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Posts tagged ‘AI infrastructure’

Oct 22

AI infrastructure spending forecasts compared: every major firm raised its 2030 number in 2026

AI infrastructure spending forecasts compared, 2026 revisions: Dell'Oro raised 2030 data center capex from $1.7T to more than $3T, Gartner raised 2030 data center systems 58% to $2.16T, hyperscaler capex consensus rose 52% to $800B, IDC raised 2029 AI infrastructure 42% to $1.08T; Citi's $3.8T 2030 AI capex is 3.1x IDC's $1.21T
Four firms raised the same AI infrastructure forecast between editions, with 2030 figures from Dell’Oro, Gartner and IDC. Click any chart to open it full size. Sources linked below, analysis by softwarestrategiesblog.com.

Every major AI infrastructure spending forecast went up in 2026, and the biggest increases landed on 2029 and 2030. Dell’Oro Group nearly doubled its 2030 data center capex outlook in six months. Gartner raised its 2030 data center systems forecast 58% in a single quarter. IDC raised its 2029 AI infrastructure forecast 42% in nine months.

The firms still disagree on how big the build-out gets. For 2030, the published annual figures run from IDC’s $1.21 trillion to Citi’s $3.8 trillion, a 3.1x gap. Most of that gap comes from what each firm counts, not from different views of demand.

This post puts the public numbers from 12 firms side by side. They are Gartner, IDC, Dell’Oro, Goldman Sachs, Citi, Morgan Stanley, McKinsey, Bain, Forrester, JLL, the IEA and BloombergNEF. It shows what each one measures, how far each has moved, what the five largest buyers are actually spending, what the power grid can deliver and what the build-out has to earn back. Where a firm published only a range or a scenario, I say so.

Every revision this year went up

Dell’Oro’s February 2026 report put worldwide data center capex at $1.7 trillion by 2030. Its August report raised that to more than $3 trillion. “Our 2030 data center capex outlook has nearly doubled since the January 2026 forecast,” said Baron Fung, vice president of research at Dell’Oro. He cited higher hyperscale capex guidance, higher projections for data center power capacity and higher commodity costs.

Gartner made the largest single-quarter move. Its 3Q26 Market Databook raised 2030 data center systems spending by $794.9 billion, to $2.16 trillion. That puts the 2Q26 figure at $1.36 trillion, so the forecast rose 58% in one quarterly update. Gartner says hyperscalers, sovereign AI programs and neocloud providers are all expanding compute capacity faster than expected.

IDC has raised its 2029 forecast twice. Its October 2025 release projected $758 billion in AI infrastructure spending for 2029. In April 2026 it said 2029 would exceed $1 trillion. In July it put 2029 at $1.08 trillion and 2030 at $1.21 trillion.

Wall Street raised the current year

Wall Street moved the most on the current year. Goldman Sachs reported in December 2025 that the consensus estimate for hyperscaler capex in 2026 was $527 billion. By August 2026, Goldman put the consensus at about $800 billion, a 52% increase. Goldman’s December note also showed how consistently the consensus has been too low. At the start of both 2024 and 2025, analysts expected roughly 20% capex growth, and actual growth topped 50% both years.

Near-term numbers moved least. IDC raised its 2026 forecast from $487 billion to $497 billion between April and July. Gartner’s 2026 data center systems figure rose $56.8 billion this quarter. Purchase orders and earnings guidance anchor the current year. The outer years are where forecasters are changing their minds.

Forecast revisions in 2026: Dell'Oro more than 76%, Gartner 58%, Goldman Sachs consensus 52%, BloombergNEF 52%, IDC 42%, Citi 11%
Six forecasters raised the same forecast between their two most recent public editions. Percent changes calculated from each firm’s published figures. Sources linked below, analysis by softwarestrategiesblog.com.

BloombergNEF and Citi raised theirs too

Two more forecasters moved in the same direction. BloombergNEF raised its forecast for US data center capacity in 2030 to 118 GW in July, 52% above its December 2025 outlook, and its 2035 forecast 83% to 194 GW. The announced US pipeline grew by another 101 GW over those seven months. Citi raised its 2026 to 2030 global AI capex estimate from $8 trillion to $8.9 trillion in March, and its AI revenue estimate for the same window from $2.8 trillion to $3.3 trillion.

Hyperscaler guidance is the anchor

2026 hyperscaler capex ranges: Amazon $200B to $220B, Alphabet $195B to $205B, Microsoft $175B to $190B, Meta $130B to $145B, Oracle $50B to $70B, totaling $750B to $830B
2026 capital spending ranges for Amazon, Alphabet, Microsoft, Meta and Oracle. Amazon gave no full-year guide, so its range is the figure reported from Goldman’s September note. Sources linked below, analysis by softwarestrategiesblog.com.

Every forecast in this post leans on what five companies say they will spend. Alphabet raised its 2026 capex guide on July 22 to $195 billion to $205 billion, up $15 billion at both ends. Meta raised the low end of its range to $130 billion to $145 billion. Microsoft guided calendar 2026 to roughly $175 billion, and coverage of Goldman’s September note cites $175 billion to $190 billion. Management said about $25 billion of that reflects higher component prices rather than new capacity. Amazon gave no full-year guide, and the same Goldman coverage puts it at $200 billion to $220 billion. Oracle’s fiscal 2027 target is $50 billion to $70 billion.

