2026 年 3 月,“算电协同” 首次被写入《政府工作报告》,标志着算力与电力系统的深度融合正式上升至国家战略层面。当前全球 AI 产业竞争正从技术赛点转向成本赛点,这一战略落地不仅能化解算力快速增长与能源供给之间的结构性矛盾,更可依托 AI Token 跨境服务输出带动电力资源数字化出口,推动我国算力竞争迈入电力系统调度与协调能力比拼的全新阶段,为数字经济出口打造新增长极。

战略落地:政策沉淀与能源转型的必然结果

算电协同升级为国家战略,是长期政策铺垫与内需驱动的必然结果。早在 2023 年 12 月,“算电协同” 概念首次出现在《关于深入实施 “东数西算” 工程加快构建全国一体化算力网的实施意见》中;2024 年,国家发改委、能源局等部门在《加快构建新型电力系统行动方案(2024—2027 年)》中,为算电协同配套项目制定了具体实施纲领,其推进节奏与我国新型电力系统建设、能源转型的时间线高度契合。

从能源供给端来看,2025 年底我国风电光伏装机占比达 47.3%,首次超过煤电;非化石能源发电量占比升至 42.9%,风光新增发电量占全社会新增用电量的 97.1%,新能源已成为用电增量的核心供给主体。但现阶段能源与算力布局仍存在明显矛盾:一方面存在结构性、时段性供电紧张的时间错配问题,另一方面是东部算力需求旺盛但绿电资源稀缺,西部绿电富集但算力需求不足的空间错配问题。算电协同不仅是解决时空错配的抓手,更承担着推动区域协调发展、保障国家算力安全、完善新型电力系统、落地 “双碳” 目标的多重使命,其核心是将我国电力系统的产业优势转化为数字经济的竞争优势。

全球竞争:算力竞争的终局是电力系统综合实力的较量

全球 AI 产业规模化落地,推动算力需求呈指数级增长,算力消耗与电力供给的强绑定成为行业发展的核心特征。IDC 数据显示,2023-2028 年全球智能算力五年复合增长率达 46.2%,2025 年我国智算规模达 1037.3EFLOPS,同比增长 43%,这背后是 AI 大模型训练与推理规模化落地带来的算力需求爆发。算力需求的快速攀升直接推高电力消耗,2025 年我国 AI 数据中心 IT 能耗预计达 77.7 太瓦时,2027 年将增至 146.2 太瓦时,五年实现 6 倍增长,电力供给端的压力持续加大。

全球 AI 产业竞争已出现阶段变化,上半场竞争聚焦芯片等核心技术突破,下半场则转向电力系统综合实力的较量,成本竞争趋势显现:低成本、稳定且绿色的电力供给是核心竞争力。

模式创新:AI Token 开创电力出口新模式

算电协同将继续推动我国电力出口模式转型。通过 “电力转化为算力、算力生成 Token”,实现电力资源的数字化跨境服务输出。与 2000 年后我国电子家电、智能手机、新能源汽车等实体商品出口不同,AI Token 属于数字服务贸易范畴,依托 API 电子传输实现跨境交付,且受 WTO 电子传输关税豁免规则保护,形成了数字经济领域的 “免税高速公路”,降低了跨境服务输出的成本与壁垒。

当前全球 AI Token 产业竞争呈现明显的层级特征,表层是 AI 模型技术的比拼,中层是算力服务能力的竞争,而底层则是电力体系综合实力的较量。在全球 AI Token 竞争中中国拥有四大核心优势:

1. 电力规模优势:2025 年全国用电量突破 10 万亿千瓦时,电力供给能力稳居全球前列;

2. 成本优势:中西部地区拥有丰富的低成本绿电资源;

3. 布局优势:“东数西算” 与 “算电协同” 双战略加持下,我国算力布局与能源布局的协同性提升;

4. 技术优势:我国 AI 模型性价比高、开源生态强大,为算力服务与 AI Token 产业提供有力支撑。

未来方向:电力和算力的双螺旋演进

未来,算电协同发展将聚焦三大核心方向——推动实现电力服务算力、算力反哺电力的双向赋能、提升能源与算力资源的配置效率。

1. 持续推进 “算力跟着电走”,推动算力设施向绿电资源集中区域集聚,最大化利用低成本绿电;2. 推动算力错峰用电落地,将非实时性算力任务合理安排在电力低谷时段;

3. 推动算力成为电网可调节资源,助力电网实现削峰填谷,提升电网运行的稳定性。

产业机遇:算电协同背景下,IT 供应商迎市场新空间

算电协同战略落地为 IT 技术供应商打开了新市场空间,IDC认为有四个机会窗口:

1. 算力电力协同调度系统开发需求爆发,全国一体化算力监测调度、源网荷储一体化管控等场景,亟需能实现算力负荷与电力供应动态匹配的智能调度平台;

2. 绿色算力基础设施技术升级需求凸显,液冷温控、高密度供电、低 PUE 数据中心改造等技术成为算力设施建设标配,绿电直连配套的数字化适配技术也迎来蓝海市场;

3. 跨域融合技术服务空间广阔,算力枢纽与新能源基地协同布局背景下,数字孪生、AI 能效优化、跨境算力服务 API 搭建等技术服务需求快速增长;

4. 中西部算力基建配套市场持续扩容,“算力跟着电走” 的布局思路下,中西部新能源富集区的算力集群建设,将带来服务器部署、算力网络搭建、边缘计算节点建设等大量项目机会。

IDC 认为,未来全球算力竞争不再局限于规模比拼,而是转向算力、电网、储能、调度的系统能力综合较量。算电协同作为我国新型基建的重要组成部分,其深度推进不仅能推动新能源高效消纳、降低数据中心运营成本、提升我国 AI 产业全球竞争力,更能优化电网资源配置、稳定居民用电成本、推动绿色 AI 服务普惠化。而以 AI Token 为纽带的电力数字化出口,也将成为我国数字经济出口的全新增长极。对于 IT 技术供应商而言,当前正处于算电协同领域的市场窗口期,企业需紧密关注国家及地方政策导向,积极融入行业生态,与电力运营商、发电集团、电力设备商、综合能源服务商等主体深化合作,才能把握产业发展机遇。

IDC相关研究报告:《IDC Perspective 电力算力协同发展趋势预测与市场研判,2025》(Doc#CHC52926825)

算电协同正在成为数字基础设施与能源体系深度融合的重要方向,也将重塑全球算力产业的竞争格局。围绕这一趋势,IDC已在算力基础设施、数据中心能源管理、电力数字化以及绿色算力发展等领域持续开展研究。未来,IDC将持续发布相关研究成果与产业洞察,深入解读算电协同的发展趋势与市场机遇,欢迎持续关注IDC在数字能源与算力产业领域的最新研究与观点。

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

OpenClaw的应用迈向规模化部署阶段,安全不再是可选附加,而是支撑其全域落地与长效运行的先决条件。

OpenClaw龙虾爆火全球,区别于以往的AI助手、聊天机器人或智能体,OpenClaw具备长期记忆能力,在本地运行,可以主动通过用户偏好的现有消息应用向用户发送消息并在后台持续运行,受到了全球用户的广泛关注并引发安装潮。对于企业来说,OpenClaw的潜力更大。理论上,OpenClaw可以查看用户的日历、阅读会议记录,并持续关注正在进行的项目。与传统需要用户主动提问或提示的方式不同,OpenClaw会主动通过消息应用如Microsoft Teams、微信、飞书、钉钉等联系用户,它还可以执行诸如浏览网页、撰写和发送邮件、编写代码、创建新agent以实现目标等操作。用户可以通过包括手机和笔记本电脑在内的多种设备与其交互。这类持续在后台运行、能够检测趋势、异常和机会的环境型agent,对知识型员工来说极具价值。它代表了一种新层次智能助手理念——不再需要人类先设想可能性并指示助手执行,而是助手能够主动确定下一步行动并识别新机会。这类能力与高度自主性息息相关,意味着助手在实现目标时可能表现出异常强大的资源整合能力,这既带来了益处,也会大幅扩大威胁暴露面,“裸奔”的小龙虾会造成巨大的安全风险,给个人、企业带来难以挽回的巨大损失。

具体来说,OpenClaw可以执行Shell/Python、访问本地文件、调用API、安装社区Skills等,这些能力会带来巨大的安全风险,IDC总结了其存在的一些安全:

