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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

Analyst Profile
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.

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.

Get the full perspective

Frank Dickson

Frank Dickson - Group Vice President, Security & Trust

Analyst Profile
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;…

When Phillip Langeberg took over IT leadership at The Resorts Companies in 2017, he faced a problem that doesn’t show up in business school case studies. He was building a modern technology organization for a company that operates at the intersection of hospitality, real estate, and recreation, and there was no obvious playbook for it.

The company runs two flagship properties in Virginia: Wilderness Presidential Resort, a 600-acre camping and recreation destination, and Massanutten Resort, a 6,000-acre, four-season property with 2,500 accommodations, ski, an indoor and outdoor waterpark, four restaurants, two recreation centers, two golf courses, and more. Its technology ecosystem has to support everything from reservations and guest engagement to point of sale, property management, marketing platforms, and operational systems across multiple venues and verticals. And as a 100% employee-owned (ESOP) company since 2015, every technology decision carries weight beyond the immediate bottom line. Every employee has an ownership stake. Getting it wrong is not just a budget problem.

“The challenge is really trying to find the software and build the ecosystem that allows all these different business verticals to work together,” said Langeberg, CTO of Resorts Companies. Benchmarks from companies far smaller or far larger weren’t useful. He needed a perspective that could meet him where he actually was.

Seeing the larger framework

That’s where IDC came in. Over more than a decade, Langeberg has used IDC analyst conversations and IDC Roundtables to validate strategy, pressure-test decisions, and benchmark against peers across industries well beyond hospitality.

In 2021, a group of IDC analysts helped him think through the right staffing model for his IT organization, making recommendations based on the company’s goals, existing systems, and where technology was heading. Knowing that the future would be shaped by AI and data analytics, Langeberg used that guidance to build those capabilities in-house, reducing reliance on external vendors before the wave hit.

More recently, as The Resorts Companies planned a 140-room hotel addition to its waterpark, the expansion became a catalyst to standardize point of sale systems across the entire resort. IDC helped cut through a crowded vendor market to identify the solutions that actually fit. “You Google point of sale vendors and there’s a ton out there,” said Langeberg. “That’s really what IDC can bring to the table, the ability to come in and say, you’re in this industry, you’re doing this, let’s figure out what software platforms align with what you guys are doing as a business.”

IDC Roundtables added another dimension, bringing Langeberg into conversation with CIOs and CTOs from industries ranging from water treatment to manufacturing. Hearing what leaders in completely different sectors were building, and what they were running into, shaped decisions he never would have reached staying inside the hospitality lane.

“Being able to have conversations with IDC analysts is really helpful because they see the larger framework,” said Langeberg. “There could be a solution I’m not considering because I’m only looking at the hospitality sector.”

The relationship becomes a platform

When IDC introduced Quanta, a new AI platform built on IDC’s proprietary research and intelligence, Langeberg recognized immediately what made it different from the general-purpose AI tools he had already been using. “If I go to one of the other AI products out there and search for something, it’s searching the internet, and we all know that everything on the internet isn’t always accurate,” he said. “Maybe it’s paid marketing material for a product, and that’s weighing the decision and ultimately weighing where you’re guiding your business.” Quanta draws from verified IDC intelligence instead, with every response citing the underlying report or MarketScape it came from.

The trust, though, didn’t come from the platform’s architecture. It came from the relationship. “It’s almost like taking the power of all the IDC analysts and putting it into a simple query engine that I’m able to use,” said Langeberg. “An AI backed by IDC’s research gives me a lot more confidence in the answers.”

Looking around the corner

With major expansions underway, including a new waterpark hotel with a redesigned check-in experience, and the Bluestone Peak 55+ active adult community, The Resorts Companies is entering its most complex period of growth. Langeberg plans to use Quanta to build multi-year technology roadmaps and model vendor decisions before committing, backed by IDC’s market intelligence.

“When I walk into that moment where I’m not sure where I need to be on something, I know that IDC is there as a partner, through their research, the AI platform, the analysts, to help us plot the right course.”

For technology leaders navigating complex, niche industries without a natural peer group, that combination of earned credibility and on-demand access is exactly the edge that’s hard to find anywhere else.