Added up, the ranges come to $750 billion to $830 billion. Goldman’s smoothed calendar-year estimate for the five is about $800 billion, nearly double the roughly $413 billion they spent in 2025. The IEA notes that the capex of five technology companies is now larger than global investment in oil and natural gas production.

Backlogs and chip sales point higher

The order books explain why guidance keeps rising. Oracle’s remaining performance obligations reached $664 billion in its fiscal first quarter, after more than $30 billion of new AI cloud contracts, and it delivered 850 MW of new capacity. Citi puts combined hyperscaler backlog at about $1.7 trillion at the end of the second quarter, up 151% in a year. On the supply side, Nvidia reported $89.0 billion in data center revenue for the quarter ended July 26, up 117% from a year earlier.

The 2030 numbers measure different things

AI infrastructure spending forecast 2030 by firm and scope: Citi $3.8T AI-related capex, Dell'Oro more than $3T data center capex, Gartner $2.16T data center systems and $1.97T servers, Goldman Sachs $1.6T and $1.4T, IDC $1.21T AI infrastructure
Latest published long-range figure from each firm, with what it counts. Goldman figures are for 2031 and 2028, the nearest years published. Sources linked below, analysis by softwarestrategiesblog.com.

Each forecast counts something different. IDC’s $1.21 trillion counts only server and storage systems running AI workloads. Servers made up 97.6% of IDC’s AI infrastructure spending in the first quarter of 2026. IDC does not count networking, power or buildings.

Gartner’s $2.16 trillion covers all servers, external storage and enterprise network equipment, AI and general-purpose combined. Servers alone account for $1.97 trillion of it. Gartner’s dataset does not separate AI servers from other servers.

Citi and Dell’Oro set the top of the range

Citi’s $3.8 trillion is the largest annual figure. Its September research projects global AI-related capex rising from $1 trillion this year to $3.8 trillion by 2030, counting every spender rather than equipment categories. Morgan Stanley put 2026 at a little less than a trillion dollars of AI capex, close to Citi’s starting point.

Dell’Oro’s more than $3 trillion is the broadest equipment-and-facilities figure. It covers servers, storage, network and physical infrastructure bought by every class of buyer. Dell’Oro breaks out the top four US cloud providers, the top three Chinese cloud providers, AI model builders and neoclouds, colocation, telecom and enterprise. Fung said the top four US hyperscalers alone could account for about half of global capex.

Gartner publishes two AI infrastructure numbers

Gartner itself publishes two different AI infrastructure numbers. Its September AI spending forecast puts AI infrastructure at $1.48 trillion in 2026 and $1.98 trillion in 2027. That category adds AI-optimized IaaS, AI semiconductors and AI devices. The 2026 figure is $606 billion more than Gartner’s data center systems figure for the same year.

Firm What it measures Latest long-range figure Earlier figure Scope
Citi Research Global AI-related capex $3.8T (2030) $8.0T for 2026-2030, raised to $8.9T in March 2026 AI-related capex by all spenders, from $1T in 2026
Dell’Oro Group Worldwide data center capex >$3.0T (2030) $1.7T for 2030 in Feb. 2026 Servers, storage, network and physical infrastructure, all buyers
Gartner Data center systems end-user spending $2.16T (2030) $1.36T for 2030 in 2Q26 Servers, external storage, enterprise network equipment, AI and general-purpose
Gartner AI infrastructure (AI spending forecast) $1.98T (2027) n/a AI-optimized servers and IaaS, AI network fabric, AI semiconductors, AI devices
Goldman Sachs Research Capex, five largest US hyperscalers $1.4T (2028) Consensus $527B for 2026 in Dec. 2025 Total company capex for Amazon, Alphabet, Microsoft, Meta and Oracle
Goldman Sachs Global Institute Baseline AI capex scenario $1.6T (2031) n/a Compute, data centers and power, modeled from Nvidia data center revenue
IDC AI infrastructure spending $1.21T (2030) $758B for 2029 in Oct. 2025 Server and storage systems running AI workloads
McKinsey Cumulative data center capex $6.7T (through 2030) n/a AI and traditional data centers, including chips, buildings and power
Morgan Stanley Global data center capex $2.9T (2025-2028) n/a Data center construction, with a financing split by source
JLL Data center sector investment Up to $3T (2026-2030) n/a About 100 GW of new capacity, real estate, fit-out and debt

Each firm’s latest public figure as of October 2026. Gartner data center systems from the 3Q26 Market Databook dataset, current U.S. dollars. Earlier figures from each firm’s prior public release.

Five curves for the same build-out

AI infrastructure spending forecasts by year 2024 to 2030 from Gartner, IDC, Dell'Oro, Citi and Goldman Sachs, close together in 2026 and 3.1x apart by 2030
Published annual figures by year from Gartner, IDC, Dell’Oro, Citi and Goldman Sachs. Dashed segments join years a firm did not publish. Sources linked below, analysis by softwarestrategiesblog.com.