  • 公网暴露+弱认证风险:OpenClaw默认端口(18789/19890)常被设为 0.0.0.0 全网监听,全球超23万个实力裸奔,攻击者不需要密码、不需要权限、不需要确认即可连接OpenClaw服务,获得权限,进行未授权访问和远程代码执行,对主机进行完全控制,是超高危风险。
  • Skill供应链风险:ClawGuard发现36.8%的ClawHub Skills存在安全问题,至少76个Skills含恶意代码,攻击者可以一次发布多个恶意Skills,用户在安装Skills时面临重大安全风险。2026年2月,ClawHacvoc大规模恶意Skills投毒,一旦用户执行恶意安装步骤,攻击者便可获取 SSH 密钥、浏览器密码、加密货币钱包私钥、云服务 API 密钥等敏感数据,或在用户设备上植入远程控制程序(RAT),实现完全系统接管。
  • Agent权限失控风险:OpenClaw 可以做Shell执行、文件访问等高权限动作,可以读写全盘、执行任意命令。当Agent做出如删文件、格式化的高危动作时,无二次确认将带来不可挽回的损失。2026 年 2 月,Meta 安全专家在测试OpenClaw 时,因 AI 处理大量邮件时遗忘 “未经确认不得操作” 的安全约束,批量删除其 200 多封工作邮件,专家只能通过强制关机物理止损。
  • 提示注入风险:攻击者可以在skills、网页、邮件、工具中嵌入恶意指令,诱导Agent执行高危操作。
  • 敏感信息明文存储:OpenClaw 将 API Key、账号凭证及会话数据明文保存在本地目录,已被主流窃密木马列为重点窃取目标,存在高风险的数据泄露隐患。
  • 高危漏洞频发:OpenClaw持续暴露高危安全漏洞,攻击者可利用这些漏洞实现远程控制或系统接管,严重威胁运行环境安全。国家信息安全漏洞库(CNNVD)发布通报,2026年1月到3月9日,共采集到82个OpenClaw漏洞,存在极大的安全隐患。

无论是个人还是企业部署类似OpenClaw的工具时,需要采取严格的安全和治理措施,IDC建议组织可以从以下几点入手进行检测和防护:

  • 网络隔离:网关绑定 127.0.0.1 ,关闭默认端口,禁止公网暴露;远程用SSH隧道/VPN/零信任,配IP白名单+强密码+MFA;防火墙阻断外部入站,仅内网/堡垒机访问。
  • 最小化权限:用普通用户启动,禁用root/管理员权限;仅开放必要文件路径,禁用删除/格式化等高危命令;关键操作强制二次确认等。
  • Skills供应链安全管控:Skills供应链扫描、安装前代码审计等。
  • 数据与凭证安全:开启数据加密,禁止明文存密钥/密码;定期更换API密钥,用密钥管理服务/环境变量注入;清理本地缓存与日志,避免敏感信息残留等;
  • 漏洞与监控:开启自动更新;开启操作日志,异常实时告警;定期用官方工具自查绑定地址、认证状态。

目前,中国众多大模型厂商陆续推出了免费版类OpenClaw智能体,积极抢占新的用户端入口。伴随类OpenClaw智能体部署节奏的加快,AI云厂、网络安全厂商以及AI安全创新型企业快速推出了OpenClaw安全风险分析与防护解决方案和 “安全小龙虾” ,帮助用户安全地使用OpenClaw。为了更好地帮助用户了解大模型安全、智能体安全的市场格局并帮助其做技术选型,IDC正式发布《IDC MarketGlance:中国大模型安全,2026Q1》(Doc#CHC53617026),市场格局详见下图:

与此同时,大模型、智能体的安全检测与防护也少不了AI的赋能即安全智能体的加持。当前众多技术服务提供商已经将安全智能体和智能体集群的能力集成到其安全解决方案中,帮助用户提质增效,用AI对抗AI、AI防护AI将成为未来大模型安全、智能体安全防护的一个重要思路与能力。IDC同期发布《IDC MarketGlance:中国安全智能体,2026Q1》(Doc#CHC53597826),希望通过IDC对于中国市场中安全智能体产品的调研来帮助用户充分地了解安全智能体相关技术的发展和市场格局,详见下图:

IDC《全球CIO议程2026年预测——中国启示》报告预测,到2030年,中国500强企业中15%的组织将因对AI智能体的管控与治理不足,引发高关注度的运营中断,进而面临诉讼、高额罚款及CIO被解雇的情况。企业管理者亟需构建一套智能体安全和治理体系来帮助企业安全地用好智能体,规避安全风险。

为了更好地帮助用户了解智能体安全检测和防护如何入手,IDC正式启动《IDC PerspectiveOpenClaw安全防护解决方案市场洞察,2026》报告、《IDC Perspective:中国智能体身份与访问控制解决方案市场洞察,2026》报告研究,欢迎大家与我们保持沟通交流,与IDC共同开展更多前瞻性与实践性研究。

IDC更多相关研究

IDC已于2026年启动AI安全技术系列研究,围绕AI原生安全架构、安全智能体成熟度评估、AI驱动DevSecOps实践路径及企业级AI治理框架展开深入分析。

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

Sophia Wang - Research Manager - IDC

Sophia Wang is a Research Manager in IDC China. She is responsible for the analysis and research of China's cybersecurity market. Her primary focus is on China's cybersecurity appliance and services market and operational technology (OT) security market. Additionally, she provides related research and consulting services for regional and global IT customers and supports their business development. Prior to joining IDC, Sophia worked in several consulting companies. She was independently responsible for consulting projects in fast-moving consumer goods (FMCG), internet, and other industries. Through market analysis and benchmarking analysis, she helped many clients solve problems in the different stages of their development. Sophia graduated from the University of Southern California with a master's degree in econometrics. She also majored in human resource management and journalism for her bachelor's degree.

AI-driven infrastructure demand is accelerating investment in datacenter networking, reshaping the Ethernet switch market.

The datacenter portion of the Ethernet switch market continued its strong growth in the fourth quarter of 2025 (4Q25), rising 63.0% year over year (YoY) to reach $9.9 billion, driven by the build-out of datacenter network infrastructure to support AI workloads.

The total Ethernet switch market, inclusive of both datacenter and non-datacenter segments, grew 35.1% YoY to reach $16.2B in 4Q25. For the full year, revenue totaled $55.1 billion, up 31.5% YoY.

Ethernet switch market highlights

  • Datacenter segment: The datacenter portion of the Ethernet switch market saw exceptional growth in 2025, with full-year growth of 53.5% YoY to reach $32.5B. High-speed datacenter switches (800G) accounted for 25.8% of 4Q25 revenues and 16.4% of full-year revenues, while 200Gb/400Gb speeds represented 43.9% of yearly revenue—reflecting rapid adoption of higher-speed networking to support AI workloads.
  • Non-datacenter segment: Ethernet switches used in enterprise campus and branch networks grew 6.4% YoY in 4Q25 and 9.1% for the full year, reflecting steady investment in enterprise infrastructure.
  • Regional performance: Ethernet switch revenues grew in all regions of the world in both 4Q25 and the full year. The Americas led with 45.4% YoY growth in 4Q25 and 40.2% for the year. EMEA posted 28.3% growth in 4Q25 and 23.1% for the year, while Asia Pacific saw 23.3% growth in 4Q25 and 23.5% for the year.

Router market highlights

The total router market, inclusive of both service provider and enterprise segments, rose 11.5% YoY in 4Q25 and increased 11.2% for the full year 2025 to reach $15.0B.

  • Service provider segment: The service provider segment (including communications and cloud SPs) made up 74.4% of total router market revenues in 4Q25 and increased 12.8% YoY.
  • Enterprise segment: The enterprise router market makes up the balance of revenues and grew 7.5% YoY in 4Q25, reflecting ongoing investment in enterprise wide area networking (WAN) connectivity.
  • Regional performance: In 4Q25, the Americas router market rose 15.9% YoY, EMEA increased 16.2%, and APJ grew 3.8%.

Vendor highlights

Vendor performance reflects the shift toward AI-driven datacenter demand.

