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Posts tagged ‘Enterprise 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

Jan 22

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

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

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

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

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

Where the bottlenecks are breaking

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

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

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

The $14.6 billion data readiness bet

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

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

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

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

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

The 2027 crossover: When agents overtake chatbots

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

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

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

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

The Security Tax on Agentic AI

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

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

Infrastructure: Dominant but decelerating

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

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

The 6% problem

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

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

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

Where the value accrues

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

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

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

Other High-Growth Segments:

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

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

May 19

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Gartner’s 2024 CEO Survey Reveals AI as Top Strategic Priority

Gartner's 2024 CEO Survey Reveals AI as Top Strategic Priority

75% of CEOs used ChatGPT in the first half of 2023, with 44% incorporating it into their jobs.

Gartner’s 2024 CEO survey finds that CEOs are on board with AI to a much greater extent than previously believed. 87% of CEOs agree that AI’s benefits to their business outweigh its risks. “Digitalization, in general, and AI, in particular, will be core innovative elements in revised business strategies, as will environmental-sustainability-based growth ideas,” writes Gartner in the report.

CEOs experimenting with synthetic video

Almost a third of CEOs have considered making and using a synthetic video of themselves. Gartner notes that Estelle Brachlianoff, CEO of the European utility services company Veolia, has posted an AI-augmented video of herself on LinkedIn and X appearing to speak in multiple languages.

Driving AI adoption

CEOs who adopt new technologies immediately drive their adoption enterprise-wide because everyone immediately sees those technologies as critical to their jobs. Seasoned CEOs know the quickest way to get a new enterprise app’s adoption rate to go up is to use it themselves and demonstrate their mastery quickly. What’s happening with AI’s adoption is faster than many CEOs expected.

Key takeaways from Gartner’s 2024 CEO survey include the following:

  • Growth dominates CEO agendas, reaching a new record in Gartner’s annual survey. “CEOs’ top business priority of growth is up 25% and is at the highest level since 2014,’ writes Financial considerations increased by 25%, cost management by 11%, and customer priorities grew by 22%. The survey points towards CEOs being more focused on profitability and margins, two signs of internal process gains to reduce operating costs and improve efficiency. The survey results point to more CEOs looking at how to get greater returns from the most expensive assets their businesses operate.
75% of CEOs used ChatGPT in the first half of 2023, with 44% incorporating it into their jobs.Gartner's 2024 CEO survey finds that CEOs are on board with AI to a much greater extent than previously believed. 87% of CEOs agree that the benefits of AI to their business outweigh its risks. "Digitalization, in general, and AI, in particular, will be core innovative elements in revised business strategies, as will environmental-sustainability-based growth ideas," writes Gartner in the report. CEOs experimenting with synthetic video Almost a third of CEOs have considered making and using a synthetic video of themselves. Gartner notes that Estelle Brachlianoff, CEO of the European utility services company Veolia, has posted an AI-augmented video of herself on LinkedIn and X appearing to speak in multiple languages. Driving AI adoption CEOs who adopt new technologies immediately drive their adoption enterprise-wide because everyone immediately sees those technologies as critical to their jobs. Seasoned CEOs know the quickest way to get a new enterprise app's adoption rate to go up is to use it themselves and demonstrate their mastery quickly. What's happening with AI's adoption is faster than many CEOs expected. Key takeaways from Gartner's 2024 CEO survey include the following: • Growth dominates CEO agendas, reaching a new record in Gartner's annual survey. "CEOs' top business priority of growth is up 25% and is at the highest level since 2014,' writes Gartner. Financial considerations increased by 25%, cost management by 11%, and customer priorities grew by 22%. The survey points towards CEOs being more focused on profitability and margins, two signs of internal process gains to reduce operating costs and improve efficiency. The survey results point to more CEOs looking at how to get greater returns from the most expensive assets their businesses operate. Ceo growth 1 • CEOs mentioning AI as one of their top two technology priorities jumped from 4% in 2023 to 24% in 2024. Technology innovation also increased from 7% to 11%, and the use of digital transformation for growth increased from 9% to 11%. It's interesting to see how CEOs are focusing on how to improve, integrate, and modernize their strategic use of technology. That category jumps from 1% in 2023 to 5% in 2024. "AI is explicitly mentioned a lot more in 2024 than it was in the 2023 survey. At the same time, mentions of "digitalization" have declined significantly, and so have mentions of e-commerce and omnichannel," writes Gartner. CEO two top strategic business priorities 2 • 34% of CEOs say that the next business transformation their enterprises will pursue after digital is AI. CEO's intentions to pursue AI as their next business transformation are nearly four times greater than their interest in operations efficiency and agility. Sustainability and ESG are a distant third priority. Just 5% of CEOs say customer experience/centricity will be a priority. the theme of the next transformation after digital • 59% say AI is the technology that will most impact their industry. AI has a four-year track record of being the top category, starting in 2020, with the percentage of CEOs mentioning it ranging between 18% to 29%. Gartner mentions in the survey results that in 15 years of asking this question and comparable ones to it, there's never been a category that emerges as dominant as AI has. In the past, CEOs believed cloud and big data technologies would be the most impactful. Previous technologies have had nowhere near the extent of impact that AI does today. "Eighty-six percent of CEOs expect AI will help maintain or grow their revenue in 2024-2025, and when asked exactly how that would happen, the top answer category was an improvement to customer experience and relationships," writes Gartner. Use AI to Help Maintain or Grow Company Revenue

Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches

  • CEOs mentioning AI as one of their top two technology priorities jumped from 4% in 2023 to 24% in 2024. Technology innovation also increased from 7% to 11%, and the use of digital transformation for growth increased from 9% to 11%. It’s interesting to see how CEOs are focusing on how to improve, integrate, and modernize their strategic use of technology. That category jumps from 1% in 2023 to 5% in 2024. “AI is explicitly mentioned a lot more in 2024 than it was in the 2023 survey. At the same time, mentions of “digitalization” have declined significantly, as have mentions of e-commerce and omnichannel,” writes Gartner.
Gartner's 2024 CEO Survey Reveals AI as Top Strategic Priority

Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches

  • 34% of CEOs say that the next business transformation their enterprises will pursue after digital is AI. CEO’s intentions to pursue AI as their next business transformation are nearly four times greater than their interest in operations efficiency and agility. Sustainability and ESG are a distant third priority. Just 5% of CEOs say customer experience/centricity will be a priority.
Gartner's 2024 CEO Survey Reveals AI as Top Strategic Priority

Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches

  • 59% say AI is the technology that will most impact their industry. AI has a four-year track record of being the top category, starting in 2020, with the percentage of CEOs mentioning it ranging between 18% to 29%. Gartner mentions in the survey results that in 15 years of asking this question and comparable ones to it, there’s never been a category that emerges as dominant as AI has. In the past, CEOs believed cloud and big data technologies would be the most impactful. Previous technologies have had nowhere near the extent of impact that AI does today. “Eighty-six percent of CEOs expect AI will help maintain or grow their revenue in 2024-2025, and when asked exactly how that would happen, the top answer category was an improvement to customer experience and relationships,” writes Gartner.
Gartner's 2024 CEO Survey Reveals AI as Top Strategic Priority

Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches