安排及病房环境。

智慧病房代表着护理流程的智能化重构，通过持续监测与动态调整，提升患者安全性并缓解护理人员压力。

预测 6｜自主化医疗设备

到2029年，50%的新型医院医疗设备将采用 AI Agent、先进传感器和边缘计算技术，实现自我优化、故障预测，并将运行时间提升50%。

医疗设备正从“被动资产”转向“主动系统”，这不仅提升运行效率，也对设备安全、运维和合规提出更高要求。

预测 7｜AI 就绪型医疗基础设施成熟度

到2029年，40%的医疗机构将因社会、文化和政治层面对数据滥用或可解释性缺失的负面反应，推迟部署人工智能就绪的医疗基础设施。

这一预测提醒行业：技术成熟并不等于社会接受。可解释性、伦理与公众信任，将直接影响 AI 投资节奏。

预测 8｜预防性医疗

到2030年，多模态人工智能将在症状出现前预测45%的慢性病和罕见病，通过更广泛的健康数据（包括可穿戴设备和多组学数据）使预防性医疗成为现实。

医疗价值链正在前移，疾病预测与早期干预将成为核心能力。

预测 9｜医疗 Agent 崛起

到2030年，50%的中国等级医院将部署 AI Agent，以提供实时决策支持和自主工作流程，准确率超过80%，同时将异常情况上报至临床工作人员。

医疗 Agent 的核心价值在于重构临床工作流，让医生将精力集中于高复杂度决策。

预测 10｜量子医学

到2030年，15%的顶级医疗机构将采用量子平台，实现诊断、模拟及数字孪生技术的100倍加速，以精准驱动复杂医疗护理。

尽管仍处早期阶段，量子计算已开始在复杂疾病建模与精准诊疗中展现潜力。

这些预测共同指向什么？

IDC FutureScape 2026 反复传递出一个重要信号：医疗健康的未来，并不是“更多 AI”，而是“更系统、更可信、更以人为中心的智能协作体系”。
技术只有在治理、信任和组织能力同步演进的前提下，才能真正释放价值。

IDC 中国高级分析师林红表示，中国医疗健康行业正站在从“数字医疗”迈向“智能体医疗”的关键节点。能够在政策的指引下将信息系统、大模型技术、基础设施与医院业务统一规划的机构，将更有能力在提升诊疗质量的同时改善患者体验。

一个面向未来的行动性判断

IDC 并不建议医疗机构“追逐所有新技术”。相反，更重要的是 优先投资那些能够同时提升医疗质量、增强信任的 AI 能力——包括可信医疗智能体、人机协同决策机制，以及真正 AI 就绪的医疗基础设施。只有将这些能力视为医疗体系的长期资产，而非短期项目，医疗 AI 才能走向可持续的规模化应用。

进一步推荐

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Erin Lin - Senior Market Analyst - IDC

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 business development in related markets. Before joining IDC, Erin had four years of experience in medical administration; then served as an analyst in the health industry for two years. Erin is familiar with healthcare institutions and related businesses, and has been gathering deep insights into the health industry and emerging medical technologies. Erin holds a master's degree in Leadership and Management in Health and Social Care from the University of Southampton, and a bachelor's degree in Clinical Medicine from Capital Medical University.

从生成式 AI 到智能体，真正的变化是什么？

过去两年，生成式 AI 在企业中的普及速度远超预期。但 IDC 指出，生成式 AI并不是终点。当 AI 只能生成内容时，它仍然是工具；而当 AI能够 感知环境、调用工具、执行任务并持续反馈结果，它才真正开始参与企业运行。

智能体正是在这一背景下出现。它不再局限于单点问答或流程辅助，而是以数字劳动力、流程协调者和决策顾问的形式深度嵌入业务流程。企业竞争的分水岭，也随之从是否部署 AI，转向“是否具备规模化、安全化、可治理地运行智能体的能力”。

在《IDC FutureScape：全球 Agentic AI 2026 年预测——中国启示》（Doc#CHC54084526，2026年1月）中，IDC 系统刻画了未来五年中国企业在智能体发展过程中将面临的十个关键转折点。

十大预测：智能体如何重塑企业的运行方式

预测 1｜数据就绪度

到2027年，如果企业没有优先构建高质量的AI就绪数据，在扩展AI解决方案时将面临幻觉频发、错误率高的问题，导致生产力下降15%。

数据质量不再只是IT部门的KPI，而是企业的生存红线。如果投喂给智能体的数据是脏的、乱的、没有经过治理的，那么企业得到的将不是效率提升，而是需要耗费更多人力去修正错误的负生产力。

预测 2｜定价

到2028年，传统的按席位收费模式将被淘汰。随着智能体作为数字劳动力接管大量重复性工作，70%的软件供应商将不得不重构其商业模式，转向按业务结果、交易量或自动化成果计费的新模式。

