Most EMEA organisations have the intent to scale AI. What they are missing is a way to execute. On May 28, 2026, IDC’s EMEA Digital Leaders Hub brought together Martina Longo, Daniel Saroff, and Giulia Carosella for a live session drawing on 12 months of conversations within the Hub and fresh IDC research. Below is a brief overview. The full recording is available on demand. 

AI maturity in EMEA in 2026: why execution, not intent, is the real gap 

IDC’s latest MaturityScape Benchmark (EMEA, N=583) tells a clear story: 63% of EMEA organisations are still in the two lowest AI maturity stages. Fourteen percent are ad hoc: scattered initiatives, no coherent strategy. Forty-nine percent are opportunistic, running pilots but without the repeatability needed to scale. Just 2% are effectively scaling AI and Agentic AI initiatives across their organizations, including unlocking AI-driven revenue growth. 

The journey maps from the (Gen)AI Scramble (fragmented, investment-heavy experimentation) through the AI Pivot (structured scaling) to the Agentic Organisation (AI embedded across operations). Most EMEA organisations are stuck in the transition between the first and second stage. The blocker is almost never ambition. It is the ability to execute. 

Why AI adoption in EMEA is stalling: five challenges organisations need to address 

Notably, 49% of EMEA organisations have already shifted their focus from launching new AI pilots to improving existing initiatives The experimentation phase is peaking, and EMEA organizations are no longer seeking new tools but instead focusing on making current AI work effectively first. But five structural challenges continue to slow progress: 

  • Competition for resources among digital initiatives 
  • Regulatory uncertainty slowing deployment decisions 
  • Resistance to process change within the business 
  • Difficulty quantifying and demonstrating AI ROI to the board 
  • Lack of executive sponsorship or organisation-wide buy-in 

These are not isolated problems. They compound each other. Without a shared language for value, the resource conversation is difficult to win. The webinar addressed all five, and the one that generated the most discussion was ROI. 

Measuring AI ROI: why cost savings are not enough 

The most common reason AI initiatives stall is not technical. It is that no one agreed upfront on what success looks like. IDC’s AI Business Value Benefit framework maps nine dimensions where AI creates measurable impact, spanning Revenue Generation and Customer Experience through to Sustainability, Time to Market, and Business Resilience. Most organisations are measuring only one or two of these dimensions and are therefore systematically underselling the value they already have. 

“Know what you want to achieve and how you will measure it” 
  — Alex Catmur, Commercial Director Digital, AtkinsRéalis 

Three practices that came up consistently in the session: 

  • anchor every initiative to a specific business outcome before selecting any tool: Start with value drivers, not technology 
  • if no executive has a stake in the metric, the initiative will eventually stall: Align to KPIs executives already own 
  • productivity gains are visible; resilience and trust are harder to quantify but equally real, and the framework accounts for both: Separate direct and indirect value 

The session also walked through a detailed case study of a global professional services firm that went from no shared ROI lens to confident scale decisions. If that is where your organisation is at present, it is worth watching the recording to hear how they structured the turnaround. 

How the CIO role is evolving in the age of AI 

IDC’s WW C-Suite Tech Survey (EMEA, N=300) makes the expectation clear: 42% of the broader C-suite now expects the CIO to lead digital and AI transformation with a major focus on creating new revenue streams. That expectation is growing faster than the formal authority that would make it achievable. 

The digital leader of the future, as IDC frames it, is an architect of three things: Workforce (orchestrating AI-fuelled change management), Resilience (modernising IT for strategic alignment), and Value (demonstrating what digital technologies actually deliver for the business). 

Three steps for digital leaders looking to prepare for this shift: 

  • not called in to implement choices that have already been taken: Get in the room before decisions are made 
  • accountability for results, not just go-live dates: Own the business outcome, not just the delivery 
  • design AI deployments to grow and withstand failure, not just to ship: Architect for scaling and recovery from the start 

The session went into considerably more detail on each of these steps, including the structural and political dynamics that make them harder in practice than they appear on paper. 

A practical AI transformation playbook: four steps that matter

The session closed with a synthesis that cuts through the complexity. Leading AI-fuelled business transformation comes down to four sequential actions: 

  • modernise architecture and data before scaling AI; pilots built on brittle infrastructure do not survive production: Fix the foundation 
  • anchor every initiative to a KPI an executive already owns; no metric, no mandate: Define the value 
  • redesign workflows and roles alongside the technology; AI layered onto old processes delivers expensive old results: Change the model 
  • run experiments to disprove bad assumptions quickly; promote only what survives to a funded pilot with a scale plan: Scale what works 

Straightforward to state, harder to execute. The webinar covers what this looks like in practice, including the Q&A that followed. 

The complete recording covers the full AI Business Value Benefit framework across all nine dimensions, the case study of a professional services firm moving from pilot to scale, the detailed best practice sessions on ROI measurement and the evolving CIO mandate, and the Q&A with the IDC analysts. If the topics covered are relevant to your organisation’s AI journey, IDC’s EMEA Digital Leaders Hub offers advisory support, benchmarks, and peer roundtables for CIOs and digital leaders navigating this transition. Reach out via the contact form to continue the conversation. 

Martina Longo

Martina Longo - Research Manager, CIO and CTO Buyer Insights

Martina Longo is a Research Manager for the CIO and CTO Buyer Insights program. Her research focuses on the emerging priorities, programs and decision making processes linked to the modern CIO/CTO agenda. This includes advancing AI from experimentation to operational…
Daniel Saroff

Daniel Saroff - Group Vice President Research and Consulting

  Daniel Saroff is Group Vice President of Research and Consulting at IDC, where he leads the research agenda focused on end-user technology leaders, including CIOs and their direct leadership teams. He oversees a team of analysts and advisory professionals…
Giulia Carosella

Giulia Carosella - Senior Research Manager

Giulia Carosella is a Senior Research Manager in IDC's AI-Fueled Business Strategies team, leading the Worldwide research program. In this role, she researches current and emerging global trends in AI?driven business transformation, examining how organizations can reinvent themselves by leveraging…

What Happened in India’s PC Market in Q1 2026? 

India’s traditional PC market (encompassing desktops, notebooks, and workstations) delivered a standout performance in Q1 2026, with total shipments reaching 4.4 million units, representing 31.1% year-over-year growth, according to IDC’s Worldwide Quarterly Personal Computing Device Tracker. Notably, this marked the third consecutive quarter in which shipments exceeded the 4-million-unit threshold, a milestone that underscores the market’s sustained momentum. The growth came even as PC prices continued to rise due to component shortages. Nonetheless, this reflects strong demand across both consumer and commercial segments, fuelled by digital transformation, enterprise investment, and large-scale education procurement. 

Why It Matters 

The Q1 2026 results signal the resilience of India’s PC market in the face of structural pricing pressures. Here’s what stakeholders should take away: 

  • Vendors should limit dependency on education-led demand and monitor the durability of government procurement pipelines. ELCOT was a major volume driver this quarter, but such big volume projects are rare.   
  • Rising prices are accelerating premiumisation, but they also risk squeezing out price-sensitive buyers in the consumer and SMB segments if unchecked. 

Market Dynamics: What Drove the Outcome? 

Three converging forces shaped the quarter’s strong performance: 

  • ELCOT education project led to notebook surge: The execution of the Tamil Nadu government’s ELCOT education procurement programme was the single largest growth catalyst. It drove the education segment to an exceptional 455.2% YoY growth and propelled the overall notebook segment to 3.3 million units, or a 55.4% YoY increase. 
  • Enterprise investment providing commercial resilience: Sustained domestic and global enterprise spending supported 24.2% YoY growth in the enterprise segment. Workstations, driven by high-performance computing needs in engineering and design, rose 31.8% YoY. 
  • AI adoption and premium segment expansion: Rising demand for AI-capable notebooks helped drive the premium notebook segment (above US$1,000) up 70.1% YoY. Consumer demand for premium devices outpaced commercial, growing 92.4% versus 56.1% YoY. 

