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Year: 2026

Executive Answer

According to IDC’s Worldwide Quarterly AI Infrastructure Tracker, spending reached $121.4 billion in Q2 2026, up 53% year-over-year and 34% sequentially as deployments paused in the first quarter resumed. The defining shift was a broadening of demand: non-accelerated, CPU-based infrastructure grew 61% year-over-year, faster than accelerated systems (+52%), as agentic AI orchestration, data-pipeline workloads, and sharply higher component prices pulled standard platforms into the buildout. IDC raised its full-year 2026 forecast to $521 billion, up from $497 billion last quarter, on higher component pricing, stronger CPU-based demand, and sustained hyperscaler capex.

Why It Matters

The Q2 2026 results confirm that AI infrastructure investment has moved well beyond initial proof-of-concept phases into a sustained, multi-year capital commitment cycle, and that demand is broadening beyond accelerated systems while component pricing becomes a primary driver of value. Enterprise technology buyers, cloud service providers, and national governments are making long term decisions about where to build, how much to spend, and which AI workloads to prioritize.

For vendors, this means a prolonged period of elevated demand across accelerated compute, high-performance storage, and supporting network infrastructure, but also a faster-moving mix as CPU-based systems for agentic AI orchestration and data pipelines take a larger share of spend and memory costs lift prices across every configuration. For enterprises, the data signals that AI capacity is becoming a structural cost of doing business at scale, that elevated memory and storage pricing will raise the cost of waiting, and that late movers risk falling behind on both performance and cost efficiency.

IDC now projects the global AI infrastructure market will surpass $1 trillion in 2029, reaching $1.17 trillion, before climbing to $1.33 trillion in 2030, above last quarter’s $1.08 trillion and $1.21 trillion.

Market Dynamics

  • Growth accelerated across most regions. The United States remained dominant at $92.8 billion (76.4% of global spend, +58.7% YoY). China (PRC) grew to $13.0 billion (10.7% share, +27.9% YoY). Western Europe was the standout among larger regions (+83.9% YoY to $6.4 billion), followed by APeJC (+28.5% to $5.9 billion). The Middle East & Africa (+108.9% to $0.9 billion) and Central & Eastern Europe (+96.7% to $0.3 billion) grew fastest, though from small bases, while Japan was roughly flat (-1.3%).

  • Accelerated compute remains the structural backbone, but non-accelerated infrastructure is growing even faster. Server spending represented 98.0% of total AI infrastructure value in Q2 2026. Accelerated systems reached $105.8 billion (+52.2% YoY), while non-accelerated infrastructure reached $15.6 billion (+61.1% YoY). Agentic AI orchestration and data-pipeline workloads are driving that demand alongside accelerated buildouts, which continue to anchor inference and training, and sharply higher memory and component prices are lifting the value of every configuration.

  • Component pricing is now a primary driver of value. Memory suppliers are raising prices as demand runs ahead of supply, and hyperscalers have secured much of the available supply through multiyear agreements, leaving enterprise buyers to absorb prevailing market rates. IDC expects elevated pricing to persist through at least the second half of 2027. AI-centric storage remained a small share (2.0%, or $2.5 billion, +45.2% YoY) of total AI infrastructure value.

Data Callout: Q2 2026 Key Metrics

Total AI Infrastructure Spending (Q2 2026)$121.4 billion
Year-over-Year Growth (Q2 2026 vs. Q2 2025)+53.3%
Full-Year 2025 AI Infrastructure Spending$318.7 billion (YoY +107.2%)
Full-Year 2024 AI Infrastructure Spending$153.9 billion
Server Share of Q2 2026 AI Spending$119.0 billion (98.0%)
Storage Share of Q2 2026 AI Spending$2.5 billion (2.0%)
2029 Forecast — AI Infrastructure$1.17 trillion
Accelerated vs. Non-Accelerated Mix (Q2 2026)Accelerated $105.8B vs. Non-accelerated $15.6B
2030 Forecast — AI Infrastructure$1.33 trillion

Analyst Insight

“The defining feature of this quarter is breadth. AI infrastructure demand is extending beyond accelerated systems as agentic AI and data pipelines scale, while rising component prices lift spending across every configuration. Together, these trends point to AI becoming a lasting part of how enterprises build, and they underpin the higher outlook for the year.”

— Madhumitha Sathish, RM, AI and Workloads, Enterprise Infrastructure, IDC

Outlook

IDC projects AI infrastructure spending will reach $521 billion in 2026, representing approximately 63% year-over-year growth; a further acceleration from the roughly 56% pace estimated as recently as last quarter, and still one of the largest absolute-dollar expansions ever recorded in a single IT market segment. The forecast implies roughly $309 billion of spending in the second half of 2026, nearly 80% above the same period of 2025. The market is now forecast to surpass $1 trillion in 2029, reaching $1.17 trillion, before climbing to $1.33 trillion in 2030, a five-year compound annual growth rate (CAGR) of approximately 33% from 2025.

What could accelerate this trajectory:

  • Faster-than-expected scaling of inference workloads as enterprise AI application deployment broadens

  • Sovereign AI program expansion in the Middle East, Southeast Asia, and Europe, driving incremental greenfield investment

  • New model architectures and AI agent frameworks require deeper, more distributed compute infrastructure

  • Emergence of non-GPU AI-centric demand (AI orchestration tooling, data-pipeline workloads, and physical AI use cases such as robotics and autonomous vehicles) extending the addressable market beyond GPU-based training and inference

What could constrain growth:

  • Power generation and grid capacity constraints, which remain the primary operational bottleneck for new data center commissioning in major markets

  • Memory and storage component pricing, which lifts server costs and can slow procurement cycles, with elevated pricing expected to persist through at least the second half of 2027

  • Expanded export controls and data-sovereignty regulations, which could reshape where AI workloads are deployed and which vendors win enterprise deals

  • Geopolitical instability in the Middle East, where growth is concentrated in a small number of large, government-backed Gulf deals; escalation of regional tensions, including the ongoing conflict involving Iran, could delay procurement decisions, complicate data center siting and security planning, or shift government priorities away from AI infrastructure investment, introducing volatility to what is currently the fastest-growing region

Investors and technology buyers should monitor Q3 2026 capital expenditure guidance from leading hyperscalers and AI platform providers, as these forward signals remain the most reliable leading indicator of near-term infrastructure demand.

Frequently Asked Questions

Why did AI infrastructure spending rebound so sharply in Q2 2026?

Q1 2026 was held back as a platform transition paused some shipments and data center power constraints left deployed hardware idle. As those pauses began to resolve, Q2 spending rose 34% sequentially to $121.4 billion, up 53% year over year. Higher memory and component pricing amplified the rebound, lifting the value of nearly every configuration. The long-term expansion cycle remains firmly intact, and IDC’s full-year 2026 forecast was revised upward, not downward, this quarter.

Which regions are emerging as new AI infrastructure centers?

Western Europe (+84% year over year) and the Middle East & Africa (+109% year over year, from a small base) posted some of the fastest growth in Q2 2026, driven by government-backed sovereign AI programs, data sovereignty requirements, and hyperscaler expansion; more than 40 countries are now funding national AI infrastructure initiatives. China grew 27.9% year over year to $13.0 billion, while the United States still accounted for 76% of global spend. APeJC also grew 28.5%, supported by national AI strategies and localized cloud service provider expansion.

What risks should buyers and vendors watch in 2026?

Power availability is the single most important operational constraint heading into 2027. Data center commissioning timelines are increasingly driven by utility capacity rather than hardware lead times. In parallel, evolving trade policy, particularly around advanced GPU exports, will continue to reshape competitive dynamics across China, the Middle East, and other emerging markets. Memory pricing is a second pressure point: hyperscalers have secured much of the supply through multiyear agreements, leaving enterprise buyers exposed to elevated prices that IDC expects to persist through at least the second half of 2027.

Is AI infrastructure demand still limited to GPU-based systems, and what’s the outlook going forward?

No. Accelerated systems still account for most spending, reaching $105.8 billion in Q2 2026 (87% of the total, up 52% year over year), but non-accelerated infrastructure grew faster, reaching $15.6 billion (+61% year over year). Agentic AI deployments increasingly rely on CPU-based front-end infrastructure to orchestrate calls to backend models, and enterprises are using standard-rack x86 systems for data-pipeline and other lighter workloads that do not require dedicated GPUs, while inference and training scale-out continue to anchor accelerated demand. Higher component pricing is also lifting the value of these systems. How much of this momentum proves to be volume rather than price will become clearer as memory supply normalizes.

For comprehensive vendor share, forecast data, and taxonomy detail, see: IDC Worldwide Quarterly AI Infrastructure Tracker®. For taxonomy and methodology definitions, see: Worldwide Artificial Intelligence Infrastructure Tracker Taxonomy, 2025.

