Corporate AI Spending Statistics 2026
By Axis Intelligence Research
Co-authors: Sarah Mitchell (AI and Machine Learning) & Sarah Davis (Digital Finance) | Last updated: August 18, 2026 | License: CC BY 4.0
Worldwide AI spending reaches $2.59 trillion in 2026, up 47% year over year, according to Gartner’s May 2026 forecast. But 55.15% of that lands in infrastructure bought by vendors and hyperscalers. For the 6,525 U.S. small employer firms the Federal Reserve surveyed, the binding constraint is not ambition. It is credit.
Quick Answer: How Much Are Companies Spending on AI in 2026?
Worldwide AI spending totals $2.59 trillion in 2026, a 47% increase over 2025, per Gartner’s May 19, 2026 forecast. According to Axis Intelligence Research, 55.15% of that total is AI infrastructure, the layer procured by technology vendors and cloud providers rather than consumed directly by enterprise buyers. On the ground, the Federal Reserve’s 2026 Report on Employer Firms finds 46% of U.S. small employer firms use AI, while only 42% of financing applicants received the full amount they sought. Axis Intelligence Research measures the distance between those two facts as the Corporate AI Funding Gap (CAFG): 35.8 points.
Key Findings
- Worldwide AI spending is forecast at $2.59 trillion in 2026, a 47% year-over-year increase, according to Gartner’s May 2026 forecast.
- According to Axis Intelligence Research, the AI Spend Concentration Ratio (ASCR) reads 55.15 for 2026, meaning infrastructure absorbs a majority of global AI spending, and it declines to 54.11 by 2027.
- The Federal Reserve Banks report that 46% of U.S. small employer firms currently use AI, but just 7% of those users have fully integrated it, which Axis Intelligence Research calculates as 3.22% of all small employer firms.
- According to Axis Intelligence Research, the Corporate AI Funding Gap stands at 35.8 percentage points as of the Federal Reserve’s March 2026 release, the distance between firms that want AI and firms that get fully funded.
- In the Federal Reserve’s January 2026 Senior Loan Officer Opinion Survey, net C&I loan demand ran +16.1% for large and middle-market firms against 0.0% for small firms, a 16.1-point spread Axis Intelligence Research computes from the published response distributions.
Source: Axis Intelligence Research — CC BY 4.0
What Is the Corporate AI Funding Gap (CAFG)?
Most 2026 coverage of corporate AI spending stops at the trillion-dollar headline. That number is real, and it is also the least useful figure for anyone running a company with fewer than 500 employees, because almost none of it is addressable by them.
The Corporate AI Funding Gap (CAFG) is an Axis Intelligence Research metric that measures, in percentage points, the distance between the share of firms that want to deploy AI and the share that actually secure the full financing they applied for. Both inputs come from the same Federal Reserve survey instrument, the same respondent population, and the same fielding window, which is what makes the subtraction meaningful rather than decorative.
Formula
CAFG = (AI users + firms planning AI within 12 months)
- (share applying for financing x full-approval rate)
Inputs and computation, snapshot March 3, 2026
| Input | Value | Source |
|---|---|---|
| Firms currently using AI | 46% | Federal Reserve Banks, 2026 Report on Employer Firms |
| Firms planning AI within 12 months | 15% | Federal Reserve Banks, 2026 Report on Employer Firms |
| AI demand, combined | 61.0% | Axis Intelligence Research calculation |
| Firms that applied for financing | 60% | Federal Reserve Banks, 2026 Report on Employer Firms |
| Applicants receiving the full amount sought | 42% | Federal Reserve Banks, 2026 Report on Employer Firms |
| Firms fully funded (60% x 42%) | 25.2% | Axis Intelligence Research calculation |
| CAFG | 35.8 points | Axis Intelligence Research |
Scope note. CAFG is denominated on all small employer firms with 1 to 499 employees, so both terms share a common base. It measures financing access alongside AI intent; it does not assert that every financing application was filed to fund an AI project, and the Federal Reserve reports the two questions separately. The Small Business Credit Survey uses a convenience sample rather than a random one. Readings are comparable year over year because the instrument and population are stable.