在智能体时代，传统的按席位收费模式将越来越难以匹配价值创造的实际形态。当一个智能体在典型场景下一天可以完成过去多个人工岗位累计才能完成的工作量时，按人头收费的定价逻辑将难以为继。

预测 3｜智能体项目失败

到2028年，69%的企业自建智能体项目将因未能实现投资回报率目标（ROI）而被放弃，因为企业难以充分认识到项目实施的实际成本和价值。

企业往往会受市场热度裹挟而仓促启动智能体项目。然而，由于未能对潜在应用场景进行深度研判，开发团队被迫仓促推进的项目，往往陷入落地即闲置的窘境。在此背景下，选择能够打通数据、应用、治理全链路，且深度契合业务场景的合作伙伴，无疑是更具可行性的路径。

预测 4｜客户体验智能体编排

到 2027 年，45% 的企业将管理跨多个渠道、应用程序和供应商的多智能体（Multi-Agent），从而实现更无缝、上下文更丰富的体验。

这里的编排并非指的是单纯的工作流配置编排，而是指构建支持多智能体动态协作的系统架构。未来的竞争不在于拥有一个超级智能体，而在于编排能力。企业建立智能体系统架构应避免过于刚性的流程，拥抱灵活的协同框架，让智能体与智能体、人类与智能体能够无缝协同工作。

预测 5｜智能体服务体验

到2029年，30%的中国500强企业将运用AI客户服务智能体，主动且个性化地联系客户，在客户尚未意识到问题时就解决问题。

服务模式将发生根本性逆转，从被动响应投诉升级为主动解决问题。这种预判式的服务能力，将在存量市场中建立起全新的差异化体验。

预测 6｜人工监督作为战略职能

到2027年，50%的AI驱动型企业应用部署将设立新的专业职位，负责监督智能体，作为合规核心，确保自主工作流中的结果可追溯。

智能体的自主性不等于无人值守。随着智能体权力的扩大，人类的角色必须从操作者转变为监督者，以确保在合规与伦理的安全边界内释放AI的能力与价值。

预测 7｜AI 卓越中心

到2027年，那些建立了成熟AI或智能体卓越中心（CoE）的企业，其创新、速度和服务质量将比竞争对手高出20%。

零散的烟囱式试点难以支撑AI的真正落地和组织的规模化创新，建立AI CoE卓越中心是弥合技术与业务鸿沟、实现跨职能规模化治理的关键组织保障。

预测 8｜岗位角色转型

到2026年，中国500强企业中40%的岗位将涉及与智能体的深度协作，重新定义传统的初级、中级和高级岗位。

人才的定义正在被改写。未来的核心竞争力不再单纯是个人执行力，而是智能体的管理协同能力，即构建、指挥、评估和优化数字劳动力工作的能力。

预测 9｜Agent 战略顾问

到2031年，60%的中国500强CEO将利用智能体进行战略决策，这一趋势由市场波动性、创新速度要求，以及董事会层面对更快决策和智能驱动决策的多重需求推动。

智能体正在从业务一线的手脚进化为董事会的外脑。通过实时处理海量数据并进行情景模拟，它能为高层决策提供人类难以企及的数据广度与速度支撑。

预测 10｜AI 对业务的颠覆性影响

到2030年，多达20%的中国500强企业将因智能体管控不力引发的高关注度事件，面临诉讼、巨额罚款，甚至导致CIO被问责。

随着智能体掌握更多自主权，缺乏透明框架和审计机制的企业将面临巨大的法律与声誉风险。

这些预测共同揭示的本质变化

IDC FutureScape 2026 反复强调一个核心结论：

智能体改变的不是某个流程，而是企业如何运行、如何决策、如何承担责任。当智能体能够自主执行任务、协调流程并影响结果，企业必须重新思考数据、架构、治理、组织和领导力的边界。

IDC 中国研究经理 孙振亚表示，中国企业正在从生成式AI 阶段迈入智能体阶段的关键窗口期，但这个过程并非是简单的技术升级，而是一项系统化的工程。FutureScape 2026 显示，智能体的规模化落地必须要有AI 就绪的数据底座、多智能体的编排平台以及完善的治理机制。对于缺乏这些关键要素的企业而言，智能体带来的可能不是机遇，而是效率与合规层面的重大风险源。

一个面向企业领导层的扩展性建议

IDC 认为，智能体并不是一项可以逐步叠加的技术能力，而是一种会持续放大组织既有优势与短板的系统性力量。当智能体开始承担决策、执行与协调角色，企业原有的数据质量、流程设计、治理成熟度以及组织协同能力，都会被迅速放大并体现在结果层面。