Source: IDC Worldwide Quarterly Artificial Intelligence Infrastructure Tracker, Q2 2026. Release date: October 2026. Methodology note: Data derived from IDC’s AI Centric workload-based view of the server and storage markets.

Juan Seminara is Research Director for Worldwide Infrastructure Trackers at IDC, where he leads quantitative and qualitative analysis of IT infrastructure markets with a focus on enterprise servers, storage, and AI infrastructure. He can be reached at jseminara@idc.com.

Questions about the process for press announcements? Contact the team at press@idc.com.

Juan Seminara - Research Director, Worldwide Infrastructure Trackers - IDC

Analyst Profile LinkedIn
Juan Pablo Seminara is the Research Director for IDC's Worldwide Enterprise Infrastructure Trackers within the Data & Analytics organization. Mr. Seminara is responsible for leading a team of analysts in charge of the product concept, roadmap, implementation, execution, and client support for IDC’s enterprise infrastructure Trackers and forecast worldwide. In his role, he oversees the quarterly interaction amongst a global group of more than 50 IDC analysts in different regions that participate in the execution and market trend analysis of all enterprise infrastructure Trackers and forecast for IDC.

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まずIDC Quantaに質問する

Ryan Smith - Content Marketing Director - IDC

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

Christina Cardoza - Content Marketing Manager - IDC

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

Datacenters are outrunning the power grid. Water supplies are stretched. Insurers and lenders want climate risk on the table before they fund your next project. And your engineers won’t act on an AI recommendation they can’t verify.

If you lead technology strategies in energy and resources, you’ve felt at least one of these pressures. The message from IDC’s latest FutureScape is straightforward: you can’t build your way out of today’s constraints. The companies that pull ahead will put AI and unified data to work where it counts, from control rooms and capital projects to subsurface engineering and mining operations, with clear limits on how much authority AI holds.

Three shifts shaping 2027 and the five years ahead

AI moves from advising to doing.

In grid operations, capital projects and refining, AI is starting to execute bounded tasks rather than just suggesting them. The emerging model is graduated authority: agents handle repeatable, low-risk work while people keep control and oversight of safety-critical and ambiguous decisions.

Physical limits are changing how the sector works together.

Global datacenter energy use is set to jump from 404 TWh in 2024 to 1,310 TWh by 2029, according to IDC’s Datacenter Facilities Index. Utilities, datacenter operators, water providers and large energy users are moving from static contracts to real-time coordination because none of them can solve these energy imbalances on their own.

Trust sets the speed limit.

Engineers, operators, regulators, insurers and lenders will accept AI outputs only if they can verify them. Physics-grounded models, unified asset and geospatial data, and process twins are becoming prerequisites for decision-grade AI.

A preview of some of IDC’s energy and resources FutureScape predictions

The report spans electric and water utilities, energy retailers, datacenter operators, oil and gas producers, miners, refiners and energy-intensive consumers. A few headline callouts:

  • Grid operations: By 2030, 40% of electric utilities will use graduated authority frameworks for AI agents to execute routine grid-control actions, cutting operator intervention by 25%.

  • Datacenters and the grid: By 2027, 50% of AI-ready datacenters will share real-time grid and compute load data with their utilities to dynamically balance energy supply and demand.

  • Water: By 2029, water utilities in datacenter-dense regions will shift 40% of capital spending from new water supply infrastructure to AI-driven reuse and demand management, deferring capital costs by five years.

  • Oil and gas: Exploration & Production organizations that skip physics-grounded domain small language models by 2027 will see 50% longer AI pilot cycles.

  • Mining: Miners that haven’t unified asset and geospatial twins by 2029 will see 20% higher unplanned downtime.

  • Climate risk: The share of energy firms embedding climate intelligence in capital decisions will rise from 20% to 55% by 2028.

Four additional predictions cover adaptive capital projects, visionary customer experience platforms for energy retailers, hybrid AC/DC microgrids and twin-trained AI in refining. Each comes with a view on what it means for your business and your IT organization.

Five moves for technology leaders now

Each prediction in the report provides potential impacts, specific actions and essential guidance for IT, lines of business and executive leaders. Across all ten predictions, five major themes stand out:

  1. Set a verification gate for every AI program. Expand scope only after a named business owner signs off on an explainability and audit-log review against an agreed accuracy threshold.

  2. Fund shared foundations once. Publish one roadmap and budget line for common data, integration and control layers, and decline requests that duplicate them.

  3. Favor integration over point solutions. Weight procurement toward unified asset, geospatial, project and customer data, and toward proven twin-to-control capabilities.

  4. Extend IT/OT governance to new data exchanges. Name an owner, access controls and a validation standard for datacenter telemetry, AI water forecasts and climate feeds before go-live.

  5. Close the leadership gap before you scale. Give every AI program an executive sponsor with cross-IT/OT decision rights, and train operational leaders on how to manage and supervise AI.

Navigate your next move with confidence

The full report goes well beyond this preview. You’ll get all 10 predictions with the evidence behind them, the business and IT implications of each, the external drivers shaping them, and concrete, owner-assigned actions and essential guidance you can take into your next planning cycle.

Read IDC FutureScape: Worldwide Energy & Resources 2027 Predictions
(IDC #US54982426)

Read the Report

IDC Energy Insights, with contributions from Jean-François Segalotto, Andrew Buss, Roberta Bigliani, Gaurav Verma and Gaia Gallotti.

 

John Villali

John Villali - Senior Research Director, IDC Energy Insights

Analyst Profile
John Villali is a Senior Research Director for IDC Energy Insights, primarily responsible for thought leadership in the area of digital strategies for the energy and utilities sector. Villali has an impressive background in power, natural gas and oil markets.…

If your AI budget grew tomorrow, where would you invest first?

Most leaders answer with a platform or a vendor. The IDC FutureScape 2027 webinar argued that the better answer is in people and governance, and that the decision stays open only for a limited time. IDC’s Rick Villars described the open window as the area where leaders “can’t solve everything this year, but making decisions and commitments now will allow you to be ready to really capitalize on this AI first transition in 2029.”

This is the second of three posts on the windows that frame IDC FutureScape 2027: The Great Rewrite. The launch blog introduced the Top 10 predictions, and the closing window post covered the cost, model, and software-factory decisions that are nearly made. Here we turn to leadership. The target sets the pace: 60% of enterprises will run a majority of their business processes AI-first by 2029, up from 13% in 2026. IDC defines AI-first as agents becoming the default starting point for how work gets done, with humans assisting and providing oversight, which is why the governance and skills decisions below carry so much weight. Webinar viewers can revisit the replay.

The talent bill: what does skipping the people investment cost?

Villars started with a cost that never appears on a hardware invoice. When leaders feel they are not getting value from their AI investment, he said, “It’s much more about the people, about the talent, the decisions that your people make about what models to use.”

The FutureScape prediction puts a number on it. Enterprises that have not invested in AI talent development and governance by 2029 will see 50% lower revenue growth than peers who do. The price tag arrives before the deadline: IDC predicts that organizations delaying AI talent investment until after 2027 will face a talent acquisition premium of two to three times, because demand outstrips supply across industries.

The same report adds a measured comparison. In IDC’s Worldwide Future of Work Survey (May 2026, n = 1,146), organizations with no return on AI reported increased revenue at half the rate of those earning the highest returns, 18.5% versus 37.0%. This is a survey comparison between groups, and the 50% figure above remains a prediction. Enterprises with a mature AI center of excellence are four times more likely to grant AI autonomy on complex work, 63% versus 15%.

Our read: IDC expects the premium to fall on organizations that delay AI talent investment past 2027, so commitments made by the end of 2027 stay clear of it.

Jobs are shifting while this plays out. By 2029, G2000 enterprises will move 30% of entry- and midlevel execution roles toward supervising and orchestrating agents. IDC expects enterprises to reskill existing workers into those roles, because a shrinking and aging talent pool leaves too few new hires to backfill them.

Audit before you commit.

The IDC guidance for the first six months is that the CIO and CHRO charter an AI center of excellence with written decision rights, a named owner for compute and token spend, and audit standards built into the technology stack before agent deployment scales further. Between six and 18 months, the center of excellence writes a learning plan into every agent deployment, with a skills target and its own budget line. Longer term, finance and HR model the revenue and talent-retention risk against the claimed savings before any headcount cut tied to agent deployment is final.

Owners: the CIO and CHRO lead, and the CFO models the risk.

Measure: the share of agent deployments that carry a funded learning plan with a skills target, plus the modeled risk set against claimed savings for each headcount cut tied to agent deployment.

The role: what does a knowledge architect do?

IDC defines knowledge architecture as the work of setting the standards, practices, and exception rules that govern what an enterprise’s AI agents know. Daniel Saroff, group vice president of research and consulting at IDC, took the next question. An enterprise architect designs how systems fit together and sets the technology standards. A knowledge architect keeps that systems thinking and adds a different job: defining “what agents know,” which means the standards, practices, and exception rules agents work within. Those rules set what good looks like and which practices are mandatory, and they decide how edge cases resolve and where the boundaries sit.