A CAFG of 35.8 says something specific: for every four small employer firms that want AI in production, roughly one and a half will clear the capital markets that year at full ask. The rest self-fund from cash flow, take partial financing, or wait.
Sarah Davis: The spread between what a firm wants to build and what its lender will underwrite is where the AI adoption curve actually gets set, and it is nowhere in the vendor decks. Read the Fed numbers together and the picture sharpens: 86% of these firms use financing on a regular basis, 59% of those carrying debt pledged a personal guarantee to get it, and the single most common reason for seeking money was covering operating expenses at 56%, well ahead of expansion at 46%. That is not a borrower base with slack to fund a model rollout. When a firm in that position adopts AI, it is buying a seat license, not building a stack.
How Is the $2.59 Trillion in Corporate AI Spending Actually Split?
Gartner publishes the segment table. It does not publish the growth rates or the shares. Those are calculated below by Axis Intelligence Research from the published figures, and the segment values reconcile against Gartner’s stated totals to within one million dollars, a rounding artifact.
AI Spending by Market Segment, 2025 to 2027
| Segment | 2025 ($M) | 2026 ($M) | 2027 ($M) | 2026 growth | 2026 share | Source |
|---|---|---|---|---|---|---|
| AI Infrastructure | 975,581 | 1,431,509 | 1,890,310 | 46.73% | 55.15% | Gartner, May 2026; growth and share by Axis Intelligence Research |
| AI Services | 436,351 | 585,527 | 759,418 | 34.19% | 22.56% | Gartner, May 2026; growth and share by Axis Intelligence Research |
| AI Software | 282,897 | 453,209 | 638,431 | 60.20% | 17.46% | Gartner, May 2026; growth and share by Axis Intelligence Research |
| AI Cybersecurity | 25,920 | 51,347 | 85,997 | 98.10% | 1.98% | Gartner, May 2026; growth and share by Axis Intelligence Research |
| AI Models | 15,494 | 32,604 | 59,161 | 110.43% | 1.26% | Gartner, May 2026; growth and share by Axis Intelligence Research |
| AI Platforms for Data Science and ML | 21,292 | 29,928 | 42,639 | 40.56% | 1.15% | Gartner, May 2026; growth and share by Axis Intelligence Research |
| AI Application Development Platforms | 6,587 | 8,416 | 10,922 | 27.77% | 0.32% | Gartner, May 2026; growth and share by Axis Intelligence Research |
| AI Data | 826 | 3,126 | 6,480 | 278.45% | 0.12% | Gartner, May 2026; growth and share by Axis Intelligence Research |
| Total | 1,764,947 | 2,595,667 | 3,493,358 | 47.07% | 100% | Gartner, May 2026 |
Two checks worth stating. Axis Intelligence Research computes AI Models growth at 110.43%, which reconciles with Gartner’s own stated figure of 110% growth in 2026 and confirms the table is being read as intended. And the AI Data segment grows 278.45% off a base of $826 million, which is arithmetically dramatic and economically small at 0.12% of the total. Percentage growth on a small base is the most frequently misquoted number in this dataset.
One point of friction with the source. Gartner’s release states that AI infrastructure will account for “over 45% of spending.” Its own Table 1 puts the 2026 figure at 55.15%. Both statements are defensible, since the quoted 45% describes a multi-year floor rather than the 2026 reading, but anyone citing the release text will understate infrastructure concentration by roughly ten points. Axis Intelligence Research uses the table.
The AI Spend Concentration Ratio (ASCR)
ASCR is an Axis Intelligence Research metric expressing AI infrastructure spending as a share of total AI spending, on a 0 to 100 scale. It answers one question: how much of the AI economy is capital equipment rather than product a business can buy and use next quarter?
ASCR = (AI Infrastructure spending / Total AI spending) x 100
| Year | AI Infrastructure ($M) | Total AI spending ($M) | ASCR |
|---|---|---|---|
| 2025 | 975,581 | 1,764,947 | 55.28 |
| 2026 | 1,431,509 | 2,595,667 | 55.15 |
| 2027 | 1,890,310 | 3,493,358 | 54.11 |
Scope note. ASCR is computed from a single forecaster’s segment definitions, so it tracks the composition of Gartner’s AI market rather than a universally agreed boundary of what counts as AI. It is a composition measure, not a measure of capital intensity per unit of output. Readings are directly comparable across years because the segment definitions are held constant within the forecast vintage.