Segment Snapshot: Winners and Laggards 

Notebooks: 3.3 million units | +55.4% YoY | Majority of total shipments 

Workstations: +31.8% YoY | Driven by engineering, design, and data-intensive sectors 

Desktops: −13.5% YoY | Elevated prices led to order deferments and cancellations 

Premium Notebooks (>US$1,000): +70.1% YoY | Consumer: +92.4% | Commercial: +56.1% 

AI Notebooks (total): +96.1% YoY  

India PC Market at a Glance: Q1 2026 

  • Total shipments: 4.4 million units (+31.1% YoY) 
  • Third consecutive quarter above 4 million units 
  • Commercial segment: 2.8 million units | Education: +455.2% YoY | Enterprise: +24.2% YoY 
  • Consumer segment: +11.7% YoY | eTailer: +23.1% YoY | Traditional retail: +8.7% YoY 
  • Top five vendors: HP Inc. (#1), Acer Group (#2), Lenovo (#3), Dell Technologies (#4), ASUS (#5) 

Analyst Insight 

“Despite persistent upward pressure on PC prices due to rising component costs, particularly DRAM and GPUs, consumer demand has remained resilient. With prices having increased over the past two quarters and expected to rise further in the coming months, proactive and transparent communication from vendors, partners, and channel stakeholders has encouraged early purchase decisions, thereby helping sustain overall market demand.” 

— Bharath Shenoy, Research Manager, Devices Research, IDC 

Vendor Landscape: Who Won the Quarter? 

HP Inc. retained the top spot with 26.6% market share, driven by ELCOT execution and solid enterprise demand (+27% YoY in commercial). Consumer growth was steady at 8.2% YoY, supported by strong large format retail (LFR) traction. 

Acer Group surged to second place with 21.3% market share, overtaking both Lenovo and Dell. ELCOT billing in the commercial segment, where it captured 25.5% share, was the key driver. A marginal 1.2% YoY dip in consumer shipments due to entry-level supply constraints was the only blemish. 

Lenovo held third with 18.3% market share. Commercial grew 32.3% YoY on enterprise and SMB strength, while consumer grew 20.8% YoY, bolstered by a gaming portfolio surge and e-commerce shipments up 58% YoY. 

Dell Technologies ranked fourth at 15.9% market share. Enterprise demand drove a 37.5% YoY segment increase, with overall commercial growing 38.7% YoY partly from ELCOT fulfilment. Consumer shipments grew 17% YoY despite pricing pressures. 

ASUS rounded out the top five at 6.9% market share. Gaming notebook demand lifted consumer growth 31.9% YoY, and an exceptional 241.1% YoY commercial surge reflected rising SMB momentum. 

IDC Outlook: What’s Next? 

The first half of 2026 is expected to sustain positive momentum, supported by favourable supply allocations, aggressive channel stocking, and front-loaded procurement ahead of anticipated price hikes. However, the second half presents a more cautious picture. 

  • What could sustain growth? Strong enterprise and SMB pipelines would drive 2Q 2026, continued AI notebook adoption, and successful rollout of products like Apple’s mass-market MacBook Neo (launched March 2026) might be some positive drivers for 2H 2026. 
  • What could slow it down? Inventory corrections in Tier 1 and Tier 2 channels, supply-side constraints, rising DRAM and GPU costs, and budget pressures in government and education segments. 
  • What should readers watch next quarter? Whether channel inventory levels normalise, how consumer demand responds to further price increases, and the pace of AI notebook adoption in SMBs. 

“Vendors have so far benefited from favourable supply allocations and are currently managing relatively high inventory levels across Tier 1 and Tier 2 channels. While enterprise and SMB segments continue to support volume-led procurement, shipments might decline in second half of the year. The government and education sectors could face challenges amid rising device prices and constrained budget allocations. The consumer segment may also witness a decline in the second half of the year unless pricing stabilizes. At the same time, Apple’s launch of the mass-market-focused MacBook Neo in March is expected to strengthen its position and potentially expand its presence across both consumer and SMB segments.” 

— Navkendar Singh, AVP, Devices Research, IDC 

Frequently Asked Questions 

Why did the PC market grow so strongly despite rising prices? 

The primary catalyst was the ELCOT education procurement project, which alone drove the education segment up 455.2% YoY and turbocharged notebook shipments. This institutional demand, combined with enterprise investments and early purchasing by both enterprises and consumers ahead of further price increases, more than offset the headwinds from elevated pricing. 

What risks could impact the PC market in 2026? 

The key risks include prolonged component cost inflation (especially DRAM and GPUs), potential inventory overhang in distribution channels, and weakening budget availability in government and education. If prices continue to climb, consumer spending moderation could also temper growth. 

About IDC 

International Data Corporation (IDC) is the premier global provider of trusted technology intelligence, advisory services, and events. With more than 1,000 analysts worldwide, IDC offers global, regional, and local expertise on technology, IT benchmarking and sourcing, and industry opportunities and trends in over 100 countries. IDC’s analysis and insights help IT professionals, business executives, and the investment community to make fact-based technology decisions and to achieve their key business objectives. To learn more about IDC, please visit www.idc.com. Follow IDC Asia/Pacific on X at @IDCAP and LinkedIn. Subscribe to the IDC Blog for industry news and insights. 

All product and company names may be trademarks or registered trademarks of their respective holders. 

For more information, contact Bharath Shenoy, Research Manager — Devices Research, or Navkendar Singh, AVP – Devices Research. 

Bharath Shenoy

Bharath Shenoy - Senior Market Analyst

Bharath Shenoy is a Senior Market Analyst at IDC India. Based in Bangalore, Bharath is responsible for tracking and analyzing market trends of PCs and Printers for Bangladesh. Prior to joining IDC, Bharath has worked with Dataxis as a Telecom…
Navkendar Singh

Navkendar Singh - Associate Vice President, Client Devices & IPDS, IDC India

Navkendar Singh is a Associate Vice President with IDC India, based in Gurgaon. His research domains encompass deep-dive research and insights in and around mobile devices, smart homes, PCs, tablets, wearables, and the printing market in India, Bangladesh, and Sri Lanka.…

AI正在深刻重塑数据架构,并显著提升企业对其的关注度。随着生成式AI与Agent技术的爆发式发展,商业智能与分析、数据目录与血缘、数据质量评分、湖仓一体、数据治理等议题,已经成为超过40%组织的首要建设重点。

这一变化的深层逻辑在于,DataAI正在形成一种前所未有的紧密交互关系,而非传统的运维流程管理。Scaling Law依然成立——高质量的数据支撑着上层AI与Agent的开发,而Agent本身又以数据库、数据湖、数仓、数据中台、数据分析平台为基础设施。更重要的是,Agent所产生的频繁交互,正在催生大量运行时数据和记忆的出现,这对数据的存储与管理提出了全新要求。

一、Data Agent:定义与趋势

IDCData Agent的定义是:利用Agent管理和治理数据,通过对话式或低代码入口实现精准查询、分析、决策,降低获取洞见的门槛。需要强调的是,Data Agent并非指向Agent工具本身,而是在广泛的数据场景中嵌入Agent能力,以实现更快速的数据集成、管理、开发、查询、分析和可视化内容生成。其核心覆盖场景包括数据集成、数据治理、数据发现、指标开发与知识管理、查询分析、Memory与上下文管理等。

从市场趋势来看,Data Agent已成为数据与分析行业未来的重要发展方向IDC预测,到2028年,60%的中国500强企业将部署企业级Data Agent;到2026年,50%将部署数据分析Agent,以自动化日常任务、加速战略决策。

二、为什么Data Agent会成为趋势

要理解Data Agent的兴起,需要从政策、技术与市场需求三个维度来看。

首先是政策环境向好。2026年作为“十五五”开局之年,政府及央国企有望加大数字化预算。国家明确要激活数据要素潜能,深化“人工智能+”,这为Data Agent提供了坚实的政策土壤。

其次是基础模型的持续演进。过去大模型能力集中在文娱对话,而当前技术突破重点已转向数学、代码、长上下文理解与任务执行。这些能力对于数据开发和管理至关重要,使得Data+AI场景更加成熟可落地。

第三,Agent标准协议和框架的成熟正在加速发展。MCP、A2A等协议的快速普及,大幅降低了数据软件之间的互操作性难度,使得Data Agent的技术成熟度足以支撑大规模应用部署