Saroff framed the shift as a change in the core question. The enterprise architect asks how systems connect. The knowledge architect asks “what does good look like and what happens at the edge?” The output changes with the question, from reference diagrams to “governed knowledge foundations.”

Do you want to see the whole webinar? Click here to watch the replay.

IDC predicts that by 2030 the traditional enterprise architect role will have transformed into a knowledge architecture role that lets AI agents evolve 50% faster. Saroff’s reasoning was brief. “Agents learn from a well governed foundation,” he said. “And without one, each deployment … repeats the last mistake and is inefficient.”

The starting point is uneven. IDC’s 2026 AI MaturityScape Benchmark Survey of 1,900 organizations in 20 markets found that only 12.8% of enterprises have reached the two most advanced stages of AI maturity, while more than 61% remain in the two least mature.

In IDC’s view, the role asks for a mix of skills. Saroff described it as part architect, part editor, and part diplomat. Business fluency matters because the person has to know how work gets done and how the business ranks its priorities. Knowledge structuring turns what individuals know implicitly into explicit rules that guide agents. Regulatory literacy means working with the risk organization to see how rules apply to agents. And because the role carries little formal authority, persuasion and demonstrated value are part of the job description, a condition Saroff noted enterprise architects have always faced.

Inventory before you stand up the role.

IDC advises that within 12 to 18 months you name an owner and write down the standards, practices, and exception rules your production agents rely on, flagging every place none exist. The function itself follows in the 18 to 36 month horizon.

Owners: the CIO or CTO names the owner, enterprise architecture leads the inventory, and a risk partner reviews it.

Measure: the share of production agents whose standards and exception rules are written down.

The proof: why are CIOs pulling forward-deployed engineers back?

Villars asked Saroff whether the knowledge architect connects to forward-deployed engineering teams, the vendor-supplied engineers who embed inside a customer to build. Saroff answered with a conversation from two weeks earlier, when he met with 20 CIOs at a forum and asked how they use these teams.

Several told him they had used the teams and were now pulling them back. In Saroff’s telling, the CIOs still saw the value, since end users and the business both love them. The pressure came from expense, which he traced to a gap the knowledge architect addresses: “How do you create the guardrails and rules around the agents?” The same gap covers getting data to agents effectively and holding the deployed engineers to the enterprise’s standards.

Without that role, Saroff said, IDC sees forward-deployed teams “less likely to meet ROI goals, less successful in the delivery, and lower end user satisfaction.” That is an early signal from one forum, and the pilot below is how you test it against your own engagements.

Our read: before you add more forward-deployed hours, check whether the agents those engineers build have anything governed to learn from.

Pilot before you scale.

Pair one forward-deployed engagement with a named knowledge owner, and compare its ROI against its stated goals and its end-user satisfaction with an engagement that has none. IDC also suggests putting a question to every AI platform and tooling vendor: how do their agents consume, and get governed by, your own knowledge standards? Hold the answer against a standing evaluation criterion.

Owners: the CIO and the business sponsor lead the pilot, and the CFO tracks return against the original goals.

Measure: ROI against stated goals and end-user satisfaction, per engagement.

The Great Rewrite is happening. Access analyst videos, blogs, and report links to stay ahead of the closing windows. Click here to visit FutureScape 2027.

Before the window narrows: what should tech leaders do first?

Each beat above carries a gated first move. In sequence:

  • Audit your AI talent and governance position before you commit budget. Charter the center of excellence, name the owner of compute and token spend, and put the funded learning plan into every agent deployment.

  • Inventory the knowledge your production agents rely on before you formalize the knowledge architecture role. Name an owner and flag the gaps.

  • Pilot a knowledge owner on one forward-deployed engagement before you scale the model. Measure return and satisfaction against a comparison engagement.

The Top 10 report frames the stakes in Villars’s words: “Organizations that delay governance, talent, and knowledge architecture investments through 2027 will find that the cost of catching up rises faster than any budget adjustment can close.”

Make the predictions work for you. IDC Quanta can connect your data to IDC research and help you gain an edge on the competition in 2027. Visit IDC Quanta Now.

What signals should you watch as the window stays open?

  • Talent pricing. If AI talent costs climb toward the two-to-three-times premium IDC expects after 2027, your window is shortening.

  • The maturity benchmark. Movement in the 12.8% of enterprises at advanced AI maturity, and in the more than 61% still foundationally stuck, shows whether the leader and laggard gap is widening.

  • Peer behavior. Watch whether other CIOs scale back forward-deployed teams or report that ROI goals fell short, and what they say the cause was.

  • Vendor answers. Track how platform and tooling vendors respond when asked how their agents follow your knowledge standards.

Review these quarterly. Bring your baseline for each measure to your IDC account team, and ask to be connected with the analyst who covers your area.

Where can you go deeper?

The research behind every prediction in this post is in the FutureScape reports at idc.com/futurescape. IDC Quanta brings those reports together with hundreds of other IDC analyst reports, so you can ask about the prediction that caught your attention and see the evidence behind it.

To hear the leadership discussion again, watch the webinar replay.

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.

IDC forecasts the Asia/Pacific professional security services (PSS) market is forecast to grow at a compounded annual growth rate (CAGR) of 10% from 2026 to 2030 , and security providers are changing what they sell, how they deliver, and how they staff. IDC’s latest Professional Security Services MarketScape, which assesses 21 providers, identifies three forces behind the shift: agentic AI moving into live delivery, a sharper sovereignty and regulatory agenda, and a wave of acquisitions. Clients want two things: security for their own AI, and AI inside to the security services they buy.

In Asia Pacific, 53% of organizations rank securing and governing AI initiatives and AI agents among the top three goals of security provider engagements, ahead of 48% worldwide. (Source: WW Cybersecurity Consulting Services Survey, Oct 2026, AP n=239)

What’s forcing security providers to change?

  • Security has become a board-level question of digital trust, operational resilience, and regulatory standing. Organizations now expect security providers to take a security initiative from strategy and architecture through implementation, training, and incident readiness. 48% of organizations expect significant funding increases for incident readiness services and security consulting, according to IDC’s Asia/Pacific 2025 Security Survey.
  • Agentic AI is now both a tool and a threat. Security providers are embedding agentic AI in SOC operations, GRC assessments, and offensive testing. Organizations, meanwhile, face AI-powered attackers, with AI-enhanced impersonation and LLM prompt injections, increasing the demand for AI-driven protection.
  • Sovereignty is now a delivery requirement. Australia’s Cyber Security Act, India’s Digital Personal Data Protection Act, and data-localization rules elsewhere are pushing security providers to establish dedicated SOCs, cleared personnel, and classified delivery centers in-country, rather than relying solely on regional or offshore hubs.

How are security providers evolving?

  • People-led, agent-assisted delivery. Security providers are shifting from road map slides to live tooling. During my evaluation of IDC’s APeJC PSS MarketScape, I was impressed by how a security provider can run an autonomous penetration-testing agent and report on autonomous test execution in under 50 hours. Another provider developed a SOC AI worker that independently triages and classifies alerts, while humans remain in the loop for critical decisions.
  • Providers now sell AI trust and agent governance. Another trend that stood out during my conversations with security providers is the emergence of AI trust and agent governance. Security providers are building agentic AI trust frameworks that cover agent discovery and registration, sandboxed testing and certification, and continuous policy monitoring for clients’ own agents. Providers are also establishing Frontier AI Security advisory practices. In IDC’s WW Cybersecurity Consulting Service Survey, 35% of organizations have already used their provider for agentic AI security advisory and 26% for AI trust services.24% have used theirs for AI bill-of-materials work.
  • Acquiring sovereign capacity. Security providers are buying and building their way to in-country capacity. A global security provider completed an acquisition in early 2026 that added 1,000+ security professionals and security operations centers across Australia and New Zealand. An IT services firm bought an Australian specialist to deepen offensive security skills and government accreditations. Another runs a defense-classified sovereign cyber defense center in Canberra. Ultimately, when I speak to CISOs or CIOs, the winning combination is the ability to move between onshore, sovereign, and offshore delivery to match risk tolerance, data residency obligations, and budget.
  • GRC moving onto AI-assisted platforms GRC is moving to platforms. One security provider’s AI-assisted platform automates control mapping against frameworks such as DPDP, PDPA, MAS TRM, and APRA CPS 234, while another layers AI accelerators onto a workflow-platform-based GRC suite. Resilience is following the same path, with dedicated recovery services joining readiness and response, and at least one security provider bundling advisory, threat intelligence, and response into a single continuous retainer. 
  • From billable hours to retainers, outcomes, and embedded engineers. The commercial delivery model is changing fast. Data from IDC’s WW Cybersecurity Consulting Services Survey indicates that only 8% of Asia/Pacific organizations run one-off, project-based engagements. Most are on continuous, embedded advisory (37%), managed or subscription services (23%), or outcome-based arrangements (20%). Forward-deployed engineers and technical staff, embedded onsite or virtually, are a core, ongoing part of the engagement.

For the technology leaders: what should you ask your security provider?

  • Show me AI in production. Ask for agentic AI, GenAI, and automation running in SOC operations, GRC assessments, or security testing, backed by measurable outcomes such as faster response times, quicker assessments, or higher analyst productivity.
  • Prove you know my regulations. Look for the ability to flex across onshore, sovereign, and offshore delivery, plus depth in your industry and in rules such as the Cyber Security Act or the DPDP Act.
  • Tell me who is behind the capabilities. Alliances with hyperscalers, cybersecurity vendors, and AI model providers signal how well AI-driven outputs are governed and tested, and how quickly you’ll get access to new capabilities.

IDC outlook: what’s next?

IDC expects that the security providers best positioned to become long-term strategic partners, rather than service vendors, will combine three elements: trusted, in-production AI-enabled tooling; a mix of onshore, sovereign, and offshore capacity; and a broad ecosystem of hyperscalers, security software vendors, and, increasingly, AI model and platform providers. Buyers can use those three elements as a checklist when they shortlist providers.

IDC’s FutureScape webinar, Agents. Sovereignty. Quantum. What Earns Trust & the Budget That Follows, picks up the budget side of the questions raised here. It draws on IDC survey data showing that more than half of surveyed Asia/Pacific buyers name cybersecurity and resiliency as the top factor in their AI budgets. On Tuesday, December 15, 2026 at 2:00 PM SGT, IDC’s Sakshi Grover and Yih Khai Wong will walk through IDC’s ten FutureScape 2027 trust predictions and the Trust Playbook, which lists what leaders should ask of their providers. Register to bring that list to your next budget conversation.

*Asia/Pacific Excluding Japan and China

Yih Khai Wong - Senior Research Manager - IDC

Analyst Profile
Yih Khai Wong is a senior research manager for IDC Asia/Pacific's Cybersecurity practice, supporting cybersecurity research and client engagements through the Asia/Pacific Security Opportunities: Trust and Resilience program. Yih Khai's area of focus is on security technologies, including cloud-native application protection, identity, endpoint and network security. He works closely with technology vendors and buyers, delivering actionable market insights and advice within the cybersecurity ecosystem. Before rejoining IDC, Yih Khai was a principal analyst covering the cloud, datacenter, and edge computing market with ABI Research. Prior to that, Yih Khai was in EY, in his capacity as an assistant director at EY's research and insights group. Yih Khai started his analyst career with IDC Malaysia as an analyst covering the enterprise applications market.

世界全体で、AI導入の最も成熟した段階に到達している組織はわずか3.1%です。61%は、最も成熟度の低い2つの段階にとどまっており、大多数を占める中間層は、この1年ほとんど動いていません。ほとんどのテクノロジーリーダーは、自社がどのグループに属しているのかを自信を持って答えられずにいます。

その不確実性が、最悪のタイミングで重くのしかかっています。

世界の組織の73％が、アプリケーションやモデル、およびその基盤となるプラットフォームへの過剰なAI支出が今後1年間のテクノロジー投資計画における大きなリスクと捉えており、ヘルスケアおよび金融サービス業界では77％に上昇します。

AI投資は、より大型化しつつあります。2026年と2027年には、500万~5,000万ドル超のレンジへと移行しつつあり、経営層のIT責任者の約70%はIT予算全体の増加を見込んでいます。

3つの意思決定、手引きなし

現在、テクノロジーリーダーは3つの意思決定を同時に迫られており、そのいずれにも確立された手引きはありません。

  1. 既存のベンダーやプラットフォームを今後も信頼し続けるのか、それとも新たなものを導入するのか。
  2. 従来のIT費用とは異なる規模で膨らむAIコストをどう管理するのか。IDCは、2029年までに世界で13億のアクティブなAIエージェントが稼働し、1日に2,400億件のアクションを実行し、エージェントのアクション提供にかかる年間コストが530億ドルに迫ると予測しています。
  3. そして、四半期ごとに厳しさを増す取締役会の問いに対して、資本配分の判断をどう正当化し、さらに次に問われたときに、その成果をどう証明するのか。

これらはいずれも、年に一度で済む取り組みではありません。取締役会や予算レビューのたびに浮上し、しばしば誰も気づいていなかった契約更新の期限と重なります。社外の基準点を持たないテクノロジーリーダーは、そのひとつひとつを、社内の確信だけを頼りに判断することになります。

この3つこそが、現時点で最も緊急性が高い課題です。どのような意思決定であっても、必要な基盤は同じです。独立して検証された市場インテリジェンス、実際のデータを用いたベンチマーク、そして、意思決定が行われるまさにその場で利用できることです。

チームが下す意思決定のために設計

IDC Quantaは、IT部門のリーダー、セキュリティ&オペレーション、財務&調達の各部門に、上記の3つの意思決定に限らず、その同じ基盤を提供します。

  • IT部門のリーダー:信頼性の高いテクノロジー/AIロードマップを描き、ベンダーの提案資料ではなくデータに基づいて、より迅速に、より確信を持ってベンダーやプラットフォームを選定できます。
  • セキュリティ&オペレーション:AIのコストとリスクが顕在化し、取締役会レベルの問題になる前に先回りして把握できます。ここで挙げたすべての意思決定を支えているのと同じ、同業他社とのベンチマークに基づくエビデンスが裏付けとなります。
  • 財務&調達:支出とベンダー価格について、財務部門が支持できる資本配分の提案を提示でき、あらゆる更新交渉に、より強い交渉力で臨めます。

厳しい精査にも耐えるもの

IDC Quantaは、一般的なリサーチへのアクセスにとどまらない、次のような具体的な強みで、それぞれの意思決定を支えます:

  • 利用可能な最大規模のピアユニバース:52か国、404,000社の組織のデータを活用し、あらゆるベンチマークに実際の市場を広くカバーします。
  • 自社のデータを、即座にベンチマーク:IT予算、AIコストの算出、ベンダーとの契約書をアップロードすると、Quantaが同じプロンプトの中でIDCのデータと照らしてベンチマークします。アナリストによる再入力も、別のツールも不要です。
  • 別のポータルも、新しいログインも不要:IDC Quantaは現在、Teams、Slackを介してあなたのチームに届き、次にMCP対応ツールへと拡張します。調達、予算、更新に関するインテリジェンスに普段お使いの環境でご利用いただけます。
  • 期限の後ではなく、前に知らせる:スケジュール機能の活用により、契約の更新、予算サイクルの開始、取締役会レビューの日程が近づくと、最新のインサイトが通知されます。

問われる前に、根拠を持って示す

実際にAI成熟度で最適化段階に到達している3.1%の組織と、到達したと思い込んでいるだけの組織のギャップは、まさに取締役会が痛い思いをして気づくたぐいのものです。それは多くの場合、予算レビューの際であり、その前ではありません。IDC Quantaは、テクノロジーリーダーが、すでに準備の整った状態でその場に臨むためのエビデンスを提供します。

まずIDC Quantaに質問する

このブログは、IDCのRyan SmithとChristina Cardozaが執筆し、2026年9月15日にidc.comで英語版が初めて公開されたものです。

Ryan Smith - Content Marketing Director - IDC

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

Christina Cardoza - Content Marketing Manager - IDC

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

Sometime in the next planning cycle, someone will ask you a question with no comfortable answer. Could you justify a double-digit increase in AI infrastructure spending to your board, and would your sovereignty audit back you up?

For a growing number of enterprises, the question is already live. IDC FutureScape 2027, IDC’s annual set of top technology predictions, places three decisions in the closing window, where the big decisions are nearly made and an opportunity is about to become a disadvantage. One of them: IDC predicts that by 2027, 45% of G2000 organizations will justify exactly that kind of increase.

The clock behind them is the first prediction in the Top 10. IDC predicts that by 2029, 60% of enterprises will run a majority of their business processes AI-first, up from 13% in 2026. AI-first means AI agents are the default starting point for how work gets done, with humans assisting and providing oversight. It is not full autonomy: IDC expects supervised autonomy, with defined escalation thresholds, to be the standard operating model for agents by 2028, so the tipping point is a governance event as much as a technology one.

Missed the webinar? Watch all ten worldwide predictions in the webinar replay.

That puts a short fuse on the calls you make now. The architecture decisions in this planning cycle are the ones you will be judged on in 18 months, and IDC predicts that by 2029, 40% of G2000 CIOs will be measured on AI unit economics.

This post extends our launch blog, IDC FutureScape 2027: The Great Rewrite Comes With Deadlines, which introduced the closing, open, and emerging windows. IDC’s Rick Villars, who presented the webinar, said the main task in the closing window is rethinking resilience, at the business level as well as the technology level. Each decision gets a section below, with clips from the live webinar. The open and emerging windows get their own posts next.