  • Cisco: Cisco’s total Ethernet switch revenues increased 13.5% YoY in 4Q25 to $4.5 billion, capturing 27.6% market share. Non-datacenter segment revenues (63.9% of Cisco’s total) grew 7.5% YoY, while datacenter segment revenues rose 26.0% YoY. Cisco’s total router revenue increased 22.5% YoY, giving the company a 30.6% market share.
  • Arista Networks: With 92.6% of its Ethernet switch revenues in the datacenter segment, Arista’s revenues grew 31.4% YoY in 4Q25 to $2.0 billion. Arista holds a 12.6% share of the total Ethernet switch market and 19% in the datacenter segment.
  • Huawei: Huawei’s total Ethernet switch revenue increased 14.0% YoY in 4Q25 to $1.7 billion, giving the company a market share of 10.6%. Huawei’s router revenue increased 6.5% in 4Q25, giving the company a 30.2% market share.
  • NVIDIA: NVIDIA’s Ethernet switch revenues, entirely from the datacenter segment, surged 192.6% YoY to $1.5 billion in 4Q25, giving it a 15.2% share of the datacenter segment.
  • HPE: HPE’s total Ethernet switch revenue (66.8% from non-datacenter) increased 11.7% YoY in 4Q25, reaching a 6.7% market share. Following the July 2025 acquisition, HPE revenues now also include Juniper.

Market dynamics

  • Demand for speed and low latency: Organizations are investing in higher-speed switches to support AI-driven and other demanding workloads, fueling growth in datacenter and high-speed segments.
  • AI workloads: The proliferation of AI applications is pushing enterprises to upgrade their networks for both bandwidth and latency improvements.
  • Global uncertainty (constraint): While macroeconomic and geopolitical uncertainty persists, it has not significantly dampened investment in critical network infrastructure.

Why it maters

  • Who should care? CIOs, network architects, IT buyers, and technology vendors should note this acceleration, as it signals a renewed investment cycle in network infrastructure.
  • Business impact: Upgrading to more reliable and faster networks enables more responsive applications, enhances employee and customer experiences, and supports faster decision-making platforms.
  • Ecosystem signal: The market’s robust growth highlights the strategic importance of network modernization, especially as organizations deploy AI and data-intensive applications.

What’s next for the Ethernet switch market

IDC expects continued momentum in the Ethernet switch market as enterprises prioritize network modernization to support AI, cloud, and real-time applications. Growth could accelerate further as AI investments increase, particularly in datacenter AI factories and as more inferencing use cases emerge. However, new supply chain concerns around memory and persistent global uncertainty are headwinds. Watch for ongoing investments in datacenter upgrades and higher-speed switch deployments in the coming quarters.

Brandon Butler - Sr. Research Manager - IDC

Brandon Butler is a Senior Research Manager with IDC's Network Infrastructure group covering Enterprise Networks. His research focuses on market and technology trends, forecasts and competitive analysis in enterprise campus and branch networks. His coverage includes technologies used in local and wide area networking such as Ethernet switching, routing/SD-WAN, wireless LAN, and enterprise network management platforms. While contributing to ongoing forecast and market share updates, he also assists in end-user surveys, interviews and advisory services and contributes to custom projects for IDC's Consulting and Go-To-Market Services practices.

Petr Jirovsky - Senior Research Director, Network Infrastructure and Services - IDC

Petr Jirovsky is a Senior Research Director within IDC's Enterprise Infrastructure global research domain. He provides quantitative insights on network infrastructure for the datacenter, cloud, and campus/branch environments as part of the Network Infrastructure and Services subdomain. Petr serves as the global lead for IDC's Network Infrastructure Trackers, which track Ethernet switches, routers, wireless equipment, and application delivery appliances and services. He also contributes to numerous custom data projects and supports the publication of market share and forecast documents for the subdomain.

Diego Anesini - VP D&A, LatAm & Director Enterprise and Telecom - IDC

Diego Anesini serves as Research Vice-President, Data & Analytics for IDC Latin America, overseeing all the Information and Communications Technologies. Prior to this position, Diego held various roles in the company. The most recent was Enterprise Infrastructure and Telecom Director for Latin America. He has extensive experience in the Telecom and IT markets, due to his more than 25 years in the industry.

OpenAI’s failed attempt to scale “pay in chat” revealed deeper structural constraints: it can neither be a commerce platform nor operate effectively as a middleman. To be a true commerce platform, it would need to build a Shopify-like ecosystem. Even if Codex could build something in a few months, it would take years to accumulate the millions of product listings, merchant relationships, transaction histories, pricing dynamics, fulfillment systems, and customer journey data required.

OpenAI must also contend with the fact that commerce platforms can easily implement their own AI interfaces, as many already are (Alibaba Accio, Amazon Agent Mode, Walmart AI, etc.). AI is merely a new front door. If OpenAI wants to become a true agentic commerce platform, it must own or control one or more of three strategic assets:

  1. The Commerce Graph (inventory, sellers, transactions)
  2. The Customer Graph (identity, purchase behavior, lifecycle data)
  3. The Product Data Graph (high-frequency usage and intent signals)

Let’s look at some potential acquisition candidates that could revive OpenAI’s agentic commerce ambitions based on strategic fit and financial feasibility.

Strategic fit criteria

  • Strength of proprietary data moat
  • Control over demand and transaction layer
  • Monetization leverage
  • Ability to reduce platform dependency.

Financial feasibility criteria

  • Approximate market capitalization
  • Acquisition realism given OpenAI’s capital structure.
  • Financing complexity
  • Regulatory and integration risk
CandidateStrategic
Impact
Financial
Feasibility
Overall
Assessment
KlaviyoHighHighOwn the customer graph
InstacartHighModerateFast cycle replenishment
EtsyModerateModerateSecondary Marketplace
BigCommerceModerateVery HighOwn the product data pipes
Cart.comLow-ModerateHighFulfillment and commerce infrastructure

Other candidates such as eBay, Shopify, Mercari are considered too big for OpenAI to acquire.

The case for Klaviyo

Own the Customer Graph

Klaviyo is a commerce customer data and lifecycle marketing platform serving 193,000+ merchants globally. It doesn’t own a marketplace it owns the customer identity layer across marketplaces. It processes billions of behavioral events, including email engagement, SMS interactions, purchase conversions, cart abandonment, and repeat buying patterns.

In its most recent fiscal year, Klaviyo delivered approximately 32% revenue growth, expanding margins, and strong free cash flow. It supports thousands of midmarket and enterprise brands and maintains net revenue retention above 109%, reflecting deep embedding in merchant workflows.

Primary value to OpenAI:

  • Deep behavioral identity graph: Access to engagement and purchase data across hundreds of thousands of merchants.
  • High-margin SaaS monetization: Recurring revenue aligned with merchant performance and retention.
  • AI-powered personalization engine: Enables OpenAI to embed agents directly into lifecycle marketing and conversion optimization workflows.

The case for Instacart

Own Fast Cycle Replenishment

Instacart is North America’s leading grocery delivery and retail media platform, partnering with 100,000+ retail locations and serving 26+ million active customers. In 2025, it processed approximately 338 million orders, generating over $37 billion in gross transaction value (GTV).

Grocery is high-frequency commerce—weekly or biweekly—creating repeated behavioral loops rarely found in discretionary retail. Instacart also operates a growing retail media business tightly integrated with shopper behavior.

Primary value to OpenAI:

  • High-frequency purchase data: Recurring basket-level signals ideal for agent habit formation.
  • Localized real-time inventory: Enables agents to optimize decisions across substitution, delivery speed, and pricing.
  • Integrated retail media monetization: Advertising revenue directly tied to purchase behavior.

The case for Etsy

Own the marketplace

Etsy operates a global marketplace focused on handmade, vintage, and specialty goods, with 100+ million active listings, 8+ million sellers, and approximately 96 million active buyers. In 2024, Etsy generated more than $12.6 billion in gross merchandise sales.

Its experience is driven by discovery rather than price, emphasizing personalization, gifting, and niche communities.

Primary value to OpenAI:

  • High-signal preference data: Deep insights into taste, gifting, and niche category behavior.
  • 2nd tier marketplace: Large enough to matter, less complex than dominant incumbents.
  • Discovery-optimized commerce: AI agents could materially improve search and recommendation quality.

The case for BigCommerce (Feedonomics)

Own the product data pipes

BigCommerce powers over 130,000 merchants across 150+ countries, generating roughly $350 million in ARR. Feedonomics provides structured product data feeds to major marketplaces (Amazon, Google, TikTok) and ad channels.