The reading that matters is the flatness. Infrastructure is growing 46.73% while the total grows 47.07%, which means the widely repeated claim that AI infrastructure is progressively swallowing the market is not what this forecast says. ASCR is essentially unchanged from 2025 to 2026 and falls by more than a point into 2027, as software at 60.20% and models at 110.43% grow faster than the hardware layer beneath them.
Sarah Mitchell: A flat ASCR is the more interesting result, and it cuts against the narrative on both sides. The build-out is not accelerating away from the application layer, which undercuts the pure-capex bubble framing. But it is also not yielding to it: fifty-five cents of every AI dollar still goes into servers, fabric, and silicon that enterprises consume through an API rather than own. Gartner’s own analyst says enterprises have not yet flexed their spending potential and calls 2026 the inflection year for that. The segment table shows the inflection has not landed yet. Models and data are the fastest-growing lines, and together they are 1.38% of spending.
How Many Companies Actually Use AI, and How Deeply?
Spending forecasts describe the supply side. Adoption surveys describe the demand side, and the two U.S. federal instruments measuring it do not agree.
AI Adoption by Firm Size and Sector
| Population | AI use rate | As of | Source |
|---|---|---|---|
| U.S. small employer firms, 1 to 499 employees | 46% | March 2026 report | Federal Reserve Banks, Small Business Credit Survey |
| All U.S. employer businesses, national rate | 19.8% | May 3, 2026 | U.S. Census Bureau, BTOS |
| Firms with 250 or more employees | 37% | May 3, 2026 | U.S. Census Bureau, BTOS |
| Firms with 100 to 249 employees | 32% | May 3, 2026 | U.S. Census Bureau, BTOS |
| Information sector (NAICS 51) | 39.7% | May 3, 2026 | U.S. Census Bureau, BTOS |
| Finance and Insurance (NAICS 52) | 33.9% | May 3, 2026 | U.S. Census Bureau, BTOS |
| Retail Trade (NAICS 44) | ~14% | May 3, 2026 | U.S. Census Bureau, BTOS |
| All employer businesses, employment-weighted | 32% | Nov 2025 to Jan 2026 | U.S. Census Bureau, CES-WP-26-25 |
The Federal Reserve’s small business survey puts AI use at 46%. The Census Bureau’s Business Trends and Outlook Survey puts the national rate at 19.8%. That is a 26.2-point divergence between two federal sources, and Axis Intelligence Research publishes it rather than picking a favorite, because the gap is informative.
The two instruments ask different questions. The Federal Reserve asks whether the business or its employees currently use AI, which captures an employee running a chatbot on a marketing draft. The Census Bureau asks whether the business used AI in a business function in the prior two weeks, a narrower operational test with a recency window attached. The Federal Reserve draws a convenience sample of small employer firms; the Census Bureau draws a nationally representative sample of roughly 1.2 million businesses across all sizes. Neither figure is wrong. They are measuring adjacent things, and any article quoting one without the other is reporting half a fact.
Depth of Integration
The Federal Reserve’s depth question resolves most of the tension. Of firms that use AI, about half are experimenting, 44% have partially integrated it into business processes, and 7% have fully integrated it.
Applied to the 46% base, Axis Intelligence Research calculates that 3.22% of U.S. small employer firms have fully integrated AI, and 20.24% have partially integrated it. Against a $2.59 trillion global spending forecast, roughly three in a hundred small employer firms have finished the job.
What those firms use AI for is unglamorous and consistent with a low integration ceiling: writing or marketing at 83%, individual productivity at 61%, planning or analysis at 51%. Reported outcomes skew positive, with 71% citing increased productivity, 39% improved quality, and 31% higher sales, while the vast majority reported no change in labor costs. The blockers are accuracy at 46% and adapting tools to business needs at 43%. For firms that have not started, the top blocker is finding tools that fit at 54%, followed by implementation and training time at 37%.
Sarah Mitchell: Accuracy at 46% and tool-fit at 43% are not procurement problems, they are integration problems, and integration is the line item nobody finances. A seat license is an operating expense a firm can start and stop. Rewiring a workflow so a model’s output is trustworthy enough to act on without a human re-checking it is a project with a payback period, and a project with a payback period is the thing you go to a bank for. That is why the depth distribution is shaped the way it is: 50% experimenting, 7% finished. The gap between those two numbers is mostly capital and calendar, not enthusiasm.
Are Banks Now Pricing AI Exposure Into Loan Approvals?
Yes, and the Federal Reserve has the receipts. This is the part of the corporate AI spending story that almost nobody connects, and it is the reason the CAFG matters.
The January 2026 Senior Loan Officer Opinion Survey included special questions asking banks how their likelihood of approving a C&I loan application had changed since the beginning of 2025, based on the borrower’s sector exposure to AI. The Federal Reserve reported three results:
- A moderate net share of banks were more likely to approve C&I loans to firms in sectors benefiting from AI.
- A major net share of banks were less likely to approve such loans to firms adversely affected by AI.
- For firms with little AI exposure, approval likelihood was unchanged.
The Federal Reserve does not publish point estimates for these special questions, so Axis Intelligence Research records them as NA in the dataset rather than inventing precision. The Federal Reserve does publish the bands behind its own vocabulary, and applying them is the useful step: “moderate” means a net share greater than 10% and up to 20%; “major” means a net share of 50% or more. Read against the Fed’s own definitions, the asymmetry is the finding. The penalty for being on the wrong side of AI is at least two and a half times larger than the reward for being on the right side.
Banks are not extending credit because a borrower adopts AI. They are withdrawing it because a borrower’s sector looks disrupted by it. Adoption does not buy a firm a better rate. Disruption costs it access.
C&I Lending Conditions by Firm Size
Axis Intelligence Research computed the net shares below from the response distributions published in SLOOS Table 1, using the Federal Reserve’s standard convention of tightening minus easing and stronger minus weaker.
| Measure | Large and middle-market firms | Small firms | Spread | Source |
|---|---|---|---|---|
| Net tightening, C&I standards | +5.3% | +8.9% | 3.6 pts | SLOOS Jan 2026 Q1; net shares by Axis Intelligence Research |
| Net stronger C&I demand | +16.1% | 0.0% | 16.1 pts | SLOOS Jan 2026 Q4; net shares by Axis Intelligence Research |
The demand spread is the headline. Net loan demand from large and middle-market firms ran +16.1%, the strongest reading in the recent series, while small-firm demand netted to exactly zero, with 14.3% of banks reporting stronger demand and 14.3% reporting weaker. Among banks citing stronger demand, 63.2% rated increased customer investment in plant or equipment as at least somewhat important.
That is the AI capital cycle showing up in the credit data, and it shows up in exactly one size class.
By the July 2026 survey, covering the second quarter and drawing responses from 56 domestic banks and 18 U.S. branches of foreign banks, standards for C&I loans were basically unchanged across all firm sizes, and banks reported C&I standards easier than the midpoints of their historical ranges since 2005. Demand stayed stronger from large and middle-market firms and basically unchanged from small firms. The size divergence persisted through two quarters of otherwise loosening conditions.
Where Small Firms Go Instead
When bank credit does not clear, firms route around it, and the Federal Reserve tracks where. The share of applicants seeking financing at online fintech lenders rose from 17% in the 2020 survey to 29% in the 2025 survey, a 12-point increase. Applicants at small banks were the most likely to be fully approved at 57%.
The cost of the detour is documented. Among firms that borrowed from online lenders, 60% reported that actual borrowing costs came in higher than expected, against 37% at small banks and 32% at large banks. Axis Intelligence Research puts the cost-surprise spread between online lenders and large banks at 28 percentage points. High interest rates and unfavorable repayment terms were the most common challenges reported at online lenders.
Sarah Davis: Twenty-nine percent of applicants now start at an online lender, and three in five of them are surprised by the price. That is not a pricing failure, it is a disclosure asymmetry, and it compounds the funding gap rather than closing it. A firm that was declined at a bank, took a higher-cost alternative, and then discovered the true cost after signing is a firm whose AI budget just moved to debt service. The Fed also asked about loan quality expectations for 2026 and banks anticipate deterioration specifically for C&I loans to small firms. The lenders are telling you which side of the divide they expect to break.
What Does the Macro Data Say About Corporate AI Investment?
Forecasts are forecasts. The national accounts are a measurement of money that actually moved, and they run considerably cooler.
| Series | Value | Period | Source |
|---|---|---|---|
| Private fixed investment, information processing equipment and software | $1,612.853B SAAR | Q2 2026 | U.S. Bureau of Economic Analysis, series A679RC |
| Same series | $1,556.469B SAAR | Q1 2026 | U.S. Bureau of Economic Analysis, series A679RC |
| Same series | $1,466.358B SAAR | Q4 2025 | U.S. Bureau of Economic Analysis, series A679RC |
| Same series | $1,352.452B SAAR | Q2 2025 | U.S. Bureau of Economic Analysis, series A679RC |
| Real GDP growth, advance estimate | 1.5% annualized | Q2 2026 | U.S. Bureau of Economic Analysis |
U.S. private fixed investment in information processing equipment and software reached $1.613 trillion at a seasonally adjusted annual rate in Q2 2026, per the BEA series retrieved from FRED. Axis Intelligence Research calculates year-over-year growth of 19.25% against Q2 2025, and 3.62% quarter over quarter.
Set that beside the 47.07% global AI spending growth in the Gartner forecast. The measured U.S. investment series is growing at roughly 41% of the rate implied by the global AI forecast. These are not the same quantity, and the comparison should be handled with care: the BEA series is U.S.-only, covers all information processing rather than AI specifically, and counts capitalized fixed investment rather than services and consumption. The BEA’s Q2 2026 advance estimate attributes the intellectual property gain mainly to prepackaged software and research and development, with real GDP growing 1.5% annualized.
Two things are true at once: real money is moving into information technology at a genuinely elevated rate, and the rate is less than half of what the AI-specific forecast headline implies.
A Comparison Axis Intelligence Research Declines to Make
Gartner’s July 27, 2026 forecast puts worldwide IT spending at $6.37 trillion in 2026, up 14.2%. Dividing the $2.59 trillion AI figure into it would yield a tidy “AI is 41% of all IT spending” statistic, and that statistic would be wrong enough to be worth refusing.
The two forecasts are separate releases from separate vintages, published two months apart. More importantly, the AI spending forecast includes AI services and AI models, categories whose overlap with the IT spending taxonomy is not stated in either release. A ratio between two differently scoped aggregates produces a number that looks precise and means nothing. Axis Intelligence Research publishes both figures side by side and does not divide them.
Global Corporate AI Investment for Context
Capital flowing into AI companies is a different quantity again from spending on AI, and the two are routinely conflated. Stanford HAI’s 2026 AI Index, published April 2026, measured global corporate AI investment at $581.7 billion in 2025, of which private investment was $344.7 billion, growing 127.5% and accounting for about 60% of the corporate total. U.S. private AI investment reached $285.9 billion against China’s $12.4 billion, with 1,953 newly funded AI companies in the United States. Stanford notes the China figure understates total Chinese AI spending given state guidance funds.
Three distinct numbers, three distinct meanings: $2.59 trillion is what the world spends on AI, $581.7 billion is what investors put into AI companies, and $1.613 trillion SAAR is what U.S. businesses actually booked as information technology investment. They are not interchangeable, and the most common citation error in this subject is treating them as though they were.
Methodology
Collection. Every figure in this article was retrieved from its issuing organization during a research session on August 18, 2026. Primary sources fetched and read: the Federal Reserve Banks’ 2026 Report on Employer Firms; the Federal Reserve Board’s January 2026 and July 2026 Senior Loan Officer Opinion Surveys, including the published Table 1 response distributions; the U.S. Census Bureau’s Business Trends and Outlook Survey analysis and working paper CES-WP-26-25; the U.S. Bureau of Economic Analysis series A679RC and the Q2 2026 GDP advance estimate; Gartner’s May 19 and July 27, 2026 press releases; and Stanford HAI’s 2026 AI Index. No figure in this article originates from a secondary aggregator or from model recall.
