Oct 8
Gartner forecasts enterprise application software spending of $496.3B in 2026 and $812.6B by 2030. CRM captures $119.2B of the $316.2B market gain, while cross-functional agentic AI cannibalizes $116B of traditional app spend.
The 3Q26 Gartner information security forecast raised exactly one growth rate.
Gartner now projects firewall equipment spending to grow 13.4% in 2027 in constant currency, up from the 7.8% in its June forecast. Network security equipment overall rises to 12.8% from 8.7%. Post-quantum cryptography (PQC) is the cause. Many installed firewalls can’t run the new algorithms, which forces early replacement.
Every other business category in the 41-category forecast kept the constant-currency outlook it had 91 days earlier.
Published September 24, the forecast puts worldwide end-user spending at $247.5 billion in 2026, up 13.6% in current U.S. dollars and 12.7% in constant currency.
Spending reaches $372.8 billion by 2030. Gartner frames that as $373 billion and a 10.8% compound annual growth rate in constant currency from 2025 through 2030.
I built this analysis from Gartner’s 3Q26 report (G00862059), the full detailed dataset of 13,489 rows covering 47 countries, 9 regions, 41 categories and 7 years, and a line-by-line comparison against the 2Q26 file Gartner released in June (G00855892).
For the 2Q26 update that first counted securing AI inside the forecast, see Gartner’s $248.2B security forecast makes securing AI the only segment accelerating through 2030.
Each bar in the hero chart stacks four parts of Gartner’s forecast in current U.S. dollars. Navy is business security software, mid-blue is security services, sky blue is network security and light blue is consumer security software. Bold figures above the bars give each year’s total, with Gartner’s constant-currency growth rate in italics.
Six tiles underneath carry the 2026 and 2030 totals, the $125.4 billion in new spending between 2026 and 2030, the 2030 firewall revision in constant currency, the change in Gartner’s 2027 firewall growth forecast and the securing AI trajectory.
Both the 56% software share and the $80.5 billion software tile include consumer security software.
Gartner splits the market into security software, security services and network security. Software keeps gaining share in every year of the forecast.
Each column in the chart below adds to 100% of total spending. Navy is security software, including consumer. Mid-blue is security services and sky blue is network security.
A panel on the right lists each market’s 2024 and 2030 spending and share. I calculated the shares from Gartner’s current-dollar figures.
Total market 2026. $247.5 billion, up $29.7 billion from $217.7 billion in 2025.
Security software 2026. $126.9 billion including $9.0 billion of consumer security software. Software reaches $207.4 billion by 2030, 55.6% of all spending, up from 49.6% in 2024.
Security services 2026. $93.5 billion. Services reach $125.4 billion by 2030, but their share falls from 39.3% in 2024 to 33.6%. Constant-currency CAGR is 7.5%, the slowest of the three markets.
Network security 2026. $27.0 billion, growing 15.0% in constant currency. It reaches $40.0 billion by 2030. Gartner raised the 2025 to 2030 CAGR for this market to 10.9%, up from 9.8% in 2Q26.
2026 to 2030 net new spending. $125.4 billion. Security software captures $80.5 billion of it, or 64%. Services add $31.9 billion. Network security adds $13.0 billion.
Gartner’s near-term call is unchanged from June. Growth slows to 11.2% in 2027 in constant currency, then decelerates to 9.6% by 2030.
Gartner names security service edge (SSE), cloud-native application protection platforms (CNAPPs), cloud security posture management, cloud access security brokers, web application firewalls, encryption and enterprise data loss prevention as the areas where 2027 budgets will grow.
It also expects AI trust, risk and security management (AI TRiSM) adoption to rise as generative AI use widens data, application and governance risk.
Gartner’s revision table compares 3Q26 against 2Q26 for all 11 subsegments. Ten of them show 0.0% growth-rate change in every year from 2026 through 2030. Network security equipment is the exception, at +4.1 points in 2027, +1.1 in 2028, +0.4 in 2029 and -0.2 in 2030.
I ran the same comparison across all 41 categories in the detailed files. In constant currency, firewall equipment is the only category Gartner raised.
It gains $1.08 billion in 2027, $1.50 billion in 2028, $1.77 billion in 2029 and $1.84 billion in 2030. The other 40 categories, including consumer security software, match the June file to the dollar in constant currency.
In the chart below, each bar is the firewall equipment increase in constant currency. Because no other category moved, the bars are also the net change for the whole market. The total is unchanged in 2025 and 2026 and turns positive in 2027, when the firewall upgrade starts.
Gartner states the cause directly. “Postquantum cryptography (PQC) requirements will drive premature hardware refreshes, initially among government, financial and defense organizations, due to the inability of many existing firewalls to support the processing demands and crypto-agility required through software or firmware updates,” the report says.
Gartner’s timing is specific. The report says “selected products may support algorithms such as FIPS 203/ML-KEM by late 2026.” Broader vendor availability follows in 2027. By 2028, Gartner expects every major firewall vendor to offer PQC-capable platforms.
Gartner expects at least 20% of customers in government, financial services and defense to upgrade in 2027, rising to more than 30% in 2028.
In the top panel, the chart compares firewall equipment spending in Gartner’s 2Q26 forecast (light blue) with 3Q26 (sky blue), in constant currency. Revised values carry dark-blue labels and the 2027 and 2030 increases are marked above the bars.
Year-over-year growth sits in the bottom panel, with June’s forecast dashed and September’s solid. Both lines match through 2026, split in 2027 and converge by 2030.
That shift shows up in one year. In June, Gartner expected firewall equipment growth to fall from 16.1% in 2026 to 7.8% in 2027. Now 2027 growth holds at 13.4%.
Gartner’s revision lifts the 2027 firewall market from $20.8 billion to $21.9 billion in constant currency, and the 2030 market from $25.7 billion to $27.6 billion.
In current dollars firewall equipment reaches $28.6 billion by 2030, still the largest network security category by a wide margin.
Every region gets the upgrade at nearly the same rate. Each region’s 2030 firewall forecast rose between 6.9% and 7.8%. North America takes $834 million of the $1.84 billion. Europe takes $414 million. Together they account for 68%.
Each bar in the next chart is one region’s 2030 firewall revision in constant currency. The label shows the dollar increase, that region’s share of the $1.84 billion total and its revised 2030 firewall market. Sky-blue bars mark North America and Europe. Lighter bars are the other seven regions.
Gartner’s argument rests on hardware. Many installed firewalls cannot meet PQC processing and crypto-agility demands through software or firmware updates. The engineering behind that is straightforward. ML-KEM public keys and ciphertexts are larger than the elliptic-curve exchanges they replace, and hybrid key exchange runs both algorithms in the same handshake.
A firewall that inspects encrypted traffic at line rate has to absorb that overhead on every session.