Join the IDC Quanta Waitlist

Christina Cardoza - Content Marketing Manager - IDC

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

Three forces reshaping European cybersecurity buying decisions in 2026 

Last week, Joel Stradling, IDC’s Senior Research Director for Global Security and Trust, and David Clemente, IDC Research Director for European Security Services, presented the latest IDC intelligence on three topics that are driving security buying decisions across Europe right now: AI agents, security platform consolidation, and digital sovereignty. 

The session was built around IDC survey data and direct input from European CISOs. Here is a summary of the key findings. The full recording is available on demand. 

What your buyers are prioritising around AI agents 

IDC projects 1.2 billion AI agents in operation by 2029, performing 217 billion daily actions across four categories: custom-configured, in-application, standalone, and bespoke agents. For security vendors, the relevant signal is what this proliferation is doing to your buyers’ security priorities and investment patterns. 

On average, 16.7% of planned AI investment worldwide is currently allocated to AI agent security and governance. That is a meaningful budget line, and it reflects a genuine gap that security buyers are trying to close: most European organizations cannot fully account for the non-human agents already operating in their environments.  

Joel framed this in the session with a question that resonates with CISOs: how many agents are running in your ecosystem right now without being part of any governance programme? The answer, frequently, is that they do not know. 

IDC’s forthcoming MaturityScape Benchmark on AI-fuelled organizations gives a precise picture of where European enterprises stand on AI maturity. The details of that data, including the significant divergence between organizations IDC categorizes as survivors versus those that are thriving, are covered in the recording and available through IDC’s research programme. 

Where the platformization opportunity actually sits 

Joel was direct about the state of the market: security is, to a degree, broken. Vendor sprawl and complexity are the most consistently cited concerns in IDC’s CISO conversations, and the move toward vendor consolidation is real and accelerating. In IDC’s December 2025 survey, 84% of respondents agreed or strongly agreed that their organization is prioritizing security platformization. 

The gap worth understanding, however, is the one between customer intent and vendor positioning. IDC’s CISO Hub data from March 2026 shows that while close to two-thirds of senior security decision-makers say they already have a platform, there is no shared definition of what that means or where it is anchored. The result is fragmented islands of platformization rather than genuine consolidation. 

For vendors, the implication is that the buying conversation is not uniform. Joel walked through how platformization incentives differ by stakeholder: a CISO is looking at visibility, proactive defence posture, and operational efficiency; a CIO is focused on speed, agility, and reduced contract complexity; a CEO is weighing business growth, risk reduction, and regulatory accountability. Understanding which stakeholder is in the room changes the pitch considerably. 

The barriers to consolidation are equally important market intelligence. Tool sprawl, technical debt, skills gaps, upfront costs, and the desire to avoid vendor lock-in are all factors slowing decisions that, on paper, buyers have already made. The session explored each of these in detail. 

What the sovereignty conversation looks like from the buyer side 

David Clemente covered how European security buyers are currently thinking about sovereignty, and it is a more complex and more urgent conversation than it was 18 months ago. 

IDC’s 2025 Worldwide Digital Sovereignty Survey asked European cloud buyers about their main motivations for choosing a sovereign cloud. The top three responses were: concerns about extraterritorial data requests, compliance with national legislation and European directives such as NIS2, DORA, the Cyber Resilience Act, and the AI Act, and the baseline desire to improve data privacy and security. 

David framed the trade-offs buyers are navigating using a cost-versus-control matrix that maps different procurement categories from commodity hardware through cloud services, AI, and sovereign or defense-grade infrastructure. The useful insight for vendors is that these positions are not stable. The past 12 to 18 months have introduced a level of volatility that has caused many European buyers to revisit assumptions they had previously considered settled, including assumptions about suppliers headquartered in the United States. 

During this time, a number of developments have shifted that calculus, and buyers who were not questioning their vendors about sovereignty a year ago are now asking them directly. For US-headquartered vendors in particular, the session covered what buyers are asking and what kinds of responses are building or eroding confidence. 

The core message from David was straightforward: vendors who have genuinely thought through their sovereignty positioning, who can offer tangible provisions and answer specific regulatory and political questions, are in a different position from those who have not. That distinction is becoming visible to European buyers quickly. 

Watch the full session on demand 

The webinar covered substantially more than this summary captures, including the full benchmark data on AI maturity, the complete breakdown of platformization incentives by buyer persona, and a detailed Q&A with both analysts. 

►  Watch the recording here  

IDC Security Summits Europe 2026 are taking place across the continent throughout the year, connecting vendors with 700+ senior security decision-makers in Madrid, Lisbon, London, Paris, Milan, Cologne, Stockholm, and Barcelona. The IDC European CISO Xchange takes place in Sitges, Spain, 8–10 November. Contact IDC for sponsorship and participation details. 

Joel Stradling

Joel Stradling - Senior Research Director, European Security

Analyst Profile
As senior research director for IDC's European Security practice, Joel Stradling leads the content and analyst team for tracking the European security segment. His main focus areas include Zero Trust Network Architecture, Managed Security Services, and Cyber Risk and Resiliency.…
David Clemente

David Clemente - Research Director, European Security

Analyst Profile
Dave Clemente is a Research Director in IDC's Security Services research practice, with a focus on managed services and professional services. His coverage areas include governance, risk and compliance, regulation, managed detection and response, incident response, cyber insurance, security budgets,…

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。

从“模型竞赛”到“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

  • 从“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》

进一步联系：

如需获取本文引用的完整数据报告、细分市场数据表格、定制化分析服务，或希望与IDC分析师进行一对一交流，欢迎与IDC联系。

请点击此处与我们联系。

Yanxia Lu

Yanxia Lu - Research Director

Analyst Profile
  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…

2026年第一季度，中国平板电脑市场出货量为811万台，同比下降4.8%。这个数字本身并不惊人，但放在“存储价格持续上涨”与“国补政策收紧”的双重背景下，其背后的结构性变化更值得关注。

从我们的跟踪来看，市场增长动能正在发生一次明确的切换：从“换机+政策”双轮驱动，逐步转向产品力与真实需求主导的理性调整阶段。这一过程必然伴随出货量的短期承压，但长期看，是市场走向成熟的必经路径。

一、消费市场：成本压力加速结构升级，价格驱动让位体验竞争

2026年第一季度，中国消费平板市场出货量同比下降5.6%。这一降幅的直接诱因是明确的：存储价格大幅上涨导致终端成本压力加剧，叠加国家补贴力度收紧，以及上一轮换机周期进入尾声。

但更值得关注的是市场结构的变化。受成本上涨影响，各厂商的优惠政策在本季度出现不同程度的收缩，200美元以下价位段市场份额出现明显下降。与此同时，终端售价同步上调，市场结构加速向中高价位段迁移。

这意味着一个重要的行业转折：市场竞争正从“价格比拼”转向“产品体验、生态协同及场景化应用”的综合较量。

在我们看来，这一转变是良性的。当成本上行压缩了价格战的空间，厂商必须依靠真正的产品差异化来维持竞争力——无论是鸿蒙带来的跨设备协同，还是iPad在芯片与内存升级后维持原价的策略，本质上都是在回答同一个问题：用户凭什么愿意花更多钱？

二、商用市场：提前备货驱动短期增长，真实需求仍待验证

2026年第一季度，中国商用平板市场出货量同比增长7.4%，表现优于此前预期。

但需要审慎解读这一增速。根据我们的调研，本轮增长主要源于行业判断后续成本将延续上涨趋势，从而主动提前备货及前置采购。相比之下，真实行业需求的拉动作用相对有限。

换句话说，Q1商用市场的增长更多是成本预期驱动的节奏前移，而非需求曲线的系统性上移。

当然，积极的因素同样存在。厂商持续深耕教育核心应用场景，拓展行业合作，同时积极部署平板AI能力升级、完善软硬件生态。这些动作长期来看有助于拓展平板在各行业数字化转型中的落地空间。

但就短期而言，IDC倾向于认为：商用市场的“真增长”验证，需要看下半年的需求是否能够接力备货驱动的增量。

三、厂商格局：头部梯队抗跌能力分化，结构布局成为关键

华为
在我们的观察中，其市场领先地位的核心支撑来自于三方面：完备的产品矩阵、鸿蒙生态的协同优势，以及中高端品牌认知。在成本上行、补贴退坡的背景下，这些能力构成了有效的抗跌护城河。此外，Q1期间春节及返校节点的节日营销与补贴转化，也起到了稳固份额的作用。