In 2026 the forecasts sit close together. IDC puts AI infrastructure at $497 billion. Gartner puts data center systems at $878.5 billion. Goldman puts capex at the five largest US hyperscalers at about $800 billion and global AI investment at $1 trillion. Dell’Oro said in February that global data center capex would approach $1 trillion.

By 2030 the curves split. Gartner’s growth slows sharply, from 71.5% in 2026 to 8.7% in 2030 in constant currency. IDC’s grows about 30% a year from 2025 to 2030. Dell’Oro’s broader figure keeps climbing to more than $3 trillion, and Citi’s AI capex line reaches $3.8 trillion.

Goldman’s year-by-year view is the steepest in the near term. It expects capex at the five largest US hyperscalers to reach $1.2 trillion in 2027 and $1.4 trillion in 2028. Those five companies alone would spend more in 2028 than IDC expects the whole world to spend on AI servers and storage in 2030.

Multi-year totals run from $2.9 trillion to $8.9 trillion

Cumulative AI and data center capex estimates: Citi $8.9T 2026-2030, Gartner $8.07T, Goldman Sachs $7.6T, McKinsey $6.7T and $5.2T for AI, JLL $3T, Morgan Stanley $2.9T 2025-2028
Cumulative multi-year totals from Citi, Gartner, Goldman Sachs, McKinsey, JLL and Morgan Stanley. Gartner summed from the 3Q26 dataset. Sources linked below, analysis by softwarestrategiesblog.com.

In April 2025, McKinsey estimated that data centers would need $6.7 trillion in capex worldwide by 2030. Of that, $5.2 trillion would go to AI data centers, within a scenario range of $3.7 trillion to $7.9 trillion. McKinsey’s total includes chips, buildings and power.

Gartner’s latest forecast puts equipment spending alone above that. Data center systems total $8.07 trillion from 2026 to 2030 in the 3Q26 dataset. Gartner’s number includes general-purpose servers and leaves out buildings and power. Even so, the comparison shows how far expectations have moved in 18 months.

The Goldman Sachs Global Institute’s baseline puts cumulative AI capex at about $7.6 trillion from 2026 to 2031 across compute, data centers and power. Goldman says this is a scenario built on stated assumptions, not a forecast. It models annual AI capex rising from $765 billion in 2026 to $1.6 trillion in 2031.

Citi’s $8.9 trillion for 2026 to 2030 is the largest cumulative figure. Morgan Stanley estimates about $2.9 trillion of global data center capex from 2025 to 2028. JLL expects the sector to need up to $3 trillion over five years to add roughly 100 GW, nearly doubling global capacity from 103 GW to 200 GW by 2030.

Forrester does not publish a data center total, but its global tech forecast points the same way. It expects AI-specialized computers, including AI servers, to account for more than 80% of computer equipment spending by 2030, up from 43% in 2024.

Power is the constraint every forecast names

Data center power forecasts: IEA 485 TWh in 2025 to 950 TWh in 2030, JLL 103 GW to 200 GW, BloombergNEF US 118 GW in 2030 up 52%, Citi new AI power 19, 29 and 45 GW
Electricity and capacity outlooks from the IEA, JLL, BloombergNEF and Citi. BloombergNEF’s December 2025 value derived from the stated 52% revision. Sources linked below, analysis by softwarestrategiesblog.com.

The IEA’s July update projects global data center electricity use roughly doubling from 485 TWh in 2025 to 950 TWh in 2030, about 3% of world demand. Consumption at AI-focused data centers grew 50% in 2025 and triples by 2030 in the IEA’s projection. Citi expects new AI-related power capacity of 19 GW in 2026, 29 GW in 2027 and 45 GW in 2028. JLL expects AI to account for half of global data center capacity by 2030, up from about a quarter in 2025.

The grid is where the dollar forecasts and the physical forecasts part ways. BloombergNEF calculates a 63 GW gap by 2033 between what AI chip shipments imply the US should build and what its own forecast says will get built, because of energy constraints. That gap is the clearest signal of which outer-year spending numbers are most at risk.

The return question got bigger

AI infrastructure financing and returns: Morgan Stanley $2.9T data center capex split by funding source, Goldman $800B capex vs $300B break-even AI revenue, Bain $6T revenue needed by 2031
Morgan Stanley’s financing split for $2.9 trillion of data center capex, Goldman’s break-even estimate and Bain’s 2031 revenue requirement. Sources linked below, analysis by softwarestrategiesblog.com.

Higher spending forecasts raised the bar for returns too. Bain’s 2026 Global Technology Report says funding AI’s compute demand would require $6 trillion in annual revenue by 2031. Existing consumer and enterprise AI applications could bring in $1.2 trillion to $1.8 trillion of that, leaving $4.2 trillion to come from new revenue. A year earlier, Bain’s estimate was $2 trillion by 2030 with an $800 billion shortfall. “AI infrastructure is being built well ahead of the demand curve,” said David Crawford, chairman of Bain’s global technology practice.

