IDC Quanta launched this year with over 175 customers across the beta program: CIOs, analyst relations and communications leaders, and technology strategists who spent months telling IDC exactly what they needed.

That’s not the usual way an intelligence platform gets built. Most AI tools ship first and adjust later. IDC Quanta did the opposite, and the difference shows up in how customers describe using it.

Months of beta testing

Nick Gartner, Director of Digital Product Management at IDC, led the beta program from the ground up, handling recruiting, communications, reporting, and every round of testing in between. He described a deliberate, expanding process that ran all the way through to general availability.

Each round fed directly into the next. The goal, Gartner explained, was never to build something because it followed a trend. It was to build something people would use every day, one that made IDC’s research easier to reach and easier to act on.

That approach is baked into how IDC Quanta works today. It’s described as the technology intelligence fabric of the AI-enabled enterprise: IDC’s structured, sourced, and defensible research, built for over 60 years, now living inside the tools where work already happens instead of requiring one more portal and one more login.

What customers kept naming: Time

Across every round of beta feedback, one theme surfaced more than any other: with more than 1,000 analysts publishing continuously, it often took a lot of time to find and digest the exact data and research customers needed. Customers said they were regularly spending a week or two working through lengthy reports just to get to the specific insight they were looking for.

Gartner put it plainly: people needed to get to trusted intelligence faster, and IDC Quanta was built to close that gap. Instead of just retrieving research, it acts more like an always-available IDC consultant. Customers can question it, reason through options with it, and even upload their own business documents for a side-by-side comparison against IDC’s data.

One beta customer, Mark Terranova of Kyndryl, told IDC that work which used to take weeks now takes minutes. Beta customers across industries, from technology providers to hospitality, said something similar: the same research now takes hours instead of the week or two it used to consume.

A platform build only on verified data

That speed opened up room for a wider set of use cases. Ask customers what they use IDC Quanta for and the answers span the map: competitive intelligence, vendor landscape analysis, market sizing, technology adoption research, and benchmarking their own internal documents against IDC’s data.

IDC Quanta doesn’t search the open web, and it doesn’t learn from anything customers upload. Every response is grounded in verified IDC research, and every answer can be traced back to its source. For leaders who need to defend a recommendation in a boardroom, that traceability is the reason they can act on what the platform tells them.

Phillip Langeberg, CTO of The Resorts Companies, Inc., described that confidence directly:

“An AI platform backed by the knowledge and research of IDC gives me a lot more confidence in the answers provided.”

Eric Walk, VP of AI and Data Platform Services at Perficient, framed it as the entire point of the tool:

“The ability to ask a question and get an insight that’s backed by specific, referenceable research is really the key to the tool.”

And for some customers, the value went beyond confidence: it changed how they thought about what was even possible. Ashley Spicer, CIO of Amarok, put it this way:

“We are getting ridiculous, previously unimaginable value from IDC Quanta. If you had told me 20 years ago that I would have access to something like this in my lifetime, I would have said no way.”

Watching the reaction in real time

Gartner said the moment IDC Quanta’s value became undeniable wasn’t in a spreadsheet of feedback scores. It was in a room. He demoed the platform live to IDC’s CIO Executive Council Advisory Board and again to attendees at IDC’s CIO event in New York, walking senior IT leaders through their own real-world use cases in real time.

The reaction, he said, was immediate: people started using it and simply kept going. After using the platform, Jolene Peixoto, VP of Corporate Communications at RELEX, described it as:

“One of the most intuitive and useful AI tools I’ve seen across analyst research portals.”

A better way to work

It would be easy to describe IDC Quanta purely as a faster way to get answers. That undersells it. The deeper shift is in how people work day to day, with IDC’s research embedded directly in email, in Anthropic’s Claude, and in the custom AI tools and workflows enterprises are already building, with no new login and no new habit to learn.

That’s also where the human side of IDC hasn’t gone anywhere. Every session includes a direct path to contact an IDC analyst, something most AI research platforms skip entirely. When a customer wants to go deeper than an AI-generated answer, they can schedule time with the expert who knows the subject, based on the exact data they were just looking at.

What happens now that it’s live

Gartner was clear that the listening doesn’t stop at general availability. IDC built IDC Quanta on months of direct customer feedback, and every future feature will start the same way, with customers telling IDC’s product team what to build next.

For teams still evaluating whether an AI research platform can be trusted, that’s the difference these customers kept coming back to: not the AI itself, but what’s behind it. What’s behind it is 60 years of verified, sourced IDC intelligence, already showing up in customers’ inboxes and inside Claude.

Want the full story straight from the person who ran the beta program? Watch our complete conversation with Nick Gartner on the IDC Quanta beta, launch, and what’s next.

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.

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:

On-Demand Webinar: The Physical AI Era: Robots as Intelligent Partners
Robots are moving from single-task tools to genuine operational partners. Watch IDC’s on-demand session to see what that shift means for your next move in Physical AI. Recorded before the July 2026 FCC robotics restriction; some market projections may not reflect this policy change.

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.

Your adtech spider-sense should be tingling. BMW spent 2023 promising owners a private dashboard, then spent this July breaking that promise in front of all of them.

A broken promise, and the ambient advertising it wasn’t

Starting in late July, drivers in more than 70 countries turned on their vehicles to find a full-screen animation for Spider-Man: Brand New Day on the iDrive display, complete with its own soundtrack and matching ambient lighting. BMW framed it as a Festive App tie-in connected to a film in which two of its vehicles appear. Owners framed it as something closer to a violation. In December 2023, BMW’s own leadership had described the dashboard as “a private space” that would never carry commercials. That promise lasted two and a half years, until the Spider-Man rollout arrived this July.

The heated-seat subscription controversy from a few years earlier hadn’t helped. BMW had asked owners to pay monthly for hardware already built into the car, backed off after a similar backlash, and never fully closed the door on the model. The Spider-Man rollout landed on the same nerve, and it’s worth asking why BMW keeps finding this particular one.

Here’s where the story gets more interesting than the outrage suggests. IDC’s FutureScape 2026 Chief Marketing Officer predictions include a forecast on exactly this category of screen.