Apple
一个值得注意的现象是：在行业普遍调价的背景下，Apple平板定价保持稳健，反而使其竞争优势进一步凸显。Q1迭代的iPad Air在芯片与内存双升级的基础上维持上一代定价，“加量不加价”的策略在本季度有效拉动了市场需求。

小米
其用户群体价格敏感度偏高，因此厂商补贴退坡及国补政策收紧对其影响较为明显。但依托于完善的产品矩阵和成熟的渠道网络，小米在本季度仍重回国内市场第三位。

荣耀
消费端成本上涨对入门级价位段形成显著压力。Q1荣耀对多款产品进行了迭代更新以优化成本结构，同时携手火火兔发布新品，加大教育场景布局。商用市场方面，伴随教育行业深耕及渠道提前备货，荣耀实现同比大幅增长。

联想
在整体市场承压背景下，联想仍保持同比增长。消费市场通过小新与拯救者系列新品持续强化主流大屏及细分小屏市场竞争力；商用方面，大客户市场优势延续，教育行业生态合作也在积极拓展。

总体来看，IDC的判断是：在成本和补贴双重变量作用下，各厂商的出货量表现越来越反映其结构性能力——包括价格带布局、生态粘性、渠道韧性及行业客户基础——而非单纯的营销力度。

四、后续展望：量减额增延续，AI能力成为下一阶段核心变量

展望2026年全年，我们对市场走势有几个明确判断：

第一，市场出货量持续承压，但销额预计保持增长。
存储价格上涨趋势预计年内存续，其他核心零部件也存在潜在涨价风险，厂商成本压力不会快速缓解。在此背景下，厂商将逐步减少低利润产品占比，加快产品功能配置升级，推动行业平均单价明显提升。“量减额增”将是全年主基调。

第二，细分赛道的重要性进一步提升。
在整体需求相对疲软的背景下，移动办公、游戏娱乐、教育学习等细分领域预计将获得更多资源投入。PC级应用、小尺寸产品、内容合作及周边配件都将获得更多市场关注。

第三，AI能力将成为下一阶段市场竞争的核心差异点。
随着AI概念持续升温及模型技术不断迭代，厂商正加速深化平板产品的AI功能部署。需要强调的是：目前AI尚未成为拉动换机的核心驱动力，但它正在成为中高端平板差异化卖点的重要构成，会议摘要、笔记生成、图文创作等场景化AI能力，正逐步激发新的消费需求并支撑价格上行。

IDC结论

2026年第一季度，中国平板市场在成本上涨与政策收紧的背景下进入阶段性调整期。
我们的核心观察是：市场增长动能正由价格与补贴驱动，逐步转向产品与体验驱动。

在整体需求增长趋缓的背景下，接下来的竞争不再是“谁更能降价”，而是——

  • 谁的生态协同更能留住用户；
  • 谁的AI能真正解决场景问题；
  • 谁在细分赛道中找到结构性增量。

这三件事，在成本上行时期值得平板行业更多的关注与思考。

本文相关报告：

IDC 《2026年第一季度中国平板电脑市场季度跟踪报告》

进一步联系：

如果你对文中提到的细分市场表现、厂商格局变化、成本趋势影响或AI能力落地路径有更深入的研究需求，欢迎与IDC中国分析师团队联系。

请点击此处与我们联系。

Quorra Liu

Quorra Liu - Research Manager

Analyst Profile
Quorra Liu is a Research Manager for the Client Systems Research team at IDC China. She is responsible for China's smart home device research. Her responsibilities include tracking the monthly and quarterly market development, conducting research in fully managed services market,…

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。

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Hangzhou has always defied expectations. Once celebrated for the serene beauty of West Lake, it reinvented itself as the birthplace of China’s digital economy through the rise of Alibaba. Now, it’s doing it again — emerging as one of China’s most serious players in artificial intelligence and embodied intelligence.

That’s why IDC Directions 2026 is coming to Hangzhou for the first time.