Federal policy points the same way. The National Security Agency’s CNSA 2.0 guidance says traditional networking equipment such as VPNs and routers should “support and prefer CNSA 2.0 by 2026, and exclusively use CNSA 2.0 by 2030.”
According to Keyfactor’s summary of the federal timeline, new National Security System acquisitions are expected to be CNSA 2.0-compliant by default from January 1, 2027, and NIST IR 8547 proposes deprecating RSA, ECDSA, EdDSA and Diffie-Hellman at the 112-bit security level after 2030, with disallowance in 2035.
Vendor roadmaps line up with Gartner’s 2027 inflection. Cisco’s Secure Firewall PQC roadmap targets ML-KEM support in Secure Firewall Threat Defense 10.5 and ASA 9.25 for general availability in late 2026.
ML-DSA signature support is planned for FTD/ASA 11.0 in the second half of 2027, and SLH-DSA support is also planned for 11.0.
Gartner names the sectors with procurement mandates first. That is why the revision lands in 2027 and fades by 2030. Gartner models the refresh as a pull-forward, with 2030 growth now slightly below the June forecast at 6.8% versus 7.0%.
I have tracked Gartner’s 2026 security number through four quarterly updates. The 4Q25 update projected $244.2 billion. 1Q26 raised it to $246.2 billion. 2Q26 raised it again to $248.2 billion. 3Q26 is the first update in that run to come in lower, at $247.5 billion.
Each bar in the chart above is the 2026 total as published in one quarterly update. The labels inside the bars show the change from the previous update, at +$2.0 billion, +$2.0 billion and -$0.8 billion.
An axis starting at $240 billion keeps the revisions visible. All four estimates sit within 2% of each other.
The entire $788 million drop is currency, not lower demand. Gartner’s revision table puts the 2026 change at -$788 million in current dollars, with 0.0% change in every 2026 growth rate. In the detailed files, the 2026 total is unchanged in constant currency.
Japan shows the currency effect most clearly. Its 2030 forecast is $1.37 billion lower in current dollars than in June, yet $49 million higher in constant currency, all of it from the firewall increase.
Gartner’s notes flag exchange-rate volatility, Strait of Hormuz disruption expected to continue into 2027, energy prices more than 50% above pre-war levels and the risk that inflation and rising interest rates erode business confidence.
Gartner expects the conflict’s main near-term effect on IT spending to be a rebalancing of sourcing, vendor relationships and regional exposure rather than a material cut.
Growth rates show where momentum is. Dollar additions show where budgets actually move. Fifteen of the 41 categories capture 76% of all new spending between 2026 and 2030.
Bars in the next chart show the dollars each of the 15 largest categories adds between 2026 and 2030, colored by market. The indigo line, read on the right axis, is the cumulative share of the $125.4 billion total.
It reaches 15% with the first category, 46% after five, 65% after ten and 76% after fifteen.
Other security software, including securing AI. +$19.2 billion, from $18.3 billion to $37.5 billion. The largest single dollar gain in the forecast.
Endpoint protection platforms (enterprise). +$11.7 billion, from $21.3 billion to $32.9 billion.
Cloud security posture management. +$9.8 billion, from $6.3 billion to $16.1 billion.
Firewall equipment. +$8.8 billion, from $19.7 billion to $28.6 billion. Post-quantum refreshes make firewalls the fourth-largest source of new dollars, ahead of cloud workload protection.
Cloud workload protection platforms. +$8.2 billion, from $7.5 billion to $15.7 billion.
Managed security operations. +$7.0 billion, from $15.6 billion to $22.7 billion. The largest services gain.
Five categories alone account for $57.8 billion, or 46% of new spending. Three of them sit in cloud security or in other security software, where Gartner counts securing AI. Endpoint protection and the firewall, a category many security leaders had written off as a replacement-cycle business, make up the other two.
Ranked by 2025 to 2030 CAGR in constant currency, cloud security takes the top three spots. Gartner’s cloud security subsegment grows from $16.6 billion in 2026 to $38.4 billion by 2030, a 24.1% CAGR and the fastest of the 11 subsegments.
In the chart, bar length is each category’s 2025 to 2030 CAGR in constant currency. The label gives the CAGR and the category’s 2026 and 2030 market size in current dollars.
Navy marks security software and sky blue marks network security. The dashed line is the 10.8% market CAGR, so every bar crosses it by at least 1.2 points.
Gartner’s own opportunity map plots the 11 subsegments on two axes. The horizontal axis is the 2025 to 2030 CAGR. The vertical axis is dollars added over the same period. Bubble size is the 2030 market. The dashed vertical line marks the overall 10.8% CAGR.
Cloud security and other security software sit alone on the right, the only two subsegments growing faster than 15%. Infrastructure protection is the largest subsegment at $62.7 billion by 2030 and adds the most dollars, about $26 billion, while growing at the market average.
Gartner places securing AI inside other security software. The report sizes the market for securing AI ecosystems and AI agents at $3 billion in 2026 and $16 billion by 2030, citing its companion analysis, Forecasting the $16.4 Billion Opportunity in Securing AI.
That makes securing AI about 16% of the other security software category in 2026 and about 43% by 2030, by my calculation.
Of the $19.2 billion the category adds over the period, roughly $13 billion comes from securing AI. The rest of the category grows from about $15.3 billion to about $21.5 billion.
Other security software is also the only category whose growth accelerates every year of the forecast, from 16.3% in 2026 to 20.1% in 2030 in constant currency. It passes enterprise endpoint protection in 2029, $31.1 billion against $30.1 billion, and finishes 2030 at $37.5 billion against $32.9 billion.
By 2030 securing AI alone, at $16 billion, is roughly the size of cloud security posture management ($16.1 billion), managed detection and response ($15.7 billion) or cloud workload protection ($15.7 billion), and larger than SIEM ($11.2 billion).
Gartner’s second AI number is larger. Gartner’s AI-amplified security forecast projects AI-amplified security, meaning existing security products with AI built in, rising from $49 billion in 2026 to $204 billion by 2030.
I covered the full AI-amplified forecast in Gartner’s AI security forecast exposes 162x services growth that still trails software 2 to 1 in new spending.
Each bar in the next chart equals Gartner’s 3Q26 total for that year. The navy segment is Gartner’s AI-amplified security spending. The light-blue segment is everything else in the information security market, calculated by subtracting AI-amplified spending from the total.
Each segment shows its dollars and its share of that year’s total. The indigo note repeats Gartner’s securing AI figures, which sit inside other security software rather than in the AI-amplified total.
Everything else, the light-blue segment, shrinks from $182.9 billion (95%) in 2024 to $168.4 billion (45%) in 2030, even as the total nearly doubles.