Screens everywhere are becoming ad-eligible surface area, and vehicles were always going to enter that picture eventually.

Read the prediction closely, though, and the BMW campaign doesn’t actually match it. Ambient intelligence works because it responds to context: a shelf tag that adjusts price by time of day, or a store display that reacts to who’s walking past. What BMW pushed was the opposite of ambient. One static animation went to every eligible car across 70 countries regardless of driver, location, or moment, landing on a screen BMW’s own leadership had promised would stay ad-free. The tie-in itself was ordinary marketing. Automakers do this constantly when their vehicles appear on screen. What made this one land as an interruption rather than an ad was the missing context: no signal telling BMW which car, which driver, or which moment actually called for it.

IDC’s consumer research backs this up. In our Pre- and Post-Purchase Promotional Demands study, 64% of consumers say ads have no or limited influence on their decisions, but 52% would click a shoppable connected TV ad when it’s actually targeted to them. The same study found that a brand’s guarantee not to resell shared data ranks among the top incentives for engagement, which is just a data-era version of the same private-space promise BMW made and broke: people let a brand into something personal only when they trust exactly what happens with it next.

IDC has been flagging this exact risk in guidance to technology buyers for nearly a year: brands need to work through real privacy and experience concerns around personalized advertising, or risk this kind of blowback.

What has to be true before ads belong in the car

The dashboard won’t stay ad-free forever, but two conditions have to be met before that shift is earned. One is economic. Streaming’s free tier, ad-supported search, and a discounted Kindle all run on a bargain consumers understand going in: pay less, or pay nothing, and ads come with it. No such bargain exists yet in the automotive category. A BMW owner paid full price, in some cases more than $100,000, for a vehicle sold on an explicit promise that the interior stayed private, so paid still means ad-free by default until automakers offer something else, an ad-supported tier or a discounted lease in exchange for opted-in content. The other condition is attentional, and it’s a moving target. A driver at SAE Level 2 has no attention to spare for a screen, ad or otherwise. A passenger at Level 4 or 5, hands off entirely, is functionally riding in a moving living room, closer to a train passenger than a driver. As the fleet climbs that ladder over the next decade, the cabin becomes a legitimate leisure environment. Entertainment content still has to earn its place there, but the case against it gets harder to make with each level of autonomy the fleet adds.

Vehicle category matters just as much, and BMW picked close to the worst one to test this on. A luxury sedan sells exclusivity, the same reason a private villa doesn’t run ads on its own television. A family minivan on a road trip is a different proposition entirely: kids already watch movies on rear-seat screens, parents already pay for anything that buys 20 quiet minutes, and a timed movie promotion might land as a genuine perk rather than an intrusion. The same content, in the wrong vehicle segment, reads differently to the person watching it, which is exactly the mismatch BMW ran into by testing a Marvel tie-in on a luxury sedan lineup instead of the family vehicles where in-cabin entertainment already sells.

The industry already learned a version of this lesson in television. IDC’s research on the collapse of the upfronts found that premium content never automatically buys a premium audience; precision targeting does. BMW bet on the content, a splashy Marvel tie-in riding a halo launch, and skipped the targeting altogether. This is a common pattern among brand leaders who came up in a Mad Men-era playbook and haven’t fully adapted to data-driven, let alone agentic, automation methods.

What decides whether the next attempt lands is whether automakers match the format to the moment: the autonomy level, the vehicle category, and a business model the owner actually opted into. Skip any of those, and the next backlash won’t be about the ad. It will be about the same broken promise, running on whichever screen BMW decides to test next.

The brands that get this right over the next five years won’t be the ones with the splashiest tie-in. They’ll be the ones who bothered to ask whether the driver actually wanted to see it.

Roger Beharry Lall

Roger Beharry Lall - Research Director, Advertising Technologies and SMB Marketing Applications

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…

Worldwide tablet shipments fell to 33.6 million units in the second quarter of 2026, down 5.2% from the prior quarter and 12.3% from a year ago, according to IDC’s Worldwide Quarterly Personal Computing Device Tracker. That’s the category’s steepest year-over-year decline in this cycle. Almost the entire drop traces back to one root cause, but the more interesting story is what happens once that cause eventually clears.

RAMmageddon finally lands on tablets

The memory supercycle that’s been reshaping PCs and phones all year has now fully hit tablets, as AI infrastructure buildouts continue to pull DRAM and NAND capacity away from consumer devices. IDC’s own PCD Forecast Assumptions had already flagged this inflection point across two consecutive quarterly cycles, projecting shipment declines accelerating from 2026Q2 onward. This quarter confirms that thesis rather than surprising it.

The pass-through, based on vendor list-price changes IDC tracked, has been fast and visible. Samsung raised list prices $40 to $280 across nearly its entire Galaxy Tab lineup in April. Lenovo followed with $30 to $70 increases on the Tab One, Tab Plus, and Yoga Tab during the same month. Then Apple raised the entire iPad line globally in June, with multiple models costing an additional 20% or more, explicitly citing memory costs rather than tariffs or a hardware refresh. When your three biggest vendors all raise prices inside ten weeks of each other, consumer demand doesn’t shrug it off.

That’s exactly what happened: consumer shipments fell 13.5% year over year, while commercial held up far better at -5.9%. Commercial and education buyers are typically locked into multi-year refresh cycles that don’t get renegotiated quarter to quarter, so they absorbed less of the shock. Consumers, buying at list price with no contract to hide behind, took almost the entire hit.

The US got the worst of both worlds

No region suffered like the United States, where shipments collapsed 31.6% year over year (even as they rebounded 7.1% sequentially off a depressed base). The US is the one market where memory costs and tariffs land at the same time. One of the largest vendors, Amazon, also saw shipments decline in its tablet lineup, compounded by a difficult comparison against 2025Q2, when many brands pulled forward inventory ahead of anticipated tariffs. As tariffs continue to pinch the supply, brands such as Samsung and Lenovo are likely going to be in noticeably better shape thanks to production outside China.

Beyond the memory shock: a harder question

Here’s the part that should worry the category more than any single bad quarter. Even before RAMmageddon, tablets were fighting for relevance in a device market that’s rearranging itself around AI. AI PCs are absorbing the productivity use case tablets spent a decade trying to own. Phones keep getting bigger, and foldables, still a rounding error in unit terms, are growing fast and pulling casual browsing and media consumption further into the smartphone. Foldables are starting to serve as a drag on 8-to-10-inch tablet demand, and cheap mini-PCs and laptops are peeling off would-be buyers at the sub-$200 end. A memory shortage clears eventually. A device that’s lost its reason to exist doesn’t recover just because supply does, and this quarter is a reminder that tablets can’t just wait out the crunch. The vendors still winning share, mostly through productivity and enterprise deals, are the ones already treating that as the real problem.

Where the silver linings are

The data isn’t uniformly bleak, and the bright spots point toward where that case might be built:

  • Detachables are holding the line. Slate tablets fell 19.8% year over year, concentrated at the low end where price hikes bite hardest. Detachables fell only 6.1%, because that mix skews toward commercial and productivity buyers who are far less price-sensitive. The market’s mix shift, forced or not, is toward tablets that behave more like PCs.
  • Lenovo is the standout. Lenovo grew shipments 26.2% year over year, the best result among any major vendor, on the strength of its productivity-leaning lineup. Huawei (+9.1%) and OPPO (+10.5%) also grew.
  • Emerging markets are picking up slack. While the US and Western Europe (-13.3%) cratered, APeJC grew 2.6%, the Middle East and Africa edged up 0.7%, and Japan jumped 5.8% year over year. Chinese vendors, in particular, are pushing volume aggressively into these regions, helped by the fact that tablets carry a smaller share of memory in their bill of materials than smartphones do, giving them more room to defend share.
  • Scale is consolidating, not collapsing. The top five vendors (Apple, Samsung, Lenovo, Huawei, Xiaomi) now control roughly 79.8% of the market, up from 77.9% just one quarter earlier. Bigger vendors have the procurement leverage to secure memory allocation that smaller players simply can’t match, so the category is concentrating around the players best equipped to weather the storm.

What to watch next

The pace of these price increases is expected to ease in the coming quarters, but supply relief isn’t immediate, and tablets will likely keep riding out elevated prices for a while longer. The vendors who use that time to push harder on productivity, AI features, and enterprise deployments, rather than just waiting for cheaper memory, will set the terms for what a smaller tablet market looks like next year. The ones who don’t are betting that a supply cycle can fix a relevance problem. That’s a bet this quarter’s data doesn’t support.

Jitesh Ubrani

Jitesh Ubrani - Director, Consumer Devices Research

Jitesh Ubrani is a Director at IDC leading a team of analysts within the Worldwide Consumer Device Trackers group, covering wearables, augmented reality (AR), virtual reality (VR), tablets, phones, PCs, gaming, and smart home devices, with a focus on market…

二季度平板出货量同比下滑4.4%,跌幅看似收窄,但水面之下的结构变化比数字本身更值得关注。商用高增长与消费疲软形成鲜明反差,成本压力正从供应链末端加速传导至前端市场。本文将结合IDC最新季度跟踪报告,梳理几个关键信号。

国际数据公司(IDC)发布了2026年第二季度中国平板电脑市场季度跟踪报告。数据显示,2026年第二季度,中国平板电脑市场出货量为796万台,同比下降4.4%,降幅较第一季度小幅收窄。但从市场表现来看,市场压力并未缓解,反而进一步放大。商用市场的大幅增长主要来自涨价预期下的需求前置,而消费市场则在成本上涨、需求提前释放等因素影响下同比下降9.8%。表面上市场跌幅收窄,实际上成本上涨正在加速向需求端传导。

商用市场高增长背后,需求前置特征明显

2026年第二季度商用平板市场出货量同比增长67.1%,成为支撑整体市场的重要力量。但本轮增长更多来自厂商延迟涨价及价格持续上涨预期下的提前采购,企业客户和渠道商集中备货,推高了第二季度单季出货量。随着7月起商用市场开启全面涨价,后续渠道库存及交付节奏将成为影响第三季度市场表现的重要变量。

“618”难以抵消价格上涨影响,消费市场进入调整期

2026年第二季度消费平板市场出货量同比下降9.8%。尽管厂商和渠道积极布局“618大促”,通过新品和促销刺激需求,但价格上涨的影响已经明显超过促销带来的拉动。同时,去年国补推动需求提前释放,低价与涨价之间的鲜明对比也进一步抑制了消费者购买意愿。

成本上涨加速头部厂商分化,市场格局出现新变化

伴随低价备料逐步消耗,成本上涨对厂商的影响进一步显现。不同厂商的产品布局、渠道能力及应对涨价的节奏有所差异,本季度市场排名和表现也出现一定变化。

  • 华为继续位列中国平板电脑市场出货量第一位,整体表现相对稳健。消费端通过MatePad Pro Max完善高端产品布局,并深化学习场景合作;商用端则受益于渠道提前备货,出货量实现较快增长。
  • Apple位居市场第二。第二季度主要销售期仍维持原有价格,在涨价前形成一定价格优势,但受成本压力逐步传导影响,6月底Apple上调其平板产品售价,后续市场挑战将增加。
  • 联想出货量上升至市场第三,是本季度头部厂商中排名变化较为明显的厂商。拯救者、小新系列新品推动其消费产品结构升级,商用端凭借大客户市场积累及需求前置同样保持较快增长,共同带动整体出货量提升。
  • 小米排名市场第四位。尽管成本压力下平板出货量仍同比下降,但降幅有所收窄。“618大促”期间小米平板7系和8系促销获得较好销售反馈,同时Redmi K Pad 2和Redmi Pad 2 SE两款新品覆盖不同价位段,对市场形成一定刺激。
  • 荣耀位列市场第五。低端消费平板市场受到成本上涨冲击,出货量明显收缩;与此同时,教育行业渠道提前备货推动其商用平板延续高速增长,成为其当季平板市场表现的重要支撑。

