Skip to content

Posts tagged ‘generative AI’

Sep 21

Gartner’s $239B AI forecast: Agentic workflows take half of GenAI model revenue

Detailed 2024–2030 GenAI revenue chart with annual spending, agentic shares and dollar allocations; intermediate agentic shares are explicitly labeled SSB scenarios.
Gartner publishes the agentic share of GenAI model revenue for 2025 (5%) and 2030 (50%). The 2024 and 2026 to 2029 bars are my estimates and are labeled SSB. By 2030, agentic revenue reaches about $69.6 billion of $139.2 billion. Source: Gartner, G00855897 and G00861842. Chart and analysis by softwarestrategiesblog.com.

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).

$239 Billion by 2030 and Where It Breaks Down

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.Four-segment annual AI market breakdown from 2024 to 2030, with exact annual levels, constant-currency growth, dollar additions, and GenAI market share.

Source: Gartner, G00855897. Chart and analysis by softwarestrategiesblog.com.
  • Total market 2026. $64.3 billion, up 60.7% year over year, adding $25 billion in net new spending.
  • AI platforms 2026. $36 billion, growing 34.7%. Data science and ML platforms account for $26.4 billion and app development platforms for $9.5 billion.
  • GenAI models 2026. $28.3 billion, growing 113.5%. Foundation models reach $23.4 billion and DSLMs and specialized models $4.9 billion.
  • 2030 total. $239.2 billion. GenAI models reach $139.2 billion and AI platforms $100 billion.

Where platform and model spending goes

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.

AI Platforms and Models Opportunities.
Source: Gartner, AI Platforms and Models Opportunities, G00855897 (June 2026). Commentary by softwarestrategiesblog.com.

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.

All annual platform subsegment spending, ratios, market shares, and constant-currency growth rates from 2024 to 2030.
Source: Gartner, G00855897. Chart and analysis by softwarestrategiesblog.com.

DSLMs Are the Fastest-Growing Segment in This Forecast

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.

GenAI Models Spending Segmented Into Foundation Models, and DSLMs and Specialized Models, 2024–2030.
Source: Gartner, GenAI Models Spending Segmented, G00861842 (September 2026). Commentary by softwarestrategiesblog.com.

The share shift tells the structural story. DSLMs were 5.5% of GenAI model spending in 2024. By 2030 they will command 24.3%.

Detailed annual foundation-model and DSLM spending, segment shares, growth rates, and DSLM dollar additions for 2024–2030.
Source: Gartner, G00855897 and G00861842. Chart and analysis by softwarestrategiesblog.com.

Slower percentage growth, larger dollar additions

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.

Six annual DSLM growth rates in both constant currency and current dollars, annual dollar additions, and a complete 2024–2030 table.
Source: Gartner, G00855897 and G00861842. Chart and analysis by softwarestrategiesblog.com.

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.

All 2025–2030 forecast revisions for foundation models, DSLMs, and combined GenAI, with reconstructed first-quarter and second-quarter levels.
Source: Gartner, G00855897, Table 2. Chart and analysis by softwarestrategiesblog.com.

Agentic AI Rewrites Inference Economics

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.

Forecast Driver Impact on GenAI Model Spending.
Source: Gartner, Forecast Driver Impact on GenAI Model Spending, G00861842 (September 2026). Commentary by softwarestrategiesblog.com.

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.

  • Agentic share of GenAI revenue. 5% in 2025, growing to 50% by 2030.
  • Spending impact. $38 billion in additional spending by 2030 from scaling agentic workflows.
  • Token economics. Consumption shifts from model calls per user interaction to model calls per completed business process.
  • Production commitments. 75% of foundation model monetization locked into multiyear commitments by 2030, up from 20% in 2025.

Gartner Forecasts Frontier Revenue Will Concentrate by 2030

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 Commands 54% of the Market Through 2030

All nine regional forecasts for 2024–2030 with annual dollar amounts, 2030 shares, and current-dollar versus constant-currency CAGRs.
Gartner’s China region includes China, Hong Kong, and Taiwan. Source: Gartner country-level dataset, G00855897. Chart and analysis by softwarestrategiesblog.com.

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.

Detailed 2024–2030 spending trajectories for the U.S., Europe, and Gartner’s China region, with annual growth, world shares, and both CAGR bases.
Source: Gartner, G00855897. Chart and analysis by softwarestrategiesblog.com.

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.

  • North America 2030. $130 billion (54.4% share), 43.4% CAGR (2025 to 2030).
  • Europe 2030. $45.1 billion (18.8% share), 40.8% CAGR (2025 to 2030).
  • China region 2030. $40.9 billion (17.1% share), 58.3% CAGR (2025 to 2030).

Open-Weight Models Are Eroding the Paid Revenue Pool

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%.

Vercel monthly open-weight shares in December, April, and August; August token and spending split; calculated spend-per-token indices.
Source: Vercel AI Gateway Production Index, September 2026. Chart and analysis by softwarestrategiesblog.com.

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.

Tokens versus spending on a single day

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.

Complete daily Vercel rankings with ten named models plus Other for both token volume and spending, and four-model spending-per-token comparisons.
Source: Vercel AI Gateway dated export, CC BY 4.0. Chart and analysis by softwarestrategiesblog.com.

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.

What This Means for Enterprise Buyers

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).

  1. Build for model routing, not model loyalty. The market is fragmenting by workload. Enterprises locked into a single provider risk overpaying for tasks a smaller, specialized model handles at the required quality. The DSLM forecast makes the case for evaluating specialized models before assuming every task needs a frontier model.
  2. Budget for agentic inference volumes. Agentic workflows consume far more tokens per completed task than conversational AI. A budget sized for chatbot-level consumption won’t survive production agentic workloads.
  3. Evaluate open-weight alternatives for routine workloads. Gartner expects 30% of high-volume enterprise inference to shift to open or enterprise-controlled models by 2030. The Vercel data shows that shift underway. Start identifying which production workloads can move now.
  4. Watch the consolidation timeline. If frontier revenue concentrates in North America and Asia/Pacific as Gartner forecasts, assess provider resilience and migration options now.
  5. Demand platform-level governance and cost attribution. The AI platform market reaches $100 billion by 2030 because enterprises need orchestration, evaluation, cost visibility, and policy enforcement. For more on the governance gap, see Gartner’s $244.2B security forecast shows enterprises spend 17x more on AI tools than securing AI itself (March 24, 2026).

Bottom line

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.

Related on Software Strategies Blog

This post is my personal analysis of Gartner’s AI platforms, models, and generative AI 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

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.

Nov 10

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

Bottom line: Identity security stands at an unprecedented crossroads, with machine identities creating greater complexity and potential chaos every security professional needs to plan for.

At Forrester’s 2025 Security & Risk Summit, Merritt Maxim, VP and Research Director at Forrester, delivered critical insights highlighting the escalating threats shaping identity security’s evolution. CISOs and security leaders find themselves navigating surging threats driven by generative AI, the rapid proliferation of non-human identities, and outdated IAM infrastructures originally designed solely for compliance.  Maxim emphasized a pressing urgency: identity strategies must adapt or risk catastrophic breaches and compliance failures.

Here’s a detailed breakdown of the top 10 insights from Forrester’s Summit, including the specific slides from Maxim’s presentation and deeper insights from Forrester’s latest data:

1. Identity Security Budgets Accelerate Toward $27.5B by 2029

IAM investment is growing explosively, set to nearly double from $13.4 billion in 2024 to $27.5 billion by 2029, driven by the escalating complexity and severity of identity-related threats such as AI-driven deepfakes, sophisticated supply-chain attacks, and rampant cloud misconfigurations. This positions IAM as cybersecurity’s third fastest-growing segment, underscoring identity security as a business-critical imperative.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

2. Hybrid IAM Still Dominates—77% Keep On-Premise Components

Despite the relentless push to the cloud, 77% of organizations continue relying on hybrid IAM deployments due to legacy infrastructure and regulatory constraints. Fully cloud-based identity management remains a distant reality, with only 9% fully transitioned. Maxim stressed hybrid IAM’s persistence, highlighting the necessity for seamless integration capabilities between on-premises systems and cloud IAM platforms.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

3. Third-party Risk Matches Compliance as a Top IAM Driver

Forrester revealed a pivotal shift: managing third-party identities (32%) is now equally critical as regulatory compliance (32%) in driving IAM investments. High-profile breaches at Okta and CyberArk underscore vulnerabilities introduced by third-party identities, necessitating robust governance models that go beyond basic compliance checklists.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

4. Static Entitlements Are Obsolete; Zero Standing Privilege Is Now Mandatory

The static entitlement model—assigning privileges during onboarding—is officially outdated. Forrester highlighted Zero Standing Privilege (ZSP) architectures as the definitive new standard, utilizing the Continuous Access Evaluation Protocol (CAEP) to dynamically assign permissions at runtime. This strategy mitigates rampant privilege sprawl, dramatically reducing attack surfaces.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

5. Identity Management Converges Across Security, Marketing, and CX

Enterprises are rapidly integrating fragmented identity management systems across marketing, customer experience (CX), fraud prevention, and security. Maxim emphasized that businesses consolidating these functions significantly improve detection speed, minimize breaches, and enhance end-user experience. Leveraging customer preference and security data together is becoming a strategic advantage.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

6. Vendor Consolidation Radically Reshapes IAM Markets

IAM vendor consolidation accelerated significantly, highlighted by major moves such as Palo Alto Networks acquiring CyberArk, Ping Identity merging with ForgeRock, and CrowdStrike purchasing Adaptive Shield. Enterprises increasingly demand integrated identity platforms combining PAM, IGA, and Identity Threat Detection & Response (ITDR), driving these high-profile acquisitions.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

7. Generative AI Exacerbates Identity Threats but Offers Transformational Defenses

Generative AI escalates identity threats dramatically through enhanced phishing and sophisticated deepfake impersonations. Conversely, GenAI’s defensive capabilities are equally transformative, enabling automated identity threat detection, rapid response, and real-time entitlement adjustments. Maxim described these dual dynamics as essential to future IAM strategies.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

8. Machine Identities Are a Critical Emerging Attack Vector

The explosive growth in non-human identities (IoT, APIs, AI agents) vastly expands attack surfaces. Enterprises urgently need automated platforms from vendors like CyberArk, Venafi, and HashiCorp to manage this surge. Forrester highlighted machine identities as a rapidly intensifying risk requiring immediate attention and robust governance.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

9. Phishing-Resistant MFA Is Dangerously Under-Deployed

Alarmingly, only 21% of companies deploy phishing-resistant MFA after breaches, despite the increasing sophistication of MFA-bypass attacks. Forrester insists enterprises must urgently adopt solutions like FIDO2 and WebAuthn. Maxim warned that neglecting these standards leaves companies dangerously exposed to credential-based compromises.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

10. Context-Aware IAM Becomes a Real-time Security Necessity

Static IAM fails against machine-speed threats. Context-aware IAM, powered by dynamic authorization, continuously assesses real-time user behavior, device posture, and threat intel. Forrester identifies this adaptive approach as critical, turning identity from a passive gatekeeper to a proactive defender, which is essential for stopping attacks before damage occurs

10. Context‑Aware IAM Defines the Future of Access Control Best Slide: Slide 21 – Runtime Context and Adaptive IAM Model The next generation of IAM is contextual, continuous, and AI‑assisted  Convergence, Consolidation, And… . Static permissions are being replaced with adaptive models that evaluate risk in real time — factoring in behavioral biometrics, device posture, and environmental signals. This “runtime context” turns identity from a passive gatekeeper into an active defender capable of making split‑second decisions as threats unfold.

Bottom Line: Adaptive identity security defines enterprise survival

Identity security has become synonymous with enterprise survival. Merritt Maxim’s compelling insights from Forrester’s 2025 Security & Risk Summit underscore a new identity imperative: convergence, consolidation, and context must drive strategic identity transformations. Following Forrester’s lead, enterprises must prioritize investment in dynamic Zero Standing Privilege architectures, integrated identity platforms, generative AI-enabled threat response, robust machine identity management, and phishing-resistant MFA immediately.  The future of enterprise resilience hinges directly on evolving identity security today.

Oct 19

Gartner’s 13 ways GenAI is improving B2B Sales is the roadmap every business needs

Gartner's 13 ways GenAI is improving B2B Sales is the roadmap every business needs

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.

