Imagine being responsible for making sure market intelligence reaches the right decision-maker before a call gets made without it, at a global technology company that manufactures, delivers services, and funds serious R&D all at once. Now imagine doing it with a team of two. For years, that’s held up. It just doesn’t scale.

That’s the team the company’s Head of Information Management runs. Her group doesn’t produce research. It makes sure the research that already exists, inside the company and from partners like IDC, gets used. “We try and make information available and findable as easily and quickly as possible,” she said, “so that if somebody’s in a meeting, they can get to it quickly, or they can come to us and we can get to it quickly.”

Two people can stay close to maybe the top few hundred decision-makers in a company of that size. But everyone else is on their own. That is exactly the gap the company is trying to close with AI and tools like IDC Quanta.

A decade of using IDC to pressure-test decisions, not just source data

Long before AI entered the picture, one of the company’s regional strategy teams built a standing cadence with IDC analysts: a call every six weeks, with her team submitting the questions two days ahead so IDC could weigh in before the conversation even started.

That extra step mattered more than it sounds. “A lot of times what would come back is, ‘No, that’s not the questions you should be asking,'” she said. Her team would come in wanting data on one topic; the analysts would redirect them to a different one entirely. On numbers the company couldn’t break out itself, IDC analysts walked through the reasoning behind a breakdown, stress-tested the logic with the team, and told them plainly when their assumptions didn’t hold up.

It’s a pattern that shows up across the company’s engagement with IDC. The relationship delivers a second opinion sharp enough to change the question being asked, well beyond data on demand.

The real cost of AI answers nobody can verify

As general AI tools spread across the organization, a new problem showed up alongside the old one: getting an answer got faster, but trusting it got slower. When a leader asks for information on a topic, her team has watched colleagues turn to whatever open AI tool is on hand, then come back with a case to make.

“I have to waste time going in and looking at who the analyst is, where they work, who they work for, is it a credible firm,” she said. “I can’t just say no, it’s not credible. I have to explain why.” In one recent instance, the firm behind numbers a colleague had pulled couldn’t even spell the company’s own name correctly.

For her, that verification tax, the time spent fact-checking an AI’s sources instead of using its answer, is the exact problem IDC Quanta removes. Because IDC Quanta’s answers are drawn from IDC’s own verified research and cite the report behind every response, her team no longer has to relitigate credibility before anyone can act on what it says.

“

That’s where firms like IDC will really excel, being able to leverage the credibility you already have, so people don’t have to question whether they can trust the number.

— Head of Information Management, Anonymous

The other detail that stood out to her: what IDC Quanta does when the answer isn’t fully available. Where some AI research tools simply decline to answer a question outside a client’s subscription, IDC Quanta tells her what it can share and names the specific reports that would close the gap. “That to me is so much more helpful,” she said. “If we’re working on a key strategic topic, well then let’s buy those two reports, and use IDC Quanta to keep pulling the data out from there.”

A partner, not just a provider

The company sees AI extending its analyst relationships further than two people could ever reach on their own. When the company rolled out AI internally, the number of questions landing on her personally dropped by half, because AI could get people most of the way to an answer without waiting on her team. She expects IDC Quanta to do the same for her own workflow: fewer questions she has to chase down herself, and more time for the harder problems that still need her judgment.

What she’s watching for next is smaller and more practical: a tool that can recognize an outdated chart she drops in and pull the updated version automatically, so she spends less time hunting for the right version and more time using what she already knows is true.

For her, that’s the real value of the relationship. “I think that’s what you need to be,” she said. “A partner, and not just a provider.”

Christina Cardoza - Content Marketing Manager - IDC

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

At AMD Advancing AI 2026, held on July 23, 2026, at the Moscone Center in San Francisco, California, AMD presented a broad AI strategy that spans cloud infrastructure, enterprise AI, client computing, robotics, and embedded systems. The event brought together developers, customers, ecosystem partners, and enterprise leaders to discuss how AMD’s end-to-end AI portfolio is evolving from silicon through software.

Financial Momentum Supports the AI Strategy

The announcements came shortly before AMD reported its second-quarter 2026 financial results. The company delivered record revenue of $11.5 billion, representing 50% year-over-year growth, while datacenter revenue more than doubled compared to the prior year. AMD attributed this growth to accelerating demand for EPYC processors, expanding Instinct deployments, and increasing AI infrastructure investments across the market.

A Vision Beyond Silicon

While hardware announcements often dominate AI events, the broader message from Advancing AI 2026 was AMD’s effort to create a unified AI development experience. The company’s strategy aims to enable developers to build AI applications on local systems, optimize them with a common software framework, and deploy them consistently across enterprise, cloud, edge, and robotics environments using the same foundational platform.

Building an Open AI Software Foundation

A key pillar of AMD’s strategy is its continued investment in open software. AMD reported that more than three million Hugging Face models run out of the box on AMD platforms, while many of the industry’s leading open-source AI projects now offer native AMD support. The company also highlighted significant growth in open-source contributions and ecosystem partnerships as part of its software expansion efforts.

ROCm Evolves from Platform to Development Ecosystem

AMD’s ROCm platform has evolved from a GPU programming environment into a broader AI software stack that supports frameworks, libraries, developer tools, and optimization capabilities. AMD positions ROCm as the common software layer that enables portability across its AI hardware portfolio, from client systems through hyperscale infrastructure.

Introducing ROCm.ai

Among the notable software announcements was ROCm.ai, an AI-driven development platform designed to help developers build, optimize, and deploy GPU applications more efficiently. ROCm.ai enables popular coding assistants such as Claude, Codex, Cursor, and Gemini to understand AMD hardware architectures and ROCm workflows natively, helping reduce the learning curve associated with GPU optimization.

AI-Assisted Performance Optimization

AMD also introduced capabilities that leverage AI to improve application performance. Through technologies such as Hyperloom, ROCm.ai can establish performance baselines, profile workloads, identify bottlenecks, tune serving configurations, generate optimized GPU kernels, and validate performance improvements. This represents a shift toward AI-assisted software engineering where optimization becomes increasingly automated.

Extending AI Development to the Desktop

AMD’s vision extends beyond the datacenter. With the Ryzen AI Halo developer platform and upcoming systems powered by Ryzen AI Max PRO 400 Series processors, AMD aims to make advanced AI development accessible on desktop-class hardware. These systems provide developers with local environments capable of running increasingly sophisticated AI workloads while maintaining compatibility with larger AMD compute environments.

Ryzen AI Halo developer platform, source: AMD, 2026

Developing Locally, Scaling Globally

A notable aspect of AMD’s approach is software consistency across deployment environments. AMD presented ROCm.ai as supporting systems that range from AI PCs and workstations to rack-scale AI infrastructure and datacenters. This approach creates a workflow where models can be developed and tested on local hardware before being deployed at cloud scale using the same software foundation.

Enterprise AI and Hybrid Deployment

AMD also highlighted a collaboration with Cisco designed to combine AMD AI inference platforms, including Ryzen AI Halo systems, with Cisco networking, security, observability, and management capabilities. The goal is to help enterprises deploy, govern, and manage hybrid and local agentic AI deployments while maintaining operational consistency across environments.

Extending AI into Robotics and Physical Systems

Another major theme of the event was AMD’s expansion into Physical AI. As AI moves beyond digital environments into machines that perceive, reason, and act in the physical world, AMD is positioning its technology portfolio to address robotics, industrial automation, healthcare, and autonomous systems. AMD estimates that the Physical AI silicon market could reach $200 billion by 2035.

Introducing the Kria AI Portfolio

To address this opportunity, AMD introduced the Kria AI solutions portfolio, extending its presence from traditional robotics control systems into higher-level robot intelligence. AMD describes this as bringing together perception, reasoning, decision-making, and control capabilities on a unified platform designed for demanding robotic workloads.