It is particularly strong in B2B commerce, where early agentic commerce value is emerging.

Primary value to OpenAI:

  • Structured product feed layer: Clean SKU, pricing, and catalog normalization critical for AI accuracy.
  • B2B commerce exposure: Early advantage in high-margin procurement automation.
  • Ecosystem leverage: Influence over product data standards feeding major marketplaces.

The case of Cart.com

Fulfillment and commerce infrastructure

Cart.com provides end-to-end commerce services, including storefronts, analytics, warehousing, and fulfillment. It has raised over $380 million and expanded through acquisitions to build a distributed logistics network.

Its platform captures operational data such as inventory velocity, shipping performance, and fulfillment timelines—data typically unavailable to discovery-layer platforms.

Primary value to OpenAI:

  • Operational telemetry: Real-world delivery and inventory data to inform agent optimization.
  • Discovery to delivery: Enables AI to reason about fulfillment constraints.
  • Multi-channel insight: Visibility across marketplaces and merchant storefronts.

OpenAI would have to buy several of these companies, top three Klaviyo (customer graph), BigCommerce (data pipes), and Instacart (fast cycle replenishment). Cart.com adds operational telemetry but is most valuable after OpenAI has control of demand and identity. Without the customer graph and habit loop, fulfillment intelligence is secondary. Etsy is a large secondary global marketplace, but Instacart delivers a marketplace and faster cycle replenishment buying activity.

Acquisitions would radically change the monetization potential of OpenAI’s commercial business. New revenue streams from AI SaaS (Klaviyo), transactions and retail media (Instacart), merchant services (BigCommerce), in addition to their enterprise contracting and consumer subscriptions. These acquisitions would make a powerful strategic statement, legitimize OpenAI’s vision of being the default interface to commerce (or at least being competitive as such), and radically rebrand the company as the AI OS for commerce.

Who owns the personal shopper?

Assuming the industry resolves the operational challenges of agentic commerce, the key question becomes: who owns the consumer interface? Consumers will likely default to a single personal shopper agent that interacts across brands and marketplaces.

OpenAI is a strong interface but has structural vulnerabilities. At best, it is a third-party app unless it secures exclusive distribution with a major device manufacturer. Even then, marketplace operators can control and meter third-party agent access—pushing OpenAI back into a margin squeeze.

More critically, platform-native competitors are emerging. A Gemini-powered Siri deployed across billions of Apple devices could be extremely difficult to displace. If OpenAI cannot effectively monetize commerce or advertising, it will depend on enterprise and consumer subscriptions—yet consumers will gravitate toward the lowest-friction interface.

Gerry Murray - Research Director, Marketing and Sales Technology - IDC

Gerry Murray is a Research Director with IDC's Marketing and Sales Technology service where he covers marketing technology and related solutions. He produces competitive assessments, market forecasts, innovator reports, maturity models, case studies, and thought leadership research. Prior to his role at IDC, Gerry spent six years in marketing at Softrax Corp. an enterprise financial solutions provider. There, he managed marketing programs that produced 4 million emails a year, multiple websites, interactive tools and product tours, an online game, collateral, and PR. Concurrently, he was Managing Editor at RevenueRecognition.com, a thought leadership site featuring partnerships with IDC and the Financial Accounting Standards Boards (FASB) which was quoted and referenced in leading industry publications such as CFO magazine, BusinessFinance, and others. Gerry spent the first half of his career at IDC advising executives from some of the world's largest software and services providers on market strategy, competitive positioning, and channel management. He was the Director of Knowledge Management Technology and conducted research on a worldwide scale including: market sizing and forecasting, ROI models, case studies, multi-client studies, focus groups, and custom consulting projects.

企業でのAIを利用したITシステム投資はもはや必然の状況になっています。特に2026年は、AIエージェントの実ビジネス適用の元年となるとIDCでは予測しており、従来のAI利用方法であった「仕事のアシスタント」からAIエージェントを実利用し、ワークフローへの組み込みによる「業務遂行のバディ(相棒)」への構造的変化の年になるとみています。

IDCが2026年3月に発表した「Worldwide AI and Generative AI Spending Guide 2026V1」では、国内AI市場支出額は2025年に2兆3,725億円から、2029年に2.9倍の6兆8,897億円に急成長し、2024年~2029年の年間平均成長率(CAGR)は36.0%に達すると予測しています。このことは、AI市場が国内IT市場の重要な一画を占めるまでになることを意味しており、2029年にはIT市場全体の20%を占めるまでになります。これらのデータからも、企業でのAI投資は必然になっていると言えるでしょう。

今回IDCが発表した「Worldwide AI and Generative AI Spending Guide 2026V1」では、
以下の重要なAIシステム導入/活用のポイントを示しています。

1.AIエージェントの急成長によるAIソフトウェア市場の成長

国内では、企業での生成AIは2025年末に5割以上の企業が実利用しているものの、その利用方法(ユースケース)が例えば翻訳、要約などの一般オフィス業務の補助的役割に留まるケースが多く、十分な価値創出が得られずに試験的導入(PoC)が失敗するケースが多く見られることが判明しています。IDCの企業ユーザー調査においても、PoCにおいて期待する効果が得られなかった経験のある企業が6割に上ることが測定されています。このような背景で、導入効果が得られやすいユースケースとして、補助的な役割のAI利用から業務ワークフローの自動化/自律化へのユースケースの移行が求められます。これを実現する手段として提供が始められたAIエージェントは市場の期待を集めており、ソフトウェアベンダーによるAIエージェント向けプラットフォーム提供やアプリケーションへの組み込みが2025年から始まり、2026年から実ビジネスへの適用が急成長するとみられます。これらのAIエージェントの急成長を加味し、AIソフトウェア市場のCAGRは48.9%と予測し、AI市場全体のCAGR 36.0%と比較して成長率が大きくなると予測しています。

2.AIユースケースのCX適用拡大

AI/AIエージェントを実ビジネスに適用するためには、どの業務のどの部分に適用するかが成功のキーポイントになります。IDCが今回発表した「Worldwide AI and Generative AI Spending Guide 2026V1」では、ハードウェア/ソフトウェア/サービスのAIテクノロジー市場分類だけではなく、ユースケース別の情報も提供しています。これによると、IT運用の自動化、ソフトウェア開発へのAI適用などが成長率の高いユースケースとして予測されていますが、特に成長率の高いユースケースとして「セールス(CAGR 46.2%)」「カスタマーサービス(CAGR 42.0%)」などのCX(顧客エクスペリエンス)への適用拡大が見込まれています。これは、AI/AIエージェントが社内業務の自動化のみならず、人間とAIが協働することによって顧客やパートナーなどの対外的なリレーションを提供する「バディ」として位置づけられるようになることを意味しています。このことは、企業がAIを活用した自動化によるコスト削減ばかりではなく、顧客対応力や市場変化への対応力を強化する活用方法にユースケースを拡大していくことを示唆しています。

まとめ

2026年の国内AI市場はAI活用の変革が起こり、AIがアシスタントからバディへの変化を起こすキックオフの年と位置付けることができるでしょう。このことは、人間とAIが協働する企業運営の再定義をもたらし、AI市場加速の引き金となり、国内IT市場の主要な一画を占めることになるでしょう。

IDCが提供するデータのご紹介

IDCはAI市場に関して、継続的かつ多層的な情報を提供し、分析を行っています。

これらのデータセットを活用することで、国内およびグローバルなAI市場の加速を可視化することが可能となります。

関連する調査やご相談について

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

Takashi Manabe - Senior Research Director, AI and Automation, IDC Japan - IDC Japan

Takashi Manabe is the Senior Research Director of AI and Automation groups in IDC Japan. Mr. Manabe's primary responsibility includes the analysis of the dynamics and trends, vendor strategies, and market sizing/modeling of Japan’s enterprise AI related market including Software, Services and Infrastructure. He also covers Security, Data Management/ Bigdata Analytics, Customer Experience and Digital Transformation market related to AI. Before joining IDC, Mr. Manabe worked at Toshiba Corporation, Toshiba America Information Systems, Inc., and Toshiba TEC Corporation. 20 over years his experience in the communications and software market, Mr. Manabe started his business as a system engineer for PBX/enterprise data communications equipment in Toshiba Corporation. He was also act as product planning and marketing manager for communication equipment/ software. He also acted as business planning, business management in Toshiba America's age for cable TV Internet business in the enterprise, security software and consumer communication market. Just prior to join IDC, Mr. Manabe worked at Toshiba TEC Corporation, for document solution such like MFP remote management system, scan OCR solution as product planning manager. Mr. Manabe graduate of Muroran Institute of Technology, Japan, holds a Master Degree of Computer Science and Engineering. He also holds a Bachelor degree in Computer Managed Machinery Systems from Muroran Institute of Technology.