因此，企业不应将智能体视为单一技术投资，而应将其纳入企业运行模式的长期演进路径来规划。这意味着：

  • 在技术层面，必须优先夯实 AI 就绪数据、智能体编排与可观测性能力，而非堆叠模型或工具；
  • 在治理层面，需将人工监督、责任边界和可追溯性制度化，而不是事后补救；
  • 在组织层面，需同步重构岗位角色、能力模型与决策流程，使人机协作成为默认工作方式；
  • 在管理层面，高管团队需要形成对智能体的共同认知，平衡效率和安全，把如何有效治理也纳入战略考量，而非单纯追求速度。

那些能够在规模化之前就完成这些准备的企业，更有可能把智能体转化为持续生产力；反之，智能体的能力越强，潜在风险也会被放大得越快。

行动指南：企业推进智能体的现实起点

结合 FutureScape 2026 的十大预测，IDC 建议企业在未来 12–24 个月内，优先从以下几个方面入手，逐步构建可持续的智能体能力：

第一，先解决基础数据问题
在引入或扩展智能体之前，对关键业务场景开展 AI 就绪数据评估，重点关注数据的完整性、语义一致性、上下文关联能力以及可追溯性。没有高质量数据，智能体带来的将更多是返工与人工干预，而非自动化红利。

第二，从高价值、低歧义的流程切入
优先选择目标清晰、结果可衡量、决策歧义较小的流程作为智能体的落地点，例如客户服务分流、内部运营协调或标准化审批支持，而非一开始就覆盖高度复杂或高风险场景。

第三，把治理与监督嵌入设计之初
在智能体架构设计阶段即明确人工介入点、升级路径与审计机制，确保所有自主决策都具备可解释性与回溯能力，而不是等到智能体进入关键流程后再补治理。

第四，建立跨职能的智能体管理机制
将 IT、数据、业务、合规与人力资源纳入同一治理框架，避免智能体成为某个部门的工具。在多智能体(Multi-Agent)场景下，统一编排、权限与责任归属尤为关键。

第五，为岗位与能力转型预留空间
提前识别哪些岗位将与 智能体深度协作，哪些能力需要被重塑，并通过培训、试点和角色演进，帮助员工适应新的工作方式。智能体的成功，很大程度上取决于组织是否准备好与智能体共事。

通过以上路径，企业可以在控制风险的前提下，逐步释放智能体的规模化价值，避免陷入技术领先但组织滞后的常见陷阱。

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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.

Government organizations across Asia/Pacific are entering a defining phase in their digital evolution. Economic constraints, heightened citizen expectations, talent shortages, and tightening regulatory mandates are converging just as digital systems shift from automation to autonomous orchestration. For government technology leaders, this is no longer about adopting another digital tool. It is about preparing institutions for agentic AI and the operating models required to use it responsibly.

What is Agentic AI and Why it Matters for the Government

Agentic AI represents a step beyond analytical or recommendation-based systems. These systems can interpret intent, plan tasks, and execute actions within policy-defined boundaries. They navigate across systems, channels, and agencies, coordinating activities that previously relied on manual intervention, casework, or administrative adjudication. In a climate where governments are expected to deliver more with fewer resources, agentic AI offers a path to fundamentally reshape how public services are delivered and managed.

Why Data Readiness is the Real Barrier to Agentic AI

This shift is already influencing investment priorities. According to IDC FutureScape: Worldwide National Government 2026 Predictions: Asia/Pacific (Excluding Japan) Implications research, in 2026, 40% of national governments in Asia/Pacific excluding Japan (APeJ) will invest 10% of their IT budget in data architecture and governance solutions to address gaps that are preventing them from realizing the benefits of agentic AI. This signals a clear recognition that data readiness —not algorithms—is now the primary barrier to scaling autonomy.

IDC survey data reinforces this outlook. While many government agencies are exploring agent-driven workflows, relatively few have moved beyond pilots. The primary barriers are not technical ambition but gaps in data quality, system integration, and oversight models. As a result, national administrations across Asia/Pacific are increasing allocations toward data management, interoperability, and governance, acknowledging that agentic AI readiness depends more on institutional foundations than on model sophistication.

Agentic AI systems require structured, traceable, and interoperable data to reason and act responsibly across high-stakes domains such as benefits administration, taxation, compliance, emergency response, and infrastructure operations. Without strong data foundations and clear policy constructs, autonomy introduces operational, regulatory, and trust risks rather than value. For government leaders, data architecture and governance are becoming strategic prerequisites for agentic AI, not supporting functions.

Strategic Forces Shaping Government Agentic Adoption

Several macro-level forces are shaping the pace and direction of agentic AI adoption in government:

  • Budgetary pressure: Fiscal constraints persist even as demand for digital service continues to expand.
  • Sovereignty and compliance: Requirements around data residency, algorithmic transparency, and accountability are tightening.
  • Workforce disruption: Structural skill gaps in cybersecurity, data engineering, compliance engineering, and MLOps remain unsolved.
  • Citizen Expectations: Citizens increasingly expect faster, more personalized, and more equitable services, influenced by private-sector experiences.