The bill: how is techflation changing AI infrastructure budgets?

Villars told webinar viewers that IDC research found over 62% of organizations saw double-digit price increases on their latest purchases in the prior three months. Software and services are starting to follow networking, storage, and processors. IDC calls this techflation: the sustained rise in technology costs driven by demand for processors, memory, and datacenter power and cooling that supply cannot match.

The techflation prediction turns that pressure into a budget decision. By 2027, IDC expects 45% of G2000 organizations to justify double-digit increases in AI infrastructure CAPEX, driven by data security and sovereignty compliance demands and by the expectation that hardware costs stay high. The bill is also getting harder to read: IDC predicts 55% of enterprises will see agentic bills rise with no published price change by the end of 2027, up from 20% today. For a CFO, that makes a baselined case, with cost and outcome measured before you commit, the price of entry.

Could you justify a double-digit CAPEX increase to your board this year, and would your sovereignty audit support it?

Organizations embedding AI into core processes will deploy more infrastructure in their own facilities, Villars said, and they are going to need to “get back into the CAPEX business.” IDC’s advice to CFOs: fund AI factories like warehouses, not incremental budgets, with multiyear committed spending plans before December 2027.

Start with an audit of security and data residency needs, within six months, so the commitment is sized to your requirements, and put legal, compliance, IT, and finance behind one sovereignty case. The multiyear hardware agreement follows, with price protection and with review and exit terms written in before you sign. CAPEX procurement cycles run longer than cloud activation, so start early, then recalibrate as costs and regulation move.

CAPEX is one answer to techflation, and sourcing is another. IDC predicts that by 2028, 50% of G2000 enterprises will adopt GPU-as-a-service over self-built infrastructure. Waiting carries a cost as well: IDC’s analysis of the macro drivers finds that delayed investment gets more expensive as costs rise.

Owners: the CIO and CFO lead, with the CTO sizing the architecture. A useful measure is IT spend as a share of revenue against your peers.

The exit: why are open models part of the cost answer?

Hardware is only half of the cost story; the other half is the model you run on it. By 2028, IDC expects enterprises to shift 60% of AI workloads from proprietary LLMs toward open-source models, using model routing (sending each workload to the model that best fits its needs) to reduce costs and hedge against vendor lock-in. This 60% counts workloads, while the AI-first figure above counts enterprises. IDC’s guidance projects AI operating costs falling 30% to 50% by 2028. Treat that as a projection that depends on routing working well, and net it against the skills and support costs the same guidance names.

Lock-in is the second driver. IDC’s FutureScape on sovereignty and regulations finds that 84% of enterprises rate freedom from AI vendor lock-in as important and that 38.8% already report switching costs and contractual lock-in. It projects that 48% of enterprises will run open source AI models in production by 2029, up from 30% today. Villars tied the two points together: cost predictability and flexibility belong in the same planning conversation, and model routing is part of how you control agent costs. IDC’s guidance is to balance lock-in against time to value, since flexibility has a setup cost.

Our read: open models are a negotiating position before they are a cost line.

A workload you can redeploy elsewhere changes your next renewal conversation. The harder part is the operating model. The guidance calls for routing that matches workloads on latency, accuracy, and cost, containerized multi-model capacity, and governance extended to open-source vulnerabilities and data residency. Procurement has to learn to evaluate open communities and support tiers, and IDC’s sovereignty research says model-serving and orchestration frameworks such as vLLM, Ray, and Kubernetes-based tooling need to be budgeted and staffed as a named 2027 item.

Owners: the CTO leads the routing architecture, with the CIO owning vendor terms and a named owner for agent knowledge standards. A useful measure is cost-to-value per workload, before and after a workload moves.

The factory: what does it take to treat the software factory as critical infrastructure?

The third decision starts with an image from IDC’s Katie Norton, who leads research on DevSecOps and software supply chain security. Picture a production line. “Every application a company ships inherits the trust of that factory,” she told webinar viewers, so a contaminated line makes everything it produced suspect and can force something like a recall.

In this prediction, the software factory means the pipelines, tools, plugins, and agents that create, build, and deliver code. IDC predicts that by 2028, 50% of large enterprises will treat those systems as critical infrastructure. AI changes the picture in two ways. It produces more code than human reviewers can absorb, and agents now take part in the factory themselves, opening pull requests, calling tools, and holding identities with access to build systems. Norton describes two ways a software factory can fail: it stops, which blocks fixes from shipping, or it is compromised, which means its output cannot be trusted. She sees the most urgency in connected products such as vehicles and medical devices, where an attacker could “ship a malicious update signed with the manufacturer’s own keys and every device will accept that as authentic.”

The risk is recognized. IDC’s DevSecOps and Software Supply Chain Security Survey from July 2026 found that 54.7% of U.S. organizations rate their business risk exposure from internally developed applications and the software supply chain as high or very high. Buyers are starting to act: IDC predicts that by 2029, 40% of critical infrastructure operators will make verified code origin and supplier control conditions of software procurement. Most still govern delivery systems as engineering productivity tools, a priority that made sense before AI multiplied the code and the agents in the pipeline.

Could you still ship a fix tomorrow if your build system were compromised or offline?

Norton’s starting advice for 2027 budgets has three steps, and the first sounds simple: “You can’t secure what you can’t see.” Inventory every pipeline, tool, plugin, and agent with access. Most organizations she talks to cannot produce that map today, so name an owner and a deadline. Add the factory to business continuity planning using the question above, and put third-party delivery tools under supplier risk management. On the budget side, she suggests consolidating delivery environments onto fewer, centrally managed platforms. Our read: consolidation concentrates risk in fewer, larger platforms, which is why the inventory comes first.

Owners: the CISO and CTO lead, with the CIO sponsoring the budget. A useful measure is the time it takes to ship a fix with the primary build system offline.

Before the window shuts: what should tech leaders do first?

Each decision has a first move that fits inside one planning cycle, and a gate that tells you when to take the next step.

  • AI infrastructure budget: audit security and data residency requirements and map sovereignty rules, then size a multiyear commitment from the result, with price protection plus review and exit terms. Recalibrate as costs and regulation move.

  • Open models: pilot routing on one workload class picked from an audit of migration candidates, and measure latency, accuracy, and cost before you fund the platform and skills at scale. Name an owner for agent knowledge standards now.

  • Software factory: inventory everything with build access and answer the ship-a-fix question before you decide on consolidation.

Villars closed the webinar with three priorities for the 2027 planning cycle: governance for agents and data, flexibility that makes cost more predictable, and leadership that can trust AI. The closing window decisions sit under the second and touch the first.

Want more FutureScape? Visit our dedicated website for FutureScape predictions, past and present.

What signals should you watch as the window closes?

Forecasts move as conditions move, so keep a short watch list. Start with memory pricing: IDC’s dedicated private cloud research expects memory prices to start easing in the second half of 2028. If supply loosens sooner or quotes soften at your next two purchases, revisit the terms of a price-protected contract, which is why review and exit terms belong in it. For open models, watch whether support tiers and communities mature, since routing savings grow with them. For the software factory, watch your inventory: consolidate delivery platforms once everything with build access is mapped.

Your IDC contacts are the fastest way to stay ahead of these signals. Ask your IDC account team to connect you with the analysts behind these predictions, and check in each quarter on what is shifting in pricing, supply, and adoption, so you can tell a blip from a change that should move your plan.

Where can you go deeper?

IDC Quanta lets you see the research behind a prediction alongside the follow-up questions it raises.

Explore the FutureScape 2027 reports at idc.com/futurescape.

Watch the webinar replay

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.

过去半年，生成式AI已经带来巨大变革：代码生成成为软件工程的标配环节，内容生产从“AI辅助”走向“人机协作”，智能客服、数据分析、营销创意等场景的渗透率快速攀升。随着企业需求从”试用尝鲜”转向”生产级部署”，承接大模型能力的主流公有云服务形态——MaaS，正成为云计算市场中最具确定性的增长方向。

国际数据公司（IDC）于近日发布的《中国AI软件市场半年度追踪，26H1》报告显示，2026H1中国MaaS市场规模达到106.3亿元人民币，占中国公有云PaaS市场的比重从2025年下半年的5.2%升至21.0%，开始对既有市场格局带来颠覆性影响。

IDC认为，对于行业用户而言，MaaS已经从可选项成为业务标配。对于技术厂商而言，MaaS也正在成为不容失手的增长主阵地。

2026H1，中国MaaS市场规模及Token调用量呈现爆发式增长

2026上半年，中国MaaS市场延续并放大了2025年以来的高增长态势。按营收口径统计，市场规模达到106.3亿元人民币，同比增长889.4%。这一增速意味着市场在一年内扩张至近9倍体量，MaaS正式迈入百亿级赛道。

按调用量口径统计，2026H1全市场Token消耗量达到2,857.9万亿，同比增长约432.5%。

增长背后：模型升级、场景放量、模态扩容

·       模型迭代——旗舰升维、Flash 达标，行业渗透加速，用户盘持续扩大。​ 供给侧看，2026 上半年模型能力与成本两道门槛同步下降。旗舰层面，Qwen3.5 ，GLM-5.2等模型在推理、编程与智能体能力上持续升级，长上下文普遍迈入百万级，大模型得以进入更多行业的核心业务环节——从“辅助办公”走向“直接承担生产任务”；普惠层面，各家 Flash/轻量版本达到可用门槛，让原本对成本高度敏感的行业第一次具备了规模化调用模型的经济性。大模型的用户盘从互联网等先行行业，扩展至金融、零售、制造、政务等更广泛的行业腹地。

·       场景挖深——大并发筑底盘，三类场景接力放量。​ 互联网自身业务中的推荐系统、搜索、对话、Coding 等大并发场景，仍构成 MaaS 收入的底盘，贡献稳定。同时增量来自于三类接力场景：一是日常办公加速普及，既包括各行业的办公流程自动化，也包括以金融投研报告撰写为代表的内容生成场景；二是智能客服向更多行业渗透，其中电商与贸易企业尤为突出；三是智能终端助手、IT 自动运维等新型场景相继接入大模型。这些场景渗透率尚低、业务链条长，对更长上下文与更强推理能力的要求，使单任务调用量与客单价同步上升，正在接棒成为 MaaS 收入增长的主要来源。

·      模态扩容——视频生成模型成为MaaS市场重要第二增长曲线。​视频生成模型在上半年跨过商业化拐点：以 Seedance 2.0 为代表的新一代模型将 15 秒视频的可用率大幅提升，短剧、广告、电商内容等生产环节开始批量引入。这一趋势直接重塑了 MaaS 的市场结构：2026H1，在整体 MaaS 规模中，大语言模型占比为 47.0%，而视频生成模型调用产生的收入贡献已达 32.7%，其余“其他模态模型”占 20.2%。视频模态已实质性地成为拉动 MaaS 增长的第二极。

MaaS 已成为必争之地，各类型玩家加速涌入：

在这一背景下，2026 年各类型厂商依托自身禀赋加速涌入 MaaS 市场：

短短半年间，MaaS 在中国公有云 PaaS 市场中的份额从5.2%升至21.0%，开始对既有市场格局产生颠覆性影响。对云厂商和模型厂商而言，MaaS 已不再是云业务中的“可选项”：它既是未来几年最重要的增量来源，也直接决定下一轮云市场竞争的话语权分配——不参与，就意味着在云计算的核心地带缺席。

  • 云厂商以算力资源、完善的模型矩阵与成熟的工具链为底座，将模型服务全面纳入云产品体系，以“模型 + 算力 + 工具链”的整体方案锁定企业级市场；
  • 大模型公司凭借技术领先与快速迭代建立品牌心智，以密集的新品发布与激进定价切入企业客户，并以订阅制与生态合作将模型优势转化为平台流量；
  • 大模型推理服务商则聚焦多模型聚合与极致性价比，以推理效率和开放性卡位“多模型时代”的流量入口，承接来自头部模型的溢出需求；
  • 运营商携全国性算力布局与深厚的政企渠道资源进场，形成第四股不可忽视的力量。

模型发布、API 降价、开源开放与算力扩容在上半年密集上演，市场竞争的烈度显著升级。凭借各自的基因优势，2026上半年，火山引擎、阿里巴巴、智谱、百度等厂商取得了优势与主要市场份额。

与此同时，大语言模型仍是主要模态。技术成熟度更高、推理成本下降更快，叠加当前企业侧需求（知识问答、代码生成、Agent 编排）大多以文本为中心，共同支撑了大语言模型在调用量与营收上的绝对主导地位。也正因如此，模态内部的竞争最为激烈，阿里巴巴、智谱、火山引擎、百度等厂商构成了大语言模型市场的主要竞争力量。

模型、推理效率构建短期壁垒，算力是长期核心护城河

MaaS 的市场前景得到数据验证，参与者类型快速扩容，竞争节奏明显加快。IDC认为，MaaS厂商的竞争要素可以分两个时间维度看：

短期看，模型能力与推理效率是最直接的抓手。一方面，旗舰模型的升级仍是获客的第一杠杆，模型的效果、迭代节奏与模型矩阵的完整度，直接决定平台流量；另一方面，在模型能力趋同的区间，推理效率成为决定性价比的关键——推理框架优化、PD分离与大规模集群调度能力，决定了厂商在同等算力下可对外供给的有效Token吞吐，也决定了价格战的底气。这一阶段的竞争本质是”效率战”。

长期看，算力是难以绕开的核心护城河：算力的获取规模、单位成本/算力性价比、异构适配能力与供应链确定性，构成了厂商最难被复制的能力。模型能力可以通过人才与资本追赶，工具链可以通过投入补齐，但大规模、低成本、可持续的算力底座需要长期资本开支与供应链关系的积累。可以预见，随着Token消耗量级继续攀升，算力话语权将向头部进一步集中，缺乏算力自主性的厂商将被迫向”轻资产模型授权+生态合作”模式迁移。

大模型平台私有化市场同步放量

与 MaaS 公有云并行，大模型私有化部署市场同步进入放量期。​ 2026 H1，大模型私有化部署市场规模达到 28.8亿元，同比增长 108.4%。需求主要来自政府、制造、能源、金融等行业，驱动逻辑正从“IT 设施建设”转向“生产环境落地”：客户的关注点也从单点模型能力转向平台的完整能力——数据安全、业务定制、自主可控与信创适配缺一不可，工程化交付与长期运维成为项目成败的关键；同时更看重对业务的支持效果，以业务指标而非模型榜单衡量投入产出。“应用+模型”一体机等形态加速交付，头部客户多期复购显现，市场正从单点试点走向规模化复制。

未来展望

MaaS市场在视频生成和通用智能体/日常办公推动下，持续高速增长。一方面，视频生成尚处于商业化放量初期，随着单次生成时长向 30 秒量级突破、可用率持续爬升，广告、短剧、电商等有真实预算的需求将源源不断转化为收入，成为增长弹性最大的板块；另一方面，日常办公等场景仍处于渗透早中期，调用量与客单价均有充足的上行空间。

商业模式加速多元化：从“资源生意”到“价值分成”。

  • 与市场规模的扩张同步，MaaS 厂商的变现方式也在快速多元化。标准 API 按量计费仍是最主流的模式；在此之外，以 Coding Plan 为代表的订阅制套餐、预定资源、按效果付费、合作分润等模式在 2026 上半年快速起量，收入结构呈现“按量付费为主、多元模式加速渗透”的格局。
  • 商业模式创新的意义不止于收入本身，更在于客户关系的重构：按量计费以低门槛赢得“入口”，订阅制与预定资源以承诺消费沉淀“确定性”，按效果付费与分润模式则试图把 MaaS 从“资源生意”升级为“价值分成生意”——厂商的收入第一次与企业客户的业务成果直接挂钩。
  • 展望未来 18 个月，商业模式将加速向“价值锚定”演进：与业务成果挂钩的计费方式有望从探索走向主流，厂商与客户的利益绑定日益加深，多元模式的收入占比也将持续提升。

增长换挡：从存量挖潜到增量开拓。国内市场，Coding 等先发大并发场景的渗透已趋近饱和，继续在存量盘子里“内卷式挖潜”，边际收益正在递减。厂商需要把增长引擎切换到两个方向：一是转化“还没进场”的客户，比如金融、医疗、政务以及大型企业的核心业务系统，它们并非没有需求，而是此前不敢用、不便用；利用专有云推理、数据不出域等方案，将这部分客户转化为 MaaS 用户盘的下一批增量，是厂商获得增速的潜在机会；二是向海外要增量，海外市场对高性价比推理服务的需求旺盛、订阅付费习惯成熟，且 API 定价与毛利空间普遍高于国内，正成为头部厂商收入结构中的新变量。

分析师观点

IDC中国高级分析师洪婉婷表示，2026 上半年是中国 MaaS 市场的重要分水岭。模型升级、场景扩容与视频生成三股力量共同驱动市场爆发式增长。短短半年，市场规模突破百亿，在公有云市场中的占比显著提升，其重要性不言而喻。往前看，市场将从‘跑马圈地’进入‘价值经营’阶段：短期，模型效率与推理成本决定入场资格；长期，算力与体系化能力决定厂商能否持续交付高质量、可信赖、可计价的服务，而这将最终决定利润池的归属。

进一步交流

如希望就《中国AI软件市场半年度追踪，26H1》定义、厂商份额及采购评估框架进一步交流，欢迎与我们联系。请点击此处与我们联系。

Catherine Hong

Catherine Hong - Senior Market Analyst

Analyst Profile
Ms. Catherine Hong is served as the role of Senior Market Analyst in Emerging Technology sector for IDC China, and her main responsibility is conducting analysis and research for the China Blockchain market. She is also in support of regional…