  • B2B sales organizations using GenAI-embedded sales technologies will reduce the time they spend prospecting and preparing for customer meetings by over 50% within two years.
  • Conversational interfaces based on GenAI will gain momentum and further revolutionize B2B selling. In 2028, they will be the driving force behind up to 60% of B2B sales interactions, up from less than 5% in 2023.
  • Centralized GenAI operations teams are also on the way, championed by Chief Revenue Officers (CROs). These teams will focus on integrating AI-driven strategies into sales and revenue operations. 35% of CROs will have GenAI operations teams online and incorporated into their companies’ strategic planning process by 2025.

The goal: find the most likely wins for GenAI in B2B Sales

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.

Source: Generative AI Use Cases for B2B Sales, Gartner, Inc.

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.

Source: Gartner, Inc. (2024) Generative AI Use Cases for B2B Sales

Mapping GenAI Use Cases Across Business Functions

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.

Source: Gartner, Generative AI Use Cases for B2B Sales.

GenAI is redefining the future of B2B Sales

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

Top ten insights CEOs need to know about GenAI going into 2025

Top ten insights CEOs need to know about GenAI going into 2025

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.

Cutting through the hype: What CEOs need to know about GenAI going into next year

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.

Top ten insights CEOs need to know about GenAI going into 2025

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.

Top ten insights CEOs need to know about GenAI going into 2025

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.

Top ten insights CEOs need to know about GenAI going into 2025

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.

Top ten insights CEOs need to know about GenAI going into 2025

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.

Top ten insights CEOs need to know about GenAI going into 2025

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.

Top ten insights CEOs need to know about GenAI going into 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.

Top ten insights CEOs need to know about GenAI going into 2025

  • 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).

Top ten insights CEOs need to know about GenAI going into 2025

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).

Top ten insights CEOs need to know about GenAI going into 2025

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.

Top ten insights CEOs need to know about GenAI going into 2025

Source: Gartner’s 2024 Gartner Generative AI Planning Survey

Sep 17

Top Seven Takeaways from Gartner’s 2024 CIO GenAI Survey

Top Seven Takeaways from Gartner's 2024 CIO GenAI Survey

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.

Strategic Insights from Gartner’s 2024 CIO GenAI Survey

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.

Source: Key findings from the 2024 Gartner CIO generative AI survey (ID G00820936). Gartner, Inc.

  • 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.


Source: Key findings from the 2024 Gartner CIO generative AI survey (ID G00820936). Gartner, Inc.

  • 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.


Source: Key findings from the 2024 Gartner CIO generative AI survey (ID G00820936). Gartner, Inc.

  • 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.


Source: Key findings from the 2024 Gartner CIO generative AI survey (ID G00820936). Gartner, Inc.

  • 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.

Conclusion

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).

Sep 15

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

 

Eighty percent of enterprises have increased their investment in GenAI over the past year, with nearly one-quarter (24%) now integrating the technology into their operations, up from just 6% in the previous year.

Capgemini Research Institute’s recent report, Harnessing the value of generative AI: 2nd edition: Top use cases across sectors, highlights enterprises’ accelerating pace of GenAI adoption and growing importance across their operations and industries.

“Generative AI is not just a technological innovation; it’s a catalyst for transformative change across multiple sectors, driving productivity gains, operational efficiency, and strategic shifts in business models,” write the report’s authors. Enterprise leaders’ sentiment underscores the increasing recognition of GenAI as a critical technology for staying competitive in an increasingly turbulent economic environment.

According to the report, GenAI’s rapid adoption across IT and marketing indicates that companies are actively integrating it into their core operations to drive tangible, measurable benefits. Capgemini’s findings highlight the need for a strong data governance framework, strategic talent development, and vigilant cybersecurity to maximize GenAI’s potential as companies scale their initiatives.

How enterprises are maximizing GenAI’s value

Capgemini found ten key ways enterprises are positioning themselves to maximize GenAI’s potential. These strategies demonstrate how all companies can potentially invest in and integrate GenAI to boost growth, efficiency, and innovation across departments and industries.

Investment surge reflects growing confidence in GenAI. 89% of large businesses with annual revenues over $20 billion are leading this investment surge, highlighting GenAI’s significance for future growth. Additionally, 73% of companies with revenues between $1 billion and $5 billion have significantly increased their GenAI budgets, showing that this trend is not limited to the largest companies. This investment trend indicates that many companies believe GenAI can drive enterprise evolution and deliver substantial returns, with many expecting double-digit productivity and customer engagement growth.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI maturity grows steadily across industries. GenAI implementations have continued to mature across industry sectors over the past year. Up from 6% in 2023, 18% of organizations will fully integrate GenAI into most or all functions in 2024. With 64% and 53% of companies enabling GenAI, high-tech and financial services lead. Retail grew 17%–40% and industrial manufacturing 14%–35%. With 53% and 47% of telecom and energy/utilities adopting GenAI, respectively, progress has been made.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI’s integration across organizational functions is growing. In one year, GenAI IT adoption rose from 4% to 27% across organizational functions. GenAI is improving enterprise productivity and innovation through this broad integration across sales, marketing, operations, and R&D. Capgemini also found that GenAI is transforming operations and creating value across all business areas.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

Productivity and customer engagement gains. Over the last year, organizations that have implemented GenAI have reported a 7.8% increase in productivity and a 6.7% increase in customer engagement. These tangible benefits demonstrate GenAI’s ability to provide real, measurable value to enterprises. Early adopters report significant improvements in key performance metrics, highlighting the strategic importance of incorporating GenAI into business operations.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

Small Language Models (SLM) are gaining momentum. 24% of organizations have implemented SLMs, and 56% plan to do so within three years. These models are cheaper and less computationally intensive than larger AI models, so many companies are piloting and eventually moving them into production. SLMs excel in industry-specific applications, allowing businesses to harness AI’s potential without the infrastructure and resource demands of larger models. SLMs are becoming a good option for companies trying to compete in an AI-driven market as they seek efficient and scalable AI solutions.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI is enabling enterprises to advance from chatbots to autonomous multi-agent systems. GenAI is helping 62% of organizations upgrade from chatbots to AI agents that autonomously manage complex goals. 48% of users use multi-agent systems, where AI agents operate independently in changing environments. As businesses automate and optimize complex processes with these systems, decision-making and operational efficiency improve across industries. AI has evolved from simple user interactions to complex, agentic use cases, as shown in the image.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI agents are accelerating the shift to autonomous operations. GenAI agents are increasingly used in enterprise automation, with 82% of companies planning to implement them in 1–3 years. These agents are evolving from supportive tools to autonomous entities that can perform complex tasks without interaction. This shift is significant, with 71% of organizations expecting AI agents to automate workflows and 64% expecting customer service and productivity improvements. AI agents are not just efficient; they are a radical shift toward fully autonomous, AI-driven operations that will transform enterprise productivity and strategic decision-making.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI is forcing major business strategy shifts. 54% of companies expect GenAI to improve their strategies, up from 39% in 2023. 40% of companies are revising their business models to stay competitive as GenAI becomes more important. As GenAI becomes more important, 74% of businesses believe they must use it to grow revenue and stay ahead of the competition.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

Strengthening data foundations is crucial for GenAI’s success. More than 60% of companies realize GenAI’s potential depends on solid data foundations. Only 51% have documented data integration processes and 46% have AI data management policies. Even enterprises that have adopted GenAI still struggle to make the most of all their external data sources. Capgemini makes it clear that for GenAI initiatives to succeed, companies need scalable, secure data infrastructure.

Tighten AI controls or risk trust and compliance. Ethics in AI deployment is a priority for 57% of organizations, which recognize the need for control mechanisms that can flex and adapt as their business goals change. While 46% have clear AI governance frameworks, 73% agree that human oversight is necessary to validate AI-driven decisions. Without strong governance, bias, and accountability issues could counteract GenAI’s benefits, so organizations must act now.

Conclusion

With 80% of organizations increasing their investment and almost a quarter already including it in their operations, GenAI is fast changing how businesses run. This general acceptance emphasizes GenAI’s importance as a main engine of efficiency and creativity, providing real advantages in customer interaction and output.

Organizations are not only embracing GenAI as AI agents and Small Language Models (SLMs) acquire traction; they are also including GenAI in their basic strategies. Those who match GenAI with their business models, make investments in solid data foundations, and develop the knowledge required to maximize its possibilities will inherit the future. They will lead in the era of artificial intelligence by doing this, establishing new benchmarks for operational excellence and creativity.