In 2025, the added value of Hangzhou’s core digital economy industries reached RMB 678 billion — accounting for 29.5% of the city’s GDP, with annual growth of 9.3%, according to the Hangzhou Municipal Bureau of Statistics. That momentum outpaces the broader national trend for digital sectors, underscoring the city’s leading position as a regional innovation hub. This isn’t a city on the rise. It’s a city already there.

Why Hangzhou, and Why Now

IDC Directions Beijing and Shenzhen have long been the go-to forum for China’s ICT community — drawing hundreds of industry participants, delivering senior analyst insights on AI, cloud, cybersecurity, and emerging technologies, and offering one-on-one sessions where ICT leaders dig into their real challenges and growth opportunities. The 2025 edition alone attracted nearly 300 industry leaders, digital experts, and investors when it launched in Shenzhen.

Now, that same calibre of conversation is coming to the Yangtze River Delta — and Hangzhou is the right city for it.

Three things set Hangzhou apart:

  • A proven innovation ecosystem. Hangzhou is home to a dense mix of startups, scaled enterprises, and specialized industry clusters. It’s not an emerging hub — it’s an active one, with real deal flow and real decision-makers already operating here.
  • Leadership in tomorrow’s technologies. Robotics, intelligent computing, and smart home industries are growing fast here, and Hangzhou’s engineering and R&D capabilities have already been showcased on a national stage at the CCTV Spring Festival Gala. This is a city that builds things.
  • Strong institutional backing. The Hangzhou municipal government’s Future Industry Cultivation Action Plan (2025–2026) is already in motion, and major national gatherings — including the China (Hangzhou) Embodied Intelligent Robot Industry Conference and the National Artificial Intelligence Industry Development Conference — have firmly established the city as a destination for serious tech collaboration.

What to Expect at IDC Directions 2026 Hangzhou

What we can already say: Curated event sessions will center squarely on Hangzhou’s flagship strength sectors—robotics, smart home ecosystems, and intelligent computing—while linking local innovation to the transformative trends reshaping China’s broader ICT industry agenda.

The event will feature keynote and breakout sessions delivered by IDC China’s most senior analyst team, with deep expertise spanning enterprise AI, emerging technology, digital economy, and industry transformation across core verticals.

Featured discussion themes will dive into tangible, high-impact topics: the rapid commercial adoption of AI Agents, full-stack cloud and computing platform evolution fueled by open-source models such as DeepSeek, and projected industrial AI investment in China set to hit RMB 900 billion by 2028.

Beyond insightful content, the event will gather a high-caliber audience pool including enterprise CxOs, top tech solution vendors, industry innovators, and institutional investors from Hangzhou and across the Yangtze River Delta—covering Shanghai, Nanjing, Suzhou and surrounding tier-one innovation cities. It offers a chance to network with regional decision-makers, benchmark industry best practices, and align long-term business strategies with local growth momentum as well as the strategic priorities outlined in China’s 15th Five-Year Plan.

Join Us in IDC Directions 2026 in Hangzhou

IDC Directions 2026 Hangzhou is where the region’s most important technology conversations will happen. Whether you’re sharpening your AI strategy, exploring opportunities in robotics and embodied intelligence, or building the partnerships that will define the next phase of your business — this is where you need to be. Register now!

Elly Hao - Senior Marketing Manager, Demand Generation - IDC China

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Elly Hao is Senior Marketing Manager, Demand Generation, at IDC China, where she drives data-backed, integrated marketing initiatives that deliver qualified leads and support business growth. A seasoned cross-functional collaborator, she has overseen key programs including the annual FutureScape campaign. This year, Elly is leading the marketing efforts for IDC Directions Hangzhou, a key strategic growth initiative.

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

Analyst Profile
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…

As enterprises move from AI pilots to full deployment, a harder problem is coming into focus. The bottleneck isn’t compute or tooling. It’s judgment. Without it, AI doesn’t make organizations smarter. It just makes them faster at making bad decisions.

Most workforce strategies haven’t caught up. When companies talk about the human skills employees need to work alongside AI, the conversation tends to collapse into the same shortlist: critical thinking, creativity, collaboration, growth mindset and so on. These are useful concepts, but they’re too vague to act on. And they are far removed from how human-AI work actually plays out.

Imagine an agent that has already triaged three incidents by the time a team lead logs in. What does critical thinking look like at that moment? What does creativity mean when a workflow designer must rearchitect a customer support process that can escalate, hand off, and learn from every interaction? Terms like “creativity” and “critical thinking” aren’t useful because they just aren’t actionable. Enterprises can’t assess them, teach them, or track whether anyone is getting better at them.

This is the gap that IDC’s new Human Skills Framework for Agentic AI is designed to address.

Breaking down the vague

The IDC Human Skills Framework for Agentic AI identifies eight clusters of human capability that organizations need to build now. Each cluster unpacks into specific, trainable subskills, the kind IT leaders can assess, teach and track.

Take critical thinking. The framework breaks it down into problem framing, assumption spotting, hallucination detection, trade-off analysis and metacognition with AI, which is the habit of asking whether AI is quietly shaping your conclusions in ways you haven’t noticed. These are more than just soft skills, or human skills. They are operational disciplines.

The same logic applies to decision-making with AI. Leaders need to be able to map accountability for AI-driven choices, recognize when outputs introduce disparate impact, and make privacy-by-design decisions before agents touch sensitive data. The framework calls these out as distinct, learnable behaviors. They matter because humans must remain in the lead. But if they lack the analytical judgement to know when to accept AI outputs or push back on them, AI becomes just a mechanism for making bad decisions faster.

Human judgement is a guardrail

Most organizations think of AI guardrails as a technical problem, something the model team or the platform vendor handles. The IDC framework pushes back on that assumption.

Human judgment is a guardrail. When an agent recommends a configuration change and a senior engineer signs off without questioning the logic chain, that’s a failure of critical thinking. It has nothing to do with the model. When a team can’t explain an AI-assisted decision to a skeptical regulator, that’s a trust-building failure, not a communications problem.

The framework is built on the premise that humans need to be explicitly trained to catch the things AI will miss. And they must do it consistently, not just when they happen to be paying attention.

The hybrid role gap

Organizations are already creating hybrid roles that sit across IT, operations and the business. IDC is seeing the rise of workflow orchestrators, risk monitors, human-agent collaboration leads. These roles typically get staffed with strong technologists. The problem is that the skills those roles actually require (facilitation, change management, cross-functional sensemaking, storytelling) often aren’t part of a technologist’s development path.

The framework gives HR and L&D leaders a map for closing that gap intentionally. Because it’s modular, it can plug into existing competency models rather than requiring organizations to start from scratch.

Saying no is a skill, too. Employees need the discipline to decline AI-driven solutions that are too risky, too irrelevant and don’t fit the context. That is a trainable behavior that appears in the framework under the strategizing AI use cluster, alongside opportunity-sensing and pilot-to-scale judgment.

The inclusion is deliberate. Organizations stuck in perpetual proof-of-concept mode, or chasing automation for automation’s sake, need leaders who can push back on bad AI ideas as clearly as they can champion good ones. That requires analytical judgment.

What to do with it

The framework isn’t meant to be read once and filed. IDC’s guidance for technology buyers, appended to the research, emphasizes keeping it alive. As AI agents grow more capable and new use cases emerge, the skills and training examples need to update alongside them. A static competency model will age out fast.

The practical starting point: map a small set of critical behaviors to specific AI use cases. Avoid long competency lists. Equip managers to coach employees on AI use day to day in the flow of work. That’s a lot more effective than just pointing people toward courses. Build cross-functional squads that experiment with AI workflows and share what they learn.

The central bet the framework makes is that organizations succeeding with agentic AI won’t be the ones with the most sophisticated models. They’ll be the ones whose people know what to do when the model gets it wrong.

A Human Skills Framework for Agentic AI
Gina Smith, PhD

Gina Smith, PhD - Research Director – IT Skills for Digital Business

Analyst Profile
As a Research Director at IDC, Gina Smith produces research in the IT education and skills sector. Her responsibilities include primary research, analysis, and the production of market insights worldwide. The New York Times bestselling author of Apple cofounder Steve Wozniak’s memoir, iWoz:…