国际数据公司（IDC）最新手机季度跟踪报告数据显示， 2026年第三季度中国智能手机市场出货量约为5,806万台，同比下降15.2%。相较上半年约4%的同比降幅，下半年整体市场规模下滑速度进一步加快。存储及其他元器件成本大幅上升所引发的终端价格上涨与产品配置方案调整，已开始明显抑制消费需求，其中安卓厂商与长尾品牌承受的压力尤为突出。

本季度市场呈现“量跌价升”的典型特征。上游成本压力持续向终端传导，导致新品定价普遍上浮，同时厂商不得不调整产品配置组合以平衡成本。消费者面对价格上涨仍需时间适应，换机周期进一步拉长，直接拖累整体出货表现。这一趋势表明，成本压力已成为当前影响中国智能手机市场走势的关键变量。

厂商格局：华为份额升至21.1%，荣耀环比微增

在整体低迷的背景下，华为凭借多样化的产品形态与相对克制的售价涨幅，延续了今年以来的强势表现，出货量实现同比两位数增长。苹果新品发布后市场欢迎度超出预期，但由于今年机型数量减少，出货量同比小幅下滑。其它中国头部品牌虽面临较大挑战，但依然展现出各自相应的承压能力。

OPPO连续四个季度稳居市场出货量前三。在 600 美元以上高端市场，Find X10登顶今年新旗舰产品标准版机型销量榜首，助力 OPPO 该价位段出货连续三个季度实现三位数同比增长。400-600 美元中高端市场，Reno 16 系列凭借均衡产品力与渠道深耕，稳居该价位段安卓单品销量第一，构成 OPPO 销量基本盘的核心支撑。子品牌一加是唯一自 2025 年至今年第三季度持续保持出货量同比正增长的品牌，为整体产品矩阵补充差异化增长动能。依托中端稳住大盘、高端持续突破、多价位段精准覆盖的产品布局，OPPO 在行业成本承压、需求放缓的周期中，展现出稳固的市场基本盘与可持续的市场抗风险能力。

vivo继续凭借稳健的产品竞争力保持稳定市场表现。历经多代产品迭代持续深耕移动影像赛道，vivo已建立起扎实的用户口碑与市场认知，全新旗舰X500系列进一步夯实高端影像旗舰的市场地位。子品牌iQOO精准瞄准年轻电竞人群，旗舰iQOO 15系列持续热销，新款iQOO 16上市后亦受到市场欢迎。主品牌和子品牌共同助力vivo在600美元以上高端市场位居安卓阵营第一。中端市场，S60系列市场表现好于上一代产品，帮助vivo位列400—600美元价位段第一，巩固销量基本盘。高端市场上影像与性能双线差异化突围使vivo在行业周期波动中展现出更强的经营韧性与市场竞争力。

Xiaomi重返折叠屏市场后，国产安卓首款阔折叠产品小米18 Fold凭借差异化形态与产品力快速打开市场，助力小米重返折叠屏市场第三；叠加全新旗舰小米18 Pro系列上市，推动小米在1000美元以上安卓市场份额位列第一。在行业上游成本高企、多数厂商收缩入门产品线的背景下，小米持续坚守普惠型市场，在 100 美元以下价位段保持市场首位，有效覆盖预算敏感型用户群体，稳固了整体销量基本盘。高端突破与普惠覆盖并行，形成差异化产品合力，使小米在成本压力与需求疲软环境中保持市场竞争力。

Honor成为本季度头部主流厂商中唯一实现出货量环比正增长的品牌，反弹态势清晰。全新 Magic 9 系列是今年新上市主流新旗舰中，唯一实现首销销量较上一代显著增长的产品。国庆假期期间持续稳居安卓旗舰销量榜首，成为高端市场增长的核心拉动力。中端市场，X80 Pro Max 系列上市后快速起量，登顶 200-400 美元价格段单品销量第一；多价格段、多产品线差异化协同发力、旗舰新品突破增量帮助荣耀在行业调整期展现出强于同业的抗风险能力与复苏动能。

价格体系：中低端市场份额持续收缩

2026 年下半年，元器件成本快速攀升对终端价格的影响已完全显现，市场定价中枢持续上移，价格结构分化态势进一步加剧。分价位段来看，受头部厂商旗舰新品发布节奏前置、产品力升级共同拉动，600 美元以上高端市场份额同比扩张 7.1 个百分点，成为本轮结构升级的主要受益区间；400-600 美元中高端价位段同步扩容，份额同比提升 4.6 个百分点，部分中低端端产品涨价升档到该价位段；200-400 美元中端价位段运行基本平稳，份额同比微降 0.7 个百分点，为全价格带中波动幅度最小的区间；而200美元以下低端市场承压显著，份额同比下降13.9个百分点。整体来看，成本压力正加速推动市场结构向中高端集中，低端市场空间被进一步压缩。

折叠屏：连续四个季度出货下滑

2026年第三季度，中国折叠屏手机出货量达到177万台，同比下降32.7%，市场已连续四个季度呈现同比下行态势。传统形态产品的创新边际效应持续递减，已难以继续激发消费者换机意愿；而率先落地阔折叠新形态的品牌，凭借差异化的产品体验取得更优的市场表现。随着苹果正式入局折叠屏赛道，叠加更多主流品牌将于 2027 年上半年集中推出阔折叠形态新品，市场关注度与消费吸引力有望逐步修复。预计国内折叠屏市场将从下一季度起止跌回暖，重新步入增长轨道。

Huawei凭借产品形态最全面的布局依然占据绝对优势，市场份额超过70%；阔折产品Galaxy Z Fold8全球热销，帮助Samsung在折叠屏手机市场稳居第二位，份额8.0%；Xiaomi同样依靠阔折新品重返市场第三位，市场份额6.4%；vivo和Honor分居第四和第五位。

后市展望：价格体系重建前，市场难有起色

展望后市，预计存储及其他物料成本将在未来几个季度依然处于高位，手机产品价格将继续保持增长趋势。大部分消费者对价格上涨仍需接受与适应的时间，换机周期恐进一步拉长。因此，即便有大量各档位新品集中发布，预计2026年第四季度中国智能手机市场表现依然难有起色。甚至到2027年大部分时间，成本压力仍将存在，市场预计仍将延续明显下滑趋势。

不过，头部厂商凭借较强的品牌拉力与相比竞争对手更有限的涨价幅度，有望在存量竞争中抢夺更多消费者。随着时间的推移，消费者势必逐步适应并接受新的手机价格体系，待新的换机节点到来，仍将更换手中设备，届时其它品牌也将迎来更多市场发展机会。

进一步交流

除本次发布的季度核心数据外，我们还提供全维度历史数据回溯、细分市场交叉分析及定制化数据模型服务。如需获取完整版数据底表或探讨专属分析维度，欢迎随时联系，我们将为您提供深度数据支持。请点击此处与我们联系。

Arthur Guo

Arthur Guo - Research Manager

Analyst Profile
Arthur Guo is a research manager for the Client Devices group in IDC China. Based in Beijing, Arthur is responsible for research and analysis of market trends, market sizing, and provincial development trends in the China mobile phone market. His…

2026 年，生成式 AI 与销售管理深度融合，中国 AI 增强型销售管理 CRM 正从传统的信息记录、流程管控，向融合智能洞察、内容生成、预测分析与任务执行的全链路平台演进。企业选型逻辑已从比拼 AI 功能数量，转向关注产品能否嵌入真实销售工作流，在权限规则约束下产出可核验的业务结果，切实提升线索质量、商机推进与服务效率。

近日，国际数据公司（ IDC） 发布了《中国 AI Enable CRM 市场份额，2026H1》报告，围绕上半年市场收入与竞争格局展开分析，为技术服务商锚定产品升级方向，也为企业用户更新 AI CRM 选型标准提供参考。数据显示，2026 年上半年市场收入达 5.971 亿元，同比增长 331.3%。头部阵营份额差距较小，前五厂商合计占据超半数市场，其余厂商贡献 47.2% 的收入，市场高速扩容的同时仍保持分散竞争格局。收入增长印证企业已开始为 AI 能力付费，但市场规模不等于场景成熟度，厂商仍需证明 AI 能力可持续嵌入销售流程，带来可量化的业务改善。

一个重要发现：2026年上半年中国AI Enable CRM市场收入同比增长331.3%，但厂商演示厅里“能跑通”的Agent，到了客户真实权限体系和异常接口面前，仍有相当比例跑不完一个完整任务链。市场在为AI能力付费，但付费的理由正在从“你有多少AI功能”转向“你的AI能不能在规则约束下把一件事做完、做对、留下可审计的痕迹”。换句话说，这一轮竞争的分水岭不是模型参数，而是任务闭环的完成率。