While AI-powered tools proliferate across the advertising (and every other) industry, most deployments remain isolated, unable to deliver the unified, cross-channel customer experiences that brands demand. IDC sees a critical gap in the market—fragmented AI products are failing to orchestrate branding and performance across the entire customer journey.

The solution lies in agentic mesh architectures for CX, where interconnected AI agents collaborate seamlessly, breaking down silos and enabling real-time, authentic engagement at scale. This shift is not just evolutionary; it is foundational for advertisers seeking to lead in the AI age.

Advertising’s agentic revolution

Let’s face it: Traditional adtech often feels fragmented, making campaign orchestration and audience targeting a challenge. This is only exacerbated by current AI deployments which often focus on single tasks and fail to capture the cross-functional nature of a client journey.

The agentic mesh for CX changes the game, deploying semiautonomous and autonomous AI agents that collaborate across departments and systems. For advertising professionals, this means:

  • Real-time, cross-channel campaign management
  • Privacy-compliant personalization
  • Unified brand messaging at scale

Best practices for AI-driven advertising

Brands need to address the agentic mesh for CX framework as they look to effectively enable AI across their Ad-tech stack. In this regard several best practices emerge:

  • Unified aata infrastructure: Build a standardized data foundation that integrates DMPs, CDPs, CRMs, and analytics platforms. This empowers agents to access real-time audience profiles, fueling dynamic creative optimization and attribution.
  • Interoperability and standards: Design advertising agents for seamless workflow handoffs and data exchange using protocols like AdCP, OpenRTB, and IAB frameworks. This ensures compliance, scalability, and consistent campaign execution.Rapid adoption and risk management: Deploy agents for critical use cases—cross-channel attribution, creative optimization, and commerce integration. Regular risk assessments help mitigate errors, ad fraud, and ensure quality AI outputs.
  • Upskilling and governance: Equip your teams for agentic AI by upskilling in programmatic buying, campaign optimization, and creative management. Clear governance and supervisory controls are essential for brand safety and regulatory compliance.

The agentic mesh advantage

Agentic AI isn’t just a buzzword—it’s transforming advertising operations by automating complex workflows, breaking down data silos, and enabling proactive, outcome-driven marketing. Industry leaders like Amazon Ads, Oracle, Adobe, and Salesforce are already embedding agentic mesh principles, even if under different monikers, to drive cross-functional collaboration and supervisory control.

The true test for brands in the AI era is not simply adopting new tools, but evolving from fragmented, siloed adtech to a fully autonomous, agentic ecosystem. This transformation requires real-time data integration, robust infrastructure, and clear protocols for agent interoperability—each a pillar of the agentic mesh advantage.

Brands that unify their advertising investments with cross-functional CX goals and KPIs, leverage no/low-code tools and agent cloning, and prioritize closed-loop measurement will accelerate their journey toward autonomous operations. Selecting partners committed to outcome-driven, agentic innovation is equally critical.

Ultimately, competitive advantage hinges on your ability to break down silos, upskill teams, and implement strong governance. Early adopters of agentic mesh principles will not only deliver consistent, brand-safe customer experiences at scale—they will define leadership in the AI age.

For more information on the Agentic Mesh for CX and how brands can apply it in the advertising context, check out:  Applying the Agentic Mesh for CX to Advertising: Orchestrating Branding and Performance Across the Entire Customer Experience in an AI Age.

Roger Beharry Lall - Research Director, Marketing Applications for Growth Companies - IDC

With over 25 years' experience leading technology driven marketing programs, Mr. Beharry Lall is now a Research Director with IDC covering Advertising Technologies and SMB Marketing Applications. He brings a unique multidisciplinary perspective, evangelizing the innovative and pragmatic use of both martech and adtech solutions for companies of all sizes. Early in his career Rog worked with an IBM subsidiary expanding into the Asian Market and subsequently, he spent over a decade at RIM (BlackBerry) building marketing leadership across new industry segments, geographies, and product categories. This background fuels his perspective as he researches enterprise customers engagement tools and tactics across the unified omnichannel.

Japan’s IT market is evolving beyond its traditional reliance on large enterprises, including public sector modernization, as a primary growth driver.

In 2026, IDC forecasts the market will reach ¥28,418.9 billion, growing 3.3% year on year, with a 6.4% CAGR through 2029. Large enterprises remain dominant, increasing their share from 53.9% (2025) to 56.0% (2029).

Japan IT market growth accelerates as mid-sized firms drive 9.5% spending surge, reshaping vendor strategy and digital transformation demand.

However, the key shift is in the rise of mid-sized companies.

  • Mid-sized firms (100–999 employees) will expand IT spending share from 19.8% in 2025 to 21.2% by 2029
  • In 2026, IT spending (excluding PC) will grow 9.5% YoY, outpacing large enterprises (8.7%)

Japan’s IT market is entering a dual-engine growth phase, combining enterprise modernization with accelerating mid-market digital transformation.

Why Are Mid-Sized Companies Accelerating IT Investment?

1. Is labor shortage forcing mid-sized firms to digitalize?

Yes, and it’s becoming urgent.

Labor shortages in Japan aren’t just a macro trend anymore. They’re showing up in day-to-day operations, especially for mid-sized companies.

Large enterprises have advantages: stronger employer brands, deeper recruiting pipelines, and more mature digital platforms. Many have already invested heavily in automation, workflow integration, and data infrastructure.

Mid-sized companies often lack both talent depth and digital maturity. They cannot compete on compensation scale or recruitment visibility. As labor shortages intensify in 2026, digitalization becomes essential for business continuity rather than a discretionary initiative.

In addition, digitalization mandates from large business partners and public sector procurement processes are cascading downstream. Mid-sized firms that fail to digitize risk exclusion from supply chains and ecosystem participation.

Bottom line: From 2026 onward, digitalization is no longer optional, it’s how mid-sized companies will stay operational and competitive.

2. Why will mid-sized firms rely more heavily on IT vendors and system integrators?

Because they don’t have the in-house capacity.

Large enterprises are increasingly building in-house digital capabilities or partnering directly with hyperscalers and advanced technology firms. Their internal IT maturity has advanced significantly.

Mid-sized companies usually can’t. Most have small IT teams, limited internal expertise, and constraints in executing complex modernization programs. So as digital transformation moves from planning to execution in 2026, they’ll depend more on vendors.

Mid-sized companies typically require:

  • End-to-end implementation support
  • Packaged, use-case-driven solutions
  • Operational scalability
  • External expertise in AI and cloud adoption

What this means for vendors: Serving this segment requires structural adaptation. Projects are smaller. Budgets are tighter. Engagement models must be leaner and outcome oriented.

3. Do mid-tier vendors have a structural advantage?

In many cases, yes.

While Tier 1 and Tier 2 vendors remain essential for large-scale enterprise transformation, mid-sized companies often require a different delivery model. Engagements are more operational, localized, and execution focused.

Mid-tier system integrators and regional IT vendors may hold an inherent structural advantage in this environment.

Their scale, cost base, and organizational focus are often better aligned with the needs of mid-sized enterprises. They’re often closer to the customer, more hands-on, and are better structured standardized delivery of outcome-driven solutions without the overhead associated with mega-enterprise programs.

Meanwhile, Tier 1 vendors are optimized for complex, multi-year transformation programs. Mid-tier vendors are often optimized for speed, proximity, and practical execution, attributes that align naturally with mid-sized companies entering digitalization at scale.

So, the fit matters. As mid-sized companies increase IT investment from 2026 onward, vendors whose size, service intensity, and geographic reach match this segment are likely to capture disproportionate growth.

4. How does cloud adoption lower transformation barriers?

Large enterprises frequently face modernization bottlenecks due to deeply embedded legacy systems and customized architectures. Transformation often requires extensive integration and long transition timelines.