Ryzen AI Embedded X100 Series

At the foundation of the portfolio is the new Ryzen AI Embedded X100 Series, which combines CPU, GPU, and NPU resources within a unified memory architecture. AMD positions these processors as platforms capable of supporting the simultaneous requirements of perception, reasoning, planning, orchestration, and real-time control that characterize modern Physical AI systems.

Ryzen AI Embedded X100 Series, source: AMD, 2026

The Kria AI Robotics Developer Platform

AMD also introduced the Kria AI Robotics Developer Platform, described as an open, turnkey robotics development environment that integrates CPU, GPU, NPU, and FPGA compute. The platform is intended to reduce development complexity while helping robotics developers move more quickly from concept and prototype to production deployment.

Kria AI Robotics Developer Platform, source: AMD, 2026

Bringing the Cloud Software Stack to the Edge

One strategically important aspect of AMD’s robotics announcements is software continuity. The new AMD Robotics Software Suite is built on ROCm, allowing developers to leverage familiar frameworks, tools, and workflows already used in cloud AI environments. AMD explicitly describes ROCm as both cloud-proven and embedded-ready, creating a common software layer between datacenter AI and Physical AI deployments.

Reducing Vendor Lock-In Through Open Standards

AMD repeatedly emphasized openness throughout its software and robotics announcements. The company highlighted support for open standards, familiar x86 development workflows, industry partnerships, and open-source technologies. In robotics specifically, AMD identified avoidance of vendor lock-in as a key priority among developers and positioned its software stack as an alternative built around portability and ecosystem flexibility.

Analyst Opinion and Conclusion

AMD’s Advancing AI 2026 announcements reflect a strategy focused on developer productivity, software consistency, and open ecosystem adoption. Rather than treating desktops, datacenters, edge systems, and robotics as separate markets, AMD is building a continuum in which applications can be developed locally, optimized through AI-assisted software tools, deployed at cloud scale, and extended into physical systems using a common architecture. This approach addresses a persistent industry challenge: the fragmentation between experimentation, production deployment, and edge execution.

Technical vision alone will not guarantee adoption, however. AMD continues to compete against an incumbent ecosystem built on years of developer investment, mature tooling, and deep integration with commercial AI applications, and shifting those established development priorities typically requires clear performance, cost, or business benefits. Pricing and total cost of ownership will matter as much as raw compute performance, particularly as enterprise buyers weigh software readiness, operational simplicity, and deployment timelines across the full lifecycle.

Success will likely depend less on hardware specifications than on ecosystem momentum: winning ISV certifications, application optimizations, framework support, and robotics middleware integrations may prove as important as introducing faster processors. The broader significance of Advancing AI 2026 is not that AMD is entering new AI segments, but that it is attempting to connect desktop, cloud, and physical AI through a common development model. Whether AMD becomes an increasingly influential platform provider across that continuum will depend on its ability to sustain software adoption and partner engagement alongside its hardware roadmap.

Mohamed Hefny

Mohamed Hefny - Senior Program Manager, Data and Analytics

Mohamed Hefny leads market research in EMEA on professional workstation PCs and solutions. He also reports on professional computing semiconductors, processors, and accelerators (CPUs and GPUs), as well as breakthroughs and trends related to the market. In addition, Mohamed is…
Malini Paul

Malini Paul - Senior Research Manager, Personal Computing Devices

Malini Paul is a research manager of Client Computing Devices team in Europe, with over 10 years of experience in the ICT industry. Currently based in London, she leads the Personal Client Computing Devices research program for Western Europe. She…

On July 28, 2026, the FCC quietly added “advanced robotic devices” to its Covered List, the roster of tech it has flagged as a national security risk, blocking new equipment authorizations for foreign-made robots. The headlines were about humanoids, the walking machines from Chinese vendors like Unitree. Within 24 hours, the story took a stranger turn: the ban also covers your vacuum.

Ban, or something narrower?

Most coverage calls this a “ban,” and we will too, because that’s the word everyone is searching for. But read the fine print and it is more of a chokepoint than a wall: already-certified models can keep shipping new units, not just running out what’s already sold, though the FCC has reserved the right to revoke that status later, there is a Conditional Approval pathway that lets vendors keep selling if they shift manufacturing to the US, and a long list of exclusions, road, rail, aircraft, underwater vehicles, medical and mobility devices, and fixed industrial arms, sit outside the rule entirely.

The FCC’s definition sweeps in any foreign-made mobile robot over roughly 4.4 pounds that senses its surroundings, moves along the ground, and connects wirelessly, a description that fits a humanoid, a quadruped, and a robot vacuum, lawnmower, or window cleaner equally well. Already-certified models keep shipping new units, not just running out what’s on shelves. It’s the next hardware refresh that’s cut off.

IDC is launching research into Physical AI across four layers of the ecosystem: Infrastructure, Software and Platforms, Services, and Devices. Robotics sits inside Devices, and today it is, by a wide margin, the biggest draw in that layer. That is why a rule written around “robots” ripples so far. The figures below cover the robot categories IDC tracks in depth, household cleaning, professional and commercial, and humanoid, not the entire global robotics market. We ran those numbers against IDC’s latest worst-case scenario, current as of August 2026, and the results flip a lot of the conventional wisdom about who is actually impacted by this restriction.

Where Robots are Consumed Worldwide 2026 chart - Shows China (21%) and US (18%)...the rest of the world (61%)

Source: IDC Worldwide Robotics Trackers, Q2 2026 (If Ban Stays Till 2030 Scenario). Household cleaning, professional and commercial, and humanoid robots combined, the categories IDC tracks in depth.

Within these tracked categories, Europe, not the US or China, is the largest robot market by consumption, at more than double America’s share. The US and China combined still do not outbuy the rest of the world here.

The US Hit, If the Restriction Holds

Put the US robot categories IDC tracks in depth on one chart, household cleaning, professional and commercial, and humanoid combined, and the picture is more forgiving than the headlines suggest: these categories still grow every year through 2030, just off a lower trajectory. The gap opens gradually, reaching about 4% below the published baseline in 2027 and 18% by 2030. On IDC’s baseline, this US total compounds at around 13% a year through 2030; under this scenario, that cools to 9%, a real deceleration, but nowhere close to the growth story stalling out.

Using IDC’s blended average selling price assumptions, that growth gap between the restriction scenario and IDC’s published baseline compounds into more than $6 billion in foregone US robotics revenue between 2026 and 2030, with the single worst year, 2030, alone accounting for above $2 billion of it.

That softer trajectory rests on a specific assumption: already certified models do not disappear. The FCC’s rule only blocks new equipment authorizations, so IDC’s scenario assumes overseas vendors keep selling models that cleared certification before the rule took effect, refreshing appearance, hardware configurations, and software features rather than filing for new authorizations. For household cleaning specifically, IDC expects those extended models to stay competitive at least through 2027, since US demand for the newest generation of features runs lower than in Europe or China, and expects vendors to lean harder on wet and dry vacuums, a category this rule does not touch, to offset lost robot vacuum volume while protecting their premium positioning. A sharper decline is still expected within two to three years once those extended product cycles run out.

Not Every Category Takes the Same Hit

The instinct is to assume this rule protects the humanoid and commercial robot builders it was written for, while consumer cleaning brands, already dominated by Chinese vendors, absorb the pain. The final numbers say the opposite. Look at each category’s own base case line against its own worst-case line, and the ones expected to benefit from this policy are the ones falling furthest behind where they would otherwise be:

The categories the Restriction was meant to protect fall furthest behind their own baseline chart.

Source: IDC Worldwide Robotics Trackers, 2026 (If Ban Stays Till 2030 Scenario). Each panel uses its own scale (millions versus thousands of units) to show the category’s own trajectory, not a comparison of absolute volume across categories.