Set against the 3Q26 totals, AI-amplified spending rises from 20% of the market in 2026 to 39% in 2028, 47% in 2029 and 55% in 2030. Treat that as an illustration of direction, since the two forecasts were built separately.
By the end of the decade, most security dollars will buy products where AI does part of the detection, triage or response work.
Gartner’s report expects AI code security assistants and cybersecurity AI assistants to automate event triage, false-positive reduction and code remediation, and it expects organizations to shift from reactive defense toward continuous threat exposure management (CTEM).
On the left, the chart shows each region’s 2030 spending in current dollars and its share of the world total. On the right is each region’s 2025 to 2030 CAGR in constant currency. The dashed line marks the 10.8% world rate, and indigo marks China and Japan, the two fastest-growing regions.
North America. $129.2 billion in 2026, 52.2% of the world. $197.7 billion by 2030, 53.0%. 11.4% CAGR. The United States alone reaches $181.1 billion in 2030.
Europe. $64.0 billion in 2026 to $91.3 billion by 2030. 8.6% CAGR, the slowest of the nine regions.
China. $10.7 billion in 2026, up 24.5% in current dollars. $18.7 billion by 2030. 15.4% CAGR, the fastest region.
Japan. $12.6 billion in 2026 to $22.2 billion by 2030. 13.1% CAGR, second fastest, with 17.6% constant-currency growth in 2026.
Emerging markets. Emerging Asia/Pacific grows at 11.1%, Sub-Saharan Africa at 10.5%, Latin America at 9.3%, and the Middle East and North Africa at 9.1%.
At the country level, China (16.2%), Indonesia (13.9%), Japan (13.1%) and Taiwan (12.4%) post the fastest constant-currency CAGRs among the 47 countries in the file.
Growth rates spread wide across the full ranking. Seven categories grow faster than 18% a year. Twenty-nine grow below the 10.8% market rate. Two shrink.
Bars rank all 41 categories by 2025 to 2030 CAGR in constant currency. Each bar carries its CAGR, and the right-hand column lists the category’s 2030 market size in current dollars. Click the chart to open it full size.
Navy is security software, mid-blue is security services and sky blue is network security. Indigo marks the two shrinking categories, and a dashed line marks the 10.8% market rate.
Network access control declines at a 17.7% CAGR, from $922 million in 2026 to $382 million in 2030. Intrusion detection and prevention systems fall at 8.3% a year, from $785 million to $548 million.
Both sit inside network security equipment, the same subsegment where firewalls, zero trust network access and network detection and response all grow. My read is that standalone network appliances are being absorbed into firewall platforms and ZTNA, which is consistent with Gartner’s comments on platform consolidation.
User authentication grows at 3.1%, the slowest positive rate in the forecast, while access management grows at 9.2% to $12.1 billion and identity governance and administration at 10.2% to $7.1 billion. My read is that identity spending is shifting from the login event to governing who and what holds access.
Readers working from the PDF of the Gartner information security forecast should check the growth columns in Table 1. For the last three rows, the growth rates appear offset by one row. The table shows 16.3% to 20.1% growth next to security consulting services and 9.0% to 5.6% next to other security software.
Gartner’s detailed dataset shows the reverse. Other security software accelerates from 16.3% to 20.1%, security consulting services slows from 9.6% to 5.0%, and security professional services slows from 9.0% to 5.6%. The dollar values in the table are correct. Every growth rate in this post comes from the detailed file.
Inventory every firewall and VPN concentrator for PQC capability now. Gartner’s refresh window opens in 2027 for government, financial services and defense. Organizations that sell into those sectors, or connect to them, will face the same questions in their own procurement and supplier reviews. Ask vendors which appliance generations support ML-KEM in hardware at full inspection throughput, and get the answer in writing.
Budget the refresh as a 2027 and 2028 capital item. Gartner’s revision adds $1.08 billion to 2027 and another $0.43 billion in 2028, then only $0.26 billion and $0.07 billion more in 2029 and 2030. Waiting for 2029 means buying when lead times and pricing reflect peak demand.
Plan for securing AI as a line item, not a pilot. At $16 billion by 2030, securing AI will be comparable in size to CSPM and MDR. Governance gaps are already visible. Gartner’s first AI governance hype cycle found 34% of enterprises govern AI with policies they only partly follow, which I covered in Gartner’s 2026 AI Governance Hype Cycle.
Push cloud security consolidation. CSPM, CASB and CWPP are the three fastest-growing categories, and Gartner lists SSE and CNAPP adoption alongside tool consolidation and cost control as 2027 budget priorities. Consolidating onto those platforms is the most direct way to fund the growth without adding consoles and contracts.
Re-test services contracts against AI-assisted operations. Services share drops from 39.3% to 33.6% by 2030. Managed security operations still adds $7.0 billion, so outsourcing is not shrinking. What changes is the mix of human hours and AI triage inside each contract, and pricing should reflect it.
How much will worldwide information security spending be in 2026? Gartner forecasts $247.5 billion in 2026, up 13.6% in current U.S. dollars and 12.7% in constant currency.
How big will the security market be by 2030? $372.8 billion, which Gartner frames as $373 billion and a 10.8% constant-currency CAGR from 2025 through 2030.
What changed in the 3Q26 Gartner information security forecast? Firewall equipment growth for 2027 rose to 13.4% from 7.8% in constant currency, driven by post-quantum firewall refreshes. Every other business category kept its constant-currency outlook from June.
Which security category grows fastest? Cloud security posture management, at a 27.6% CAGR from 2025 to 2030, reaching $16.1 billion.
How large is the securing AI market? Gartner sizes securing AI at $3 billion in 2026 and $16 billion by 2030, counted inside other security software.
All market sizes are Gartner end-user spending from the 3Q26 detailed forecast file (G00862059), in current U.S. dollars unless noted. All growth rates and CAGRs are constant currency, matching Gartner’s reporting convention, with 2024 as the constant-currency base year.
Revisions compare the 3Q26 and 2Q26 (G00855892) detailed files category by category in constant currency, which separates forecast changes from exchange-rate effects. Dollar additions, shares, rankings, regional splits and the AI-amplified ratio are my calculations.
Securing AI figures ($3 billion in 2026, $16 billion by 2030) are Gartner’s, as stated in the 3Q26 report. The AI-amplified figures come from Gartner’s August 2026 AI-amplified security forecast.
Earlier analysis in this series:
For each month’s AI agent attacks, exploited CVEs and breaches with primary sources, see my monthly AI security news briefing.
This post is my personal analysis of Gartner’s information security research and does not represent my employer.
Sep 21
Agentic workflows will drive 50% of generative AI model revenue by 2030, up from 5% in 2025. Gartner published that projection on September 17, 2026, in its forecast analysis of the generative AI models market (G00861842). The number changes how every enterprise buyer should read the firm’s $239 billion AI platforms and models forecast.
