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Posts tagged ‘AI infrastructure’

Jan 22

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Gartner 4Q25: $4.71T AI market proves agentic AI and data readiness are the only race that matters

Only 43% of organizations say their data is ready for AI. Meanwhile, AI Data spending is compounding at 155% annually. That’s six times faster than the infrastructure buildouts grabbing headlines. That disconnect defines the enterprise AI landscape in 2025.

Gartner’s 4Q25 AI Spending Forecast (December 17, 2025) projects $4.71 trillion by 2029. But I’ve been digging through the segment data, and the story isn’t the topline number. Four subsegments within Gartner’s AI Data market are growing between 136% and 178% CAGR. AI Infrastructure? Just 29.25%. The money is following the bottlenecks.

“Nearly everything today, from the way we work to how we make decisions, is directly or indirectly influenced by AI,” says Carlie Idoine, VP Analyst at Gartner. “But it doesn’t deliver value on its own. AI needs to be tightly aligned with data, analytics, and governance to enable intelligent, adaptive decisions and actions across the organization.”

McKinsey’s 2025 State of AI survey (1,993 participants, 105 countries) found 88% of organizations now use AI in at least one business function. But two-thirds remain stuck in pilot mode. Just 6% qualify as “AI high performers,” meaning organizations where more than 5% of EBIT comes from AI. The gap between adoption and value creation is where the real spending story unfolds.

Where the bottlenecks are breaking

Every high-growth segment in the forecast eliminates a constraint that stalls production of AI.

Synthetic data generation addresses the labeled data shortage. You can’t train models without it, and real world data comes with privacy constraints, bias problems, and collection costs that don’t scale. Data governance enforces quality standards because ungoverned data produces ungoverned outputs. Hallucinations, compliance violations, and bias incidents trace directly back to data quality failures. Data integration software connects fragmented sources. Most enterprise data sits across dozens of systems that don’t communicate.

“With AI investment remaining strong this year, a sharper emphasis is being placed on using AI for operational scalability and real-time intelligence,” says Haritha Khandabattu, Senior Director Analyst at Gartner. “This has led to a gradual pivot from generative AI as a central focus toward the foundational enablers that support sustainable AI delivery, such as AI-ready data and AI agents.” Infrastructure enables these capabilities. Data readiness and agentic AI determine whether they generate returns.

The $14.6 billion data readiness bet

Gartner tracks AI Data as a unified market with four subsegments. The aggregate grows from $134.35 million in 2024 to $14.59 billion by 2029. That’s 109x, making it the fastest-growing major category in the forecast.

Synthetic Data Generation: 178.29% CAGR, $40.71M to $6.80B. The fastest-growing subsegment adds $6.76 billion in new spending by 2029. A 167x increase from a small 2024 base. Gartner predicts 60% of data and analytics leaders will encounter failures in managing synthetic data by 2027, which explains why governance spending is accelerating in parallel.

AI Data Governance: 163.75% CAGR, $14.82M to $1.89B. Starting from just $14.82 million in 2024, this subsegment grows 128x by 2029. Legal and compliance teams won’t accept the alternative. When AI systems produce ungoverned outputs, the liability exposure is unacceptable.

AI Data Integration Software: 137.13% CAGR, $71.73M to $5.38B. The largest AI Data subsegment by 2029. Connects fragmented data sources, delivering context that transforms generic models into systems that understand specific business operations.

AI Ready Datasets: 136.16% CAGR, $7.09M to $520.45M. These are prepackaged, curated datasets structured for AI and ML workflows. Think labeled image libraries for computer vision, cleaned financial datasets for forecasting, and domain-specific corpora for fine-tuning LLMs. Organizations buy them to skip the months of data collection, cleaning, and annotation that delay projects. Smallest subsegment by revenue, but 73x growth signals enterprises are willing to pay for time to production shortcuts.

The 2027 crossover: When agents overtake chatbots

Agentic AI: 118.73% CAGR, $15.04B to $752.73B. This is the single most dramatic dollar growth in the forecast. Agentic AI expands from $15 billion to $753 billion by 2029. That’s 50x. Nothing else comes close.

Gartner predicts the crossover will happen in 2027. Chatbots peak at $264.75 billion that year, while Agentic AI surges to $371.40 billion. By 2029, Agentic AI is 3.3x larger ($752.73B vs. $228.50B).

McKinsey’s data reinforces the trajectory: 62% of organizations are experimenting with AI agents, 23% report scaling them in at least one function. But scaling remains limited. Most organizations deploying agents are only doing so in one or two functions, primarily IT service desk and knowledge management.

Organizations building chatbot-only strategies should note that the category dominating 2025 and 2026 is projected to decline after 2027.