Goldman frames the same question at the company level. In a September 25 note, strategists led by Ryan Hammond estimated the five largest US hyperscalers need roughly $300 billion in annual AI revenue to break even on about $800 billion of 2026 spending. That is the steady-state revenue needed to cover depreciation and running costs, not a one-year payback. Goldman estimates AI cloud revenue is running only about $70 billion above the pre-AI trend, leaving a gap of roughly $230 billion a year. It also expects the four largest cloud providers to issue about $250 billion in bonds in 2026, roughly a third of capex, up from $108 billion in 2025.

Who funds the build-out

Morgan Stanley’s financing map shows how much of the build-out sits outside hyperscaler cash flow. Of $2.9 trillion in data center capex through 2028, it expects $1.4 trillion to come from hyperscaler cash flows and $800 billion from private credit. The remaining $700 billion comes from private equity, venture and sovereign capital, corporate debt and securitized credit. Citi offers the counterpoint, saying hyperscalers are generating cash returns of roughly 30% on invested capital from their infrastructure spending.

The forecasters name the same constraints. Dell’Oro says growth depends on “the sustainability of investment, power availability, and supply chain conditions.” It adds that enterprise investment “remains constrained by uncertain AI returns.” Gartner warns that memory prices have more than doubled over the past 12 months and expects the pressure to last through 2027.

Each firm’s forecast has risen with hyperscaler guidance. If that guidance flattens, the outer-year numbers, which moved most this year, are also the ones most likely to come down.

How to read these forecasts

  • Match the scope before comparing numbers. Check whether a figure counts AI-only systems, all data center equipment, or equipment plus buildings and power. The same build-out can measure $1.2 trillion or $3.8 trillion in 2030.
  • Weight near-term figures more heavily. 2026 numbers moved least this year because orders and guidance anchor them. Forecasts for 2029 and 2030 changed by 42% to 76% within a year.
  • Track hyperscaler guidance first. Dell’Oro and Goldman both tie their revisions to hyperscaler capex plans, and the top four US hyperscalers could make up half of global capex.
  • Separate spending from returns. None of these forecasts measures whether the spending pays off. Bain’s $4.2 trillion revenue gap is a different question from how much gets spent.
  • Watch power before price. BloombergNEF’s 63 GW US gap and the IEA’s doubling of data center electricity use set the physical ceiling on outer-year dollar forecasts.
  • Expect the next edition to move again. Goldman’s data shows consensus estimates fell short of actual capex growth in 2024 and 2025.

Frequently asked questions

How much will the world spend on AI infrastructure in 2026? It depends on scope. IDC forecasts $497 billion for AI server and storage systems. Gartner forecasts $878.5 billion for all data center systems and $1.48 trillion for its broader AI infrastructure category. Goldman Sachs estimates about $800 billion in capex at the five largest US hyperscalers.

How much will be spent on data centers by 2030? Dell’Oro forecasts more than $3 trillion in worldwide data center capex in 2030. Citi projects $3.8 trillion in global AI-related capex in 2030. Gartner forecasts $2.16 trillion in data center systems spending. IDC forecasts $1.21 trillion in AI infrastructure.

Why do AI infrastructure forecasts differ so much? They count different things. IDC counts AI server and storage systems. Gartner counts all data center equipment. Dell’Oro adds physical infrastructure and every class of buyer.

How much are the hyperscalers spending in 2026? The 2026 ranges for Amazon, Alphabet, Microsoft, Meta and Oracle add up to $750 billion to $830 billion. Goldman’s calendar-year estimate is about $800 billion, nearly double the roughly $413 billion they spent in 2025.

How much revenue does AI need to justify the spending? Bain estimates $6 trillion in annual revenue by 2031, with $4.2 trillion still to be found beyond existing AI applications. Goldman estimates the five largest US hyperscalers need about $300 billion in annual AI revenue to break even on 2026 spending.

How much power will data centers use by 2030? The IEA projects 950 TWh worldwide in 2030, about double 2025. BloombergNEF forecasts 118 GW of US data center capacity in 2030.

Which forecast was revised the most in 2026? Dell’Oro’s 2030 data center capex outlook, which rose from $1.7 trillion in February to more than $3 trillion in August.

How I built this analysis

All figures come from each firm’s public press releases, earnings releases, research pages and news coverage of their notes, linked below, plus the Gartner 3Q26 Market Databook and its dataset. I calculated the percentage revisions, Gartner’s cumulative totals, the sum of the hyperscaler capex ranges, BloombergNEF’s December 2025 value from its stated revision, and the 2Q26 Gartner data center systems figure. That figure is the 3Q26 value of $2,157.6 billion less Gartner’s published revision of $794.9 billion. Where a firm published a scenario rather than a forecast, the post says so. Charts are my own work.

My work at Dassault Systèmes includes analyst briefings with IDC, which had no role in this post.

More analysis from this blog

This post is my personal analysis and does not represent my employer.

Sources

Forecast and research firms

Company results and coverage

Oct 19

Gartner’s $9.76T IT spending forecast: servers will outspend all IT services by 2030

Gartner IT spending forecast 2024 to 2030 rising from $5.04 trillion to $9.76 trillion, stacked by data center systems, software, IaaS, IT services, communications services and devices, with six KPI tiles
Gartner’s 3Q26 worldwide IT spending forecast by segment, 2024-2030, with six tiles on the numbers that matter most. Click any chart to open it full size. Source: Gartner (G00862058, Sept. 25, 2026), analysis by softwarestrategiesblog.com.

Gartner’s latest IT spending forecast puts worldwide IT spending at $6.39 trillion in 2026 and $9.76 trillion by 2030. Spending grows 13.2% in 2026 in constant currency, or 14.5% in current dollars, and compounds at 11.2% a year from 2025 to 2030.

One subsegment drives that growth more than any other. Server spending grows from $386.5 billion in 2025 to $1.97 trillion in 2030. That $1.58 trillion gain is 38% of the $4.18 trillion the IT market adds over the period. By 2030, the world spends more on servers than on all IT services combined.

Gartner calls building AI compute capacity “the largest infrastructure build-out in history.” This edition makes the build-out bigger. Gartner raised its 2030 total by $816.8 billion from last quarter, and data center systems account for $794.9 billion of that increase.

I built this analysis from Gartner Market Databook, 3Q26 (G00862058), published September 25, 2026. Segment totals, growth rates and revisions come from the report. Subsegment and regional figures come from the 7,896-row dataset Gartner publishes with it, which covers 19 subsegments, nine regions and 47 countries. The shares, dollars added and comparisons are my calculations.

Servers take 38% of new IT spending

Gartner IT spending forecast line chart showing server spending rising from $0.23 trillion in 2024 to $1.97 trillion in 2030, passing all IT services at $1.93 trillion
Servers against all IT services, all software and communications services, 2024-2030. Growth labels in constant currency. Source: Gartner (G00862058), analysis by softwarestrategiesblog.com.

Server spending nearly doubles in 2026, up 84.4% in constant currency to $722.4 billion. It grows another 64.7% in 2027 to $1.19 trillion. After that the curve flattens: 26.8% in 2028, 16.0% in 2029 and 9.5% in 2030.

Even with the slowdown, servers reach $1.97 trillion in 2030, $45 billion more than the $1.93 trillion Gartner forecasts for all IT services. In 2025, IT services spending was nearly four times server spending.

Gartner explains why the numbers keep rising. Hyperscalers, sovereign AI initiatives and neocloud providers are all building next-generation data centers. Gartner says servers are increasingly viewed “as revenue-generating assets capable of monetizing AI workloads over many years,” not as ordinary hardware. Backed by hyperscaler capital spending, sovereign AI programs and private capital, AI infrastructure is becoming “both a technology platform and an investable asset class.”

The dataset does not separate AI-optimized servers from other servers, so I report the full server line. Gartner’s commentary leaves little doubt about where the growth comes from.

AI infrastructure and AI software take nearly half

Three subsegments map most directly to AI: servers, AI-optimized IaaS, and AI platforms and models. Together they add $1.96 trillion from 2025 to 2030, or 47% of all new IT spending.

AI-optimized IaaS grows fastest of all 19 subsegments, at a 55.3% CAGR. Spending more than doubles in 2026 and reaches $199.7 billion in 2030. AI platforms and models grow at a 42.8% CAGR, from $39.3 billion to $239.5 billion.

Traditional IaaS adds $287.4 billion at an 18.6% CAGR, so the cloud does not lose ground. Total IaaS reaches $687.7 billion in 2030.

Bar chart of 19 Gartner IT spending subsegments by dollars added from 2025 to 2030, led by servers at $1,584 billion
All 19 Gartner subsegments by dollars added from 2025 to 2030, with constant-currency CAGR and 2030 spending. Source: Gartner (G00862058), analysis by softwarestrategiesblog.com.

Servers add more than the next five subsegments combined. Infrastructure software, enterprise application software, traditional IaaS, vertical-specific software, and AI platforms and models add $1.52 trillion together. Servers alone add $1.58 trillion.

Segment 2025 2026 2030 2026 growth CAGR 2025-2030 Added 2025-2030 Share of new spending
Data center systems $505.5B $878.5B $2,157.6B 71.5% 32.6% +$1,652.1B 39.5%
Software $1,270.9B $1,464.5B $2,500.9B 14.1% 14.0% +$1,230.0B 29.4%
IaaS $222.2B $291.6B $687.7B 29.1% 24.6% +$465.5B 11.1%
IT services $1,491.6B $1,556.8B $1,925.6B 3.4% 4.6% +$434.0B 10.4%
Communications services $1,299.5B $1,352.5B $1,519.3B 2.6% 2.6% +$219.8B 5.3%
Devices $790.3B $847.2B $970.1B 5.9% 3.8% +$179.7B 4.3%
Total IT spending $5,580.0B $6,391.1B $9,761.2B 13.2% 11.2% +$4,181.2B 100%

Spending in current U.S. dollars. Growth rates and CAGRs in constant currency. Dollars added and shares calculated by Software Strategies Blog from Gartner’s 3Q26 dataset (G00862058).