最后,企业技术架构正在螺旋式演进。2025年头部企业将预算优先投入湖仓一体与数据治理;随着基础设施完善,2026年企业希望通过Agent直接获取洞见、提升决策效率并降低人力成本

三、为什么一定是Data Agent有更大需求

Agent可以在各个场景落地,但Data Agent会成为最先落地的方向,这可以从三个维度来回答。

从需求端看,企业的核心资产是数据。企业在对外合作和对内管理中沉淀了庞大的经营、财务、代码等数据资产。企业真正需要的,不是通用Agent,而是能够处理内部全流程的Data Agent。

从商业闭环看,Data Agent的盈利模式最容易实现。相比C端Agent仍处探索阶段,Data Agent面向企业客户,部署后可以快速显现效率提升。未来付费模式可能转向Tokens消耗或RaaS(结果即服务),且Data Agent的“每Token价值”比文字、视频内容更加清晰可衡量。

从技术迭代看,数据市场已发展数十年。所有企业都已接受并落地了各种数据底座。在已有IT基础之上,企业进一步“+AI”更容易实现,且能够保持技术延续性。

四、Data Agent2026年会发展到什么程度

当前,Data Agent市场呈现出明显的供需错配:技术厂商的投入热情超过了客户的实际需求意愿。2025年厂商密集推出Data AI Agent产品,但在需求侧,企业仍处于基础设施建设和数据治理过程中,对新技术缺乏完整认知。这意味着2026年仍需厂商持续的市场培育

OpenClaw的爆火远超预期,这一现象值得数据厂商深思。它反映出企业和用户真正需要的是“互操作性”和“主动性”。映射到数据层,这恰恰需要Data Agent来更好地接入和管理企业数据与Memory。

但必须承认,Data Agent的商业模式仍然未定。当前企业明确投入算力与数据,但对上层Agent功能是否单独付费尚未形成共识。Agent是附加价值还是下一代必需品?是单独付费还是默认“+AI”即为未来产品形式?市场倾向于后者,但尚未有定论

此外,Data Agent最快落地的场景不是营销,而是商业分析。企业当前对AI的认可仍集中在“降本增效”上。Data Agent擅长执行内部数据工作,但复杂多变的营销场景仍需能力提升。

五、可能的抑制因素有哪些

在讨论抑制因素时,需要先明确一个判断:数据的高价值不会改变,但未来的话语权不一定仍在数据厂商手中。在AI逐步替代多种场景功能的趋势下,掌握数据的可能是AI软件而非传统数据产品。当前来看,做中间层(衔接数据与AI出口的综合引擎)的企业更有机会

另一个重要判断是:Data Agent市场似乎不会出现“DeepSeek/Manus时刻。数据市场更符合稳定增长路线,难以复现AI领域的轰动效应。数据厂商真正的护城河,在于多模态数据管理、治理与垂直行业经验

此外,轨道偏移也可能带来不确定性。智慧办公等Personal Data产品可能颠覆传统SaaS交付逻辑,企业核心资产将从经营数据扩展为“经营数据+员工办公数据”,但市场目前缺乏对应产品。

最后,合成数据可能影响高质量数据的价值。虽然尚未完全成熟,但当合成数据的价值高于真实数据时(拐点取决于成本与准确率对比),数据资产价值可能被重构。

六、技术厂商应该怎么应对

面对上述趋势与挑战,技术厂商需要从四个方面着手应对

第一,打造轻量化的数据平台。企业不会再经过漫长周期建设颠覆性数据底座,而是通过AI实现快速数据集成与应用,包括流式集成、Data Flow管理、本体与指标分析等。

第二,从被动工具型转向主动任务型。借鉴OpenClaw,让企业用户通过对话指令跨SaaS执行数据管理和分析

第三,强化运行时安全。2026年预计会有更多厂商涌入Agent Infra赛道,保障Agent运行时安全与企业数据安全。

第四,关注Agent带来的数据变化。用户与Agent交互产生大量指令、记忆、中间态数据,厂商需尽快打造DataAI之间的中间层,这将成为用户核心关注点。

七、市场应该如何选型

需要明确,Data Agent不是某一具体功能的代称,而是实现数据全流程AI自动化的总集。IDC调研显示,企业最希望建设的Data Agent类型包括:用户合规监管Agent、自动分析Agent、知识搜索Agent、动态执行优化Agent、自动决策Agent、Text2SQL Agent等。

在IDC《Data Agent MarketGlance 2026Q1》中,整体市场被划分为:Data Agent基础设施、数据集成与治理、平台厂商与通用智能体、轻量化工具与插件、垂直行业智能体、开源项目、安全方向。

从市场格局来看,传统Data Infra厂商正利用AI搭建数据开发入口;SaaS厂商基于客户资源拓展Data Agent能力;AI初创公司以轻量化插件快速对接数据格式,模仿Manus路径吸引用户。

分析师观点

IDC中国高级分析师李浩然表示,Data Agent将在2026年迎来快速落地。技术厂商需明确区分Data AgentOpenClaw/AI办公软件的竞合关系,将自身开发经验沉淀为Skills和Mem-kit,加快轻量化部署。

IDC同行,抢占Data Agent的战略先机

IDC长期追踪全球与中国Data Agent市场,已发布《Data Agent市场图谱2026Q1》《Data Agent市场预测,2026》《金融和零售行业Data Agent最佳实践》等报告,即将发布《中国Data Agent厂商评估,2026》《Data Agent开发平台技术能力评估》等重磅报告,并可为企业提供定制化场景评估与选型服务。

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Leo Li

Leo Li - Senior Market Analyst

Leo Li is a senior market analyst on artificial intelligence (AI) and big data for IDC China. He conducts research and analysis on AI and big data for the China and worldwide markets. He is also involved in regional and…

AI is redrawing the rules of the partner ecosystem faster than most organisations can adapt. Last week, Stuart Wilson, IDC’s Senior Research Director for Partnering Ecosystems, and Andreas Storz, Senior Research Manager in the same practice, shared IDC’s latest research on what that means in practice for vendors, partners, and distributors operating across Europe and beyond. Drawing on survey data from more than 1,000 established partners, direct feedback from IDC’s European Partner Advisory Board, and real-world vendor examples, they made the case that the ecosystem is not simply evolving: it is being structurally reset. Here is a brief overview. The full recording is available on demand. 

How AI is eroding traditional partner revenue streams 

Stuart opened with a finding that will resonate with anyone tracking partner economics right now: AI is systematically compressing the lifecycle phases where partners have historically earned the most. Implementation, integration, and basic support are not disappearing, but they are becoming thinner and, in a growing number of cases, absorbed directly into vendor platforms. IDC has documented specific examples of how this compression is already playing out at scale, with leading vendors publicly committing to timelines and automation levels that would have seemed ambitious just 18 months ago. 

What makes this moment different from previous platform shifts is the speed and simultaneity of the impact. AI is hitting vendor economics, partner margins, and customer expectations at the same time. Partners who are waiting for the dust to settle before repositioning are likely to find the window has already closed. 

Where partner value is growing: advisory, AI governance, and outcome-based services 

The compression of execution-heavy activities does not mean the overall ecosystem opportunity is shrinking. IDC’s data points clearly to a redistribution of spend toward higher-order roles: AI solution design and agent creation, governance and compliance services, industry advisory, data engineering, and reusable marketplace IP. These are areas that reward deep domain knowledge and customer trust rather than delivery capacity. 

Customers are also changing how they expect to be served. Rather than relying on a single partner to cover the full lifecycle, they increasingly want a coordinated network of specialists. That shift has direct implications for how vendors structure their ecosystems and how partners think about collaboration rather than competition. The recording covers the full breakdown of where IDC sees demand growing and shrinking, and what partners are doing today to get ahead of it. 

Agentic marketplaces and the new partner go-to-market playbook 

Andreas Storz walked through a structural shift in how technology solutions are discovered and bought. Marketplaces are moving inside products, and IDC is seeing real evidence that customers are making decisions before a formal procurement process ever begins. This compresses buying cycles, introduces new buyer personas including business users and domain specialists who are not traditional IT buyers, and moves partner influence upstream into phases where most partner programs have little presence today. 