Multistep reasoning, tool integration, and repeated validation steps multiply the inference events behind every completed business process. Gartner puts the impact at $38 billion in additional spending by 2030 from scaling agentic workflows alone.
Falling inference prices won’t offset it. Cheaper units unlock deeper automation, and deeper automation drives up token volume per task faster than prices fall. Gartner calls this a structural tailwind for GenAI model spending.
That consumption flywheel sits inside a market growing from $39 billion in 2025 to $239 billion by 2030 at a 42.8% compound rate, per Gartner’s June 25 forecast (G00855897). I built this analysis from both Gartner reports and Gartner’s country-level dataset, which covers 1,316 rows across 9 regions and 7 years. For the agentic spending crossover that sets up this post, see Gartner’s $5.95 trillion AI forecast puts the chatbot era on a 2027 deadline (August 5, 2026).
Dollar figures are current U.S. dollars. Gartner reports growth in constant currency, so its rates won’t always match growth calculated from the dollar totals. The regional CAGRs here are my calculations from Gartner’s dollar data.
Gartner splits the market into two halves. AI platforms grow from $26.3 billion in 2025 to $100 billion by 2030, a 30.0% CAGR. GenAI models grow from $13 billion to $139.2 billion, a 59.9% CAGR.
GenAI models overtake AI platforms in 2027. By 2030, models command 58% of total spending.
Gartner maps all four segments by 2030 market size and CAGR. Foundation GenAI models reach $105.4 billion at a 55.2% CAGR. DSLMs reach $33.9 billion at 83.4%, the fastest growth rate in the forecast.
Data science and ML platforms carry roughly three times the spending of app development platforms. The ratio rises from about 2.8 to 1 in 2025 to 3.3 to 1 in 2030.
Domain-specific language models and specialized GenAI models grow at an 83.4% CAGR from 2025 to 2030. That outpaces foundation models at 55.2% and AI platforms at 30.0%. The segment goes from $1.6 billion in 2025 to $33.9 billion by 2030.
The share shift tells the structural story. DSLMs were 5.5% of GenAI model spending in 2024. By 2030 they will command 24.3%.
Gartner’s growth rates for DSLMs decelerate from 452.0% in 2025 to 34.5% in 2030. The dollar additions do the opposite. Each year adds more net new spending than the last, climbing from $1.3 billion to $8.6 billion.
The 2Q26 revisions confirm the direction. Gartner raised its 2030 DSLM forecast by $14.3 billion and cut its 2030 foundation model forecast by $32.1 billion. The pattern holds in every year. DSLM revisions climb from $189 million for 2025 to $1.3 billion for 2026 to $14.3 billion for 2030. Foundation model revisions run negative every year, from $2.7 billion for 2025 to $32.1 billion for 2030.
Gartner’s September analysis names agentic workflows as the single largest driver of GenAI model spending growth through 2030. The mechanism runs on volume, not price.
Agentic workflows consume more tokens per completed task than conversational AI. Each autonomous process generates multiple inference events as task complexity rises, demanding advanced reasoning, larger context windows, and repeated validation. I tracked the agentic spending crossover in Gartner forecasts agentic AI will overtake chatbot spending by 2027 (February 16, 2026). The September data shows the trajectory accelerating.
Gartner identifies three forces operating at once. Agentic workflows add $38 billion. Multimodal expansion adds $32 billion. Open-weight substitution and inference internalization remove $25 billion. The net effect grows the revenue pool while shifting where the money lands.
Gartner expects two or three suppliers to dominate the GenAI LLM marketplace in North America and Asia/Pacific by 2030. It forecasts the top two vendors will hold 85% of the foundation model revenue pool in those regions. The firm’s 2025 market share data shows Anthropic, OpenAI, and Google together accounted for 63% of enterprise spending.
The concentration thesis goes beyond model quality. Sustained investment in model development, inference capacity, reliability, security, integration, and global distribution creates a cost structure that favors vendors able to keep reinvesting at scale.
Gartner places GenAI in the Trough of Disillusionment in 2026. In that phase, enterprises lean toward frontier models delivered through their incumbent SaaS providers. That preference narrows the field for standalone frontier model companies and sets up a winner-take-all race to become the model provider software vendors choose. For how every credible forecast sizes this market, see my roundup of agentic AI forecasts and market estimates, 2026.
North America holds near 54% of worldwide spending from 2024 through 2030. Spending rises from $13.2 billion in 2024 to $130 billion in 2030, a 43.4% CAGR from a 2025 base of $21.5 billion.
The United States alone reaches $33.1 billion in 2026 and $123 billion by 2030. Gartner’s China region, which includes Hong Kong and Taiwan, grows fastest of the three at a 58.3% CAGR, from $4.1 billion in 2025 to $40.9 billion in 2030. Europe grows at 40.8% to $45.1 billion, with the United Kingdom at $4.8 billion in 2026, France at $2.2 billion, and Germany at $1.9 billion.
Gartner projects 30% of routine, high-volume enterprise GenAI inference will run on open or enterprise-controlled models by 2030, up from 5% in 2025. That substitution takes $25 billion out of the GenAI model revenue pool by 2030.
Production traffic already shows the split. Vercel’s AI Gateway routes tens of trillions of tokens a month between production applications and AI labs. In August, open-weight models processed 56% of gateway tokens but accounted for 14% of estimated spending, according to Vercel’s September production index. In December 2025, open-weight token share was 7%.
The mix is moving fast. In a post Chamath Palihapitiya amplified on September 19, Guillermo Rauch reported a snapshot of 78.4% open-weight and 21.6% closed-weight token volume.
Vercel’s September 18 daily export shows DeepSeek V4.1 Flash at 59.3% of all token volume. GLM 5.3 Flash took 7.5%, DeepSeek V4 Flash 0731 took 2.7%, and Kimi K3 took 2.5%.
Spending looks different. Anthropic accounted for 64% of estimated gateway spend in August. In July, Anthropic took 65.1% of spend on 30% of token volume, and its average price per token ran 4.4 times the average across every other lab.
The September 18 export makes the gap concrete. Claude Opus 4.8 accounted for 13.7% of estimated spend, Claude Opus 5 for 9.0%, Claude Sonnet 5 for 5.5%, and Claude Sonnet 4.6 for 4.3%. DeepSeek V4.1 Flash, with 59.3% of tokens, accounted for 5.1% of spend.
Vercel’s data covers traffic routed through its gateway, not the whole market. Its spend figures are estimates based on list prices. It shows the same pattern Gartner forecasts but doesn’t prove Gartner’s $25 billion number.
For buyers, the lesson is to budget for workload mix, not token volume alone. The models moving the most tokens aren’t the ones capturing the most spend. Route suitable workloads to lower-cost models and save premium models for work that justifies the price.