Formulas. CAFG = (AI users + firms planning AI within 12 months) − (financing application rate × full-approval rate) = (46 + 15) − (60 × 0.42) = 61.0 − 25.2 = 35.8 points. ASCR = (AI infrastructure spending ÷ total AI spending) × 100. Segment growth rates are computed as (2026 value ÷ 2025 value − 1) × 100. SLOOS net shares follow the Federal Reserve’s published convention: for standards, the share tightening minus the share easing; for demand, the share reporting stronger minus the share reporting weaker.
Verification. All arithmetic was recomputed in Python as a separate pass. The Gartner segment values sum to their published totals within $1 million across all three years, a rounding artifact. The computed AI Models growth rate of 110.43% reconciles with Gartner’s independently stated 110%. The Federal Reserve financing-outcome shares of 42%, 36%, and 22% sum to 100%. Every prose number in this article has a matching row in the accompanying CSV with source URL and retrieval date.
Where values are not published. The Federal Reserve does not release point estimates for the SLOOS special questions on AI exposure and loan approval. Those rows carry NA in the dataset with a method note recording the Federal Reserve’s own definitional bands. No value was estimated to fill the gap.
Source discrepancies published rather than reconciled. The 26.2-point divergence between the Federal Reserve’s 46% and the Census Bureau’s 19.8% AI adoption rates reflects different question wording, recency windows, and sampling frames, and is presented as a documented divergence. Gartner’s release text describing AI infrastructure as “over 45%” of spending differs from its own Table 1 value of 55.15% for 2026; Axis Intelligence Research uses the table and flags the discrepancy.
Registry note. The Axis canonical figure for 2026 worldwide AI spending is $2.59 trillion, per Gartner’s May 19, 2026 release. Earlier Axis pages citing Gartner’s $2.52 trillion figure reference the January 2026 forecast vintage, which the May release superseded. This page owns the firm-level funding and credit-access figures; AI Spending Statistics 2026 owns the global market-size series.
About This Dataset
The CSV accompanying this article is the fact table. It contains 107 rows covering worldwide AI spending by segment, U.S. firm-level AI adoption by size and sector, small business financing outcomes, bank lending standards and demand by firm size, and U.S. national accounts investment data. Every row carries the metric, value, unit, as-of date, geography, segment, source organization, source document, source URL, retrieval date, a primary-source flag, an Axis-calculated flag, a method note, and a data type. Rows marked axis_calculated = yes correspond to formulas disclosed in this article.
Coverage: 2025 to 2027 forecast horizon; U.S. and global.
Format: UTF-8, comma-separated, one header row, ISO 8601 dates, raw numeric values with units in a separate column.
License: CC BY 4.0.
Cite as: Axis Intelligence Research, Corporate AI Spending Statistics 2026, 2026.
Citation Formats
APA Axis Intelligence Research, Mitchell, S., & Davis, S. (2026). Corporate AI spending statistics 2026: $2.59 trillion in spend and the credit funnel deciding who gets in. Axis Intelligence. https://axis-intelligence.com/corporate-ai-spending-statistics/
MLA Axis Intelligence Research, et al. “Corporate AI Spending Statistics 2026: $2.59 Trillion in Spend and the Credit Funnel Deciding Who Gets In.” Axis Intelligence, 18 Aug. 2026, axis-intelligence.com/corporate-ai-spending-statistics/.
Chicago Axis Intelligence Research, Sarah Mitchell, and Sarah Davis. “Corporate AI Spending Statistics 2026: $2.59 Trillion in Spend and the Credit Funnel Deciding Who Gets In.” Axis Intelligence, August 18, 2026. https://axis-intelligence.com/corporate-ai-spending-statistics/.
Frequently Asked Questions
Does adopting AI improve a company’s odds of getting a business loan?
Not on its own. The Federal Reserve’s January 2026 Senior Loan Officer Opinion Survey found that banks became more likely to approve C&I loans to firms in sectors benefiting from AI exposure, and materially less likely to approve loans to firms in sectors adversely affected by it. Approval likelihood for firms with little AI exposure was unchanged. Lenders are assessing sector-level disruption risk, not rewarding individual adoption decisions, so a firm’s industry classification carries more weight in this calculation than its software stack.