整体来看,第二季度头部厂商的竞争已经不再单纯取决于产品和价格。在涨价的大环境下,厂商的布局结构、渠道能力及运营效率正在成为影响市场份额变化的重要因素。

后续展望:成本上涨延续,厂商寻求价位升级与需求平衡

成本上涨对平板市场的影响预计将在今年下半年进一步显现,并持续至明年。价格上涨将一定程度上延长消费者换机周期,厂商也可能通过调整新品上市节奏和产品结构应对成本压力,市场供需双方的变化将共同影响整体规模。

在成本压力持续的背景下,夯实2000-3000元主流价位段的产品竞争力是稳定市场规模的基础。尽管成本迫使平板售价不断上涨,大量消费者对平板的价值定位仍然锚定在2000-3000元档位,在这一区间的布局和提升是稳固出货量规模的关键。

引导需求向3000-5000元中高端价位段升级,将成为厂商改善产品结构、缓解成本压力的重要方向。相比单纯涨价,厂商更需要通过性能升级、使用场景拓展及AI能力增强,为中高端产品创造更充分的溢价空间。

长期来看,成本上涨或将成为平板市场的持续变量,厂商需要在成本传导、产品升级与消费承受能力之间寻找新的平衡。未来市场走势不仅取决于厂商能否通过产品创新支撑更高价格,也取决于消费者是否愿意为更高端的产品和新的使用场景支付更多。

IDC结论

2026年第二季度,中国平板市场出货量同比降幅有所收窄,但成本上涨带来的实际市场压力进一步放大。商用市场高增长更多体现为需求前置,消费市场则已明显受到价格上涨影响。未来,成本压力将推动市场结构升级,厂商能否通过产品创新和场景拓展引导消费升级,将成为实现规模与效益平衡的关键。

进一步交流:

本文数据及观点均源自IDC《中国平板电脑市场季度跟踪报告,2026Q2》。如需了解细分价位段、渠道库存、行业垂直场景等详细数据,或针对特定厂商策略进行定制化分析,敬请联络IDC中国终端系统研究团队。

Quorra Liu

Quorra Liu - Research Manager

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

写在之上的主权

2026年7月16日,Noetra Inc.与NVIDIA共同宣布,将启动一个国家级AI计算平台项目,用于开发日本自主的多模态基础模型。

Noetra是一家基础模型开发公司,由44家日本大型企业联合出资,涵盖IT、制造、材料、建筑、出行、金融、电信等多个行业,其中索尼集团、软银、NEC和本田扮演核心角色。该平台隶属于日本经济产业省主导的大型计划——FRONTia项目,总投资规模达1万亿日元,仅首年投入就高达3,873亿日元。平台被定位为全球首个面向“物理AI”的国家级AI基础设施。

其核心采用NVIDIA Vera Rubin AI Factory,理论AI性能(FP8)至少是日本现有顶尖AI计算平台ABCI 3.0的30倍。项目计划于2027年4月动工,2028年6月投入运营。做个直观对比:仅首年投资额,就已超过日本2025年国内AI基础设施市场整体规模的一半(数据来源:IDC《全球季度AI基础设施追踪报告》,2026年Q1)。这笔投入大概率集中在硬件建设上,而非后续的持续运营。

这里的关键词是“物理AI”,而最直接的落地场景就是机器人。FRONTia项目的全称——“面向AI机器人与物理AI的多模态基础模型开发”——已经把机器人写进了使命里,不是配角。就在Noetra发布消息的同周,发那科、安川电机、川崎重工等日本机器人及制造业巨头也承诺,将基于同一套开放模型栈(NVIDIA Cosmos、Isaac GR00T)进行开发。该平台的算力将帮助这些模型规模化,应用场景覆盖工业自动化、养老照护、手术辅助和零售。机器人和基础模型之间是相互成就的关系:机器人需要模型变得更智能,模型也需要从多样化的真实应用中获取反馈来迭代。根据IDC Robotics Tracker的数据,日本商用人形机器人市场预计将从2027年的142亿日元增长至2030年的470亿日元以上,出货量增长超过五倍。当然,人形机器人只是冰山一角,整个市场还包括工业机械臂、物流机器人,以及清洁、园艺等服务类机器人。可以说,这场国家级物理AI布局,本质上是对“机器人将成为日本下一个AI大市场”的一次战略押注。

消息一出,市场反应两极分化:有人盛赞它是“全球首创、自主可控、全日本协同”的壮举,也有人讥讽它是“对单一供应商的过度依赖”。但这两派可能都没说到点子上。日本把物理AI基础能力建设中的“算力主权”交给了外部伙伴——NVIDIA,因为后者确实掌握着最前沿的算力与架构。与此同时,44家民企各自持有Noetra少量股权,并贡献各自的实际数据和试验场,日本则希望掌握这些数据以及基于它们构建的模型。目前,日本既不是完全独立,也不是彻底受制于人——真正的难题还在后面。这种模式确实可能加深依赖,但日本并非只能被动接受,它仍有空间去塑造这种关系的走向。现在,比主权分配更关键的成败因素是:这套安排究竟能不能产出真正可用的成果。

关键不是主权,是执行

从执行层面看,曾主导日本本土基础模型Sarashina开发的负责人将出任本项目经营管理负责人,而PLaMo背后的Preferred Networks负责人将担任联合研发主管,并借调工程师参与实际开发。两者构成管理与技术的双支柱。日本国内屈指可数的、真正有从头构建基础模型经验的人,被放在了指挥链的核心位置,技术上算是有底子。

但风险也恰恰来自同一处。模型能做成什么样,前提是那44家企业愿意交出真金白银的现场数据。但对任何一家公司来说,现场数据都是核心机密,是差异化竞争力的来源。要让大家把数据和竞争对手放进同一个池子里,首先得建立一套完善的数据治理与安全机制——数据存在哪、谁能看、怎么保护,这些必须事先说清楚。即便跨过了信任这道坎,如果每家公司都要求模型优先照顾自己的需求——这家要针对自家产品做优化,那家要优先覆盖自己的业务领域——最后很可能搞出一个“对谁都不够好”的基础模型。关键在于,项目能不能始终坚持把基础模型作为一个统一整体来推进,有没有足够的战略定力。