Palihapitiya went further on September 19, predicting the top three models would be open source within 12 months. He named Nebius, Iren, Baseten, Together, and Fireworks as the clouds he expects to benefit. That’s his forecast, not something the usage data establishes.
Gartner reports AI budgets are getting a harder look, and money is moving to providers that can prove their value on cost, speed, and reliability. Here are five takeaways for leaders making AI platform and model decisions over the next 12 months. For how security spending fits this picture, see Gartner’s $248.9B security forecast makes securing AI the only segment accelerating through 2030 (July 6, 2026).
Gartner’s forecast puts a number on the tension running through every enterprise AI budget. The market nearly quadruples from $64 billion in 2026 to $239 billion by 2030. Agentic workflows drive half of GenAI model revenue by the end of that window.
Open-weight models absorb a growing share of token volume while frontier providers keep the lion’s share of spend. The $38 billion agentic addition and the $25 billion revenue pool reduction pull the market in two directions at once.
Enterprises that build for model routing, budget for agentic consumption, and negotiate multiyear commitments with the surviving frontier providers will be better positioned in 2030. Enterprises that treat AI spending as a single-vendor procurement decision will not.
This post is my personal analysis of Gartner’s AI platforms, models, and generative AI research and does not represent my employer.
Aug 5
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.
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.
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 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.
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.”
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.
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.
Feb 10
Over 57% of employees are using personal GenAI accounts for work. A third of them admit to uploading sensitive data into tools their security teams haven’t approved. Meanwhile, agentic AI is proliferating through no-code platforms and vibe coding, creating attack surfaces most CISOs can’t see, let alone govern. And quantum computing? No longer a 10-year planning horizon. It’s a 2030 action deadline.
Gartner’s Top Trends in Cybersecurity for 2026 report, released February 5, 2026, identifies six forces reshaping how CISOs must operate. These cut across governance, AI adoption, identity, workforce, and cryptographic strategy simultaneously. None of them is incremental.
The trends report lands alongside Gartner’s updated Forecast: Information Security, Worldwide, 2023–2029, 4Q25 (G00843183, December 18, 2025) and the Forecast Analysis: Information Security, Worldwide, 2026 (G00838442, February 5, 2026), which together project global information security spending reaching $244.2 billion in 2026, up 13.3% in current U.S. dollars. I’ve tracked this forecast through multiple quarterly updates. The trajectory keeps steepening. The six trends below explain where that money is going and why.
“Cybersecurity leaders are navigating uncharted territory this year as these forces converge, testing the limits of their teams in an environment defined by constant change,” said Alex Michaels, Director Analyst at Gartner. “This demands new approaches to cyber risk management, resilience, and resource allocation.”
Before getting into the six trends, context matters. Gartner’s 4Q25 forecast shows the three major security segments all growing at double-digit constant currency rates in 2026:
Source: Gartner Forecast: Information Security, Worldwide, 2023–2029, 4Q25 Update (G00843183). Constant currency rates.
Cloud security remains the fastest-growing subsegment at 28.8% growth in 2026. Nothing else comes close. The combined cloud security market (cloud security posture management, cloud access security brokers, and cloud workload protection platforms) is projected to reach $32.4 billion by 2029, with a 25% CAGR in constant currency. I’ve been watching this subsegment accelerate for three quarters straight. CSPM alone is growing at a 31.30% CAGR.
Cloud security spending reaches $32.4 billion by 2029. CSPM leads at 31.30% CAGR. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)
This is the trend that touches everything else on this list. Employees and developers are deploying AI agents through no-code/low-code platforms and “vibe coding” at a pace that outstrips security governance. Unmanaged AI agent proliferation. Unsecured code. Compliance violations that most security teams don’t even have visibility into yet. That’s the picture Gartner is painting.
Gartner’s recommendation is blunt: cybersecurity leaders must identify both sanctioned and unsanctioned AI agents operating within their environments, enforce access controls and data guardrails, and develop incident response playbooks specific to agent-driven threats.
“While AI agents and automation tools are becoming increasingly accessible and practical for organizations to adopt, strategic cybersecurity planning for these technologies is essential,” said Michaels. Cybersecurity leaders must work cross-functionally to manage agentic AI adoption, identifying sanctioned and unsanctioned AI agents, enforcing data access controls, and developing incident response playbooks.
The spending data backs this up. Gartner’s 4Q25 forecast projects the AI-amplified security market reaching $160 billion by 2029, up from $49 billion in 2025. Gartner is clear that this isn’t additive spending. It represents the portion of existing security products that now embed AI capabilities. But the expectation tells the story: over 75% of enterprises will use AI-amplified cybersecurity products by 2028, up from less than 25% in 2025. Vendors that don’t embed AI will lose shelf space. (For more on AI security platforms, see Gartner’s Top Strategic Technology Trends for 2026, which predicts that over 50% of enterprises will use AI security platforms to protect their AI investments by 2028.)
Regulators are getting personal. Boards and executives now face direct liability for compliance failures. Not just organizational fines, but individual accountability. The penalties for inaction have moved from theoretical to career-ending. Across multiple jurisdictions simultaneously.
Gartner advises cybersecurity leaders to formalize collaboration across legal, business, and procurement teams to establish clear accountability for cyber risk. Align control frameworks to recognized standards. Address data sovereignty concerns before they become enforcement actions. The organizations doing this well are treating regulatory preparedness as a core security function, not an annual compliance checkbox.
This is where the spending data gets interesting. Gartner’s forecast shows security consulting services growing from $24.2 billion (2024) to $36.6 billion (2029), adding $12.4 billion in five years. Security professional services follow a similar trajectory: $27.3 billion to $40.8 billion, adding $13.5 billion. Organizations are buying outside expertise because they can’t build regulatory competence fast enough in-house. I’ve been covering these numbers for three quarters, and the services growth is the part of the forecast that keeps surprising me.
Infrastructure protection adds $26.4 billion between 2024 and 2029, the largest absolute growth of any subsegment. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)
Gartner predicts advances in quantum computing will render the asymmetric cryptography that organizations rely on unsafe by 2030. Four years. That’s the window to adopt post-quantum cryptography alternatives before “harvest now, decrypt later” attacks start cashing in on data that adversaries are collecting today.
Organizations need to identify their cryptographic deployments, assess data sensitivity and lifespan, and prioritize cryptographic agility. That last phrase keeps coming up in my conversations with CISOs. The ability to swap encryption methods without re-architecting entire systems. Swapping an algorithm is one thing. Doing it across a production environment without downtime is an entirely different problem.