Partner Advisory Board members were frank about what this looks like from the front line. One dimension that generated particular discussion was the growing scrutiny customers apply to every new AI investment: 

“Customers scrutinize every purchase order. We are having to prove the ROI to the last penny before new AI work is approved.” — IDC Partner Advisory Board member, November 2025

Co-sell models are adapting accordingly. The shift away from field-led selling toward digital and telemetry-driven motions is not a future state: it is already shaping how the most forward-leaning vendors are structuring partner engagement today. The recording covers what that looks like in practice and what partners need to do to remain visible in these new buying journeys. 

Why vendor partner programs need a fundamental redesign for the AI era 

The structural conclusion Stuart and Andreas reached is that most partner programs in operation today were designed for a world that is rapidly ceasing to exist. The incentive structures, metrics, and engagement models built around resale transactions and implementation milestones are misaligned with where ecosystem value is now being created. Vendors that do not address this gap will find themselves losing the partners best positioned to deliver AI-driven outcomes to customers. 

The research points to a dual imperative: accelerating existing partners toward AI-centric delivery models while simultaneously cultivating a new generation of AI-native partners who bring differentiated industry IP and a very different set of expectations around how vendor relationships should work. How to run both strategies in parallel, without letting either undermine the other, is one of the more complex programme design challenges IDC is helping clients navigate right now. 

“AI-native competitors without legacy delivery models are coming. If we don’t pivot, we will be disrupted.” — IDC Partner Advisory Board member, November 2025 

The Q and A that followed also surfaced sharp questions on how AI model providers are disrupting established alliance hierarchies for global systems integrators, and whether the net effect of all this change will be a more consolidated or more fragmented ecosystem. Stuart’s answer to the latter was more nuanced than a binary either/or, and worth hearing in full. 

The complete recording includes data from the IDC EMEA Partner Survey data (N=1,001), a detailed breakdown of the dual partner strategy framework, and a live Q and A with both analysts. If the topics covered resonate with your ecosystem strategy, IDC’s Partnering Ecosystems practice offers advisory support, custom research, roundtables, and strategic workshops tailored to vendors, distributors, and partners navigating this transition.  
 
Our experts are always happy to continue the conversation. Simply reach out via the contact form.  

Stuart Wilson

Stuart Wilson - Senior Research Director, EMEA Partnering Ecosystems

Stuart Wilson is senior research director for IDC’s Europe, Middle East & Africa (EMEA) Partnering Ecosystems program. With over two decades of global experience, Stuart focuses on the rise of complex, connected ecosystems and how platform models are reshaping routes…
Andreas Storz

Andreas Storz - Senior Research Manager, EMEA Partnering Ecosystems

Andreas Storz is senior research manager for IDC’s Europe, Middle East & Africa (EMEA) Partnering Ecosystems program. Based in the US, Andreas focuses on the evolution of go-to-market models, new digital value chains and the wider impact on partner ecosystems,…

Digital accessibility is shifting from a compliance requirement to a key driver of inclusion, productivity, and innovation in the modern workplace. This blog explores where European organizations stand today and outlines a practical, technology-driven approach to turning accessibility into a competitive advantage. 

What Is Digital Accessibility in the Workplace? 

Digital accessibility refers to the design, development, and delivery of digital technologies, products, and services in a way that ensures they can be perceived, understood, navigated, and interacted with by all people, consistent with the principles established by the W3C Web Accessibility Initiative. This applies regardless of ability or disability and includes individuals with physical, sensory, cognitive, and neurodivergent conditions, such as impairments related to vision, hearing, motor function, speech, and information processing, as well as situational or temporary limitations. 

It encompasses not only compliance with accessibility standards and guidelines, but also the proactive inclusion of diverse user needs throughout the entire lifecycle of digital experiences, enabling equitable, independent, and dignified access for everyone. 

In simple terms, in today’s AI-enabled workplace, accessibility is no longer just a legal box to tick. It is a design choice that determines who gets to fully participate, innovate, and grow. 

The State of Digital Accessibility in European Organizations 

European legislation on workforce accessibility is comprehensive but uneven. Countries such as Italy, France, Germany, and Poland enforce employment quotas for people with disabilities, while the UK and Denmark rely on antidiscrimination and reasonable-accommodation laws. To harmonize these differences, the European Commission introduced the Disability Employment Package and the Strategy for the Rights of Persons with Disabilities 2021 to 2030, outlining shared approaches to inclusive recruitment, workspace adaptation, flexible working, and assistive technology adoption. 

Despite this robust framework, execution lags. IDC research shows that diversity and inclusion, including accommodation for people with disabilities, ranks near the bottom of EMEA organizational priorities at just 27 percent, well behind talent retention and reskilling. Around 30 percent of employees say their organization has not adopted any digital accessibility solution at all. Interestingly, employees are more optimistic than their employers. One in two believe AI is already improving digital accessibility and will help close the digital divide. 

The real issue is not missing legislation. It is the gap between what companies say they will do and what they actually deliver. 

How to Build a Digital Accessibility Strategy 

So how can organizations close that gap? IDC’s The Four Tech Pillars to Create a Digital-Accessible Work Environment lays out a closed-loop, five-step journey that keeps accessibility moving instead of getting stuck in a one-off project. 

  1. Start by listening. Assess what employees actually need through surveys, one-on-ones, and functional and contextual evaluations.  
  1. Review your technology stack. Evaluate hardware, software, productivity suites, and assistive tools for compatibility and gaps.  
  1. Embed accessibility early. Integrate accessibility into design and procurement, turning standards such as WCAG and EN 301 549 into mandatory checkpoints.  
  1. Establish governance. Define clear roles, responsibilities, escalation paths, and cross-functional ownership.  
  1. Measure impact. Use data to track ROI and support CSRD reporting on productivity, inclusion, and compliance.  

This should be seen as a continuous loop rather than a checklist, one that evolves alongside people, technology, and regulation. 

The Four Technology Pillars of Digital Accessibility 

Assistive technologies alone are not enough. IDC identifies four interdependent technology pillars, all supported by a foundation of best practices. 

  • AI-enabled assistive technologies: AI-driven screen readers, image and audio descriptions, captioning, and voice input integrated into mainstream collaboration platforms, matching the right technology to the right user.  
  • Accessible-by-design AI-driven platforms: HR, productivity, and collaboration tools built from the outset to meet EU accessibility standards, shifting remediation earlier and reducing long-term costs.  
  • AI-empowered configuration and orchestration layer: A governance backbone that sets standards, automates testing, and scales accessibility across workflows, teams, and vendors.  
  • AI, data, and analytics: Privacy-preserving analytics on usage, barriers, and outcomes to demonstrate ROI, support ESG and DEI reporting, and anticipate future needs.  

Underlying these pillars, best practices, including executive sponsorship, shared accountability, training, and continuous feedback loops, ensure that strategy translates into everyday operations. Without them, even the most advanced technology stack risks remaining underutilized. 

Why Digital Accessibility Is a Competitive Advantage 

Digital accessibility is no longer just about compliance. It plays a critical role in attracting diverse talent, enabling innovation, and responding to increasing ESG scrutiny. European regulation provides the framework, but culture, technology, and governance determine the outcome. 

By combining a closed-loop approach with the four technology pillars and strong best practices, organizations can move beyond risk mitigation and position accessibility as a true competitive advantage. 

Want the full picture? For the European legislative landscape and where organizations stand today, see IDC’s Digital Accessibility for the Workforce: European Legislation and Organizations’ Responses (IDC #EUR154346826, March 2026). For a practical technology playbook, refer to The Four Tech Pillars to Create a Digital-Accessible Work Environment (IDC #EUR154347126, April 2026). 

And if you would like to explore what these trends mean specifically for your business, our experts are always happy to continue the conversation. Simply reach out via the contact form

Erica Spinoni

Erica Spinoni - Senior Research Analyst, WW AI-Enabled Future of Work & EMEA Practice Lead

As member of the global FoW team, Erica also leads the group’s EMEA-focused research, exploring how new workplace models and technologies such as AI and automation are transforming employee experience and productivity across Europe, the Middle East, and Africa. She…

For years, the security platform was the Pinocchio of enterprise technology. It looked like the real thing. It told a convincing story. Vendors put it on stage and pulled the strings, and the puppet moved beautifully. Then you went backstage and found the strings. The telemetry was siloed. The policies were fragmented. The dashboards required a UN interpreter to reconcile. Analysts were manually stitching together context that the platform was supposed to handle automatically. The nose, in other words, was growing.