Data center systems account for 97% of the revision

Gartner IT spending forecast revision versus 2Q26 by segment, 2026 to 2030, with data center systems up $795 billion in 2030
Change from Gartner’s 2Q26 forecast by segment, 2026-2030, in $ billions. Source: Gartner (G00862058, Table 2), analysis by softwarestrategiesblog.com.

The size of the revision shows how fast Gartner’s view of AI infrastructure is changing. Compared with the 2Q26 forecast published in June, data center systems rise $56.8 billion in 2026, $299 billion in 2027 and $794.9 billion in 2030. Their 2027 growth rate rises by 26.2 percentage points, to 55.1%.

Almost everything else moves sideways or down. Software is trimmed in every year, by $9.4 billion at most. IT services is cut every year, by $13.1 billion in 2026 and $24.4 billion in 2027, and Gartner slows its 2026 services growth by about half a percentage point. Gartner says enterprises are moving budgets toward AI infrastructure and software, so services revenue arrives more slowly.

Devices fall $20.8 billion in 2026. Gartner attributes that cut to “memflation.” Demand from data center systems has more than doubled memory prices over the past 12 months, and those costs are cascading into storage, servers, networking, PCs and other devices. Higher prices raise spending per unit but cut the number of units sold. Gartner expects the effect to last through 2027 before hardware prices start to normalize.

Four raises in a row for 2026

Gartner forecast for 2026 IT spending by edition, rising from $6.08 trillion in October 2025 to $6.39 trillion in September 2026
Gartner’s forecast for 2026 worldwide IT spending by edition. Earlier editions from Gartner press releases. Source: Gartner (G00862058 and earlier editions), analysis by softwarestrategiesblog.com.

This is the fourth straight increase in Gartner’s 2026 forecast. In October 2025, Gartner expected 2026 IT spending to reach $6.08 trillion. It raised that to $6.15 trillion in February, $6.31 trillion in April and $6.37 trillion in July. The 3Q26 databook puts 2026 at $6.39 trillion, about $310 billion above the October 2025 forecast.

The September increase for 2026 is small, at $22.6 billion. The big change is in the outer years: 2028 rises $548.5 billion and 2030 rises $816.8 billion. Gartner now expects the AI build-out to run longer, not just faster.

Agentic arbitrage puts $234 billion of app spending at risk

Gartner says software spending is broadly stable this quarter, but “a structural shift is emerging beneath the top-line numbers.” It calls the shift agentic arbitrage, which it says is often described as the “SaaSpocalypse.”

Gartner estimates that about $234 billion of enterprise application spending is exposed to agentic arbitrage through 2030. AI agents increasingly run workflows without using a software interface, which weakens seat-based licensing.

Separately, Gartner expects cross-functional agentic AI to consume $116 billion of application software spending by 2030. Enterprises would shift from buying separate applications to agentic platforms that orchestrate work across CRM, ERP, supply chain management and other systems. Gartner expects the near-term impact to be modest because of adoption and switching constraints. It expects the shift to speed up in the later years, favoring vendors that control orchestration, workflow execution and enterprise context.

The databook’s enterprise application software line reaches $795.4 billion in 2030. Gartner’s standalone application software forecast, which I analyzed earlier this month, puts the market at $812.6 billion, because the two reports use different scopes.

Where the money goes

North America adds $1.99 trillion from 2025 to 2030, or 48% of new IT spending. It also takes $885.1 billion, or 56%, of new server spending. China adds $372.0 billion in servers, or 24%, and Europe adds $161.8 billion.

Business buyers account for 92.5% of the increase. Consumer spending grows $312.9 billion over five years, and phones and connectivity make up $300.2 billion of that.

Two subsegments lose ground. External controller-based storage peaks at $42.3 billion in 2027 and falls to $39.8 billion by 2030. Unified communications declines 1.8% a year and is the only subsegment smaller in 2030 than in 2025.

Gartner also flags risks outside the market. It expects disruption around the Strait of Hormuz to continue into 2027, with energy prices more than 50% above pre-war levels. Gartner expects companies to change sourcing, vendors and regional exposure rather than cut overall IT spending. It also warns that inflation and higher interest rates could return, which would weaken business and consumer confidence.

What technology leaders should do

  • Budget for memflation now. Gartner expects memory-driven price increases on servers, storage, PCs and other devices to last through 2027. Lock in refresh pricing early or plan for fewer units per dollar.
  • Treat AI infrastructure capacity as a sourcing decision. AI-optimized IaaS grows at a 55.3% CAGR. Compare neocloud, hyperscaler and on-premises capacity on total cost, not list price.
  • Reprice seat-based software before renewals. Gartner’s $234 billion exposure estimate means agents will challenge per-user licensing. Ask vendors how pricing changes when agents do the work.
  • Expect services partners to change their pitch. IT services grows at 4.6% a year and is revised down every year. Providers will push AI implementation work to grow, so ask for outcome-based contracts.
  • Watch the outer years. Most of the $816.8 billion revision lands in 2028 to 2030. Server growth slows to 9.5% by 2030, so server growth peaks in 2026 and 2027.