“Post-quantum cryptography is reshaping cybersecurity strategies by prompting organizations to identify, manage, and replace traditional encryption methods, while prioritizing cryptographic agility,” said Michaels. “By investing in these capabilities and prioritizing migration now, assets will be secured when quantum threats become a reality.“
The encryption market in Gartner’s 4Q25 forecast grows from $1.04 billion in 2023 to $2.04 billion by 2029 at an 11.95% CAGR. A 2.0x increase. For what has historically been one of the slower-growing security subsegments, that’s a significant acceleration. Quantum urgency is changing the math.
AI agents are breaking traditional IAM models. Plain and simple. Identity registration and governance, credential automation, and policy-driven authorization weren’t designed for autonomous machine actors that can initiate actions, access data, and interact with systems without human intervention. The scale problem compounds fast: when every employee can deploy dozens of AI agents, the identity surface area explodes.
Gartner recommends a targeted, risk-based approach. Invest where gaps and risks are greatest. Leverage automation where possible. The practical starting point is understanding which AI agents carry the most privilege and the least oversight. Those are your highest-risk identities right now, and most organizations haven’t inventoried them.
The identity market is already significant. Gartner’s 4Q25 forecast shows identity access management growing from $18.7 billion (2024) to $29.0 billion (2029), adding $10.3 billion in five years. That’s before the full scale of agentic AI identity requirements hits the market. IAM vendors that solve machine-actor identity at scale will capture a disproportionate share of that $10.3 billion growth.
AI-enabled security operations centers are enhancing alert triage and investigation workflows. The technology works. But deploying AI into a SOC doesn’t automatically reduce headcount needs. It changes the skill mix. Analysts who excelled at manual triage need different capabilities to oversee AI-driven workflows. Organizations are discovering this the hard way. That’s an organizational transformation challenge, and throwing more technology at it doesn’t help.
“To realize the full potential of AI in security operations, cybersecurity leaders must prioritize people as much as technology,” said Michaels. “Strengthening workforce capabilities, implementing human-in-the-loop frameworks into AI-supported processes and aligning adoption with clear strategic objectives will be critical to maintaining resilience as SOCs evolve.”
The talent dimension makes this harder than it already sounds. ISC2’s 2024 Cybersecurity Workforce Study, published in October 2024, documented a global workforce gap of 4.8 million professionals, a 19% year-over-year increase. The active workforce flatlined at 5.5 million (up just 0.1%). The numbers are brutal: 25% of organizations reported cybersecurity layoffs in 2024. 37% faced budget cuts. 90% report skills shortages. 58% believe the shortage puts their organization at significant risk. On the spending side, managed security services are growing at 11.1% in 2026, the fastest rate in the services segment. Organizations can’t hire fast enough, so they’re buying managed SOC capacity instead.
Existing security awareness programs are failing. Full stop. A Gartner survey of 175 employees conducted between May and November 2025 found that 57% use personal GenAI accounts for work purposes, while 33% admit to uploading sensitive information to tools their organizations haven’t sanctioned. Those numbers should alarm every CISO reading this. A third of your workforce is actively feeding proprietary data into tools you can’t audit.
Gartner recommends shifting from general awareness training to adaptive behavioral programs that include AI-specific tasks. Generic compliance videos won’t cut it here. The organizations getting this right are making approved GenAI tools easy to access and unsanctioned tools hard to justify. Trying to ban GenAI outright just drives usage underground and costs you talent.
Strengthening governance, embedding secure practices, and establishing clear policies for authorized GenAI use will reduce exposure to privacy breaches and intellectual property loss. The governance gap on GenAI usage is, in my view, the most underestimated risk on this entire list. Every other trend has a spending line item attached to it. This one requires behavioral change, which is harder to buy.
Gartner’s year-by-year spending trajectory shows the acceleration curve these six trends are riding:
Source: Gartner Forecast: Information Security, Worldwide, 2023–2029, 4Q25 Update (G00843183, December 18, 2025). Current U.S. dollars.
CSPM and CASB lead all security categories with 31% and 26% CAGR through 2029. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)
Three of the six trends (agentic AI oversight, IAM for machine actors, and GenAI awareness) are fundamentally about the same problem: autonomous AI systems operating inside enterprise environments without adequate governance. The other three (regulatory volatility, post-quantum readiness, and AI-driven SOCs) are the structural forces those governance failures will collide with. That convergence is the signal about where 2026 budgets need to go.
The organizations that will navigate this environment successfully are doing three things simultaneously:
Mapping their AI agent footprint now. If you don’t know how many AI agents are operating across your environment, sanctioned and unsanctioned, you can’t govern what you can’t see. Gartner’s 75% AI-amplified product adoption projection by 2028 means this window for establishing control is narrow.
Building cryptographic agility into their architecture. The 2030 quantum deadline means migration planning starts in 2026, not 2028. The encryption market’s 2.0x growth reflects early movers. Late movers face rip-and-replace costs that compound every quarter they wait.
Investing in people alongside AI tooling. AI-enabled SOCs work when human operators have the skills to oversee them. The ISC2 data is unambiguous: a 4.8 million professional gap growing at 19% year-over-year. Managed security services growth at 11.1% tells you where CISOs are finding capacity.
Gartner’s numbers aren’t projections anymore. They’re procurement trends already hitting finance systems. The $244.2 billion flowing into information security this year will fund agentic AI governance, quantum migration, and SOC transformation, whether your organization participates or not.
Bottom line: CISOs planning for 2027 are watching their competitors buy the tools they’ll be scrambling for in 18 months. The data says move now.
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.
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.
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.
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.
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.
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.
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.”
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.
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’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:
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 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.
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.
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.
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 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.
Oct 19
Generative AI (GenAI) ‘s potential for streamlining the most time-consuming processes in B2B sales is just getting started. As businesses increasingly rely on AI to enhance efficiency, automate routine tasks, and personalize customer engagement, GenAI is set to become a critical differentiator in the race for B2B sales and market leadership.
Gartner’s recent report, 13 Generative AI Use Cases for B2B Sales, provides an analysis of where GenAI is helping improve B2B sales now and in the future.
“Generative artificial intelligence (GenAI) is reshaping the sales technology landscape, offering innovative solutions in areas such as prospecting, sales analytics, forecasting, and sales enablement. Tools infused with GenAI capabilities are embedded in use cases across the sales function, supporting key priorities such as revenue growth, GTM, cost optimization, and risk mitigation,” write the authors of Gartner’s study.
In defining and ranking the most valuable use cases of GenAI in B2B sales, Gartner examined where the technology is being most effectively applied to improve sales operations, increase seller productivity, and fuel future transformation.
The following multidimensional grid defines the use cases by value and feasibility.
Gartner evaluated each use case for GenAI in B2B sales by scoring them on two key factors: business value and feasibility. The figure below shows the breakout of value and feasibility factors Gartner has used as a framework to rank the 13 use cases: “While we’ve defined the dimensions of value and feasibility according to our research criteria, companies are encouraged to customize these parameters to align with their own business needs,” the report states.
Gartner also provides a GenAI use-case pipeline as part of their analysis to graphically explain how the 13 AI-driven strategies or use cases are distributed across business functions, including marketing, sales, and customer success.
The goal is to help organizations identify and take action on the use cases that will deliver the most significant potential impact. Gartner advises that use cases that span multiple stages of the pipeline typically deliver greater overall business value, making them strategic targets for investment. Additionally, the pipeline acts as a guide to identifying the relevant stakeholders within the organization, enabling more focused discussions and alignment on AI implementation priorities.
Within the next three years, GenAI will emerge as one of the main factors that differentiate the most efficient and financially successful B2B sales organizations. With CROs creating operations teams to scale AI improvements across every phase of the sales process and sales teams using AI to automate reporting and manually-intensive tasks, GenAI is supposed to revamp the time-consuming work that gets in the way of selling.
Gartner’s analysis highlights that AI-driven strategies will soon dominate, with significant gains in efficiency and customer engagement. The message is clear: for sales organizations looking to stay ahead, embracing GenAI is not optional—it’s essential. Those who act now will position themselves as leaders in the evolving world of B2B sales, while those who hesitate risk being left behind.
Oct 6
CEOs and C-level executives, including line-of-business leaders managing enterprises, no longer have time for AI hype—they need actionable plans that deliver measurable results.
Every CEO I know has a Gen AI tech trends deck ready for board meetings. They’re all impatient for results.
Gartner’s 2024 Generative AI Planning Survey, published yesterday, reflects how impatient CEOs and their teams are gaining traction with GenAI pilots and AI initiatives. The survey involved 822 business executives from North America, Europe, and Asia/Pacific across eight corporate functions.
Key insights from the GenAI planning survey include the following:
11.3% to 19.7% cost savings are expected from GenAI, with the lowest in finance and highest in marketing and HR, as predicted by CEOs and C-level leaders.
87% of CEOs/C-suite are driving GenAI adoption in areas like sales and finance, pushing top-down initiatives for implementation.
Legal departments: 26% rolling out GenAI for contract review in 6 months; already widely used for legal research and analysis.
19.7% cost savings in marketing driven by GenAI, making it the most impacted department for efficiency gains.
28% of leaders cite technical challenges as the top barrier to GenAI implementation, followed by talent acquisition (26%) and costs (24%).
69% of GenAI-advanced companies focus on upskilling staff, while 64% are creating new AI-specific roles to meet talent needs.
Rhetoric into results is the new mantra of the C-suite going into 2025.
That’s especially the case with GenAI.
Board members are worried they’re about to get lapped or, worse, see their companies become gradually irrelevant by competitors who are more focused on making GenAI pay than they are. The greater the acuity and insight of how to turn GenAI into a competitive strength, the greater the speed at which an enterprise executes and gets solid results. Speed isn’t optional anymore, it’s table stakes to compete.
Just as every business needs to keep challenging itself to find new paths to reinvent itself to make AI a competitive strength, the same holds for working professionals. There has never been a better time to double down on new skills and master AI tools, technologies, and knowledge.
The following are ten insights every CEO needs to know about GenAI going into 2025:
Over the next 12-18 months, GenAI will boost productivity by 22.6%, outpacing revenue growth at 15.8% and cost savings at 15.2%. While cost efficiency and revenue gains matter, the most immediate and substantial impact will be on operational efficiency. Gartner predicts that enterprises that prioritize GenAI integration will see significant increases in both workflow optimization and financial performance.
Source: Gartner’s 2024 Gartner Generative AI Planning Survey
30% of leaders plan to reduce headcount by 3% to 5% in 2024 due to GenAI-driven automation, with an overall average savings of 4.6%. These reductions will primarily affect roles tied to repetitive or manual tasks as organizations seek to streamline operations. Another 18% anticipate more minor cuts of 1% to 3%, while 14% expect deeper reductions of 8% to 10%, signaling that GenAI’s impact will vary by function. Only 10% foresee no layoffs.
Source: Gartner’s 2024 Gartner Generative AI Planning Survey
87% of sales teams are following CEO or C-suite directives to implement GenAI, demonstrating a top-down strategy that prioritizes AI for revenue growth and a more significant competitive advantage. Supply chain (79%) and finance (74%) also see intense executive pressure, indicating that leadership views AI as critical for optimizing operational efficiency and financial management.
Source: Gartner’s 2024 Gartner Generative AI Planning Survey
84% of organizations prioritize embedding GenAI into existing applications as the top method for enabling their use cases, with 34% making it their first choice. Customizing existing models (74%) and training custom models (65%) follow, while only 59% opt for stand-alone tools. Enterprises are focusing on integrating GenAI within their current systems to drive efficiency and impact rather than relying on isolated or siloed solutions.
Source: Gartner’s 2024 Gartner Generative AI Planning Survey
HR leads GenAI budget allocation at 7.1%, followed closely by customer service (7.0%) and finance (6.9%). Across functions, business leaders plan to allocate 5.4% to 7.1% of their 2024 budgets to GenAI initiatives, including spending on technology licensing and employee deployment costs. Gartner observes that this shows a solid commitment to embedding GenAI across departments, with HR and customer service prioritizing it for operational efficiency and innovation.
Source: Gartner’s 2024 Gartner Generative AI Planning Survey
54% of C-level executives prioritize privacy concerns as the top GenAI risk, followed closely by misuse (49%) and job displacement fears (48%). These top concerns highlight the critical need for strong governance and risk management frameworks and plans to ensure ethical, secure AI deployment. CEOs need to step up the pace on this now if they’re going to compete in this dimension of their business in 2025.
Source: Gartner’s 2024 Gartner Generative AI Planning Survey
According to 28% of leaders, technical implementation, talent acquisition (26%), and governance issues (25%) are the top three barriers to GenAI adoption. North America struggles more with measuring value (30%), while Europe faces higher cultural resistance (24%). These barriers highlight the need for focused strategies to overcome implementation and talent gaps across regions.
32% of service-centric industries struggle with measuring value from GenAI initiatives, significantly more than asset-centric industries. The top barriers for both include the cost of running AI, technical implementation (32% each), and getting the necessary talent (28%). To excel, enterprises need to address these common challenges and tailor strategies that overcome sector-specific obstacles, including data availability (28% for service-centric industries).
Source: Gartner’s 2024 Gartner Generative AI Planning Survey
Customer service leads GenAI adoption with 40% using real-time speech and text translation, followed by marketing (38% with chatbots and digital humans), sales (34% with generative business intelligence), HR (29% for job descriptions and skills data), supply chain (30% for chatbots and code generation), finance (22% for coding assistance), legal/risk (17% for legal research), and procurement (18% for contract lifecycle management).