最终成败,取决于执行质量

衡量这个项目成败的,不是GPU数量、公共资金规模,甚至不是是否“完全自主”,而是能否在保持统一基础模型方向的同时,协调好44家企业的个性化诉求,并且不被各自的最优需求带偏。技术执行能力是必要条件。剩下的关键是:一种能抵御外部干扰、保护项目长期发展的治理模式。相比“44”这个数字,真正影响走向的,是决策重心究竟落在哪。

开发路线图分为三步:2026财年推出推理基础模型,2028财年推出全模态基础模型,2030财年实现“真实世界原生AI”。这项计划有没有自律性,首先就看2026财年的推理基础模型阶段——它会选择“不做什么”。是能克制范围,还是一开始就铺得太开?此外,物理AI落地时,尤其涉及人身安全的场景,验证工作耗时漫长。怎么在AI本身的快速迭代和必要的谨慎之间找到平衡?怎么控制节奏?这些都是难题。

随着公告落地,日本国内首屈一指的AI计算平台将于2028年6月正式上线。而在此之前的这段等待期——对日新月异的AI行业来说绝非短暂——将重点考验平台如何主导模型构建。而整个项目能否产生真正的价值,分水岭就在于那44家企业是否真的愿意拿出核心数据。如果提供的数据范围不足,预期影响势必大打折扣。

但做一个好模型,和做成一个有实效的国家级项目,是两回事。归根结底,还得看每一家参与企业能不能在自己的领域真正用起来。虽说各家的参与度有深有浅是常情,但要避免的是,大家还没想清楚对自己意味着什么,就稀里糊涂地往前走。这种观望和犹豫,背后是巨大的机会成本。我们认为,只有那些能提前想明白“怎么用”的企业,才能在物理AI的竞争中站稳脚跟。

如需进一步了解IDC关于“基础设施如何成为AI发展底座”的洞察,以及未来十年将塑造日本市场的关键选择,请下载IDC Directions Tokyo相关演示资料。若希望了解您的企业如何顺应日本快速演进的AI基础设施格局,并在下一阶段变革中保持竞争力,请填写此表单,与IDC分析师交流。

本文作者:Shinya Kato —— IDC日本,AI与自动化高级研究经理

IDC推荐的相关资源:

Shinya Kato

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

Shinya Kato is a Senior Research Manager at IDC Japan, focusing on AI infrastructure and related computing technologies in the Japanese market. Even before the recent surge in AI demand, he had been tracking GPU as accelerators and has since…

中国企业出海用云早已不是”把服务器搬到海外”,而是一场关于全球经营能力的军备竞赛————某种意义上,它正在开辟中国企业全球化的“云上第二战场”,不亚于一场数字时代的诺曼底登陆。云厂商正在助其将全球经营能力、合规能力和AI能力同步迁移到海外数字底座上。国际数据公司(IDC)最新发布的《中国企业出海用云市场预测,2026—2029》显示:2025年中国企业出海用云市场规模达到52.3亿美元,同比增长31.0%,远高于中国企业的整体IT支出增速,以及中国本土云计算市场增速。到2029年,这一市场将增长至165.6亿美元,四年翻三倍。

但真正值得关注的,不是”蛋糕有多大”,而是”蛋糕怎么分、谁来分、根据什么分”。结合IDC近期对120家中国出海企业的用云情况调研,我们试图回答一个问题:在这场165.6亿美元的暗战里,赢家和输家将被如何决定?

出海用云的本质变了:从“买资源”到”买全球经营能力”

早期企业出海上云,核心诉求是IDC机房、服务器、带宽——先把业务跑起来再说,是”成本导向”的逻辑,谁便宜买谁。而如今进入深水区,企业关心的是全球业务能否稳定运营、快速复制、持续增长,用云逻辑已切换为”业务导向”:全球基础设施覆盖、网络优化、安全合规、数据治理、容灾备份,乃至数据库、AI、大数据等PaaS能力,一个都不能少。

调研数据佐证了这一点:44.2%的出海企业采用出口模式,42.5%设有海外办事处,41.7%已建立全资子公司——出海模式高度复合。这意味着市场需要的是覆盖部署、运维、合规和本地支持的一揽子方案,而不是只满足单一“建站”需求。

还停留在”卖资源”逻辑的云厂商,竞争优势将持续下滑。未来市场的增量,不只来自”更多企业开始采购海外云”,更来自”已出海的企业开始采购更深、更重、更长期的云能力”。客单价和黏性的密码,藏在能力深度里,而不是机架数量里。

多云是默认选项,中国云攥着“主钱包”,本地化服务能力是关键

IDC调研显示:84.2%的出海企业正在使用中国背景云厂商,同时77.5%的企业也在使用国际知名云厂商——两者并不互斥;61.7%的出海企业已经是多云用户,其中又有62.2%的企业将50%以上的云支出给了中国云;未来两年42.5%的中国企业海外用云支出将继续增加,54.2%的企业将优选中国背景云厂商,其次是国际云厂商。

再看购买决策因素,调研结果几乎颠覆了传统认知:中国企业海外用云选择服务商时,最高比例(53.3%)的企业优先考虑的因素是本地化服务能力,技术实力(49.2%)、服务稳定性(46.7%)紧随其后,而品牌知名度仅15.8%。

这是一个重要信号:在出海用云市场,”认知资产”让位于”可落地能力”。客户不为Logo付费,只为”你能不能在我的目标市场帮我把事办成”付费。品牌换不来订单,本地交付团队才换得来。

区域不是选择题,是战略布局题:东南亚要速度,中东非要规格,欧洲要合规,北美要本地化

从区域来看,2024-2029年中企出海用云市场规模增长引擎极其清晰:中东非将以40.0%的CAGR领跑,亚太区(36.8%)紧随其后,再次是欧洲(31.5%)和美洲(26.1%)。不同区域的云服务商优先选择偏好分化:东南亚、中东非优先选择中国背景云厂商,而北美、拉美优先选择国际云厂商,欧洲区域参半。