Household cleaning is nearly the entire US volume across these tracked categories, and it is also the most resilient category in relative terms, trailing baseline by just 4% in 2027 and 18% by 2030. Professional and commercial robots fall further behind, from a 29% gap in 2027 to 43% by 2030. The same lifecycle extension approach applies here too, incremental hardware and software upgrades on already deployed fleets rather than new authorized models, but higher stakes deployments and shorter replacement cycles make it a thinner cushion than in household cleaning, though a commercial robot’s higher price tag makes a real US manufacturing commitment worth pursuing for some vendors under the FCC’s Conditional Approval process.

Humanoid robots, the category this restriction was ostensibly designed to protect, see the deepest cut of all: a 41% gap in 2027 widening to 58% by 2030. Look at the dollars and it is starker still: nearly four out of every five dollars of the category’s projected 2030 US revenue evaporates under the worst case. Volumes are still small, which is also why the percentage swings look so dramatic, so this is not a story about finished robots piling up at customs. It is about how dependent even vertically integrating US builders, Figure, Apptronik, Boston Dynamics, still are on China for the motors, actuators, batteries, and rare earth materials that go into every unit. Overseas humanoid vendors have the same shelf life extension playbook available, keeping already approved units in the field and iterating through incremental hardware, software, and AI upgrades, but it buys far less cover here: IDC does not expect US based humanoid builders to reach scaled mass production until the end of 2028, so there is no domestic backstop ready to absorb the growth that foreign models can no longer supply. IDC estimates China accounts for 82% of global humanoid shipments today. Until US builders reach that scale, whatever badge sits on the chassis, the bill of materials behind it still reads China.

Europe Absorbs the Redirect, Not China

The scenario does not just subtract US demand; it has to send that displaced hardware somewhere. IDC’s model nudges professional and commercial and humanoid forecasts higher across EMEA and other parts of the world, particularly the UK, France, Germany, and Eastern Europe, as Chinese vendors redirect resources toward markets open to them. China’s own domestic forecast barely moves. It is a story about Europe, already the largest buyer of these tracked robot categories, getting a little larger still.

What It Means for Vendors

Reshoring consumer robotics specifically is unlikely given thin margins; this accelerates a supply chain diversification trend already in motion rather than starting a new one. Tesla sourcing its own AI chips for Optimus across TSMC and Samsung’s Texas fab is one model other vendors may follow: hedge the component that carries the most geopolitical risk, chips today, magnets and actuators next, rather than moving final assembly to the US.

Expect that hedge to widen the gap inside devices rather than close it. Humanoid and commercial builders have the margin and the government backing to build out US supply for the parts that matter most. Household cleaning vendors do not have that room, so the category most exposed today stays exposed, even as it turns out to be the one best riding out the restriction. And expect more vendors trying to maintain a US foothold to pair with independent US software partners rather than their own bundled stack, since a software partner is far cheaper to Americanize than an entire supply chain. Tennant’s hardware running on Brain Corp’s independent BrainOS is one example of that model.

The Bottom Line

The vacuum detail is what makes this restriction legible to a general audience, a strange, sticky hook for a story about industrial policy. But it’s not the point, and neither is the word “ban.” The point is that a single trade rule, applied evenly on paper, lands completely unevenly in practice, and not in the direction most people assumed.

Household cleaning, already dominated by foreign brands and squarely in the crosshairs, shrugs this off best. Humanoid robotics, the category built with US flags on the marketing decks, takes the deepest percentage hit, because protecting the badge on the finished robot does nothing to protect the motors, actuators, and rare earths inside it. And the biggest beneficiary of the US-China standoff is not the US or China, it is Europe, already the largest buyer of these tracked robot categories, absorbing the redirected volume without a policy of its own.

So, what does the restriction really mean? Not that the US is closing its doors to robots, and not that China’s lead breaks, because China was never actually leading global consumption in the first place. It means the market is quietly sorting into two tracks: mass market categories that keep growing around the US, and higher value categories where the appearance of protection outpaces the reality of supply chain independence. The vendors and the policymakers who read that correctly get a head start on everyone still parsing what the FCC actually wrote.

Update: See how IDC’s modeling holds up against outside expert debate in our follow-up, FCC Robotics Ban: Checking the Expert Debate

Getting a Head Start of Your Own

Reading this restriction correctly is one edge. Having a full view of where Physical AI is headed next is another. Two ways to build on it:

Upcoming Webinar: How the US Ban is Reshaping the Robotics Market: Global Robotics Outlook
The July 2026 restriction is already reshaping who wins in robotics — and not in the direction most people expected. Join IDC live on October 14 (11 AM EDT | 5 PM CET) for the full 2026–2030 forecast across humanoid, autonomous mobile, and commercial service robots, plus the regional and competitive shifts this policy is setting in motion. Can’t make it live? Register anyway — the recording will be sent to you after.

Fragmented data. Blind forecasts. Competitive gaps.
If any of that sounds familiar, you’re not alone — it’s what every robotics manufacturer, systems integrator, and enterprise tech leader runs into once they start taking Physical AI seriously. IDC is the only research firm covering the full value chain, platforms and software, infrastructure, services, and devices, as one coordinated program. Explore the Physical AI Hub to see the whole picture, or Contact us to talk through what it means for you

Ryan Reith - Group Vice President, WW Device Trackers - IDC

Ryan Reith is the Group Vice President for IDC's Worldwide Device Tracker suite, which includes mobile phones, tablets, wearables, and most recently AR/VR. His teams research focuses on the quantitative aspects of the mobile device industry, including market sizing, forecasting, vendor market share analysis, and technology trends. His current responsibilities include engaging with mobile device OEMs, supply chain, distributors, and the financial industry to discuss market trends and forward looking analysis.

Navkendar Singh - Associate Vice President - IDC

Navkendar Singh is a Associate Vice President with IDC India, based in Gurgaon. His research domains encompass deep-dive research and insights in and around mobile devices, smart homes, PCs, tablets, wearables, and the printing market in India, Bangladesh, and Sri Lanka. He is also involved in building IDC's successful channel research programs for these domains at city and state levels. Navkendar also leads research related to analyzing the role of devices, emerging business engagement models, the impact of emerging technologies on devices, and emerging personas related to Future of Work.

Ask an AI vendor to prove its own security, and a good one will show you the architecture: where tenant isolation lives, what screens a file before it reaches the model, who verifies the rating. That’s the review IDC Quanta walked through in Five Questions to Ask Before You Trust an AI Vendor’s Security Claim.

There’s a second review most security teams skip. Every AI vendor runs on other vendors: cloud infrastructure, identity providers, monitoring tools, backup services. Their compliance claim is a chain, not a single link. If any vendor in that chain is weak, unverified, or simply unnamed, the vendor’s own SOC 2 report describes a foundation nobody actually inspected.

Four Questions for Your Vendor’s Vendor List

A security team doesn’t need a hundred-point audit to close this gap. Four questions do most of the work:

  • Can you name every vendor in your security stack, by domain?

  • Which certifications does each of those vendors carry, and can I verify them independently?

  • What happens if one of those vendors fails a certification renewal?

  • Who owns the relationship with each vendor, and how is that relationship reviewed?

A vendor with real architecture behind its claims answers all four without hesitation. A vendor that reframes the question (“we’re SOC 2 compliant, so this doesn’t apply”) is telling you it hasn’t looked.

What “Zero Trust” Actually Means Once You Name the Vendors

Most vendors describe their security stack as “zero trust” and stop there. That word does no work on its own. It becomes checkable the moment a vendor names who’s actually running each layer, and what’s actually at stake if one of them underperforms.

IDC Quanta’s stack, published in its Security Overview, names 24 vendors across 11 security domains: endpoint protection, identity and access, network security, monitoring, cloud security, and more. SentinelOne and JAMF handle endpoints. Entra ID and AWS IAM handle identity. Zscaler and Check Point sit in the network layer. Sumo Logic and 7AI, a 24/7 managed detection provider, staff the monitoring seat.