Frequently asked questions

How much will the world spend on IT in 2026? Gartner forecasts $6.39 trillion, up 13.2% in constant currency and 14.5% in current dollars.

How much will the world spend on IT in 2030? Gartner forecasts $9.76 trillion, an 11.2% CAGR in constant currency from 2025.

Which segment grows fastest? Data center systems, at a 32.6% CAGR. Among subsegments, AI-optimized IaaS grows fastest at 55.3%.

How much of the growth comes from servers? Servers add $1.58 trillion from 2025 to 2030, or 38% of new IT spending.

What is memflation? Gartner’s term for memory price inflation driven by data center demand. Memory prices have more than doubled over the past 12 months, raising prices across hardware categories.

What is agentic arbitrage? Gartner’s term for AI agents running workflows without software user interfaces, which reduces reliance on seat-based licensing. Gartner estimates $234 billion of enterprise application spending is exposed through 2030.

How I built this analysis

Segment totals, growth rates, revisions and quotes come from Gartner Market Databook, 3Q26 (G00862058). Subsegment, regional and customer-type figures come from the dataset Gartner publishes with the report. I calculated constant-currency CAGRs from 2025 to 2030, dollars added from 2025 to 2030, and shares of new spending. Earlier 2026 forecast figures come from Gartner’s October 2025, February 2026, April 2026 and July 2026 press releases. Charts are my own work from that data.

Earlier analysis in this series:

This post is my personal analysis of Gartner’s research and does not represent my employer.

Sources

Aug 5

Gartner’s $5.95 trillion AI forecast puts the chatbot era on a 2027 deadline

Spending on the chatbots and assistants embedded in enterprise software peaks at $272.6 billion in 2027 and then shrinks every year through 2030. Gartner buried that projection inside the 2Q26 update of its worldwide AI spending forecast, published July 24, and it matters more than the headline total. By 2030, embedded chatbot spending falls back to $205.8 billion, a hair above its 2025 starting point.

Embedded agenticAI takes the money instead, growing from $88.2 billion in 2025 to $1 trillion by 2030, an 11.4x expansion inside a single software category.

None of that slows the topline. Worldwide AI spending reaches $2.67 trillion in 2026, up 49.5% from 2025, on its way to $5.95 trillion by 2030. The figure Gartner published in May was $2.59 trillion for this year. Ninety days later, the client-facing number runs $74.8 billion higher, and the firm added $496.8 billion to its comparable 2025 through 2030 outlook in a single quarter.

Four tables below show where the money lands, which segments stall, and what Gartner changed its mind about between April and July.

Where $5.95 trillion lands

Infrastructure stays the biggest line through 2030 at $2.79 trillion, even as its share of total spending slides from 55% in 2025 to 46.9% at the end of the window. AI-optimized servers alone reach $981.7 billion by 2030, a 3.4x jump from 2025, and AI processing semiconductors add another $656.4 billion. O

One caution before quoting the total anywhere. Gartner’s note flags the forecast as a view across the whole AI value chain, so the chip and the server it ships inside both get counted. Read $5.95 trillion as the size of the AI economy, not as net end-user budgets.

Devices carry more of the infrastructure number than most readers expect. Business and consumer AI devices combine for $904.4 billion in 2030, and $647.4 billion of that is consumer hardware, the AI PCs and phones landing in shopping carts rather than data centers.

Growth flattens fast after next year. Total spending rises 49% in 2026 and 36% in 2027, then steps down to 21%, 18% and 15% through 2030, while infrastructure decelerates from 51% growth this year to 9% at the end of the forecast.

Of the $883.8 billion in net-new AI spending arriving in 2026, infrastructure absorbs $502.5 billion, or 57 cents of every new dollar. The shape of the curve says the buildout peaks now and software inherits the growth.

Farther down the board, AI cybersecurity at $220.9 billion and AI agents and assistants at $219.9 billion finish 2030 within $1 billion of each other. Gartner created the agents category only this quarter.

Agentic AI crosses $1 trillion inside enterprise software

Gartner rebuilt its segmentation this quarter, splitting cross-functional and consumer agents out of AI software and adding consumer agents to the forecast for the first time. The new structure exposes a replacement cycle the old rollup hid. Inside enterprise software, agentic AI overtakes chatbots in 2027, the same year chatbot spending tops out, and from that peak to 2030 the embedded chatbot line surrenders $66.8 billion.

Cross-functional agents, the ones that work across software from multiple vendors, start from a base of zero. Gartner books $347 million for cross-functional agentic AI in 2026 and $78.1 billion in 2030, a number it raised this quarter on the thesis that these agents begin cannibalizing traditional SaaS by decade’s end. The ceiling matters as much as the curve. Against $1.21 trillion in total 2030 AI software spending, $78.1 billion says the incumbents hold the decade, because data access, integration complexity and execution reliability hold the category back from serious SaaS competition until 2030, in Gartner’s read.

The buy-versus-build verdict is just as lopsided. Agent builder platforms, the tooling for constructing your own agents, reach only $12.6 billion by 2030, so embedded agentic AI outspends them nearly 80 to 1. The first production agent most companies run will ship inside software they already own. Gartner describes exactly that race, with vendors across software categories embedding agentic AI to defend their installed bases against cross-functional challengers.