基于本次研究，我们梳理出行业五大核心趋势。当前市场仍处于从单点辅助向业务闭环演进的阶段，功能发布与真实落地效果尚存差距。企业选型应聚焦实际任务链表现、数据治理能力与投入产出效果，而非仅依赖功能演示。

趋势一：从记录系统到行动系统：Agent执行能力成为AI Enable CRM的竞争边界

统 CRM 以客户信息沉淀、销售过程留痕、流程管控为核心，云化阶段进一步打通了渠道与业务系统；生成式 AI 普及后，智能问答、内容生成、辅助决策开始嵌入销售工作流。进入 2026 年，AI 增强型 CRM 正向 Agentic 形态演进：智能体不仅能理解销售目标，还可直接调用 CRM 及外围系统，完成客户信息采集、商机判断、跟进执行与服务处理。系统价值正从 “记录已发生的业务” ，转向 “推动下一步行动并输出可审计结果” 。

验证真实的 Agent 能力，不能只看问答或推荐效果，需完整考察自然语言输入、任务拆解、工具调用、数据回写的全链路闭环，以及数据缺失、权限不足、接口异常等场景的容错机制。自主执行不等于全流程无人化，合同、报价、回款、客户风险等高影响动作，仍需人工确认或规则兜底。市场竞争的核心边界，已从功能覆盖广度，转向 Agent 执行范围、数据知识可用性、流程编排与治理能力的综合比拼。

趋势二：从功能堆砌到语义统一与能力复用，一致性决定规模化上限

用大模型与标准化接口的获取门槛持续降低，单纯接入模型、增加对话入口已难以形成长期差异化。销售流程中的客户、商机、合同、回款等核心对象，需要与企业组织架构、行业规则、指标口径、权限体系形成统一业务语义。行业方向正从按角色提供独立 Agent，转向打造统一 AI 工作台，将数据、业务与领域知识抽象为可复用能力模块，让智能体在全销售流程中统一继承上下文、调用权限与执行边界。

业务语义层的核心价值，是把 “能回答问题” 升级为 “知道该调用哪项业务能力、遵循哪套规则”。企业选型时，应核查同一业务对象的跨模块口径一致性、Agent 权限与现有体系的继承性，以及任务失败后的补偿、重试与人工接管机制；跨系统联动场景还需验证数据映射与全链路审计能力。只有能力可配置、可复用、可追溯，AI CRM 才能从标杆项目走向规模化复制。

趋势三：高频刚需场景率先规模化，自主决策类 Agent 仍待成熟

当前国内 AI 增强型 CRM 的落地呈现清晰的成熟度梯度：智能问数、知识库检索、坐席辅助、工单分类、合同审核、智能报价等高频、边界清晰的场景，已进入规模化应用阶段；销售预测、商机健康度评估、跟进内容生成、客户流失预警等场景已得到验证，但仍需结合企业数据质量与流程执行力持续优化。市场收入的快速增长，首先来自这些价值直观、落地门槛较低的场景。

线索挖掘、多语言内容生成、行业合规话术管控等仍处于试点阶段；营销全链路 Agent、深度办公生态融合、自主决策型智能体等方向潜力可观，但距离大规模落地仍有差距。场景数量不等于使用深度，企业应围绕解决率、处理时效、预测准确率、提效幅度、转化效果等指标建立评测体系，明确区分 “可演示” 与 “可稳定运行”，并按风险等级设置人工审核与流程回滚机制。

趋势四：生态协同、本地交付与合规能力成为选型核心考量

AI 增强型 CRM 的竞争已从单一产品能力，延伸至模型、云平台、办公入口与行业服务的生态协同层面。成熟的产品底座、存量客户基础与跨领域资源，正成为产品规模化扩张的重要支撑。与此同时，数据不出域、国产模型适配、多模型切换、私有化部署、数据脱敏、全链路审计、沙箱安全执行，已成为中大型企业采购的核心准入条件。

企业选型不宜仅凭 “支持私有化”“适配国产模型” 这类标签做决策，还需验证目标环境下的完整功能可用性、模型切换效率、系统集成难度与版本升级路径，明确实施交付、数据治理、模型调优与运维的责任边界。国际化产品的全球功能发布，不代表国内市场可直接落地使用，本地合规进度与交付节奏同样需要重点核查。

趋势五：商业模式多元演进，行业复用能力决定长期竞争力

AI 增强型 CRM 的商业化，正在传统席位订阅基础上，拓展出场景增值、资源用量、智能体调用等多元收费维度。厂商间的竞争差异，更多体现在行业流程理解、数据沉淀与交付能力上。按业务结果收费仍处探索阶段，供需双方需明确价值衡量指标，厘清各项成本的分摊规则。

规模化复制的核心，是将各行业项目经验沉淀为可复用的业务规则、流程模板与交付方法论，而非依赖定制化开发。企业采购时，应建立覆盖席位、资源消耗、系统对接、持续运营的总体拥有成本模型，并将线索质量、商机转化、服务效率等业务结果纳入续约评估。能否将项目经验沉淀为标准化产品、持续兑现可核验的业务价值，将直接决定厂商的长期竞争力。

IDC 给技术提供者的建议

建议一：以业务结果而非模型参数规划产品。围绕客户的销售目标、现有流程、量化指标和风险边界设计Agent，优先打通规则明确、风险可控的完整任务链，对合同、报价、回款和客户风险等高影响动作保留人工复核与程序兜底，并通过异常处理演示证明产品的执行可靠性。

建议二：将业务语义层和Skill复用能力作为产品底座。统一客户、商机、合同和回款等核心对象的口径，打通组织权限、数据血缘和审计机制，把可调用的业务能力标准化、服务化，避免统一交互入口与底层流程能力脱节，并将跨系统接入后的语义一致性和失败补偿纳入验证。

建议三：把数据治理和可信执行前置到项目初期。帮助客户厘清主数据、指标负责人和数据域边界，保障关键结论能够追溯至原始单据或交互记录。采用确定性程序与大模型协同执行，将置信度分流、人工审核、操作留痕、阻断回滚和持续回归测试纳入产品与交付流程，降低核心销售业务的运行风险。

建议四：按客户约束完善部署方案和生态协同。明确公有云、专属云、混合部署与纯私有化的实际支持范围，推进国产软硬件适配、多模型接入和办公生态连接器建设。对实施、系统集成、数据治理、模型调优及后续运维作出清晰约定，让客户能够判断上线条件、服务责任和持续运营投入。

建议五：以行业资产复用和可量化成效支撑规模化。聚焦两到三个核心场景及优势行业，将共创项目中的规则、流程和知识沉淀为可配置产品，合理控制定制范围并持续反馈产品迭代。建立清晰的订阅、用量与服务计费体系，帮助客户测算完整TCO，以问题解决率、处理时效、准确率和销售转化等可核验结果推动采购、续约与扩容。

IDC中国企业级应用软件市场高级研究经理徐文婷认为， 2026年中国AI Enable CRM市场进入从功能辅助向业务执行演进的关键阶段，问数、客服和合同审核等高频场景正在规模化，销售预测、客户洞察和跟进建议等场景持续验证，营销侧全链路和自主决策型Agent仍需突破。未来，厂商的竞争力将由模型接入能力转向可复用的业务语义、可治理的任务执行、合规的本地交付和可量化的销售成效。

AI Enable CRM的竞争正在从“场景覆盖竞赛”转向“失败场景治理竞赛”。高频场景之所以先规模化，本质上不是因为它们更“智能”，而是因为它们的错误可以被人工兜底、责任可以被追溯、风险可以被定价；而自主决策型Agent迟迟难以放量，核心卡点也不在模型能力，而在于失败后的归因、补偿与责任边界尚未形成可复用的机制。对厂商而言，下一阶段的差异化不在于多覆盖几个场景，而在于能否把“权限不足时的降级、接口异常时的补偿、跨系统映射失败时的审计”做成标准化能力；对企业而言，选型时应把压力测试从“功能演示”转向“异常路径演示”，要求厂商现场跑通数据缺失、权限冲突、回滚接管三类场景。谁能把失败场景治理好，谁才真正具备规模化复制的资格。

进一步交流
AI Enable CRM的选型与落地，最终要回到真实任务链和异常路径上验证。IDC将持续跟踪高频场景的规模化进展、Agent失败场景治理机制与按业务结果计费的演进。欢迎厂商与企业就产品能力验证、TCO测算与行业复用路径进一步交流，也欢迎提供真实落地案例，共同校准下一阶段的竞争坐标。请点击此处与我们联系。

Wenting Xu

Wenting Xu - Senior Research Manager

Analyst Profile
Wenting Xu, senior research manager with IDC China's Enterprise Application Research, focuses on research and analysis of the China enterprise system and software area. She provides intelligence and consulting services for local and multinational corporation IT vendors. Wenting has developed a deep…