Mid-sized companies face fewer structural constraints.

While legacy platforms may exist, system environments are typically less complex. As infrastructure-as-a-service (IaaS) and cloud-native platforms expand in Japan, cloud adoption reduces both cost and complexity barriers.

Cloud changes the equation by enabling:

  • Faster deployment
  • Lower upfront costs
  • Scalable digital infrastructure
  • Easier integration of AI-related capabilities

And that last point is important. In 2026, spending on AI (models, data, agents) is expected to expand rapidly. Cloud environments allow mid-sized companies to adopt these capabilities without large-scale architectural overhauls.

In simple terms: Cloud reduces friction and that speeds everything up.

What Does This Dual-Engine Growth Mean?

Japan’s IT market is not fragmenting, it’s expanding from two different directions at once. Large enterprises continue invest and modernize. Mid-size companies are stepping up as a real growth driver.

Going forward, growth will be shaped by:

  • Sustained large-enterprise modernization
  • Accelerating mid-sized digital transformation
  • Expanded AI adoption across both segments
  • Increased reliance on scalable cloud platforms

What Should IT Vendors Do Next?

Focus more seriously on the mid-market.

Growth won’t come only from large enterprise deals anymore.

It will increasingly come from:

  • Reaching more mid-sized customers
  • Delivering repeatable, outcome-driven solutions and
  • Aligning pricing and delivery to smaller-scale projects.

The takeaway:

Vendors that adjust early to this dual-engine reality will be in the strongest position to capture the next phase of growth in Japan’s IT market.

Contact IDC for deeper insights, or connect with our analysts to discuss what this means for your business.

Hitoshi Ichimura - Senior Research Manager, Software, Services, and IT Spending, IDC Japan - IDC Japan

Hitoshi Ichimura is responsible for the market analysis of overall Japan IT spending, based in Tokyo. In this role, he is responsible for the market analysis of IT Spending research by vertical, company size and region. His main area of research involves IT Spending market forecast and trends for the Japan financial industry local area and SMB segment. Ichimura is also involved in various custom research projects in the area.

In today’s technology market, certainty has become a luxury. AI adoption is accelerating, but unevenly. Partner ecosystems are fragmenting, consolidating, and recombining at speed. Go‑to‑market models are collapsing into customer‑led buying journeys, and leadership teams are being asked to make high‑stakes decisions with incomplete, fast‑aging information.

Broad market reports, benchmarks, and best practices retain significant intrinsic value as foundations for strategy. Yet decisions that are deeply contextual, ecosystem‑specific, and time‑sensitive often require additional layers of insight beyond these core inputs.

The reality is that strategy is no longer about understanding “the market” in the abstract. It is about understanding your market position, your partners, and your customers, right now. Increasingly, the most important questions leaders are asking sound like this:

  • How is AI changing buying behavior and economics in our customer base?
  • Which partners are actually driving growth, influence, and outcomes – and which no longer align with our direction?
  • How does our ecosystem strategy compare to competitors in EMEA, not just globally?
  • Where are customers genuinely willing to invest, and where are they experimenting, delaying, or pushing back?

These are not questions that generic insight can answer with confidence, because the answers depend on your installed base, your partner mix, your regional footprint, your commercial model, and your competitive posture. In short, strategy has become situational.

Faced with this uncertainty, many organizations default to gathering more data: more dashboards, more surveys, more internal analysis. But volume is rarely the issue. The real challenge is relevance. Internal data lacks external context. Global averages mask regional and sector nuance. Lagging indicators arrive after decisions have already been made. What leaders need instead is interpretation, synthesis, and external validation that is designed around the decisions they actually need to take.

This is why we see a growing shift toward custom insight. High‑performing organizations increasingly start with the decision, not the dataset. Whether the challenge is AI monetization, partner strategy, ecosystem prioritization, or route‑to‑market design, the work begins by asking what choice must be made in the next three to six months, and what evidence is required to make it with confidence. From there, insight is built backwards.

Critically, the most effective custom projects blend signals rather than relying on a single method. Partner surveys reveal capability gaps, investment priorities, and friction points across the ecosystem. Customer surveys surface willingness to pay, buying behavior, trust dynamics, and expectations around AI, services, and outcomes. Qualitative interviews add depth and context, while ecosystem and competitive analysis connects those findings to broader market forces. The value does not sit in any one input, but in how those inputs are connected and translated into strategic implications.

We consistently see customer and partner insight deliver the greatest impact when applied to a small number of high‑value areas:

  • AI and agentic AI strategy, including pricing, packaging, economics, and partner roles
  • Ecosystem and partner optimization, from role clarity to performance segmentation and investment focus
  • Go‑to‑market and route‑to‑market evolution, particularly in EMEA’s fragmented markets
  • Executive alignment, creating a shared, evidence‑based fact base for leadership teams
  • External storytelling, using proprietary insight to support thought leadership and market influence

This is where insight turns into action. In many engagements, the report itself is not the most important output. The real value is decision confidence: knowing that a strategic move is anchored in how customers and partners are actually behaving, not how we assume they are behaving.

There is also a powerful dual role at play. Custom insight supports internal strategy and decision‑making, but it can simultaneously fuel external influence. Proprietary findings can shape executive narratives, strengthen partner and customer communications, and differentiate a company’s point of view in an increasingly noisy market. When insight is designed with this dual purpose in mind, it becomes a strategic asset rather than a one‑off deliverable.

This matters now more than ever. Across EMEA, partner ecosystems are being reshaped by a set of interlocking forces: AI economics, consolidation, shifting alliance hierarchies, collapsing route‑to‑market models, sovereignty pressures, and the rise of in‑product and marketplace‑led buying. Many of these shifts are subtle in isolation but powerful in combination.

Understanding which are leading indicators, which are mid‑cycle effects, and which are lagging consequences requires more than surface‑level analysis. It requires insight grounded in real partner and customer evidence, interpreted through an ecosystem lens.

The bottom line is simple. In a market defined by AI acceleration, ecosystem complexity, and regional divergence, generic insight is no longer enough. Organizations that are pulling ahead are those that ask better questions, invest in insight tailored to their context, and use research as a decision tool rather than a reference document.

If these questions resonate, you’re not alone. Most technology leaders we work with are already grappling with how AI, ecosystem change, and buyer behavior are reshaping their growth models – and are looking for concrete, evidence‑based answers they can act on. Through bespoke research and advisory projects, we help clients translate partner and customer insight into tangible business benefits: sharper internal intelligence for decision‑making, clearer ecosystem strategy, and insight‑led assets that can be used confidently with partners and customers alike.

This perspective is also captured in our 15 Key Trends Shaping EMEA Partnering Ecosystems report, often used as a starting point for bespoke client work. Contact us to learn more about our ecosystem research, custom solutions and advisory portfolio.

IDC’s ecosystem lens

IDC’s ecosystem research focuses on value creation, margin capture, and strategic influence. We analyze how partners orchestrate outcomes, how they align with customer buying journeys, and how they evolve their business models to stay relevant. You can find more information here

If you have any further questions, drop them in the form here

Stuart Wilson - Senior Research Director, EMEA Partnering Ecosystems - IDC

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 to market and partner engagement frameworks.

The McKinsey/Lilli incident should be read as a market signal. Once a system can see proprietary knowledge, shape work products, and connect to tools, it stops being a productivity layer. It becomes part of the operating core.

That matters because most companies are still thinking about AI risk in yesterday’s terms: data leakage, bad outputs, brand reputation damage. Those are serious issues, but the bigger risk becomes delegating authority to AI systems.

Implications now and next

Right now, most enterprises are still in a relatively contained phase. AI drafts, summarizes, searches, recommends. When something goes wrong, the damage is often painful but contained. A team wastes time. A document is exposed. A workflow stalls. Trust takes a hit.

The practical implication is clear: the severity of failure scales with an agent’s capabilities and permissions.

In a future shaped by a well-established agent economy, the perspective must evolve.

When AI agents end up touching X% of knowledge work, Y% of customer interactions, and Z% of routine approvals, the risk profile changes completely. The issue is not just whether an attacker can see something. It is whether they can quietly influence what the business does. This is the shift boards and executive teams need to understand.