I have sat through more of those briefings than I can count. The slides were gorgeous. The architecture diagram had arrows pointing everywhere, suggesting a kind of unified, harmonious security nirvana. The gap between the deck and the deployment was, shall we say, significant.

That gap has finally started to close, and the puppet has become a real boy.

IDC’s research finds that organizations now running modern security platforms in production are delivering measurably better outcomes across threat detection, operational efficiency, cost management, and business resiliency. The story has moved from aspirational to architectural, and it is worth unpacking exactly what that transformation looks like.

What a security platform actually is

Let me be precise about the definition, because vendors still stretch this term like taffy, and Pinocchio’s nose did not get that long without some help.

A security platform is not a vendor’s portfolio of products bundled under one invoice. It is an integrated collection of security capabilities delivered through a unified architecture, management plane, and data model. The critical distinction is that platform components share telemetry, policy, analytics, and automation natively, rather than through custom connectors bolted on after the fact by a professional services team charging by the hour. That last arrangement is what the old platforms actually were. It just did not look that way on the slide.

IDC’s research across multiple vendor studies, including Check Point, Palo Alto Networks, and CrowdStrike, consistently points to six structural elements that define a genuine security platform. These are not features to check off a procurement list. They are architectural commitments that determine whether a platform actually delivers or simply repackages the fragmentation problem under a shinier brand.

Unified telemetry and shared data model. A platform aggregates signals from endpoints, networks, cloud environments, identities, workloads, applications, and data repositories into a common data architecture. The operative word is “common.” Rather than asking analysts to manually pull context from separate consoles and reconcile it by hand, the platform normalizes and enriches signals automatically. The result is cross-domain visibility that supports more accurate threat prioritization and closes the blind spots that emerge when identity, network, and workload context all live in different zip codes. Greater aggregation unlocks greater value: the more telemetry flows into a shared model, the more the analytics engine can do with it.

Centralized policy and management. A unified management plane is one of the clearest signals that an organization is running a real platform rather than a curated collection of tools. Security controls are defined once and enforced consistently across hybrid, multicloud, and on-premises environments. This matters because configuration drift is one of the most reliable sources of security gaps I see in my research. When multiple tools are administered independently, inconsistencies accumulate quietly, like technical debt, until something breaks in a way that makes headlines. Centralized policy eliminates that drift and simplifies governance, audit reporting, and compliance validation as a bonus.

Integrated analytics and threat intelligence. Platforms embed analytics and intelligence across functional domains rather than isolating detection engines inside separate products. Intelligence feeds and behavioral analytics inform prevention, detection, and response in a coordinated manner, so a risk signal in one domain can immediately influence controls in another. An anomalous identity behavior can trigger network access restrictions before an analyst has finished reading the alert. The output is not simply more alerts, which would be the opposite of helpful. It is contextualized insight that lets security teams act on what actually matters rather than chase noise across a dozen different consoles.

Automation and orchestration. Automation is central to the operational value a platform delivers, and I want to be direct about why. Platforms incorporate automated workflows for investigation, remediation, credential lifecycle management, certificate issuance, patching, and policy enforcement. Orchestration capabilities reduce manual effort and accelerate response times across those workflows. Most importantly, automation lets security teams manage increasing complexity without proportional increases in headcount. In a market where skilled security talent is harder to find than a reasonable parking spot in San Francisco, that is not a marginal benefit. It is a structural necessity.

Response across control planes. A platform spans multiple control planes, including identity, endpoint, network, cloud workload, and data security, rather than optimizing a single domain in isolation. Value emerges not only from the breadth of that coverage but from the architectural integration across domains. Controls operate cohesively rather than independently, so a detection in the endpoint layer informs the response in the identity layer without requiring manual handoffs between teams who may not even share an org chart. As digital environments expand, this integrated coverage directly reduces the gaps that arise when controls are deployed in functional silos and expected to somehow coordinate on their own.

Operational simplification. I save this one for last because it is the most underappreciated element of the group, and frankly the one I hear security leaders mention most when they get candid over a coffee. As organizations accumulate tools over the years, the resulting complexity introduces inefficiencies, alert fatigue, integration fragility, and processes that vary depending on which analyst happens to be on shift. A platform consolidates workflows, minimizes dashboard-switching, standardizes operating procedures, and reduces the overhead of managing multiple vendor relationships simultaneously. Fewer tools requiring independent configuration. Fewer integration points to babysit. Fewer procurement cycles. Streamlined audit evidence collection. Lower training requirements, because analysts work within a consistent environment rather than context-switching across systems that each have their own logic and quirks. Operational simplification does not mean reduced capability. It means architectural coherence, and in an environment defined by talent shortages and relentless digital expansion, coherence is a genuine competitive advantage.

Four outcomes, regardless of who built it

IDC measures platform value through structured interviews with organizations running platforms in production, capturing before-and-after data across detection and response times, staffing requirements, downtime, incident frequency, compliance effort, and tool consolidation. Operational improvements are converted to financial value using standardized assumptions for labor costs, productivity, and risk, analyzed through a three-year discounted cash flow model. I am not accepting vendor claims at face value. I am talking to the customers actually living with the outcomes.

What IDC consistently finds falls into four patterns, regardless of the technology domain or deployment scope:

  • Faster, more contextual threat detection and response
  • Reduced operational complexity
  • Lower security-related costs
  • Business enablement and revenue protection

The platform is live. The hard part just started.

I want to be straight with you: becoming a real boy is not a one-afternoon project. Platform adoption is both an architectural and an organizational transformation, and organizations that treat it as a straightforward product deployment tend to learn otherwise rather quickly.

The most common friction points include disentangling legacy workflows and brittle integrations accumulated over years; reengineering detection logic and response playbooks rather than simply migrating telemetry; managing extended coexistence periods where parallel systems add temporary complexity; and navigating the organizational realignment that comes when automation and centralized policy management reshape roles that people have held for a long time.

None of these challenges disqualify the platform approach, but they do argue strongly for phased deployment, deliberate tool consolidation, and treating the operating model as part of the transformation rather than a problem to solve after go-live. Pinocchio did not become real by wishing hard. He earned it.

Go deeper: The full research is worth your time

My colleagues and I go considerably deeper on all of this in the full IDC Perspective, Defining and Implementing Security Platforms: Differentiating “PowerPoint” from Engineering Reality, including the complete measurement methodology behind the business value findings, a detailed breakdown of implementation challenges, and best practices for organizations at every stage of platform adoption. The puppet has become a real boy. This is the research that shows you what that looks like in practice, and what it takes to get there. If you are a security leader thinking through platform strategy, this is where to start.

Frank Dickson

Frank Dickson - Group Vice President, Security & Trust

Frank Dickson is the Group Vice President for IDC’s Security & Trust research practice.  In this role, he leads the team that delivers compelling research in the areas of AI Security; Cybersecurity Services; Information and Data Security; Endpoint Security; Trust;…

核心洞察

AI 产业化正从“模型竞赛”迈入“应用深水区”。2025 年,中国 AI 应用公有云服务市场规模突破 137 亿元人民币,已显著超过大模型训推公有云市场的 79.4 亿元。IDC 认为,这一结构性变化表明:企业客户正从“探索模型能力”转向“为业务价值付费”。未来 12–18 个月,能够将 AI 封装为行业应用、并支持智能体(Agent)工程化的云厂商,将成为新一轮增长的主导者。单纯提供模型 API 或通用算力的服务商,将面临被市场边缘化的风险。

模型竞赛应用深水区

AI 产业化正从“模型竞赛”步入“应用深水区”。谁能将 AI 能力真正嵌入业务流程、带动规模化落地,谁就将在未来的云服务竞争中赢得先机。那些能够将 AI 从“演示 Demo”转化为“业务系统”的厂商,正在加速拉开与跟随者之间的差距。IDC 追踪了公有云上 AI 应用市场,以及支持 AI 应用的大模型训推平台市场,可以看到公有云上 AI 市场格局正在发生巨变。