IDC data shows these forces converging as agentic AI moves from conceptual exploration toward early operational pilots. Government leaders increasingly see agentic capabilities as tools for accelerating workflows, improve decision support, and enhance service quality. However, integration, governance, and compliance remain the primary obstacles to scaling beyond pilots. Without deliberate management, these crosscurrents risk fragmented investments and new digital silos. Addressed strategically, they can accelerate modernization while reinforcing public trust.

How Agentic AI Transforms  Government Functions

Agentic AI opens up new opportunities across three core government domains:

1. Operational orchestration – Agent-driven systems can coordinate multi-step workflows that span multiple agencies or departments, reducing handoffs and administrative lag. This is particularly valuable in benefits processing, regulatory inspections, tax compliance, procurement, licensing, and infrastructure operations, where complexity and interdependence are high. IDC surveys show that a growing share of Asia/Pacific government agencies are prioritizing orchestration capabilities over standalone task automation, marking a shift in architectural strategy.

2. Citizen service delivery – Agentic AI capabilities enable proactive, context-aware, and personalized interactions. Agents can identify citizen needs, trigger workflows, prompt follow-ups, and escalate cases to human supervisors when required. This directly supports government priorities around service equity, responsiveness, and improved case resolution outcomes.

3. Decision support for policy and planning – Agentic systems can synthesize data, model scenarios, and present options for policymakers during crises, planning cycles, or resource allocation exercises. This does not replace human authority; it expands the analytical capacity available to decision-makers when time and complexity are constraints. Across all three domains, trust is the central requirement. Agentic systems deliver sustainable value only when paired with auditability, human oversight, and transparent accountability structures. Without these safeguards, autonomy becomes a liability —especially in regulated or politically sensitive environments.

 What Government Technology Buyers Must Do Now

For CIOs, CTOs, Chief Digital Officers, and procurement leaders, the transition to agentic AI raises several practical considerations: Institutional readiness is the first barrier.

Many agencies continue to rely on siloed legacy systems, inconsistent data definitions, and limited interoperability. Agentic AI amplifies these weaknesses. Without mature integration, clean data, and consistent metadata, autonomy is either unsafe or impractical.

Governance must be built into the workflow.

Because agentic systems act rather than merely recommend, governments must design for traceability, audit trails, human-in-loop controls, and clear escalation paths from the outset. Policy and sovereignty alignment cannot be retrofitted after deployment.

Operating models and workforce must evolve.

Agentic AI reshapes work patterns rather than simply reducing labor. While agencies currently rely heavily on external system integrators and cloud providers, new internal roles in agent orchestration, compliance engineering, and lifecycle management will become essential over time.

The message for technology buyers is clear: agentic AI is not merely a technology decision. It is an institutional capability decision.

Procurement and Vendor Evaluation for Agentic AI

As governments move beyond proofs of concept, procurement teams must distinguish between true agentic platforms and offerings that simulate autonomy through scripted automation or interfaces. IDC recommends evaluating vendors against criteria such as:

  • Orchestration of multi-step, cross-system workflows
  • Integration and interoperability across legacy and multi-cloud environments
  • Auditability, explainability and documentation
  • Alignment with sovereignty and policy mandates
  • Support for open standards and architectural portability
  • Clear responsibility models across the autonomy lifecycle.

Governments that structure RFx around interoperability, auditability, and policy alignment will be better positioned to deploy agentic capabilities responsibly without increasing regulatory or operational risk.

The Leadership Mandate for Agentic AI in Government

Agentic AI is no longer distant. It is a leadership mandate. As economic pressure, regulatory expectations, workforce disruption, and citizen demands intersect, government leaders must move beyond isolated pilots toward responsible orchestration at scale.

That mandate requires alignment across strategy, data foundations, governance, and operating models. Agencies that establish these foundations will translate agentic AI into resilience, accountability, and measurable public value. Those that do not will remain locked in pilot mode—unable to scale autonomy without unacceptable risk.

Register now for the live webinar on 24 February 2025 at 1:30 pm SGT to join IDC in charting the agentic future with confidence.

Ravikant Sharma - Research Director, Government Insights for Asia/Pacific - IDC

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Ravi Kant Sharma serves as the Research Director for Government Insights for Asia/Pacific (excluding Japan and China) (APEJC) at IDC’s office in Bangalore, India. He is tasked with guiding public sector agencies and the tech industry by collaborating with them in strategic planning. He has led numerous complex consulting engagements, represented IDC at industry events, and hosted workshops focusing on understanding how digital investments impact key economies in Asia/Pacific within the context of global trends — all while collaborating with IDC’s global Government Insights teams.