The Compliance Chain Nobody Audits

Here’s the harder question: does the vendor’s own infrastructure carry the certifications it claims to inherit? A vendor can be SOC 2 compliant on paper while running on infrastructure partners who aren’t, and the sales deck will never mention the difference.

IDC Quanta’s infrastructure runs on AWS (SOC 1, 2, 3, and ISO 27001), Azure (SOC 2 and ISO 27001, 27017, and 27018), and Snowflake (SOC 2 Type II, ISO 27001, and PCI DSS). The other tools in the stack, including Datadog, Grafana, and the identity and observability vendors, each carry their own SOC 2 certification. That’s the extended fourth-party layer, the vendors your vendor depends on, that most procurement checklists never reach. It asks whether that whole dependency chain is compliant too, with proof you can actually see, rather than stopping at the vendor’s own certificate.

Want to see more? Visit the IDC Quanta product page.

Where Quanta Stands

Quanta’s own answers are published at a public link, so a security team can verify them independently instead of taking this piece’s word for it. The 24-vendor, 11-domain stack is named in the Security Overview, along with the certification each infrastructure and tooling partner carries. BitSight rates Quanta at 800 out of 900 as of July 2026, an externally verified number rather than a self-reported one. The vendor relationships behind that score sit with the same security team that owns tenant isolation and the pen-test cadence covered in the first piece, so the fourth-party review and the first-party review are never separated internally.

See IDC Quanta for Yourself

Compliance chains are only as strong as their weakest, least-verified link. A security review that stops at the vendor’s own SOC 2 report has checked one link and called it a chain. Ask for the vendor list next. Quanta’s is posted at trust.idc.com, verifiable in the time it takes to read this sentence rather than the weeks a typical security questionnaire takes to clear.

Book a demo to have all your questions answered about IDC Quanta and the security that protects you and your data.

Ryan Smith - Content Marketing Director - IDC

Ryan Smith is the Director of Content Marketing at IDC, where he leads brand-level content and social media strategy, aligning research insights with compelling storytelling to engage technology decision-makers. With a background in both IT and marketing, Ryan brings a unique blend of technical understanding and creative strategy to his work. He’s also a seasoned storyteller, speaker, and podcast host who believes the right message, told the right way, can drive both trust and transformation.

依存を抱えた主権の獲得

2026年7月16日、Noetra株式会社とNVIDIAは、国産のマルチモーダル基盤モデル開発に向けた国家規模のAI計算基盤を立ち上げると発表しました。Noetraは、ソニーグループ、ソフトバンク、NEC、ホンダの4社を中核に、IT、製造、素材、建設、モビリティ、金融、通信など国内大手44社が出資する基盤モデル開発企業です。経済産業省の大型事業(FRONTiaプロジェクト)の下、総額一兆円で初年度は3,873億円が投じられ、フィジカルAI向けで世界初を謳う国家規模のAIインフラの整備を進めます。この中核となるNVIDIA Vera Rubin AIファクトリーは、国内の代表的なAI計算基盤ABCI 3.0と比べて理論上のAI性能(FP8)で少なくとも30倍を超え、2027年4月から構築を開始、2028年6月の稼働を予定しています。規模感を示すと、運用よりも構築への配分が大きい初年度投資額だけでも、2025年の国内AIインフラ支出額の半分を上回る規模に相当します(IDC Worldwide Quarterly AI Infrastructure Tracker 2026Q1 Release)。

この基盤で開発されるのは、フィジカルAI向けの基盤モデルです。そのフィジカルAIが現実世界と接するインターフェースの一つが、ロボティクスです。昨今はヒューマノイドが注目されがちですが、ITの観点で本質的なのは、多様なロボティクス技術やエッジをつなぎ、最適化し、そこから得たデータを判断へと束ねることにあります。そこには産業競争力に寄与する大きな機会が広がり、具体化はこの基盤で何を生み出せるかにかかっています。

この発表に対し「世界初・国産・オールジャパン」と讃えるのも、「一社への依存」と切り捨てるのも本質を外しています。本件では、日本はフィジカルAI基盤の開発における計算の主権を外部に預け、計算とアーキテクチャの最先端はNVIDIAが担います。一方、民間44社がNoetraへ薄く資本参加し、現場データと実証フィールドを持ち寄り、日本は現場データとモデルの所有権を持とうとしています。純然たる自立でも従属でもない、依存を抱えた主権の先に、日本の挑戦があります。ただし、技術の利用が一層の依存となる場合も考えられます。与えられた枠組みに乗るだけでなく、どのように関係を構築していけるかも日本に問われていきます。さらに、主権の配分よりも成否に関わる肝心な問いは、この体制で本当に使えるものが作れるかどうかにあります。

肝心なのは「主権」ではなく「実行」

そこで、ここから先は実行にあたって現実を見る必要があります。国産基盤モデルSarashinaの主導者が経営を担い、同様にPLaMoを開発したプリファードネットワークスの経営者が共同研究開発の統括責任者としてモデル開発を率い、同社エンジニアが出向して実働することで経営と技術統括の両輪を担います。日本で基盤モデルをスクラッチで作れる希少な人材が指揮系統の中枢に入ることで、技術的な実行力が裏打ちされます。

一方でリスクもこの体制にあります。この基盤は、44社が自社の現場データを持ち寄って初めて動きます。しかし、各社にとって現場データは機密情報であり差別化要素。これを競合と同じ器に預けるには、どこに保管し、誰がアクセスでき、どう守るかというデータ管理とセキュリティの枠組みが必須となります。さらに、これをクリアしてデータを預けられたとしても、各社が供出の見返りに自社の都合をモデルに求め始めれば、ある社は自社製品への最適化を、別の社は自社ドメインの優先を望み、基盤モデルは「誰にとっても最適でない汎用」へと薄まる可能性があります。多くの声を退けて基盤を一本に保つ規律があるかが問われます。

成否は「どのように仕上げられるか」

この事業を測る指標は、GPUの数でも、国費の額でも、主権の有無でもありません。44社の個別最適の要求を調整し、基盤を一本に保つプロダクトマネジメントの規律を持てるかどうかです。技術的な実行力という必要条件は満たされています。残るは十分条件、つまり出資者の声から開発を守るガバナンスです。ここでは44社という数よりも、この体制の重心が実質どこにあるかが開発の方向を左右します。

開発のロードマップは、2026年度から推論基盤モデル、2028年度にオムニモーダル基盤モデル、2030年度に実世界ネイティブAI、という三段階で構成されています。この事業に規律があるかどうかは、まず2026年度に着手する推論基盤モデルが「何を作らなかったか」に表れます。規律により絞り込めるか、すべてを抱え込み方向を見失うか。また、フィジカルAIの実運用にあたっては、人命に関わる場合など、現実世界での検証に長い時間を要します。この慎重さとAIそのものの速い変化とをどう両立させるのか。時間もまた問われています。

今回の発表により、国内無二のAI計算基盤が2028年6月に稼働します。日進月歩のAIにおいて決して短くはないそれまでの期間で問われるのは、この体制でどのように舵を握り、モデルを作り上げていけるかです。そして、この試みが名に値する価値を生むかどうかの分水嶺は、44社が本当に自社の中核データを差し出すかにあります。中核データの提供範囲が十分でなければ期待された効果は限定的になる可能性があります。

もっとも、優れたモデルができることと国家事業としての成否は別です。これは結局のところ、参加する各社が、自社の現場で活用の道を見出せるかにかかっています。関与に濃淡があるのは自然なことである一方、避けるべきは、自社にとっての位置づけを定められないまま、中途半端に担ぎ続けることです。その時間が大きな機会損失になる恐れがあります。自社での活かし方に明確な答えを出せた企業から、フィジカルAIの競争で確かな位置を占めていくとみています。

著者:加藤慎也 シニアリサーチマネージャー、AI and Automation – IDC Japan

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Shinya Kato - Senior Research Manager, AI and Automation - IDC Japan

Shinya Kato is a Senior Research Manager at IDC Japan and is responsible for the data analysis and forecasting team of Japan enterprise infrastructure market. He analyzes the impact of product technology, service offerings, and marketing strategies on enterprise infrastructure market and provides market forecasts, focusing on the domestic enterprise storage systems market. Through understanding technology adoption trends, he also provides insight into emerging devices such as flash, accelerators, and quantum computing. In addition to researching the HPC and AI infrastructure markets, he is also investigating new consumption models such as Hardware-as-a-Service, to help stimulate the market. Prior to joining IDC, he spent more than 10 years at Silicon Graphics, which was later acquired by HPE, where he held various domestic positions in sales, marketing, and business development. He has covered a wide range of businesses, from infrastructure hardware and container-based data center facilities to digital asset management, industrial virtual reality, and software for media & entertainment. He also served as a product manager for enterprise internet security software and appliances at the emerging vendor. He holds a Bachelor of Economics degree from Rikkyo University.