AI 应用公有云服务:137.3 亿元,应用落地成为核心战场

2025 年,中国 AI 应用公有云服务市场保持高速增长,市场规模突破 137 亿元人民币。在这一赛道上,头部云厂商凭借全栈 AI 能力和丰富应用场景占据领先地位。

百度智能云以 30.7% 的市场份额位居第一,依托包括智能客服、内容创作、知识管理等全面的企业级 AI 应用场景实现广泛落地。阿里云凭借智能语音、客服及视觉 AI 能力,在智能办公、营销创意等场景表现突出。腾讯云依托视觉 AI 能力、智能客服等在消费互联网、媒体、金融等场景持续发力。华为云则凭借盘古大模型在政务、金融、制造等行业的深度耕耘,稳居第四。

AI 应用市场的本质竞争,已从模型参数的军备竞赛转向场景价值的落地之争
用户所需要的,并非孤立的模型 API 调用,而是一个能够真正解决业务问题、提升效率的完整应用。无论是智能客服、内容生成、数字人营销,还是企业知识库问答、代码辅助开发,云厂商需要将大模型能力封装为开箱即用的产品,方能打动最广泛的企业级客户。考虑到这一点,领先厂商均应将 AI 应用服务的投入重心,从底层模型能力向行业解决方案、数据接入、工作流编排等“最后一公里”能力快速倾斜。

应用背后的算力暗流:大模型训推市场持续扩张

AI 应用市场的繁荣并非凭空而来。每一次智能客服的响应、每一次营销文案的生成,背后都是大模型推理能力的消耗;而企业为打造差异化应用所进行的模型微调与训练,则构成了另一层刚需——大模型训推公有云服务市场。该市场虽然规模小于应用层,但其增长稳定性与客户粘性更高。

2025 年,大模型训推公有云服务市场规模达到 79.4 亿元人民币,呈现出与前文 AI 应用市场不同的竞争格局。

阿里云以 42.2% 的市场份额遥遥领先,凭借在 AI 算力领域的长期积累和完善的 MLOps 工具链,成为大模型训练和推理的首选平台。华为云(13.1%)依托昇腾 AI 芯片和全栈自主可控能力,在政企市场获得广泛认可。亚马逊云科技(7.1%)则凭借全球化的 GPU 资源和先进的模型训练框架,在出海企业和外资企业中保持优势。

大模型训推市场的快速增长,背后有三大驱动力

第一,生成式 AI 应用爆发驱动训推需求激增。
从文本生成到图像创作,从代码辅助到多模态理解,生成式 AI 应用的繁荣带来了对模型训练和推理的海量需求。企业不仅需要调用预训练模型进行推理,更需要基于自有数据对模型进行微调,以打造差异化的 AI 能力。

第二,智能体(Agent)应用推动复杂推理需求。
随着智能体从概念走向落地,多步骤任务规划、工具调用、长上下文推理等复杂能力成为标配。这对模型的推理效率、并发能力和响应延迟提出了更高要求,也推动企业寻求更专业的训推服务。

第三,算力调度、管理和优化成为刚需。
大模型训练和推理对 GPU 算力的需求呈指数级增长,但算力资源稀缺且昂贵。如何高效调度异构算力、优化模型推理性能、降低单位 Token 成本,成为企业面临的核心挑战。这催生了 AI 算力管理平台、模型推理优化、弹性扩缩容等一系列专业服务需求。

市场隐含的分化信号

值得注意的是,训推市场的增长并非均匀分布。头部三家厂商(阿里云、华为云、亚马逊云科技)合计占据超过 62% 的市场份额,而中小型 AI 算力服务商正在被加速挤出。IDC 判断,算力调度效率与模型优化能力正在取代“裸算力价格”成为客户选择的关键因素。这意味着,未来训推市场的集中度还将进一步提高,缺乏工程优化能力的算力提供商将难以维持竞争力。

IDC 展望:四个不可逆的市场趋势

趋势一:AI 产业化进入深水区,应用价值成为核心衡量标准

Token 经济的兴起降低了企业试用 AI 的门槛,但真正的商业价值在于应用落地。未来,能够提供端到端 AI 应用解决方案、或支持企业快速构建行业专属应用的厂商,将在竞争中占据优势。IDC 认为,市场正在从“技术可行性驱动”向“业务 ROI 驱动”加速迁移。

趋势二:训推一体化平台成为主流采购标准

随着模型迭代速度加快和应用场景复杂化,企业需要无缝衔接模型训练、微调、部署、推理的全流程平台。训推一体化不仅能够提升开发效率,更能通过持续优化降低 AI 应用的总体拥有成本(TCO)。IDC 观察到,2025 年已有超过 35% 的头部企业客户在选型时将“是否具备训推一体化能力”作为核心评估指标。

趋势三:多云与混合云策略成为常态

考虑到数据安全、成本优化和供应商风险,越来越多的企业采用多云策略部署 AI 应用。这要求 AI 云服务厂商提供开放的 API 标准、灵活的部署选项和跨云的一致性体验。单一云绑定策略正在被企业客户重新审视。

趋势四:行业垂直化与场景精细化并行

一方面,金融、医疗、制造、教育等行业对垂直领域 AI 应用的需求日益增长;另一方面,营销创意、智能办公、客户服务、代码开发等通用场景也在持续深化。厂商需要在“行业深度”和“场景广度”之间找到平衡。IDC 预计,未来两年内,行业定制化 AI 解决方案的增速将超过通用型 AI 应用。

IDC 建议:厂商与用户应如何行动

对云厂商的建议

  • 提供模型转向提供业务模板 + 低代码 Agent 构建能力,降低企业落地门槛。
  • 投资训推一体化的工程能力,而非单纯扩大算力池。算力效率管理将成为差异化竞争的关键。
  • 主动拥抱多云生态,避免锁定策略带来的客户流失风险。

对企业用户的建议

  • 优先选择具备行业解决方案 + 训推闭环能力的云厂商,避免被单一模型或单一算力源绑定。
  • 关注跨模型迁移成本,在选择模型 API 或训推平台时,将标准化与开放性纳入长期评估体系。
  • 在智能体(Agent)类应用上,建议从非关键业务场景(如内部知识问答、辅助写作)起步,逐步向自动化流程演进。

IDC 中国研究总监卢言霞表示中国 AI 公有云服务市场正处于从‘技术驱动’向‘价值驱动’转型的关键期。Token 经济打开了市场天花板,但只有真正解决业务问题的 AI 应用,才能为企业带来持续价值。未来,兼具模型能力、应用生态和工程化落地能力的厂商,将引领 AI 产业化的下一波浪潮。

本文相关报告:

IDC《中国AI软件市场半年度追踪,2025H2》

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Yanxia Lu

Yanxia Lu - Research Director

  Yanxia Lu is a research director, focusing on big data and artificial intelligence (AI). Her responsibilities include big data information management platform, and big data analytics and applications. She is also involved in research on AI technology and enterprise…

过去一年,中国AI软件私有化市场交出了一份不错的成绩单:计算机视觉92.5亿元,语音语义118.6亿元,机器学习平台稳定增长。但真正值得关注的,不是这些数字和名词,而是一个更根本的结论:中国私有化AI市场已经走出技术演示阶段,进入以场景深耕、工程化交付、多模融合为核心的深水区竞争。 在这个阶段,单纯的大模型能力或算法排名不再决定胜负,谁能把行业知识、私有化数据与可规模化的产品能力真正结合,谁才能在下半场胜出。与此同时,市场仍高度分散——多数赛道CR3低于50%——这既说明竞争激烈,也意味着格局远未锁定。