In that future, the most important question will no longer be, “Was data exposed?” It will be, “What decisions were shaped by a compromised system?” If an agent can reprioritize work, alter recommendations, steer analysts toward the wrong evidence, or trigger downstream actions, then integrity matters as much as confidentiality.

At IDC, we expect more than 1 billion actively deployed AI agents by 2029, executing roughly 217 billion actions a day, and forecasts agentic AI will exceed 26% of worldwide IT spending, or $1.3 trillion, that same year.

With that in mind, any vendor or buyer building a Lilli-like platform should now assume they are operating decision infrastructure, not productivity software.

Recommendations for CIOs and CISOs

For CIOs, the priority is to run AI as a governed platform. Standardize approved frameworks, connectors, and protocols; separate confidential and public data; make agents inherit the user’s permissions rather than granting broad ambient access; and maintain a live inventory with owner, version, data sources, and external tool access for every production agent.

Just as important, design for bounded autonomy: default to read-only assistance, push high-impact or irreversible actions behind scoped tools and policy engines, and report three numbers monthly:

  • % of agents with write access,
  • % of critical workflows requiring human approval,
  • X minutes to disable an agent, model, or connector.

For CISOs, the core mistake to avoid is treating AI as a feature instead of a system. Threat-model it with AI-native frameworks such as MITRE’s Adversarial Threat Landscape for Artificial-Intelligence Systems (ATLAS), National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework (AI RMF), and Open Worldwide Application Security Project (OWASP)’s GenAI and agentic guidance, then red-team the whole chain: inputs, retrieval, memory, tool use, rendering, and agent-to-agent handoffs.

The control baseline should be familiar but upgraded for agents: least-privilege tools, isolated memory, validated inputs, sanitized outputs, full logging of tool calls and decisions, security operations center (SOC) integration and an AI-specific incident playbook with a real kill switch.

This is one of the few cyber investments with a measurable business case. And one to keep in mind if you have a chance to review our Future Enterprise Resiliency and Spending (FERS) survey series. In the Future Enterprise Resiliency and Spending Wave 10 survey, from January 2026, you will notice that organizations are already funding AI/agent security and governance at near parity with the rest of the AI stack, accounting for 16.7% of planned AI investment on average worldwide.

What CEOs should tell their boards

CEOs should tell boards three things:

First: “We are not just deploying AI; we are delegating authority.”

Second: “Success will be measured by controlled autonomy, not by agent count.”

The board pack should show:

  • number of production agents
  • number with external tool access
  • percent with high-impact permissions
  • percent under red-team coverage
  • mean time to revoke access
  • third-party dependencies per critical workflow.

Third: “This is now a supply chain and resilience issue.”
In other words, a weak link in any model provider, cloud platform, connector, or data source can disrupt core workflows, not just expose data.

Once again, our FERS survey series can offer some context as we evaluate these three statements. In the Future Enterprise Resiliency and Spending Wave 8 survey, from October 2025, security, risk, and compliance was among the areas most immune to budget reduction worldwide at 25%, and 29% of organizations ranked “Enhancing cyber recovery and resiliency” among the top areas for significant budget increases in 2026.

To close, let’s consider the opposite perspective.

The right board question is no longer, “How many agents do we have?” It is, “How much authority have we delegated, to which systems, under what controls?”

Boards do not need more demos. They need evidence that management can set clear bounds on autonomy, observe it continuously, and shut it down fast.

Alessandro Perilli - Vice President, AI Research - IDC

Alessandro Perilli is a Vice President leading the Agentic AI Platforms and Strategies research program. Alessandro’s core research coverage includes emerging AI technologies, global AI market trends, the state of enterprise AI, and sovereign AI.
Key takeaways from IDC’s Telco Forum 2026 Barcelona

On March 1, 2026, IDC brought together senior telecom leaders, vendors, system integrators, cloud leaders, partners, and media in Barcelona to examine how the industry is evolving in an AI-driven world. The discussions reinforced a clear message: telecom transformation is no longer theoretical. It is structural, financial, operational and increasingly sovereign. Drawing on insights shared across the event; this blog captures the major themes shaping telecom strategy through 2030.

The four megatrends shaping telecoms through 2030

There are 4 compounding megatrends that have been reshaping the sector since 2022. Looking back, the telco industry has moved through a rapid succession of technological focal points: Network APIs as foundational enablers for exposing network capabilities; Generative AI as the entry point for process automation; and Agentic AI in 2025, which introduced autonomous decision-making into customer experience, network management, and enterprise solutions. In 2026, the critical new frontier for AI is inferencing, the shift from model training to real-time, distributed AI workload execution, and it is this transition that is forcing telcos to fundamentally rethink their infrastructure architecture and competitive positioning. Underpinning all of this are the four defining themes of 2026:

  • Structural transformation is intensifying. Business model reinvention is not new for telcos, but the pace has accelerated. Four distinct strategic paths are now in play simultaneously: the TechCo transition (embracing network-as-a-platform models), Delayering (separating ServCo, NetCo, and InfraCo entities to optimize asset utilization), Consolidation, and a redefined form of Convergence, that focuses on bundling fixed-mobile-satellite services and designed to lock in ARPU and reduce churn.
  • Network investment is tapering. The cyclical CAPEX peak from 5G non-standalone rollout has passed in high and middle-income markets. IDC forecasts a 1.5% decline in global telecom CAPEX in 2026, bringing the total to $320 billion, with CAPEX intensity projected to fall from 22% in 2024 toward 18% by the end of the decade. The drivers are multiple: the one-off FTTP spending peak is fading, satellite partnerships are smoothing access and transport investment, and a structural CAPEX -to-OPEX shift is underway as telcos increasingly rely on ISVs and cloud providers for virtualization and AI. The freed-up cash is flowing into shareholder returns, strategic investments, and targeted digital infrastructure plays.
  • AI adoption is crystallizing around inferencing and sovereign AI. In 2025, the buzzword was agentic AI. In 2026, it is inferencing, and the telcos have a genuine structural advantage to capitalize on it. With distributed infrastructure, low latency, and deep regulatory trust in their home markets, telcos are positioned to become national AI factories, delivering sovereign AI solutions to governments, healthcare systems, and regional enterprises. IDC’s survey data shows that AI compute spending is approaching a pivot point in 2027, when inferencing will overtake training as the dominant driver of AI infrastructure investment. Telcos that expand their data center footprint and deepen relationships with co-location providers now are positioning ahead of that curve.
  • LEO satellite partnerships are becoming strategic. Starlink has established an early lead as the preferred satellite partner for telcos globally. Use cases vary significantly by geography – D2D and satellite broadband in the US and Canada’s large, underserved coverage areas, disaster recovery in Europe’s dense markets, and transport and backhaul across Asia Pacific and Australia. What is clear across all regions is that the satellite-terrestrial boundary is dissolving into a hybrid connectivity model, and the telcos that forge the right partnerships now will have a differentiated coverage story that competitors simply cannot replicate terrestrially.

Balance, pivot, revolt: the transformation imperative

Telcos’ internal transformation focus for 2026 can be framed with three sharp words: balance, pivot and revolt.

Balance is the defining tension of 2026. According to IDC’s C-Suite Tech Survey (September 2025, n=45 telecom respondents), 52% of telco C-suite leaders have AI implementation as a top three priority, but 50% simultaneously have technology modernization as a top three priority. These can be complementary investments as telcos cannot get full value from AI if they have not addressed legacy system complexity, data governance gaps, and architectural debt; but they also compete as telcos must decide between investing in new capabilities that promise significant gains vs. unglamourous IT modernization initiatives which have often been neglected for years. This is at a time with funds for transformations are finely balanced: Telecom CAPEX is declining, though IDC forecasts a modest 5.2% growth in spend on operations and monetization systems in 2026, reaching $54 billion, as telcos invest in IT systems to monetize the billions in CAPEX invested in rolling out new wireless and fixed networks. 5.2% growth is far from a blank cheque, every dollar deployed in IT must demonstrably either cut cost or support new revenue.

The autonomous networks aspiration illustrates this balancing act with particular clarity. TM Forum data from 2025 shows that only 4% of operators self-reported achieving Level 4 autonomous network status, yet 85% aspire to reach that level by 2030. That is an extraordinary gap. According to IDC’s EMEA Telco Transformation Survey (July 2025, n=150), the barriers are familiar: interoperability failures and the persistent lack of a single source of truth in network data. Notably, these are precisely the same barriers that have constrained AI adoption more broadly.