How much of the $2.59 trillion AI market can a mid-sized company actually buy?
The addressable portion is the software, services, and model layers. Axis Intelligence Research puts AI software at 17.46% of 2026 spending, AI services at 22.56%, and AI models at 1.26%, against AI infrastructure at 55.15%. The infrastructure majority is bought by hyperscalers and technology vendors and reaches enterprise buyers indirectly, priced into cloud and API rates rather than sold as a line item.
Why do Federal Reserve and Census Bureau AI adoption numbers disagree so much?
They measure different things. The Federal Reserve asks whether a business or its employees currently use AI, capturing informal employee use. The Census Bureau asks whether the business used AI in a business function during the prior two weeks, a narrower operational test. The Federal Reserve samples small employer firms via a convenience sample; the Census Bureau uses a nationally representative sample across all firm sizes. The resulting 26.2-point gap is a definitional artifact, not a data error, and both figures should be quoted with their definitions attached.
What share of small businesses have fully integrated AI rather than experimenting with it?
According to Axis Intelligence Research, 3.22% of U.S. small employer firms have fully integrated AI, calculated from the Federal Reserve’s finding that 46% use AI and 7% of those users have completed full integration. A further 20.24% have partially integrated it, and roughly half of all AI users remain at the experimentation stage.
Is small business loan demand rising with the AI capital expenditure cycle?
No. Axis Intelligence Research computes net C&I loan demand at +16.1% for large and middle-market firms against 0.0% for small firms in the January 2026 SLOOS, a 16.1-point spread. Among banks reporting stronger demand, 63.2% cited increased customer investment in plant or equipment as at least somewhat important. The capital expenditure cycle registers in one size class and is invisible in the other.
What happens to a firm’s AI budget when bank financing falls short?
It typically becomes debt service at a higher rate. The share of applicants approaching online fintech lenders rose from 17% in the 2020 Small Business Credit Survey to 29% in the 2025 survey. Among online-lender borrowers, 60% reported borrowing costs higher than expected, against 32% at large banks, a 28-point spread that Axis Intelligence Research reads as the practical cost of the funding gap.
Which sectors have the highest and lowest business AI use rates?
Per Census Bureau BTOS data as of May 3, 2026, the Information sector leads at 39.7% and Finance and Insurance follows at 33.9%, both above the 19.8% national rate. Retail Trade sits near 14%. Expected use over the following six months runs about 42% in Information, 39% in Finance and Insurance, and roughly 17% in Retail Trade, so the sector ordering is stable rather than converging.
Is AI infrastructure spending taking an increasing share of the AI market?
The Gartner forecast says the opposite. The Axis Intelligence Research AI Spend Concentration Ratio reads 55.28 for 2025, 55.15 for 2026, and 54.11 for 2027. Infrastructure remains the majority of AI spending but its share is flat into 2026 and declining into 2027, as AI software at 60.20% growth and AI models at 110.43% growth outpace the 46.73% growth in infrastructure.
What would make these figures change materially?
Four events. Gartner’s next quarterly AI spending forecast revision, which has already moved the 2026 total from $2.52 trillion to $2.59 trillion within a single year. The Federal Reserve’s October 2026 SLOOS, particularly if the AI-exposure special questions are repeated. The BEA annual update to the national and regional economic accounts scheduled for September 30, 2026, which will revise the investment series. And the 2026 Small Business Credit Survey fielding, which will produce the next CAFG reading.
Related Axis Intelligence Research
- AI Spending Statistics 2026 – the global market-size series and the enterprise ROI gap
- AI Investment Statistics 2026 – capital flowing into AI companies and investment concentration
- AI in Business Statistics 2026 – the adoption reality gap between survey and operational measures
- AI Statistics 2026 – market size, adoption, and the value gap
- AI Inference Cost Statistics 2026 – per-token pricing against rising total enterprise budgets
- AI Data Center Financing Statistics 2026 – bonds, private credit, and the infrastructure funding stack
- AI Data Center Statistics 2026 – electricity, capacity, and hyperscaler capital expenditure
- Cybersecurity Spending Statistics 2026 – the securing-AI budget category