Mar 10

Gartner Predicts Solid Growth for Information Security, Reaching $287 Billion by 2027

Gartner Predicts Solid Growth for Information Security, Reaching $287 Billion by 2027

Image created in DALL-E

AI continues to become more weaponized with nation-state attackers and cybercrime gangs experimenting with LLMs and gen AI-based attack tradecraft. The age of weaponized LLMs is here.

At the same time, multi-cloud-based infrastructures more businesses rely on are coming under attack. Exfiltrating any identity data available from endpoints and then traversing a network to gain more access by collecting more credential data is often the goal.

Cyberattacks that combine AI and social engineering are just beginning  

Attackers have a version of human-in-the-middle, too, but their goal is to unleash AI’s offensive attack capabilities within social engineering campaigns. Last year’s social engineering-based attacks on MGM, Comcast, Shield Healthcare Group, and others serve as a case in point.

CrowdStrike’s 2024 Global Threat Report finds that cloud intrusions jumped 75% last year. There was a 76% increase in data theft victims named on data leak sites and a 60% increase in interactive intrusion campaigns. Worse, 75% of attacks were malware-free, making them difficult to identify and stop. There was also a 110% YoY increase in cloud-conscious cases.

PwC’s 2024 Digital Trust Insights Report finds that 97% of senior management teams have gaps in their cloud risk management plans. 47% say cloud attacks are their most urgent threat. One in three senior management teams is prioritizing cloud security as their top investment this year.

Gartner sees a more complex threatscape driving growth

Gartner’s Forecast: Information Security and Risk Management, Worldwide, 2021-2027, 4Q23 Update report predicts the information security and risk management market will grow from $185 billion in 2023 to $287 billion in 2027, attaining a compound annual growth rate of 11% in constant currency.

Nation-state attackers are picking up the pace of their stealthy AI arms race. They’re looking to score offensive first victories on an increasingly active digital battlefield. Gartner predicts that in 2027, 17% of the total cyberattacks and data leaks will involve generative AI.

Another key assumption driving Gartner’s latest forecast is that by 2025, user efficiency improvements will drive at least 35% of security vendors to offer large language model (LLM)-driven chat capabilities for users to interact with their applications and data, up from 1% in 2022.

Gartner has also factored in the surge in cloud attacks and the continued growth of hybrid workforces. One of their key assumptions driving the forecast is that “by the end of 2026, the democratization of technology, digitization, and automation of work will increase the total available market of fully remote and hybrid workers to 64% of all employees, up from 52% in 2021.”

Gartner Predicts Solid Growth for Information Security, Reaching $287 Billion by 2027

Source: Gartner, Forecast Analysis: Information Security and Risk Management, Worldwide, Published February 29, 2024

Source: Gartner, Forecast Analysis: Information Security and Risk Management, Worldwide, Published 29 February 2024

Key takeaways from Gartner’s forecast

Market subsegments predicted to see the most significant growth through 2027 include the following:

  • Gartner has high expectations for Zero Trust Network Access (ZTNA) growth, stating the worldwide market was worth $575.7 million in 2021 and predicting it will soar to $3.99 billion in 2027, attaining a 31.6% CAGR in the forecast period.
  • Identity Access Management (IAM) is predicted to grow from $4 billion in 2021 to $11.1 billion in 2027, attaining a 17.6% CAGR. Identity Governance and Administration software is predicted to grow from $2.8 billion in 2021 to $5.77 billion in 2027, attaining a 12.8% CAGR.
  • Endpoint Protection Platforms (EPP) are predicted to grow from $9.8 billion in 2021 to $26.9 billion in 2027, achieving a 17.2% CAGR.
  • Threat Intelligence software is predicted to grow from $1.1 billion in 2021 to $2.79 billion in 2027, growing at a 15.6% CAGR through the forecast period.
  • Cloud Access Security Brokers (CASB) is predicted to grow from $928M in 2021 to $4.75 billion in 2027, attaining a CAGR of 30.2%. Gartner believes that the market share of cloud-native solutions will continue to grow. They are predicting that the combined market for cloud access security brokers (CASBs) and cloud workload protection platforms (CWPPs) will reach $12.8 billion in constant currency by 2027, up from $4.6 billion in 2022. Gartner continues to also see strong demand for cloud-based detection and response solutions that include endpoint detection and response (EDR) and managed detection and response (MDR).
« Older Entries