东南亚是最重要的增量市场。制造转移、跨境电商、消费互联网、金融科技在同一时期同步爆发,形成叠加效应。调研显示:出海东南亚的中国企业中,有53.7%的表示未来会增加云投入,57.4%的优先选择中国背景云,但它的关键词不是”容易做”,而是”碎片化”:多国家、多语言、多支付、多税制、多监管,云架构必须天生支持多地区、多币种、多合规配置。

中东非是”高规格、高投入的第二增长极”。主权资本和政府主导投资持续加码数据中心与AI基建,需求偏项目型——大项目多、订单大,但销售和交付高度依赖本地伙伴网络和认证体系。

欧洲则是”要求最高但粘性也强的市场”:数据隐私、数据驻留、消费者保护的要求全球顶尖,前期投入大、培育周期长,但一旦跨过合规门槛,客户稳定性、复购率和长期价值通常高于其他区域市场。

相比之下,北美对中企而言切入最难,54.4%的出海北美的中国企业未来优先选择国际云,难点不只在竞争,更在于监管与合规风险、品牌门槛和技术成熟度要求。

最大的黑马不是互联网,是制造业

论存量,互联网行业仍以20.6亿美元占据2024年出海用云的半壁江山。但论增速,制造业以43.2%的CAGR位居最高——到2029年,制造业出海用云规模将达46.2亿美元,逼近互联网行业的七成。

背后的逻辑是制造业出海正在经历从”卖设备”到建厂、建渠道、建服务网络的转型。汽车、新能源、动力电池、消费电子、智能硬件和IoT等制造型行业,正逐步成为中企出海云市场的关键增量来源。汽车与新能源产业链的全球化,带来车联网、远程诊断、全球供应链协同等复合型云需求;消费电子和IoT行业则通过设备接入、远程控制、数据分析和多语言运营,推动端云协同和后端服务价值提升。这类产业型需求比互联网需求释放更稳健、预算周期更长、客户黏性更强,是云厂商”长期支柱”所在。

终局变量:从“上云”到”上AI云”

AI是这份预测中最大的增量变量。随着大模型能力增强、推理成本下探,中企海外用云将从”计算、存储、网络为主”升级为以智能算力和AI平台能力为核心“,GPU资源、推理服务、向量数据库、MaaS的需求将在海外持续放量。从云+AI支撑的应用场景来看,AI多语言与本地化运营将是最先落地的创新场景。

165.6亿美元的市场里,不会有”躺赢者”。成本压力(61.7%的企业视其为最大挑战)与数据主权合规(70%的企业靠”选择具备当地合规资质的云服务商”降险)是两道硬约束,会持续把需求推向头部云厂商,未来五年纯资源售卖者出局,综合能力提供商上位。谁能把”本地化运营 + 合规治理 + 多云管理 + AI应用“打包成标准化、可复制的组合方案,谁就能在这场云上诺曼底里率先抢滩。

IDC 相关研究

围绕中企出海用云相关行业需求、典型案例、区域特点等,IDC 将持续开展系统性研究,包括但不限于:

  • 《中国企业出海用云市场预测,2026-2029》(DocC#CHC54275926,2026年5月)
  • 《中国泛互联网行业出海案例洞察,2026》(即将发布)
  • 《中国企业出海用云调研,2026》(即将发布)

进一步交流:

如需进一步了解中企出海用云相关研究内容,或咨询 IDC 在云计算、AI 基础设施及数字化转型领域的其他研究成果,欢迎与我们联系

Rachel Liu

Rachel Liu - Research Director

Rachel Liu is a research director for China’s Cloud and Services group. Her research covers public cloud, private cloud, edge cloud, industrial cloud, intelligent computing, and IT services. She is responsible for research plans, research execution and management, data tracking…

2026年全球基础大模型迭代进程显著提速,Tokens新经济也带动行业应用场景持续细分、落地需求持续深化,推动大模型开发平台进入能力升级与场景落地的关键迭代期。当前,头部大模型厂商已围绕高端推理、大规模部署、长上下文、智能体、代码工程、企业知识库集成等核心场景,搭建各类行业专有模型体系,大模型产业化应用正式从通用调用走向垂直深耕。在此行业背景下,大模型开发平台的产品核心价值与发展逻辑发生根本性转变,不再局限于单一模型封装与基础能力输出,而是朝着模型中立兼容、Agent智能化、安全合规落地、数据知识贯通、全流程运营赋能等方向全面演进。

近日,国际数据公司(IDC)特别针对中国市场发布了《IDC MarketScape:中国大模型开发平台厂商评估,2026》(Doc# CHC54109426,2026年8月)研究报告,基于市场参与者深度调研,平台厂商能够据此明确产品升级重点,打造差异化竞争优势。企业端可依此更新大模型开发平台选型规则,助力 AI 项目落地。产业从业者与投资者也可明晰赛道核心壁垒与发展走向。

基于对主流厂商的深度调研与横向对比,我们梳理出当前企业选型中必须重新审视的五个核心方向。这五个趋势将引领大模型开发平台发展新方向,是企业用户在实际落地中正在真实面对的决策分水岭。

趋势一:模型生态开放化——由单一模型调用走向模型中立生态与一体化场景交付

在大模型开发平台的模型选择/评估与应用适配功能上,头部厂商都完成了最新主流大模型的封装和多模态大模型集成生态以及行业模型适配,2026年,基础大模型迭代速度明显加快,全球市场的头部厂商构建了高端推理,大规模部署, API 密集型长上下文场景,Agent场景,代码/软件工程场景,企业知识库/应用集成场景,前沿模型等专有模型。针对于这些领域的模型调用,大模型开发平台厂商应保持模型中立 + 多模接入,主流平台的公有云、私有化部署版本都需兼容多家模型,避免厂商锁定。平台上的行业专有模型沉淀需要加速。

在工具链与应用开发支持方面,过去两年的市场中,买方选型的首要因素几乎都是价格,而且由于大模型迭代速度太快,买方通常选择直接调用大模型API,而不进行二次调优。但未来,企业将有更多垂直场景落地,这些场景离不开智能体的开发。因此,大模型开发平台在工具链上的完备性,在用户选型中的重要性将不断提高。从 “模型调用” 到 “场景交付” 需要构建覆盖数据治理→模型微调→RAG 知识库→Agent 编排→推理部署→监控运维一体化工具链,降低工程化门槛。也需要提供编码模式、和通过低代码 / 零代码可视化拖拽的模式让各类客户群体可以快速搭建 AI 应用。

趋势二:Agent 成为平台标配——依托强化学习打造自主任务执行体系

大模型开发平台Agent 相关功能成为标配:其一,用户市场需要基于开源模型的进行行业化知识增训,并追求训练后的模型效果。相比传统的增量训练和微调,市场对Agentic强化学习的能力需求增加,各厂商都在平台产品中推出基于大模型的强化学习增强能力。其二,很多头部供应商都在平台中逐步实现和智能体相关功能的深度打通,如Harness, MCP, Skills等, 原生提供上下文管理+记忆机制(支持 100K + 上下文,结合向量记忆与反思能力,实现复杂任务链式执行)、工具调用(原生对接数据库、API、代码解释器,形成 “感知 – 决策 – 执行 – 反馈” 闭环)等能力,适配复杂任务执行能力的Agent。

趋势三:安全体系升级——企业级管控完善,私有化部署转向常态化刚需

安全合规与私有化部署正在成为大模型开发平台落地企业场景的刚性需求。面向政企客户的数据风险诉求,平台需要完善全方位企业级安全管控能力,覆盖数据脱敏、分级权限管控、全链路审计日志、模型水印、可解释性(XAI)等多元能力,实现数据使用可追溯、模型行为可管控、知识产权可保护,有效化解大模型应用的数据泄露、模型滥用等风险。

行业层面,政务、金融、中央及国有企业出于数据主权与信息安全考量,普遍优先采用本地部署或者专属集群部署模式。这就要求大模型平台的私有化部署版本具备良好的软硬件适配能力,能够兼容国产本地芯片与自主操作系统。未来,单纯具备基础模型能力的平台将难以满足准入要求,完整的安全合规框架、成熟的私有化交付方案,会成为厂商获取政企项目的核心竞争门槛。

趋势四:数据链路革新——搭建端到端集成通道,打通知识检索与数据闭环

对于采用 RAG 的用户,在将来自企业内外部数据做成外挂知识库的过程中,需要选择专门的数据库来存储知识,这一数据库既可能是全新的向量数据库,也可能是具备向量引擎能力的传统数据库,未来也可能需支持多模态检索(图搜文、图搜图)以及支持 GraphRAG。另外,大部分企业还是存在不同程度的数据烟囱现象,数据分散、未标注、归集难、管理复杂是用户普遍会遇到的问题。建立端到端的自动化数据集成管道,减少跨数据存储库的数据集成工作,能够加快应用的上线。另外,针对于智能体数据反馈、数据回流、模型与数据集管理、规则干预等功能还需进一步优化。

趋势五:成本管理和运营服务演进——全周期模型运营赋能业务价值落地

2024年,中国企业级公有云MaaS市场按调用量统计的规模仅为114万亿Tokens,而到2025年,这一数字跃升至1944万亿Tokens,同比增长约16倍。在价格与成本方面,企业Token的日均消耗正在快速增长,尤其是各类Agent类产品的出现,进一步放大了Token的使用规模。随着规模化智能的到来,提供公有云版本厂商需要关注的不是单纯降低单位Token的价格,而需结合‌任务成功率、重试率、Token 消耗效率‌综合测算,场景适配度与总拥有成本(TCO)‌才是关键衡量指标 。‌‌

运营服务能力正逐步成为大模型开发平台公有云版本差异化竞争的重要组成部分。当前众多头部技术供应商持续完善平台的模型运营配套功能,形成覆盖 AI 应用全生命周期的运营支撑体系。相关能力涵盖多模型智能调度降本、统一管控合规审计(全链路调用追踪,智能告警防超支、精细化成本归因分析),同时支持提示词调优、业务工作流持续优化、合规风险评估以及落地效果与业务价值追踪等多元模块。

随着企业 AI 应用规模化推进,用户不再仅仅满足模型能够运行,更加关注资源可控、成本可视、效果可持续迭代。完善的运营服务可以帮助企业合理规划 Token 消耗,定位成本增长点,持续优化智能体与业务流程。未来,标准化、一体化的成本管理与模型运营工具,将降低企业持续运维 AI 应用的门槛,成为平台吸引政企客户、实现商业价值落地的重要加分项。

IDC给技术提供者的建议

建议一:平台需坚持模型中立化布局,兼容多家通用与行业专用大模型,公有云与私有化版本统一适配多模态模型,规避客户厂商锁定问题。加速沉淀各垂直行业模型资产,搭建从数据治理、模型微调、RAG 到 Agent 部署运维的全链路工具链,配套低代码开发组件,帮助企业告别单纯 API 调用模式,快速完成行业场景落地交付。

建议二:将 Agent 强化学习能力作为平台核心标配功能,面向行业开源模型提供定制化知识强化训练能力。打通 MCP、工具调用等智能体底层组件,搭载超长上下文记忆、任务反思模块,构建感知、决策、执行、反馈闭环架构。依托模块化智能体组件,支撑企业搭建复杂链式任务智能体,适配业务复杂流程自动化需求。

建议三:搭建一体化企业安全管控体系,落地数据脱敏、分级权限、操作审计、模型水印、可解释性模块,全方位管控数据与模型风险。打磨私有化部署方案,深度适配国产芯片与操作系统,满足央国企、政务、金融等领域数据要求。把合规架构与私有化交付作为核心竞标优势,突破政企项目准入壁垒。