Ask a vendor if your data is safe and you’ll get a yes. Every vendor says yes. In IDC’s advisory conversations with enterprise security teams, that’s the pattern that comes up again and again: the review process collects reassurance. It rarely collects evidence.

Enterprises that get this right don’t stop at a compliance label. They check for automated evidence and audit trails. They look at model monitoring and explainability. And they weigh a vendor’s actual implementation track record: real deployments, real customers willing to go on record. That’s exactly the review your own IT and security team will run on any AI vendor before signing off, whether that vendor is IDC or anyone else.

The Review That Isn’t a Review

A typical vendor questionnaire asks:

  • Do you encrypt data at rest?

  • Do you have SOC 2?

  • Is there an incident response plan?

These are yes/no questions, and yes/no questions get yes/no answers, regardless of whether the underlying control actually holds up under pressure. A security review that can be passed with a checklist only proves one thing: someone filled out a form correctly.

What “Compliant” Actually Means Depends on Who’s Asking

SOC 2 Type I confirms controls exist on a given day. Type II confirms they held up over a period of months. Both show up as “SOC 2 compliant” on a sales page. Neither tells you whether tenant data can bleed across customer environments, whether prompt injection is screened before it reaches a model, or whether your data trains anything. Compliance frameworks are a floor, not a finding.

Download the IDC Quanta Security Brief before your IT Security asks for it.

Architecture Beats Attestation

The security teams that get this right stop asking whether a vendor is compliant and start asking to see it. Show me the encryption key management setup. Show me where tenant isolation is enforced: application-layer controls that keep one customer’s data from ever touching another’s, not just a policy written down on paper. An architecture diagram is harder to fake than a checkbox. Then ask what happens to an uploaded document in the sixty seconds before it reaches the model. Is it screened for prompt injection, meaning malicious instructions hidden inside the file itself, before the model ever sees it?

Five Questions That Change the Conversation

Five questions is a short list on purpose. Security teams don’t have time to run a hundred-point audit on every AI vendor pitching them this quarter.

  1. Where, specifically, is tenant isolation enforced?

  2. What happens to a file between upload and model ingestion?

  3. Is customer data used to train any model, yours or a third party’s?

  4. Who verifies your security rating, and how often?

  5. What’s your actual pen-test cadence, confirmed against the audit log rather than the sales deck?

Want to see IDC Quanta in action? Book a Demo now.

Where Quanta Stands on Those Five Questions

Your own IT and security team will ask us these same five questions before Quanta clears procurement, so we might as well answer them here. And yes, we’re aware of the obvious catch: IDC also owns Quanta, so treat this section exactly like we just told you to treat every vendor’s answers. Verify it. Every spec below is published at trust.idc.com and open for a security team to check.

  1. Tenant isolation is enforced at the application layer. Token-derived identity and SQL scoping keep one customer’s data invisible to every other Quanta user.

  2. Every file uploaded to Quanta passes malware scanning and prompt-injection detection before it ever reaches the model. That’s the same sixty-second window this piece just asked every vendor about.

  3. Customer data trains nothing. Not Quanta’s models, not a third party’s. That’s a permanent commitment, built into the platform rather than a setting anyone could quietly change.

  4. Who verifies the rating? BitSight does, continuously. Quanta scored 800 out of 900 as of July 2026. SOC 2 Type I is compliant today. SOC 2 Type II and ISO 27001:2022 are both actively in progress.

  5. The pen-test cadence is confirmed against the audit log: annual third-party testing plus continuous vulnerability scanning, backed by a 24-vendor, 11-domain zero-trust stack with a monitoring team watching around the clock.

The Path Forward

None of this requires a bigger budget or a longer questionnaire. It requires asking for evidence. Vendors with real architecture behind their claims will show you exactly where each control lives, Quanta included. Pointing back to the checklist is what’s left when there’s nothing else to show. to show.

If you want to run this exact review against Quanta, the specs, certs, and policies are self-serve at trust.idc.com.

Ryan Smith - Content Marketing Director - IDC

Ryan Smith is the Director of Content Marketing at IDC, where he leads brand-level content and social media strategy, aligning research insights with compelling storytelling to engage technology decision-makers. With a background in both IT and marketing, Ryan brings a unique blend of technical understanding and creative strategy to his work. He’s also a seasoned storyteller, speaker, and podcast host who believes the right message, told the right way, can drive both trust and transformation.

生成AIへの投資は、国内市場でも急速に広がっています。IDCの「Worldwide AI and Generative AI Spending Guide 2026V1(2026年2月発行)」によれば、国内の生成AI市場は2025年の約4,745億円から2029年には約3兆2,739億円へと、4年間で約6.9倍、年平均(CAGR)約62%のペースで急拡大する見通しです。そして生成AIが成熟するにつれて、その使われ方も変わっていきます。汎用的なチャット用途にとどまらず、業務の一つひとつの場面に合わせて個別に最適化され、現場に溶け込む形で活用されるようになります。

用途が広がるなかで、生成AIをパブリッククラウドではなく、自社が管理する環境で動かす——いわゆる「プライベートAI」で運用するケースも増加し、生成AIの市場においても無視できない領域となることが強く見込まれます。

なぜプライベートAIなのか? 企業が「コントロール」を選ぶ5つの理由

というのも、プライベートAIには、パブリッククラウド上の生成AIにはない強みがあるからです。すなわち、

  • 機密性:競争力の源泉であるデータを社外に出さずに済むこと
  • データソブリン:データの所在地が自社の管理下にあるためコンプライアンス対応が容易であること
  • カスタマイズ性:自社のデータに深く最適化できること
  • 規制適合性:AIの運用が業種ごとの規制に対応しやすいこと
  • コストコントロール:運用量やトークン量の爆発的増大によって懸念されるコストを自らコントロールしやすいこと

などが挙げられます。本稿では、この「プライベートAI」がなぜいま注目されるのかをIDCのデータを基に解説し、そして国内市場にどのような機会を生むのかを紐解いていきます。

ユーザーのIT支出が向かう先:産業・ユースケース別市場機会

IDCは、生成AIによる価値創出が、汎用的なアシスタント用途から、各業界固有のデータDNAに深く根差した業界特化型ユースケースへとシフトしていくと見ています。共通するテーマは、単なる補助にとどまらず自律的に行動するAIエージェントが、業界固有のデータと密接に統合されることで、単純な効率化を大きく超える価値を生み出すという点です。そこで、産業別のユースケースの今後について展望してみましょう。

製造業:設計図が経営リスクになるとき

製造業は、設計図・製造プロセス・規制対応情報といった膨大な非構造化データの宝庫です。たとえば、3D設計から部品表作成、サプライヤー交渉、発注までを複数のAIエージェントが連携実行する「設計・調達の自律ワークフロー」は3D設計、部品表(BOM)作成、サプライヤーとの交渉、発注を連鎖的に処理することで、リードタイムの劇的な短縮につながります。さらに、競争力の源泉である設計データは、社外のネットワークに乗せること自体がリスクとなり得ます。また、生産工程で得られる各種データも、内容によってはレイテンシの観点からは遠くのAIデータセンターでの処理では間に合わないケースが多く存在し、この点だけでもパブリッククラウドについてマイナスの評価を下さざるを得ない理由となります。

金融・保険:ハルシネーションが許されない領域

金融・保険は、業務の性格上正確性が絶対条件として要求されるうえ、厳格な規制下で正確性が問われるにもかかわらず、汎用AIではハルシネーション等の懸念が払拭しきれない領域です。実際、取引・市場・ニュースを並列監視する複数のエージェント群がリスクを即時に遮断するリアルタイム不正検知、AI投資顧問エージェントによる資産運用の高度化など、ユースケースは多岐にわたります。これらはいずれも「最も外に出せないリアルタイムの取引データ」を扱うものであり、プライベートAIの必然性が高い領域です。監視システムでは高い即応性が求められることや、機微な情報を保護するケースも多いのが特徴です。

医療:あらゆる中で最も機微なデータ

医療・ヘルスケアでは、症状・既往歴を対話形式で収集し、診療科の振り分けと緊急度の判定を自律実行するトリアージエージェント、ウェアラブルデバイス・遺伝子・生活習慣データから個別の医療プランを継続的に生成するパーソナル予防医療などが期待されます。いずれも患者の診療情報や遺伝子情報という極めて機微なデータを扱ううえ、医療情報の取り扱いに関する規制も厳格であることから、データを自社の管理下で完結させられるプライベートAIとの親和性は際立って高いと言えます。

政府・公共機関:義務としての主権

官公庁・パブリックセクターでは、行政文書や規定・ガイドラインをRAGで検索・応答させ、業務効率化と住民対応力の強化を狙う動きが目立ちます。『IDC FutureScape: Worldwide Security and Trust 2026 Predictions — Japan Implications』(IDC #JPJ53026425、2025年12月)での2029年までに政府の3分の1がソブリンAIを求めるという予測とも重なり、国内完結を前提とした案件が積み上がっていくものとみられます。また、市場規模は限定的であるものの、データの秘匿性や実運用時のネットワークからの独立性が最高レベルで求められる防衛領域においても、プライベートAIのユースケース拡大が見込まれます。

その他:あらゆる分野で高まるプライベートAIのユースケース

このほか、以下の業種などでもプライベートAIのユースケースが広がると考えられます。すなわち、

  • 物流・輸送:リアルタイムの交通・荷量・天候を統合した配車エージェントによる完全自律ルート最適化や、需要・コスト・人口動態を自律分析して新たな配送モデルを編成するラストワンマイル新サービス
  • 小売・EC:購買体験のパーソナライズや需要予測と連動した自動発注、価格・プロモーション戦略の自律最適化
  • 教育:AIチューターによる個別学習設計や採点・フィードバックの自動化など

などが挙げられ、いずれも産業特化型のユースケースとして立ち上がりつつあります。

これらは扱うデータの機微性・要求される保護レベルに応じてパブリックとの使い分けが進むと見られますが、企業固有のデータを競争力の源泉とする度合いが高まるほど、プライベートAIを選ぶ合理性も増していきます。

いずれにも共通するのは、これらの分野・ユースケースがIDCの言う「産業ユースケース」、すなわち産業の特性に由来する独自データと深く結び付いているという点です。汎用的な生産性向上タスクならパブリックで十分であるとしても、上記のように産業分野それぞれに最適化されたAIが日常業務の現場に展開されてゆく状況にあっては、自社データの利活用が今後のAI導入の決定的要因となると言っても過言ではないでしょう。そして、これは企業・組織が扱うデータについて主体的な権利と行動をとることの上に成立します。このデータへの主体的権利と行動はデータソブリンの確立と言い換えることができます。だからこそ、データソブリンに基づいて行動することが、AIをより業務に即した場面に導入し、成功と成長を実現するための必要条件となってくるのです。

データ主権は単なるコンプライアンスではなく、それ自体が需要を生み出す

そして、このデータソブリンの領域については、具体的業務へのAIの導入という観点から捉えなおすと、国内でのビジネスに強みと豊富な経験を持ち、素早い対応が可能なベンダーに市場機会が期待されることが展望できるでしょう。というのも、データソブリンを要請する理由は業務上の必然性はもちろんのこと、IT主権・運用主権・規制対応・地政学リスクといった、個別のビジネスとは別の力学で動く要素にも由来しており、この点については国内での顧客との長きにわたる関係が経験知として蓄積されていることが大きな意味を持つからです。

そして、IDCでは、2027年までに日本のトップ1000企業の60%がAI主権の確保を追求し、非パブリックのホスティング、オープン技術、地域パートナーを組み合わせると予測しています。『2026年 国内AIテクノロジー利用動向調査』(IDC #JPJ53497126、2026年4月)でも、(データソブリンに立脚する)ソブリンAIを重要課題/検討課題とする企業においては、最大の関心事は「データ所有権の担保」であることが分かっています。また、前述の通り、2029年までに政府の3分の1が機密分野でソブリンAIを求めるとも見ています。このようなことから、ソブリンAIを前提とし、国内完結が要件になる業種・ユースケースという観点からも、業種ごとの知識や規制動向に関して現場に由来する知識とノウハウを多く有する、すなわち国内市場に強みを持つベンダーに市場機会があると言えます。

自社の強みを認識した上でのスコープ設定を

その意味では、自社の強みと弱みを整理し、強みの分野で収益性を高める判断も必要です。しかしながら、不都合な現実から目を背けるわけにはいきません。生成AIのモデルそのものの開発力あるいは性能やGPUの調達力では、現在世界で高いシェアを有しているハイパースケーラーやプラットフォーマーとの正面からの競争は、「規模の戦い」に陥るため、好ましい結果をもたらすとは言えないでしょう。

だからこそ、戦い方を変え、オープンウェイトモデルを土台に据えることも視野に入れ、日本語処理という自国の強みを乗せ、パートナーと共にデータソブリンが最大の価値を発揮する領域を固めることが求められます。また、RAGの構築、日本語特化モデルの実装、国内のビジネスに即したガバナンスやオブザーバビリティの運用、液冷対応のマネージドサービス——これらはいずれも、プライベートAIならではの市場機会であり、ユーザーの個別の現実に沿った価値提供が可能な領域であると言えるでしょう。

当然ながら、ハイパースケーラーやOEMのエコシステムに乗るほど、ベンダーロックインのリスクも高まるという事実=落とし穴もあります。自らの強みを認識して、どのレイヤーを自社で握り、どこは割り切って組むのか。その線引きの巧拙が、そのまま競争優位に直結し、勝者とその他のグループを分ける分岐点となることは言うまでもないでしょう。

規模のパブリッククラウド、鋭さのプライベートAI

プライベートAIは確かに魅力的な市場です。ただし、それは広大なAI市場の一部でしかないことを意識する必要があり、この市場の成長性とユースケースの拡大のみを見ていると理解を誤りかねません。すなわち、当面の間はパブリックAIが主流であることをわすれてはならないのです。

ここで、生成AI市場の支出が実際にどこで発生しているのか、IDCのWorldwide AI and Generative AI Spending Guideに基づき、その構図を整理しておきましょう。

国内の生成AI支出について、ソフトウェア関連の支出をデプロイ先で分けると、プライベートAIのインフラとして重要なオンプレミス(専有環境を含む)が占める比率は2024年でおよそ27%、残りの約73%はパブリッククラウドです。しかも予測期間の終盤(2029年)に向けて、オンプレミスの比率はむしろ22%へと微減し、パブリッククラウドは78%まで高まります。