Pivot means making deliberate choices about where to invest and what to sequence. Data quality, accessibility, security are all in focus in 2026. This is represented in telcos making positive investments to overhaul their network inventory systems and updating their data governance policies and infrastructure from customer data down to the network. For autonomous networks, IDC’s research points to a more granular, domain-specific approach gaining traction: telcos are identifying specific use cases, service assurance and fault management are the top automation priorities for EMEA telcos in 2026, and targeting specific domains (IP access, RAN, and core) for Level 4 capability. This is far more tractable than a blanket push to full autonomy. On the people side, 97% of telcos recognize gaps in their talent base for developing and using AI at scale. Sixty-five percent are investing in AI-enabled learning tools, and 58% are expanding internal upskilling programs, but with only 42% currently offering skills training, there is still a meaningful gap between recognition and action.

Revolt is the urgent call to fix customer commercialization before AI finally demolishes the buying behaviour telcos have relied on for decades. For example, a UK mobile subscriber paying £15 per month for 10GB, regularly consuming just 6GB, with known Disney+ and international roaming usage, was on renewal offered to take a device upgrade, to increase their data rate to 30GB for £18 or unlimited data for £24. There was no demand signal for a device, no upsell of complementary services, and no personalization of any kind. The customer found a 40GB plan with the same mobile provider on a comparison site for £7.50, a 50% ARPU reduction and 400% value giveaway. Comparison sites have been established for well over a decade empowering consumer to find the best deal with some manual effort. Today’s consumers and enterprises, however, are already beginning to use AI to undertake similar comparisons with far less manual effort.

The point is not just that this particular offer was poorly designed. The point is that the entire commercial model relies on customer inertia, and AI is systematically dismantling that inertia. As AI agents increasingly make purchasing decisions on behalf of consumers and enterprises, operators that cannot demonstrate differentiated, personalized value in real time will find their customer bases eroding with a speed and scale unlike anything seen before.

5G: from product to platform, and the 6G horizon

The back half of the 5G lifecycle represents an inflection point, but only if operators change their frame of reference. Core mobile remains solid: IDC projects a 2.0% CAGR in global mobile connections through 2029. The world will exceed 9 billion mobile connections within the next two years, surpassing the current global population of 8.3 billion. In saturated markets, however, the growth lever has shifted decisively from subscriber acquisition to retention and value extraction — which brings the customer experience and commercialization issues directly back into focus.

The bigger opportunity lies in the shift from 5G as a product to 5G as a platform. For the first five years of 5G, operators sold speed, latency, and connection density. The next phase is less about branding a connection as 5G and more about 5G as the underlying infrastructure that enables XR, drones, V2X, private 5G, and RedCap solutions to be viable, scalable, and mobile. The challenge is that these use cases do not scale in the millions the way mobility or FWA does, they scale in tens of thousands. That requires a fundamentally different approach to network architecture, back-end systems, and, critically, business models.

Integration complexity remains the most significant brake on enterprise 5G adoption. 46% percent of enterprises cite it as the primary adoption barrier. The solution is less ego and more ecosystem: operators need to be willing to play a back-end role in partner-led solutions rather than insisting on front-facing primacy. 74% of enterprises express interest in network slicing; 49% plan to increase fixed wireless access investment; 58% say they are interested in satellite connectivity, but many still have significant misconceptions about what satellite-to-device actually delivers today. Expectation management is part of the product.

On 6G, If the industry maintains the ten-year generational cycle, 6G commercial launches would begin around 2029. Technical specifications are still in the study phase at 3GPP. The defining features of 6G, AI-native architecture enabling autonomous self-optimization, integrated sensing that turns every cell tower into a radar station, quantum-resistant security, and new terahertz spectrum, collectively point toward a network that moves AI out of the data center and into the physical world. The concept of “physical AI,” or what one operator CTO termed “kinetic tokens,” suggests that 6G will not merely support AI-driven applications but will provide the real-time connectivity substrate that makes physical AI, autonomous robots, connected vehicles, intelligent infrastructure, a viable commercial reality.

The enterprise connectivity opportunity: vast, varied, and underserved

Enterprise connectivity budgets are growing. IDC’s Future Enterprise Connectivity Infrastructure and Services Survey (August 2025, n=758) shows that 37.5% of enterprises increased their connectivity budget by more than 10% over the last two years. For 2026, that proportion rises to 44%. The primary drivers are cloud migration, SaaS usage, AI, video, IoT and device density are driving up bandwidth requirements. Four in ten enterprises saw bandwidth demands increase by more than 50% over the past year. Among organizations with over 10,000 employees, 17% saw their bandwidth demands double. Retail and financial services lead in cumulative bandwidth growth, but the opportunity is sector-wide: only 40-46% of enterprises are at an advanced or market-leading stage of connectivity maturity. The majority are still on the journey and actively looking for guidance.

The question of who captures this opportunity, however, is not straightforward for network service providers. When IDC asked enterprises which provider types they see as best and worst placed to address their future WAN requirements, cloud providers ranked first at 29%, followed by IT partners at 28%, with network service providers third at 23%. More pointedly, in the “worst placed” ranking, network service providers came second. The reasons cited: not treating customers well 35%, limited IT and network capability 28%, and difficult to work with 26%.

This is a reputational and structural challenge, not just a product one. Cloud providers are perceived as having broad network capability, even though they fundamentally depend on telco partners for last-mile delivery. IT partners are perceived as having deep industry expertise, expertise that telcos themselves possess but has not been to communicate or commercialize effectively. The gap is therefore not simply about capability. It is about perception. Perception shapes purchasing decisions, which in turn shape market reality.

Encouragingly, telcos’ “best placed” positioning has improved in recent years as operators have prioritized customer experience and simplified portfolios to deliver more flexible, scalable, and accessible services aligned with enterprise demand. Network as a Service, or NaaS, is central to this shift. NaaS is a cloud-based delivery model in which connectivity, bandwidth, security, and routing are provisioned and consumed on demand via APIs or self-service portals. It abstracts the underlying physical infrastructure and allows enterprises to scale, configure, and optimize network resources without directly owning or managing hardware. Enterprise sentiment toward NaaS remains mixed, 32% said they could make it easier or cheaper for a service provider to manage their networks and security, and 26% said they could simplify self-managed network operations. But 19% said they would not want to be locked into one service provider’s platform regardless of the benefits, and 10% remain unfamiliar with NaaS entirely. The education gap is significant and closing it will require more than technical refinement. It demands commercial clarity, stronger communication, and deeper customer relationships. Ultimately, this is not just a transformation in network architecture. It is a transformation in trust, positioning, and perceived value.

The bottom line

The IDC Telco Forum 2026 in Barcelona surfaced a market that is, in many respects, more coherent in its direction than at any point in recent years, but also more demanding of execution discipline than most operators have yet demonstrated.

The opportunity in AI inferencing and sovereign infrastructure is real and structurally aligned with telcos’ natural positioning. The satellite-terrestrial convergence is creating a coverage differentiation story that was not available five years ago. The enterprise connectivity market is expanding, budget-rich, and hungry for strategic guidance. And 5G, finally maturing beyond its early-product phase, is approaching its platform moment.

But against each of these opportunities sits a structural challenge that must be addressed in parallel: legacy system complexity is limiting AI value extraction; autonomous network ambitions are outpacing organizational readiness; commercial and CX systems are still leaving significant value on the table; and enterprise perception of telcos’ breadth and quality of service lags behind the reality.

The telcos that will win this decade are those that treat these not as separate workstreams but as a single integrated transformation, one where the investment in networks, IT modernization, talent, customer experience, and ecosystem partnerships compounds into a durable competitive position. The window is open. The question, as always, is execution.

For more information on IDC’s telecom research, including the newly launched Satellite and NTN research program, contact your IDC account manager or drop your details in here.

Download a copy of the State of the Telco Market ebook here.

Masarra Mohamad - Senior Research Analyst, European 5G Enterprise Strategies - IDC

Masarra Mohamed is a senior research analyst specializing in analysing the connectivity and communications services markets, focusing on the changing networking requirements, trends, and competitive dynamics that support enterprises in their digital transformation. She explores how enterprise network strategies evolve to enable cloud, AI, and security.