Consumer agents barely register yet in dollar terms. Gartner carries $26.9 million for consumer agentic AI in 2026, then $17.7 billion in 2027 as paid consumer agents arrive at scale, building to $51.8 billion by 2030. Adding consumer agents and assistants lifted Gartner’s 2030 total by $133 billion. For how these agent numbers stack against other analyst estimates, see my roundup of agentic AI forecasts and market estimates, 2026.

AI security expands 8.5x and splits in two

AI cybersecurity grows from $25.9 billion in 2025 to $220.9 billion in 2030, an 8.5x expansion at a 53.5% compound rate. Spending in the category grew 140% in 2025, and Gartner models another 98% jump this year. AI cybersecurity and AI data were also the only two markets left completely untouched between the 1Q26 and 2Q26 forecasts, which makes security the steadiest conviction in the entire model. For the standalone security spending outlook, see my breakdown of Gartner’s 2Q26 information security forecast.

Two markets move at different speeds inside the category. AI-amplified security, meaning AI capability inside security tooling, carries the volume and reaches $204.5 billion by 2030. Securing AI, the discipline of protecting AI systems themselves, runs smaller and faster, from $1.5 billion in 2025 to $16.4 billion in 2030 at a 60.4% compound rate.

Set the security numbers against the agent forecast and an exposure gap opens. The 2030 outlook has enterprises running $1.08 trillion of embedded and cross-functional agentic software while spending $16.4 billion to secure AI systems, roughly $66 of agentic software for every $1 of securing-AI budget. AI observability and governance tooling adds just $3.9 billion more. A software wave that large riding on a security ratio that thin is the budget argument CISOs should be starting now.

The fastest growth goes to whatever cuts the bill

Rank every segment by compound growth and one pattern jumps out, because the fastest-growing lines are the ones that make AI cheaper. Synthetic data generation leads the entire forecast at a 142.5% compound rate, expanding 84x from $146 million in 2025 to $12.2 billion in 2030. AI-ready datasets, the licensed real-data alternative, peak at $583 million in 2029 and then decline, leaving synthetic data outselling licensed data 22 to 1 by 2030. Gartner is forecasting the substitution of purchased data itself.

Domain-specific language models tell the same cost story at larger scale. DSLMs and specialized models grow 210% in 2026 and compound at 84.5% through 2030, rising from 12.2% of all model spend to 24.3%. Cost pressure also explains the strangest revision in the update. Gartner cut $57.8 billion in cumulative dollars from its generative AI model forecast while raising the segment’s 2026 growth rate from 110% to 117%, which nets out to more deployments running on cheaper models and smaller checks. Arunasree Cheparthi, a senior principal research analyst at Gartner and one of the forecast’s authors, said in the firm’s July 20 platforms and models announcement that spending “is shifting toward providers who can demonstrate clear value.”

What Gartner changed in 90 days

Between the April forecast and this one, Gartner added $500.8 billion to AI infrastructure across the 2025 through 2030 window, the largest revision in the update, and did it while flagging memory-related price increases. The note calls infrastructure demand inelastic to that pricing pressure, because hyperscalers keep buying AI-optimized servers on the conviction that model capabilities improve through 2030.

Software took the other side of the trade. AI software gained $191.9 billion and application development platforms picked up $10.7 billion. Gartner lifted the 2026 app-dev growth rate from 28% to 39% as enterprises build custom AI applications and demand usage tracking to prove the spend. The cuts land on everything that resembles consulting or plumbing. AI services lost $80.7 billion across the window, with every single year revised down, and platforms for data science and machine learning lost $68.2 billion, including an 8% cut to 2027 alone.

The services cut hides a structural shift rather than a retreat. Gartner still sizes AI services at $1.25 trillion in 2030, but the growth belongs to indirect services, which compound at 34.5% and pass direct, consulting-led engagements in 2028. Direct AI services compound at 15.7%, less than half the indirect rate. Buyers are routing transformation budgets through software and cloud purchases instead of billable hours.

Three dates to plan against

2027 is the year chatbot spending tops out and agentic AI takes over inside enterprise software, which gives any vendor still selling assistant-branded features through the end of next year to ride what growth remains. By 2028, indirect services pass consulting-led engagements and infrastructure growth drops to 15%, so the buildout stops flattering everyone’s numbers. And 2030 arrives with $1.08 trillion of agentic software guarded by $16.4 billion of securing-AI spend. The first two dates decide where the money goes, and the third decides what happens when it arrives unprotected.

This post is my personal reflection on Gartner’s AI spending research from an industry analyst perspective. It does not represent my employer.

Source: Gartner, Forecast: AI Spending, Worldwide, 2025-2030, 2Q26, Kay Arnott, Jon Erensen, Amarendra, Adrian O’Connell, Arunasree Cheparthi, Naresh Singh, Peter Middleton, Hardeep Singh, Shailendra Upadhyay, Rishi Padhi, 24 July 2026, G00855896.

Jan 22

1 Comment

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

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

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.