もちろん、シェアがすべてではありませんし、オンプレミスの伸びが鈍いわけではありません。推定支出額自体は5年で約27倍という猛烈な成長です。これは成熟化が進むIT市場の中でも桁外れの成長性を持つ分野です。言い換えれば、プライベートAIはパブリッククラウドを追い抜く必要はなく、この成長率で複利的に拡大を続けさえすれば、それ自体で非常に大きく、収益性の期待できる市場となることが見込まれるのです。そして、この高成長を後押しする要因――ソブリンAIの必須要件化、データ所有権要件、規制圧力――は、循環的でも選択的でもなく、構造的なものである。だからこそ、プライベートAIはベンダーがそのシェアのゆえに退けるべきではない重要なセグメントであると言えます

追い風の中、逆風を理解してこその戦略

AIユースケースの適用範囲が拡大し、それに加えて政府や規制産業全体でソブリンAIの必須要件が進みつつあることが、プライベートAIを本格的な規模の市場へと押し上げる真の原動力となっています。そして、この追い風は強く、かつ構造的なものでもあります。しかし同時に、逆風も直視しておく必要もあります。『IDC FutureScape: Worldwide AI-Fueled Business Strategies 2026 Predictions — Japan Implications』(IDC #JPJ53025425、2025年12月)では、2028年までに国内多国籍企業の70%が地域ごとにAIスタックを分割し、統合コストが3倍に膨らむと予測しています。また、メモリの国際的な需給逼迫や、AI対応データセンターのキャパシティ不足も足かせになります。

では、この機会を掴むベンダーと掴めないベンダーを分けるものは何か。それは、次の4つの要素が揃っているかどうかであると考えられます。すなわち、

  • 実戦で鍛えられたソブリンAIの専門知識と運用ノウハウ
  • 包括的なポートフォリオ
  • 新たなパートナーエコシステム
  • AIをパイロットから本番運用へと導く、実効性のある本番実装を支える変革支援力

この4つをプライベートAIの市場で兼ね備えたベンダーが、次の成長局面でも成長の果実を手にすることができるでしょう。

これまで見てきたように、プライベートAIの市場では、国内市場に強みを持つベンダーが確信を持って次の一手を打てる立場にあります。残るは、市場全体を展望したうえでどれだけ速く動けるか。特に技術の進化が速いAIの領域において、勝負を決する最大の要素の一つは、この速度であることも忘れてはならないでしょう。

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。

出典(すべてIDC)

  • 『2026年 国内AIテクノロジー利用動向調査』(IDC #JPJ53497126、2026年4月)
  • 『2026年 国内AIインフラおよびAI向けITインフラサービス市場動向分析:推論が主導する競争軸の転換』(IDC #JPJ54233926、2026年3月)
  • 『2025年 国内クラウド市場 テクノロジー動向分析:エージェンティックAI時代のテクノロジースタック』(IDC #JPJ53018625、2025年10月)
  • 『IDC FutureScape: Worldwide AI and Automation 2026 Predictions — Japan Implications』(IDC #JPJ53019825、2025年12月)
  • 『IDC FutureScape: Worldwide AI-Fueled Business Strategies 2026 Predictions — Japan Implications』(IDC #JPJ53025425、2025年12月)
  • 『IDC FutureScape: Worldwide Security and Trust 2026 Predictions — Japan Implications』(IDC #JPJ53026425、2025年12月)
  • 『2025年 国内ユーザー企業調査:産業分野別デジタルビジネス展開動向と課題』(IDC #JPJ53025825、2025年7月)
  • 『2025年 国内金融IT市場動向調査:「モダナイゼーション」後の金融IT市場の展望』(IDC #JPJ53860825、2025年12月)
  • 『国内AI市場予測、2026年~2030年』(IDC #JPJ53498426、2026年6月)

参考データ(IDC)

  • Worldwide AI and Generative AI Spending Guide(IDC #IDC_P33198)
  • Worldwide Quarterly AI Infrastructure Tracker(IDC #IDC_P37251)

菅原 啓 (Akira Sugawara) - Research Manager, AI and Automation - IDC Japan

一貫してテック系リサーチ業界を歩み、20年以上にわたり技術動向からブランド分析まで幅広い領域をカバー。実務と調査の両面に基づく知見を強みとする。 2016年から2021年までIDC Japanに在籍し、スマートフォンやAR/VRなどのクライアントデバイス分野を担当。業界内におけるIDCのプレゼンス向上に貢献するとともに、新聞や雑誌など外部メディアへ市場データを提供。2021年から2025年にかけては日系大手ITベンダーにてマーケットインテリジェンス業務を主導し、AIや量子コンピューティングといった先進分野を中心に、ITサービス市場データの整備、新規プロダクトの市場性予測、競争・市場分析を行った。 2025年からは再びIDC JapanにてAI(生成AIを含む)および関連技術、およびそれらを活用したソリューションの市場動向を、ベンダーとユーザー双方の視点から分析を担当している。 【専門の分野/テーマ】 AI全般 ITサービス DX

This is the final post in Meet IDC Quanta, a short series showing what the product actually does, starting with the portal, then your inbox, and now when you’re already working in Claude.


Imagine you’re the account executive on a procurement software deal, and leadership on the buyer’s side just reached out: they want twenty minutes before the broader vendor review starts. Real conversation. No marketing slide deck, no long product tour. They’ll expect you to know their IT spending trajectory, where the competitive field is moving, and why now is the moment to act.

A fast reply won’t cut it here. You need an actual plan, reports pulled, trackers cross-referenced, findings stitched together. You could do that by hand, or you could open the AI tool sitting in front of you and ask it to build the whole thing.

One tool, every surface you’re already in

Most people assume IDC Quanta is a portal you visit. It’s actually a fabric: the same IDC intelligence, methodology, and research base, running everywhere you work. That’s Claude Chat, Cowork, and Code. It’s Claude for Chrome and the Microsoft Office add-ins, too. The deepest workflow integration lives in Cowork, which is where both scenarios below happen. One connection, live through Claude’s MCP integration (the open standard that lets Claude connect directly to outside data sources), with an IDC skill built in to handle the jobs sellers and buyers actually run.

You don’t switch tools to get IDC data into your work. You just ask.

Ask for the plan

Open Cowork and type it straight to Claude:

Build an account plan for a call with <your target account’s> procurement team, grounded in IDC’s data on their IT spending trajectory, the competitive landscape for procurement software, and any buying signals worth flagging.

Claude Cowork meets IDC Quanta MCP connector 1

Claude comes back with a couple of quick multiple-choice questions, like which vertical cut or which time horizon. Pick your answers, and it gets to work. A few minutes later, a fully structured account plan is sitting in a Word document. Spending trends are mapped out. Competitive positioning gets a clear read. Buying signals surface where they matter. The kind of document that used to take an analyst half a day now takes the length of your coffee.

Claude Cowork results from IDC Quanta Query

Watch it show its work

Here’s the part that actually matters. Open the document and you’ll notice something most AI tools never bother to do: it tells you which parts came from where. IDC-sourced data sits clearly apart from what Claude reasoned on top of it. Nothing blends together into an unlabeled wall of confident-sounding text.

That distinction is the whole point. When your VP asks where a number came from, you’re not guessing. You can point to the line and say exactly what it is: IDC research, or Claude’s synthesis. The document already told you.

Same intelligence, the other side of the table

Now flip seats. You’re the buyer, evaluating procurement software vendors, and you need an independent shortlist, not a vendor’s pitch deck dressed up as analysis.

Same tool, same Cowork window. Ask Claude:

Give me a shortlist of the top three options based on IDC’s worldwide procurement applications research, and generate an Excel evaluation scorecard I can share with my team.

Claude builds the scorecard from the same shortlist of the three vendors actually leading the field, sourced from IDC’s Worldwide Procurement Applications Market Shares, 2025 (IDC #US53723426, June 2026). The IDC-grounded data and Claude’s analysis are labeled separately again. Your team sees exactly what’s evidence and what’s reasoning, no matter which side of the deal they’re sitting on.

Everything You Need, in the Tab You’re Already In

Our first blog showed you a portal built for hard questions with real citations. Our second blog put that same intelligence in your inbox. This closes the series on the biggest move yet: IDC Quanta living inside the tools you use, Claude included, where the work happens. A seller and a buyer can each walk into the same negotiation better prepared, neither one leaving the screen in front of them.

If you’re already using Claude, the IDC Quanta connector is one prompt away. If you’re not yet a customer, book a demo and bring the account you’re working right now.

Ryan Smith - Content Marketing Director - IDC

Ryan Smith is the Director of Content Marketing at IDC, where he leads brand-level content and social media strategy, aligning research insights with compelling storytelling to engage technology decision-makers. With a background in both IT and marketing, Ryan brings a unique blend of technical understanding and creative strategy to his work. He’s also a seasoned storyteller, speaker, and podcast host who believes the right message, told the right way, can drive both trust and transformation.

At the IDC Quanta launch webinar, Joe Bradley, CTO at IDC, made the case for why trusting an AI-powered answer shouldn’t require faith; it should require an architecture you can actually inspect. You’ve heard “AI-powered” enough times this year that a healthy dose of skepticism is the right response. Fair. So instead of asking you to trust that IDC Quanta gets its answers right, here’s what Bradley says is actually happening under the hood when it does.

Bradley breaks it down into two layers. The first is an MCP server: essentially a pipe that gives Claude direct access to IDC’s data, the trackers, the forecasts, the market figures. It also carries instructions for how that data is structured and how to use it. The second is IDC’s own Claude plugin, which goes further, Bradley explains. It shapes how the AI reasons about that data, tells it what’s relevant for what purpose, and requires it to surface a source before handing over any answer. Put together, when someone asks a question, Claude isn’t searching a phrase in a database, Bradley says. It’s reasoning with IDC’s own methodology built into the process.

That’s the theory. Here’s what it looks like in practice.

The Moment It Earned Trust

In the product demo, Bradley walked through a fictional scenario: Marcus Chen, a Senior FP&A analyst at Vantix Security, is building a market forecast ahead of a CFO review. He is projecting 16% growth in a segment his company competes in. He asked IDC Quanta to check that number against an external benchmark, right inside Excel.

In under a minute, Quanta surfaced IDC’s actual forecast for that market: 12.1% growth through 2029, with the category decelerating to single digits in the later years. His model hadn’t caught up to where the market was actually headed. That’s the payoff of the architecture above. The answer arrived with its source attached.

Why Even Build an App

Technical buyers reasonably ask why IDC needs its own app when Claude and ChatGPT already exist. IDC isn’t positioning itself as a competitor to the assistants people already use daily. It’s building something with a narrower job.

The case for IDC Quanta comes down to control over how IDC’s own data gets handled and delivered. It exists because of what only a dedicated app can guarantee: a single place that collects everything relevant across an IDC relationship, a direct line to a live analyst when the automated answer isn’t enough, and data handling built on tenant isolation, enforced access controls, and audit logs that capture every user action. Which raises the next question technical buyers ask first.

Provenance You Can Check Yourself

Bradley’s last test for anyone skeptical of AI-generated answers is provenance. Can you verify where it actually came from? In a second demo, he showed IDC Quanta processing a strategy document sent over email, then breaking its answer down into cited data cuts, each one tied explicitly to the filters and definitions behind it.

Nothing here is asserted without a source attached, and nothing requires trusting the AI’s summary over the underlying data itself. That’s the actual answer to “how do you know it’s not confidently wrong”: you don’t have to take Quanta’s word for it. You can check.

See It Yourself

The architecture, the demo, and the sourcing are easier to evaluate firsthand than to take on faith. Request a demo, or talk to your IDC account team if you already have one.

Ryan Smith - Content Marketing Director - IDC

Ryan Smith is the Director of Content Marketing at IDC, where he leads brand-level content and social media strategy, aligning research insights with compelling storytelling to engage technology decision-makers. With a background in both IT and marketing, Ryan brings a unique blend of technical understanding and creative strategy to his work. He’s also a seasoned storyteller, speaker, and podcast host who believes the right message, told the right way, can drive both trust and transformation.

This is the second post in Meet IDC Quanta, a short series showing what the product actually does, starting with the portal, then your inbox, then wherever you’re already working in Claude.


It’s 4:47 PM. You’re prepping for tomorrow’s board meeting, and a question just landed that wasn’t on your list: how does your cloud infrastructure position compare to what’s shifted in the market over the last two quarters. You could open a portal, log in, run a search, filter by date, read three reports, and synthesize an answer. Or you could pull up your phone in the elevator.

Most executives don’t have a research workflow. They have an inbox. IDC Quanta was built on that premise: the fastest tool for a time-pressed leader is the one they never have to open, because it’s already open.

What it actually does

IDC Quanta in email lets you ask a question and get a sourced IDC answer back in about 60 seconds. It works the way you’d already expect email to work, which is exactly the point.

  1. Compose an email to brief@quanta.idc.com, from your phone, your laptop, whatever’s in front of you.

  2. Ask the question in plain language. No query syntax, no keyword tricks. “Which cloud infrastructure vendors gained share in EMEA in the past 12 months?” is a complete request.

  3. Send it, and keep moving. A structured, sourced answer arrives in about 60 seconds, fast enough that you can send the question walking into a meeting and have the answer before you sit down.

  4. The reply carries its own receipts. Every claim in the answer traces to a specific IDC source. If you want to go deeper, “View in IDC Quanta” drops you straight into the full portal conversation, context intact.

No login screen to remember, no new app for IT to vet. And no tab-switching mid-meeting. The intelligence comes to the inbox. The inbox doesn’t change to accommodate it.

Where this earns its keep

The mechanic is simple. What makes it valuable is what it replaces across a week of an executive’s actual work:

  • Before the call.
    A prospect meeting is in twenty minutes and you need a current read on their competitive positioning. Email the question on your way to the conference room. The answer’s there before you sit down, cited and ready to use.

  • Benchmarking your own thinking.
    Attach a competitive deck or account plan to your email along with your question. IDC Quanta reads it alongside its own research base and flags where your internal view and IDC’s tracked data disagree. You’re getting your own analysis checked against the numbers.

  • The follow-up nobody has time to chase.
    Someone asks a sharp question in a meeting and the honest answer is “let me get back to you.” Now that follow-up takes one email and about a minute. Who else on your team wishes they had that?

  • Board and investor prep, compressed.
    The night-before scramble for one more data point doesn’t need the whole deck reopened. One email, one sourced answer, dropped straight into the slide.

Why this isn’t just a fast AI reply

A lot of tools will answer an email question with confidence. Confidence isn’t the same as being right, and it isn’t the same as being defensible in a room full of people who will ask where the number came from. Every IDC Quanta answer draws on IDC’s research base: 1,000+ analysts across 100+ countries, tracking 15B+ data points and 800K+ companies annually. That’s the citation trail behind every answer. It’s what makes an answer dropped in your inbox worth repeating in the room.

Get it in your inbox

If you’re already an IDC Quanta customer, brief@quanta.idc.com is live. Send the question you didn’t have time to research properly and see how fast a sourced answer actually moves.

If you’re not yet a customer, request a demo, and we’ll show you what it looks like when your inbox starts acting like a research team.

Ryan Smith - Content Marketing Director - IDC

Ryan Smith is the Director of Content Marketing at IDC, where he leads brand-level content and social media strategy, aligning research insights with compelling storytelling to engage technology decision-makers. With a background in both IT and marketing, Ryan brings a unique blend of technical understanding and creative strategy to his work. He’s also a seasoned storyteller, speaker, and podcast host who believes the right message, told the right way, can drive both trust and transformation.