The Security Tax on Agentic AI

AI Cybersecurity: 73.90% CAGR, $10.82B to $172.01B. AI agents introduce attack surfaces that traditional security architectures weren’t built for. Gartner’s Hype Cycle for Application Security, 2025 (July 2025) projects that through 2029, over 50% of successful attacks against AI agents will exploit access control issues via direct or indirect prompt injection. The 16x growth in AI Cybersecurity spending reflects enterprises grappling with that exposure.

Production AI deployment requires security architectures designed for agentic systems. That’s a capability most organizations don’t have yet.

Infrastructure: Dominant but decelerating

AI Infrastructure remains the largest absolute spending category: $624.76 billion in 2024, growing to $2.25 trillion by 2029. McKinsey (August 2025) projects hyperscalers alone will spend $300 billion in capex over 2025. Their April 2025 analysis projects $5.2 trillion in data center investment by 2030.

But at 29.25% CAGR, infrastructure grows slower than every other major AI market except Services (26.93%). Market share drops from 54.6% of total AI spending in 2024 to 47.8% by 2029. The buildout is real. Differentiation happens elsewhere.

The 6% problem

Only 6% of organizations qualify as AI high performers despite 88% adoption. McKinsey’s analysis shows high performers are 3x more likely to redesign workflows around AI rather than layering it onto existing processes. They’re also 3x more likely to have committed executive leadership driving AI as a strategic priority.

The 155% CAGR for AI Data reflects organizations investing to close that gap. The 2027 chatbot-to-agent crossover marks the inflection point when autonomous capabilities surpass conversational interfaces in market size.

Gareth Herschel, VP Analyst at Gartner, frames the pressure: “D&A is going from the domain of the few to ubiquity. At the same time, D&A leaders are under pressure not to do more with less, but to do a lot more with a lot more, and that can be even more challenging because the stakes are being raised.”

Where the value accrues

Organizations positioned to capture value from this transformation may not be the ones building the biggest data centers. The Gartner data suggests they’re investing in capabilities that make AI systems work at enterprise scale: data readiness, governance, integration, and security.

AI Data Market (aggregate): 155% CAGR, $134M to $14.6B (109x)

  • Synthetic Data Generation: 178% CAGR, $41M to $6.8B (167x)
  • AI Data Governance: 164% CAGR, $15M to $1.9B (128x)
  • AI Data Integration: 137% CAGR, $72M to $5.4B (75x)
  • AI Ready Datasets: 136% CAGR, $7M to $520M (73x)

Other High-Growth Segments:

  • Agentic AI: 119% CAGR, $15B to $753B (50x)
  • AI Cybersecurity: 74% CAGR, $11B to $172B (16x)
  • AI Infrastructure: 29% CAGR, $625B to $2.25T (4x)

Gartner’s 4Q25 data points to a directional shift: AI spending is moving from infrastructure-first to data and capabilities-first architectures. The organizations treating data readiness as an afterthought are the ones most likely to stay stuck in the 94% that never make it past pilot.

Dec 22

Data readiness and security are driving AI’s $4.7 trillion run

Gartner Projects $4.7 Trillion AI Market by 2029 as Security and Data Drive Growth

Gartner’s most comprehensive AI spending forecast reveals the fundamental growth catalysts. AI-ready data predicted to deliver a 155% CAGR. Cybersecurity at 74%. Agentic capabilities crossing 50% of software spend by 2028.

Infrastructure gets the headlines. Hyperscalers are spending over $300 billion on data centers in 2025. McKinsey projects $5.2 trillion in data center investment by 2030. NVIDIA Blackwell deployments are driving 76% growth in accelerated server spending.

Gartner’s newly released Forecast Analysis: AI Spending, 4Q25 (December 17, 2025) tells a different story about where the acceleration is happening. Global AI spending reaches $1.8 trillion in 2025 and $4.7 trillion by 2029 at 33% CAGR. The growth catalysts:

  • AI Data. 155.4% CAGR. Spending increases 7x as enterprises recognize AI-ready data is non-negotiable for scaling.
  • AI Cybersecurity. 73.9% CAGR. From $26 billion to $172 billion. Over 50% of successful AI agent attacks will exploit prompt injection through 2029.
  • AI Models. 67.7% CAGR. Reasoning models underpin 70%+ of agentic AI applications by 2029.
  • AI Software. 47.0% CAGR. Agentic capabilities cross 50% of application software spend by the end of 2028. Non-agentic spending declines starting in 2027.

Infrastructure dominates absolute spending ($965 billion in 2025, growing to $2.25 trillion by 2029). At 29.2% CAGR, it’s the slower-growth segment. The acceleration is in data, security, and agentic capabilities.

The infrastructure buildout in context

The hyperscalers are building at a pace that strains global power grids. Dell’Oro Group’s Q2 2025 analysis shows worldwide data center capex up 43% year-over-year, with accelerated server spending surging 76% on NVIDIA Blackwell deployments. Amazon, Google, Meta, and Microsoft are collectively spending over $300 billion on data center infrastructure in 2025. CreditSights estimates aggregate hyperscaler capex reaches $602 billion in 2026, with approximately 75% earmarked for AI.

Gartner’s forecast aligns with infrastructure volume. AI-optimized server spending jumps 49% in 2026, representing 17% of total AI spending. GPUs account for over 90% of AI-optimized server spending on training throughout the forecast period. Infrastructure is table stakes. The differentiation is elsewhere.

Gartner’s bubble chart mapping 2026 growth rate (X-axis) against 2024-2029 CAGR (Y-axis), with bubble size representing 2025 spending. AI Data sits alone in the upper right quadrant. AI Cybersecurity and AI Models cluster at 70%+ CAGR. AI Infrastructure anchors the center as the dominant bubble. Source: Gartner Forecast Analysis: AI Spending, 4Q25, December 2025.

Gartner’s AI spending forecast by market, 2024-2029

The maturity gap

McKinsey’s 2025 State of AI survey explains why growth rates matter more than absolute spending for most organizations. 88% of organizations now use AI in at least one business function, up from 78% a year ago. Only 6% qualify as “AI high performers”, capturing meaningful enterprise-wide financial impact. Only 1% describe themselves as “mature” in AI deployment. Gartner’s CFO survey found just 11% of finance leaders from organizations implementing AI reported seeing actual financial returns.

The bottleneck is rarely compute. Gartner identifies three categories of readiness: infrastructure, data, and human. For every 100 days of AI implementation, 25 or more days may be consumed solely by change management and workforce resistance. Sharing work tasks with an AI agent, trusting results, and managing handoffs. That’s a fundamental shift in how employees work.

What the growth rates signal

AI cybersecurity’s 73.9% CAGR reflects a threat model shift. Security teams are spending because AI agents introduce attack surfaces that traditional security architectures weren’t designed to address. Gartner projects that over 50% of successful attacks against AI agents will exploit access control issues via prompt injection through 2029. By 2028, over 75% of enterprises will use AI-amplified cybersecurity products for most use cases, up from less than 25% in 2025.

AI data’s 155.4% CAGR signals enterprises are finally investing in foundations. The smallest segment by absolute spending is the fastest-growing because organizations scaling beyond pilots are discovering that AI-ready data isn’t optional. Labeled, annotated, quality-checked. By 2029, 61% of data integration software spend will focus on delivering GenAI-ready data, up from 8% in 2025. Synthetic data becomes dominant. 77% of data used for LLM training will be synthetic by 2029, up from 4% in 2025.

Agentic AI is reshaping software economics. By the end of 2028, software with agentic capabilities crosses 50% of total application software spend, up from 2% in 2024. Starting in 2027, non-agentic software spending declines. Investment in reasoning models underpins 70%+ of agentic AI applications by 2029. Open-source agentic frameworks will power more than 75% of enterprise AI agent deployments by 2028, eroding proprietary platform pricing power.

The inference shift is underway. By 2029, 66% of AI-optimized IaaS spending supports inference, not training. The balance shifts as embedded fine-tuned models become the norm in production applications.

Forecast assumptions by segment

AI Services. By 2029, 50% of all AI projects moving into production will be GenAI-centric, up from 12% in 2025. POC abandonment rates improve from 60% in 2024 to 35% in 2029. Specialized AI services command 20-30% price premiums.

AI Software. From 2027, spending on software without agentic capabilities starts declining. By 2027, one-third of agentic AI implementations will use combinations of agents with different skills for complex tasks.

AI Models. Starting in 2027, the shift toward in-house domain-specific language models constrains new spending in the specialized model market. Open-source model adoption erodes proprietary pricing power through 2029.

AI Platforms. By 2029, over 60% of enterprises will adopt AI agent development platforms to automate complex workflows. By 2030, enterprise application portfolios will include 40% custom applications built using AI-native development platforms, up from 2% in 2025.

AI Infrastructure. Export restrictions keep Chinese ASPs at about 50% of North American levels throughout the forecast. By 2026, NVL72 will become the de facto standard for large clusters. By the end of 2027, all hyperscalers will have reaffirmed Ethernet as their primary networking choice for AI workloads.

Devices. By 2029, more than 99% of PC microprocessors will have integrated on-device AI functionality, up from 15% in 2024. By 2027, efficient small language models will enable advanced GenAI to run locally on smartphones without cloud reliance.

The capital flow

The 2026 Gartner CIO Survey found GenAI and traditional AI among the most common technology areas selected for funding increases. 84% and 81% respectively. Nearly two-thirds of U.S. VC deal value went to AI companies in the first three quarters of 2025.

By 2027, the majority of AI buyers will define business outcomes from project launch. The market matures from technology-first experimentation to outcome-driven deployment. That shift from supply-push to demand-pull separates organizations capturing value from those still running pilots.

The infrastructure buildout continues. The growth signal is clear. Data readiness, security architecture, and agentic capabilities are where the acceleration is happening.