建议四:适配向量数据库、GraphRAG、多模态检索知识库构建能力,解决企业知识库搭建痛点。打造端到端自动化数据集成管线,破除内部数据孤岛,简化多源数据归集流程。完善智能体数据回流、数据集管理、规则干预功能,形成数据采集、知识加工、推理使用、迭代优化的完整业务闭环,缩短 AI 应用上线周期。

建议五:转变定价逻辑,摒弃单纯低价策略,基于 Token 利用率、任务成功率等指标核算整体使用成本。上线全生命周期运营平台,实现用量监控、超支预警、成本溯源、合规审计一体化管理。配套提示词优化、业务迭代运营服务,帮助客户管控 Token 消耗,以精细化运营服务打造产品差异化竞争力。

IDC人工智能研究经理程荫表示 2026年基础大模型迭代提速带动Tokens经济火热,产业化应用向垂直场景深耕,大模型开发平台迎来全面升级。未来行业将呈现五大核心趋势:模型中立兼容与全链路场景交付、Agent智能化能力成标配、安全合规及私有化部署成为政企刚需、端到端数据知识集成持续完善,精细化成本分析与运营服务等,技术供应商需要根据市场需求保持创新,持续打造平台的差异化竞争力

进一步交流

大模型开发平台的选型窗口正在收窄——模型中立、Agent成熟度、安全合规、数据闭环和TCO管控,每一项都直接关系到未来三年的落地成本和业务弹性。这篇文章只揭示了五大趋势的轮廓,真正的决策需要结合企业自身的场景权重、数据基础和部署约束来综合判断。


IDC此次MarketScape评估已覆盖主流厂商的详细能力对比,如果您正在推进平台选型或预算规划,欢迎与我团队直接沟通,获取针对您具体场景的分析视角。 点击此处联系我们。

Anne Cheng

Anne Cheng - Research Manager

Anne Cheng is a research manager in IDC China whose research focuses on the AI and big data markets. She collaborates with IDC's regional and global consulting teams and is involved in the business development of related markets. Prior to…

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.

AI has moved from the edge of the technology agenda to the center of enterprise strategy. But the real story of 2026 is execution, and how few organizations have actually mastered it.

IDC has just published the IDC MaturityScape Benchmark: AI-Fueled Organization Worldwide, 2026, a study of 1,900 organizations across 20 markets. It measures how far enterprises have really progressed on AI across four dimensions, strategy, governance, people, and technology. The benchmark maps each to five stages of maturity, from an ad hoc “AI scramble” to the optimized “AI-fueled organization.” The picture it paints is sobering: aspiration is nearly universal, but maturity remains the exception.

Progress is real, but slow

Meanwhile, more than six in ten (61.3%) are still in the two least mature stages, ad hoc and opportunistic. The worldwide mean maturity score barely moved, edging up to 2.43 from 2.39 a year ago.

That near-flat result is the headline finding. The AI market is evolving at an extraordinary pace. Tooling, expectations, and competitive pressure are all escalating, but execution isn’t keeping up. The broad middle remains stuck in the early stages.

Yet beneath the flat average, a leading cohort is pulling away: the share of organizations reaching the optimized stage jumped from 0.4% to 3.1% in a single year. Becoming an AI-fueled organization is clearly achievable and a sustained journey.

“IDC’s 2026 benchmark confirms the disconnect between the AI supercycle’s rapid infrastructure buildout and organizations’ ability to execute. Strategy is outpacing delivery, talent remains the main barrier, and AI is still limited to incremental efficiency gains, not growth,” says Andrea Siviero, Vice President of AI-Fueled Business Strategies at IDC.

The gap between perception and reality

The most revealing finding comes from comparing how organizations believe themselves with how they actually score. IDC asked every respondent to self-characterize its AI approach, segmenting them into “survivors” and “thrivers.” The results are humbling: of the organizations that identified as thrivers, only about one in six reached the managed or optimized stages when scored against IDC’s methodology. In other words, roughly five out of six self-described AI leaders are, by IDC’s assessment, still operating in less mature stages.

The gap concentrates in the dimensions that are hardest to fake at scale. A company can readily claim a board-level AI strategy, but that ambition is repeatedly undercut by governance that hasn’t been formalized into enforceable controls, data and platform foundations that can’t support production, and a workforce that hasn’t been brought along. Strategy is outpacing delivery.

Where the real work lies

The dimension data explains why. Governance has advanced furthest, which is a notable shift, and the reason IDC elevated it to a distinct fourth dimension this year, as trust and risk management became prerequisites for scaling AI. Technology, by contrast, is the least mature dimension of all, with the highest share of organizations still stuck at ad hoc, held back by persistent gaps in data quality, integration, and production grade platforms.

Across the regions, North America leads worldwide, with roughly double the share of organizations at the most advanced stages compared with other regions, while EMEA and Asia/Pacific trail and cluster closely behind.

What comes next

This is the starting point for a deeper conversation. Regional editions and industry-focused deep-dives are also rolling out, giving leaders a sharper view of how they compare within their own market and sector, and where the fastest gains are.

“Organizations are recognizing that scaling AI isn’t just a technology challenge; it requires a deliberate strategy, strong governance, and a culture ready to embrace AI-driven change. Those that align business priorities with a coordinated AI road map will be the ones to turn experimentation into measurable business value,” adds Xiao Liu, Research Manager, AI-Fueled Business Strategies at IDC.

The winners of the next phase won’t be those with the loudest AI ambitions, but those who close the gap between strategy and execution, with disciplined governance, modern data foundations, and a workforce ready to work alongside AI.

Xiao Liu

Xiao Liu - Research Manager

Xiao is a Research Manager for IDC's AI-Fueled Business Strategies program. In her role, she leads the research program in the APAC region. The program examines how AI and emerging technologies accelerate digital maturity and enable new business models, helping…
Andrea Siviero

Andrea Siviero - Vice President, AI-fueled Business Strategies

Andrea Siviero leads IDC’s AI-Fueled Business Strategies Global Research Group, which examines how AI and other Emerging Technologies accelerates digital maturity, enables new business models, delivers measurable business value (ROI), supports practical use cases, and strengthens the organizational capabilities required…