以下,我们基于IDC最新数据,分三个细分市场拆解这一趋势。

计算机视觉:92.5亿元大盘,CV2.0时代加速到来

2025年中国计算机视觉AI软件私有化市场规模达到92.5亿元。在这一成熟市场中,头部厂商凭借场景深耕能力占据领先地位。

市场格局上,商汤科技以19.5%的份额居首。作为国内计算机视觉领域的开创者,商汤凭借深厚的算法积累与超大规模训练能力,在城市安全、零售、汽车等场景保持领先。海康威视以16.7%的份额位列第二,依靠硬件与软件深度整合的产品矩阵以及遍布全国的渠道网络,在安防与工业视觉领域具备不可忽视的规模优势。创新奇智(9.3%)主攻工业制造场景的视觉质检与缺陷检测,已在汽车、电子制造等头部客户中实现批量复制;电信AI公司(7.5%)与大华股份(7.1%)则分别凭借运营商生态和安防硬件深度,在公共安全与智慧园区场景持续渗透。

值得关注的是,计算机视觉市场正经历从“CV 1.0”到“CV 2.0”的深刻变革。传统的计算机视觉以感知为核心,依赖针对特定场景训练的专用模型,一个场景一套算法,部署成本高、泛化能力有限。而随着视觉大模型的崛起,CV 2.0正在重新定义这一市场——从多模型到统一大模型解决多场景问题,从单模态感知到图文多模态理解,从闭集识别到开集推理,从单纯的“看”到“看懂、会搜、能生成”。CV 2.0呈现出几个核心特征:一是统一大模型替代多模型,大幅降低部署和运维成本;二是多模态融合,实现跨模态对齐与“万物检索”能力;三是生成式视觉,从感知延伸至创作;四是端侧与边缘智能,视觉Agent开始落地。这一轮转型将进一步拉大不同厂商之间的技术代际差距,市场格局可能在近年内出现新一轮洗牌。

语音语义AI软件:118.6亿元,大模型重塑竞争格局

大模型的引入使得语音语义AI从“听得清、听得懂”向“答得好、能办事”演进,智能体能力的增强成为厂商差异化竞争的新焦点。2025年中国语音语义AI软件私有化市场规模达118.6亿元,是三大细分领域中体量最大的赛道,也是大模型技术渗透最为深入的私有化场景。自然语言处理(NLP)、语音识别与合成,已成为政务热线、金融客服、医疗记录、企业智能办公等场景的标配基础能力。

市场格局中,科大讯飞以15.6%的份额领跑,凭借在教育、政务、医疗三大核心赛道超过二十年的深耕积累,以及星火大模型的本地化部署能力,科大讯飞在语音语义私有化市场建立起高壁垒的护城河。百度智能云(14.2%)依托文心大模型在语言理解与生成领域的领先性能,以及在政务、金融等行业的广泛布局,紧随其后。阿里云(10.4%)与腾讯云(8.9%)则以云厂商的综合生态优势在企业级NLP私有化部署中持续渗透,尤其在大型企业的混合云场景中具备一体化交付的优势。整体来看,语音语义赛道的其他厂商占比超过50.9%,市场仍处于高度分散状态,区域系统集成商与垂直行业方案商构成了市场的长尾主体。

机器学习平台软件:向大模型工程化平台演进

对于私有化部署市场而言,机器学习平台的核心价值在于帮助企业构建自主可控的AI能力。在数据安全合规要求较高的金融、政务、能源等行业,私有化机器学习平台成为企业训练行业专属模型、沉淀AI资产的关键基础设施。2025年机器学习平台软件私有化市场保持稳定增长,成为企业构建AI能力的重要基础设施。

范式以30.4%的市场份额位居第一,凭借AutoML自动化机器学习技术和在金融、零售等行业的深度积累,持续领跑市场。华为云(25.0%)依托全栈AI能力和政企客户资源,稳居第二。星环科技(2.5%)作为大数据与AI融合的代表厂商,也在积极拓展机器学习平台市场。值得注意的是,该市场“其他”厂商占比高达42.1%,显示市场仍处于相对分散状态,竞争格局尚未固化。

机器学习平台市场呈现出几个显著特征:一是从传统ML向大模型工程化平台演进,涵盖大模型微调、RAG知识库、Agent开发的全栈AI工程化平台;二是AutoML与低代码成为标配,降低AI开发门槛、提升模型生产效率成为平台竞争的关键;三是云厂商与AI厂商差异化竞争,云厂商依托基础设施和生态优势,AI厂商则凭借算法能力和行业解决方案取胜。

结语:私有化市场的下半场,拼的是可规模化的行业深度

综合来看,中国私有化AI市场正呈现出几个明确趋势:垂直场景成为增长核心驱动力,而非通用API调用;CV 2.0重塑视觉市场,推动从感知走向理解与生成;技术融合加速,多模态与Agent成为下一竞争高地;竞争格局持续演变,头部厂商份额仍相对分散,未来12个月有望进一步洗牌。

IDC预测,2026年中国AI软件私有化市场仍将保持强劲增长势头,尤其是随着大模型私有化成本的进一步下降与工具链的成熟,中型企业市场将成为新的增量战场。在这场持久战中,谁能把行业Know-How真正转化为可规模化交付的产品力,谁就能在私有化市场的下半场赢得先机。

本文IDC相关报告:

IDC《中国AI软件市场半年度追踪,2025H2》

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Telecom operators are entering a new phase of digital infrastructure and AI monetization. That was the clear signal at FutureNet World in April 2026. The conversation is no longer about whether AI belongs in telecom. That question has been answered. The real question is which operators can turn experimentation into measurable commercial value reflected in P&L statements.

What is emerging is not simply a new set of AI use cases. It is a broader redefinition of what telecom providers can offer. For enterprise customers, the product is shifting from raw connectivity to orchestrated outcomes. The winners will not necessarily be the operators with the biggest AI infrastructure footprints or the most GPU capacity. They will be the ones that can combine connectivity, cloud, edge, automation, and governance into trusted, SLA-backed services.

That is the framing from IDC’s panel on April 23, 2026, “Unlocking New Revenue Opportunities by Monetizing AI and Digital Infrastructure” at FutureNet World London. The panelists were senior telecom executives: Emilio Varas Jiménez, Customer Fulfilment Head of AI and Operations Improvement, Vodafone; Franck Morales, Senior Vice President, Marketing and Business Development, Orange Wholesale International; Natali Delić, Chief Strategy and Digital Officer, Telekom Srbija; and Martin Rueckert, Chief AI Officer, Tallence AG.

AI monetization starts at home

External AI monetization has to be built on internal proof points. Before operators can credibly take AI capabilities to enterprise customers, they need to show they can deploy them inside their own business.

This matters because enterprise buyers are increasingly skeptical of AI promises not backed by operational experience. Telcos that can demonstrate AI-driven improvements in field service operations, assurance, employee productivity, and customer experience will be in a stronger position to package that expertise into enterprise services.

For telecom executives, this means the first monetization opportunity is often not a customer-facing AI product. It is the ability to refine, prove, and operationalize AI internally and then externalize that know-how as a service.

From connectivity to business outcomes

The enterprise buying center is changing. Businesses are looking beyond bandwidth or basic cloud access. They want guaranteed performance, sovereign routing, managed AI-enabled operations, and real-time decisioning environments that align with business risk and revenue goals.

This is especially visible in financial services, manufacturing, and retail, where latency, compliance, and uptime have direct commercial consequences. In these environments, value comes from combining multiple layers: network slicing, APIs, edge compute, AI orchestration, and managed service delivery.

Telecom providers are being pulled higher up the stack. The role of the operator is evolving from connectivity provider to orchestrator of digital infrastructure outcomes. 

Orchestration is becoming the real differentiator

Enterprises do not want to decide what runs in the public cloud, what belongs at the edge, and what must stay on-premises. They want those decisions handled for them, governed, compliant, cost-predictable, and reliable.

That changes the basis of competition. Operators that can orchestrate workloads across hyperscaler, edge, and on-premise environments build a durable market position. Those that cannot risk turning AI infrastructure into a commodity layer with limited pricing power.

The long-term value in AI and digital infrastructure will accrue to operators who can integrate, operate, and govern AI-enabled services at scale.

Edge AI will favour smaller, purpose-built models

For most real-time telecom and enterprise use cases, the requirement is not maximum model size. It is deterministic performance, low latency, and auditability.

That favors smaller, domain-specific models and, in some cases, non-transformer architectures, particularly in industrial automation, remote diagnostics, and real-time network decisioning.