Source: Gartner’s 2024 Gartner Generative AI Planning Survey
76% of mature AI organizations actively recruit additional headcount for existing roles to meet GenAI talent needs, significantly more than the 52% of less mature organizations. They also prioritize running AI literacy programs (67%) and upskilling staff with GenAI skills (67%) to ensure their workforce remains competitive. Mature organizations are also more likely to create new roles for GenAI (67%) and establish AI centers of excellence (45%), showing their commitment to both talent acquisition and long-term AI capability development.
Source: Gartner’s 2024 Gartner Generative AI Planning Survey
Sep 17
For 87% of CIOs, generative AI (GenAI) represents more than a technological advancement—it’s a career-defining opportunity.
Gartner’s 2024 CIO Generative AI Survey finds that GenAI is gaining momentum with CIOs, with 95% believing in the technology’s significant potential to improve their organizations. A significant obstacle: a gap between CIOs and their C-suite peers — tempers their optimism.
While CIOs recognize AI’s potential to unleash productivity gains and improve customer experiences, only a fraction of the C-suite sees it as an urgent priority. Closing that gap underscores CIOs’ essential role in championing GenAI by committing to excel at learning every aspect of the new technology and how it can deliver long-term value to their organizations.
The top seven takeaways from Gartner’s survey provide CIOs with a roadmap on how to take a practical, pragmatic approach to bridge the gaps across the C-suite and help their organizations get results from their GenAI strategies.
Gartner’s latest CIO survey on GenAI provides insights into how IT leaders can capitalize on the technology’s significant impact, from career growth and expertise development to helping CIOs achieve more support across the C-suite. Each takeaway focuses on how CIOs can leverage AI to drive success for themselves and their organizations.
Here are the survey’s seven most insightful takeaways:
More CIOs are starting to view GenAI as a career-enhancing opportunity. Eighty-seven percent of CIOs see GenAI as a pivotal career advancement opportunity, with 44% of those proficient in AI strongly affirming this view. GenAI’s rapid adoption in organizations is proving itself a technology capable of delivering productivity gains and is increasingly becoming a skill and expertise essential for career advancement. For CIOs leading AI initiatives, it’s not just about technology—it’s about positioning themselves as visionary leaders qualified to step into more senior positions. To maximize this opportunity, CIOs need to prioritize the development of AI strategies that demonstrate clear, measurable business outcomes. Continuous learning and certification programs are given to any IT professional, especially CIOs, who want to maintain a competitive edge and have their careers capitalize on GenAI’s growth trajectory.
Source: Key findings from the 2024 Gartner CIO generative AI survey (ID G00820936). Gartner, Inc.CIOs are more focused than ever on increasing their acumen about GenAI. CIOs are rapidly becoming the in-house experts on AI, with 52% now rating themselves as proficient or advanced, up from 38% nine months ago. This growing expertise is crucial, as 67% of CIOs are tasked with leading AI initiatives, often sharing this responsibility with other C-suite members. Gartner recommends that CIOs deepen their AI knowledge further and foster a culture of AI literacy across their teams to capitalize on this trend. Providing targeted training for IT and business leaders to ensure that AI strategies are fully integrated into broader business goals is quickly becoming table stakes.
Disconnect between CIO optimism and C-suite prioritization. Despite 95% of CIOs believing in the potential for GenAI to deliver value, the survey reveals a disconnect with the C-suite—only 21% of CIOs who consider themselves highly knowledgeable about AI believe their C-suite sees it as a high priority. This gap suggests a need for more effective communication and strategic alignment. CIOs need to focus on translating AI’s potential into language that resonates with the C-suite. Regular briefings and ROI-focused presentations can help bridge this gap and elevate AI as a top priority for all executive leaders.
Source: Key findings from the 2024 Gartner CIO generative AI survey (ID G00820936). Gartner, Inc.CIOs are leading the charge in AI implementations. CIOs are increasingly in charge of GenAI initiatives, with 48% of CIOs responding to the survey indicating that they are the main executives responsible for these initiatives. Another 28% are part of the team responsible for developing AI strategy. This central role places CIOs at the forefront of digital transformation, requiring them to be strategic leaders and hands-on practitioners. CIOs need to establish clear governance frameworks and metrics for AI initiatives to ensure success and alignment with broader organizational goals. Additionally, partnering with other C-suite members, such as the CFO and CMO, can help secure the necessary resources and support for AI projects.
Focus on productivity gains. GenAI is proving effective in streamlining operations and improving efficiencies organization-wide, with 74% of CIOs citing productivity as its top business value. AI also improves customer experience (49%) and helps streamline digital transformation (31%). These priorities demonstrate AI’s multifaceted role in modern businesses. Gartner recommends that CIOs integrate AI into crucial or core organizational areas, ensuring that AI initiatives align with organizational objectives and are designed to deliver measurable, scalable outcomes.
Concerns over AI hallucinations. Although GenAI holds great potential, there are significant risks. According to 59% of CIOs, the biggest worry is “hallucinations” or misleading or incorrect outputs. In close succession, 44% and 48% of CIOs express concern about privacy violations and false information spread by malicious attackers. These dangers highlight the importance of solid governance, ongoing oversight, and continued investments in cybersecurity. According to Gartner, CIOs need to prioritize creating AI ethics guidelines and investing in auditing tools. Gartner also notes that reducing these risks will require cultivating a culture of accountability and transparency.
C-Suite engagement in AI is growing but still lags. The survey shows that while C-suite engagement with AI is growing, 42% of CIOs note increased investment in understanding AI, and 53% still consider their peers novices, highlighting a critical need for further education and alignment. CIOs need to take the lead and champion targeted AI education and strategy sessions to close this gap, ensuring AI initiatives are fully supported and integrated into the organization’s strategic goals.
In Gartner’s 2024 CIO Generative AI Survey, GenAI is more than a technological advancement—it’s a strategic imperative for CIOs seeking business transformation and career advancement. GenAI is rapidly becoming a cornerstone of modern enterprise strategy, with 87% of CIOs seeing it as a career-enhancing tool and 95% as a business value driver.
With only 21% of CIOs seeing AI as a high priority for their executive peers, the journey is difficult. 74% of CIOs are focused on productivity gains, but the C-suite is cautious. CIOs must gain AI expertise and lead the way in aligning AI initiatives with organizational goals to mitigate risks like AI hallucinations through robust governance. CIOs can use GenAI to achieve business success and career growth by strategically navigating these dynamics to cement their role as digital visionaries.
Bibliography:
Struckman, C. (2024). Key findings from the 2024 Gartner CIO generative AI survey (ID G00820936). Gartner, Inc. https://www.gartner.com/document/820936 (Client access required).