This has major implications for investment strategy. Operators that assume the future of edge AI depends on pushing large language models closer to the endpoint may be overestimating both enterprise demand and the technical fit. In many scenarios, the commercial opportunity will come from deploying the right model, not the largest one.

Agentic AI remains one of the most talked-about areas in the market, but many enterprise pilots are still failing to reach production. [Source: attribute to panelist name or add IDC data reference.] The problem is not only technical capability. It is trust.

For agentic AI to be backed with an SLA, enterprises need confidence that decisions are bounded, explainable, and auditable. In regulated and mission-critical environments, free-form reasoning is not enough. Determinism matters. Governance matters. Standards alignment matters.

Telecom providers looking to monetize agentic AI should focus on domain-constrained deployment models. The path to commercial scale is likely to come from tightly scoped, standards-aligned agents that can operate within controlled decision environments.

Sovereign AI infrastructure brings opportunity and risk

Sovereign AI demand is real, particularly in regulated sectors, but that does not mean every operator should rush to build large-scale local AI factories.

There is significant capex risk in overbuilding. If utilization remains low or infrastructure cycles shorten faster than expected, operators could face stranded assets within three to five years. [Per panel discussion, April 23, 2026. Add IDC data reference if available.]

The more sustainable approach is hybrid and multi-cloud by design: combining hyperscaler or neo cloud computing, edge resources, and targeted sovereign deployments where regulation or national security requirements justify them. The key is to align infrastructure investment with verified demand rather than hype-driven positioning.

The bottom line

Telecom AI monetization between 2026 and 2028 will be defined less by model ownership and more by execution discipline. Operators that can prove value internally, orchestrate hybrid environments effectively, deploy trusted and auditable AI, and match infrastructure investment to real demand will be best positioned to capture new revenue and higher margins.

The market is past experimentation. The next phase belongs to operators that can industrialize AI as a commercial capability.

Masarra Mohamed

Masarra Mohamed - Senior Research Analyst, Communications Platform as a Service

Masarra Mohamed is an expert in digital infrastructure, cloud, AI, and communications platform-as-a-service (CPaaS). She leads IDC’s global CPaaS research and advisory practice, shaping the firm’s perspective on API-driven communications, customer engagement platforms, and their convergence with contact centre and…

When IDC CEO Lorenzo Larini chose China as one of his first major destinations as a new chief executive, he wasn’t following a script. He was reading the data. 

Four months into his tenure, Larini is heading to Beijing to see firsthand what IDC’s analysts have been tracking for years: a market in a fundamental shift that most Western executives are still processing from a distance. He asked to meet with robotics companies. He wanted to see the technology himself. For Kitty Fok, IDC’s Managing Director in China and a 20-year veteran of the market, that instinct signals something important. 

“The energy in China right now, the innovation, the change since COVID, it is something very different from six years ago,” says Fok. “I really want him to feel that after his visit.” 

If your mental model of China was formed before the pandemic, it is time for an update. 

What does it mean that China is now a technology innovation engine? 

China is no longer just the world’s manufacturing center. It is one of the most active technology innovation markets on the planet, and the products, platforms, and business models coming out of China are already reshaping global competition. 

That transformation was built on three structural forces IDC has tracked across its 77 dedicated China research programs: the scale of China’s manufacturing base, the size of its domestic population and the demand it generates, and the external pressures that have pushed Chinese companies to build solutions from the ground up. 

Chinese vendors now lead globally in smartphones and electric vehicles. Robotics is where this next wave becomes impossible to ignore. IDC’s newly launched research on embodied intelligence projects China’s robotics market growing from $1.4 billion today to $77 billion in five years. That is 94% compound annual growth, every single year. Chinese robots are already shipping to Asia Pacific, Europe, and North America. This is not a future trend. It is already happening. And when you build for a billion people first, export follows naturally.  

Now look at the software layer. Alibaba built one of the most sophisticated e-commerce and logistics networks in the world. TikTok changed how a generation consumes content. And last year, DeepSeek moved global markets. Not because it was built with the most advanced compute infrastructure available, but because it was built without it. 

“We couldn’t get enough GPU from Nvidia because of restrictions,” says Fok. “So we had to change. DeepSeek happened because we didn’t have enough compute power. We had to innovate and create a different algorithm.” 

Necessity is a very effective R&D strategy. Western companies not watching this closely are working from an increasingly incomplete picture. 

What is the Belt and Road Initiative and why does it matter for global business? 

China’s innovation story doesn’t stop at its borders. That’s where the Belt and Road Initiative comes in. 

Modeled on the ancient Silk Road, it is the Chinese government’s coordinated strategy to help Chinese companies expand globally. In practice, the government establishes diplomatic relationships in target markets, state-owned enterprises move in first and build infrastructure, and private Chinese companies follow with a path already cleared. 

“The government itself is meeting with governments along the Silk Road, creating a better environment,” says Fok. “The state-owned enterprises set up infrastructure. Then all the other Chinese companies can go along with that.” 

The result is a wave of Chinese companies entering markets in the Middle East, Africa, and Europe that operate differently from what Western incumbents are used to: greater pricing flexibility, faster customization, and business models most Western companies haven’t encountered before. If you sell into those markets, you have new competition. If you want to reach Chinese enterprises expanding globally, they are actively looking for IT partners who understand their destination markets. Both are opportunities most Western companies haven’t fully accounted for. 

What Western companies get wrong about China 

The most common mistake IDC has observed across decades of working with multinationals is treating China as a sales geography rather than a distinct market. 

“The first mistake is assuming that anything that works in the US can sell in China,” says Fok. “I have seen American companies translate their product catalog into Chinese and hope that it will sell. It does not work like that.” 

China is a hardware-centric market. Even security companies need to sell appliances, not just software. When AI arrived, buyers expected AI devices alongside AI services. The product, the messaging, and the go-to-market approach all need to be built for the market, not translated into it. 

The second mistake is chasing the wrong benchmark. The competitive density in China is real, but a multinational doesn’t need to win it all. 

The key is finding the right slice. IDC helps companies identify their real addressable market in China, not the theoretical total, but the segment where a multinational can realistically compete and win. 

What does IDC uniquely offer for China strategy? 

That ground-level intelligence is what IDC has been building for approximately 40 years in China. IDC was the first foreign company to receive a media license in the country. Kitty Fok has spent 20 of her 30 years at IDC working in the Chinese market. 

Today, IDC operates 77 dedicated China research programs and has more than 70 analysts based in China. IDC’s nearest Western competitor has roughly 20. That presence matters because China is not a single market. Its provinces operate at different levels of maturity, its industries are distributed unevenly, and understanding which segment is addressable for a foreign company requires people talking to buyers and vendors every week, not a global report applied from the outside. 

“Sometimes we need to talk to the headquarters of a multinational to help them understand why China needs a different strategy,” says Fok. “Why it needs to be more than a sales office. That conversation requires local knowledge, not just market data.” 

IDC’s China intelligence works in both directions: helping Western companies enter China with the right strategy, and tracking how Chinese enterprises are expanding globally so Western companies in those markets know what’s coming. 

The signal in Larini’s visit 

Which brings the story back to Lorenzo Larini. 

Japan and China are the two largest single markets for IDC in Asia, and Larini is visiting both within months of taking the role, alongside IDC CFO Tiziana Figliola.  

He’s not just scheduling meetings. He is looking to meet with robotics companies and see the technology firsthand.  

According to Figliola—who lived and worked in China prior to becoming CFO at IDC— making this visit with Larini is a signal of how seriously IDC is weighing China as a driver of its next phase of growth. “What Larini will find is not the China of 2019. The technology is different. The competitive landscape is different. The pace of change is different in ways that are hard to appreciate without being there,” said Figliola.

For Fok, Larini’s visit matters beyond IDC. Since COVID, many Western executives have quietly stepped back from China, and a six-year gap in direct engagement is a long time in a market moving this fast.

That is the invitation for any Western executive weighing a China strategy. Come and see it. And when you are ready to act on what you see, make sure the intelligence behind your decisions comes from people who have been watching this market for decades. 

IDC has 70 analysts in China right now. The data is there. The question is whether you are ready to use it. 

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.

Christina Cardoza - Content Marketing Manager - IDC

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