AI Agent Adoption in 2026: Enterprise vs SMB

For large companies in 2026, AI agents are a question of how: KPMG's Q3 2026 AI Pulse found 62% of US organizations with $1 billion or more in revenue building, deploying or developing them, up from 53% a quarter earlier. For the smallest US businesses, the question is still mostly whether. In the Census Bureau's latest two-week reading, published 8 October 2026, 23.9% of businesses with 1 to 4 employees used any kind of AI. A 40,000-person enterprise and a 12-person agency are on different curves, and treating "AI agent adoption" as one trend hides the most useful part of the story.

This guide puts the two side by side, starting with one official survey that asks every firm size the same question, then comparing drivers, blockers, use cases and budgets. The size data measures AI of any kind, because no official survey yet reports agents by firm size; where we discuss agents specifically, we use surveys of large companies and of agent builders, and label them. For the full lists of figures, see our small business AI adoption statistics and the AI agent adoption statistics hub; for the large-company view, see enterprise AI agent adoption trends.

AI use by firm size: one survey, one question

The cleanest comparison comes from the US Census Bureau's Business Trends and Outlook Survey (BTOS), which puts the same question to employer businesses of every size every two weeks: "In the last two weeks, did this business use Artificial Intelligence (AI) in any of its business functions?" From 1 to 49 employees the answer barely moves, sitting between 21.5% and 24.8%. It climbs from 50 employees and reaches about 36% for firms with 100 or more.

Table 1. US businesses using AI, by number of employees. Source: US Census Bureau, BTOS cycle 202620, collected 21 September to 4 October 2026 and published 8 October 2026. Share of employer businesses answering yes. Covers AI in general; agents are not reported separately.

Three patterns stand out, and the last corrects a figure that still circulates:

• The step comes between 50 and 100 employees. Firms with 100 or more employees use AI at about 1.5 times the rate of the smallest (36.4% against 23.9%, our arithmetic).
• The largest class is noisy. The 250-plus reading fell from 44.0% to 36.5% between consecutive fortnights. Averaging the six readings collected 13 July to 4 October 2026 gives 39.5% for 250-plus, 33.2% for 100 to 249 and 22.8% for 1 to 4 (our arithmetic).
• Small-firm use is rising. It went from 17.0% in the first reading of the current question (collected 17 to 30 November 2025) to 23.9%, so Census's May 2026 line that fewer than 20% of firms with four or fewer employees used AI is out of date.

Two caveats apply. Census changed the question on 17 November 2025, from AI used "in producing goods or services" to AI used "in any of its business functions", and started a new series because of the level shift: the old series ended at 10.0% nationally and the new one began at 17.3%, so never compare current figures with pre-December 2025 ones. And BTOS covers employer businesses only; JPMorganChase Institute finds employer firms adopting AI at nearly twice the rate of nonemployer firms, so one-person businesses likely sit below every figure here.

Europe shows the same gradient, only steeper

Eurostat finds the same pattern across the EU with a wider gap: in 2025, large enterprises used AI at more than three times the rate of small ones (our arithmetic). Its survey of 157,000 of the EU's 1.53 million enterprises leaves out firms with fewer than 10 people.

Table 2. EU enterprises using AI, by size, 2025. Source: Eurostat, "Use of artificial intelligence in enterprises", data extracted December 2025, page last edited 2 June 2026. Covers AI in general; agents are not reported separately.

The two surveys ask different questions over different periods, so compare the slope within each table, never the levels across them.

What each survey means by "adoption"

Recent adoption rates for US small businesses run from about 24% to 76%, mostly because of who was asked and what counted. Only the last three rows ask about agents.

The one agent-specific size split

Among the surveys in our research, only LangChain's State of Agent Engineering (fielded 18 November to 2 December 2025) reports agent use by company size. Overall, 57.3% of respondents had agents in production: 67% at organizations with 10,000 or more employees, with 24% more developing them, and 50% at organizations under 100 people, with 36% developing.

Read that as a picture of builders. 63% of respondents work in technology and 49% at companies under 100 people, so the "small" group is mostly tech teams that already build agents, which says little about a typical bakery or accounting practice. LangChain's 2024 report, State of AI Agents, put production use at 51%; it is a different edition and sample, so the two figures are not a trend.

Two adoption profiles

An enterprise has people, process and existing systems in abundance. It is rarely short on headcount; it is short on capacity to grow output without growing cost in step. So it asks whether an agent can safely scale a workflow across many teams, regions and compliance regimes without adding risk.

A small business is the mirror image: short on people and specialist skills, and unable to justify a full hire for many jobs that still need doing. It asks a more basic question first: does AI apply to my business at all? Firms that answer yes mostly use it to help the staff they have. Among the smallest US firms using AI, 42.8% use it to supplement or enhance a task an employee already performs and 11.1% for a task nobody did before (Census AI supplement). For some small firms an agent does stand in for a hire they could not otherwise make, but that is the minority case. If you are still mapping the concept, our explainer on what an AI agent is sets the foundation.

Note that "SMB" has no single definition. Census classes run from 1 to 4 employees up to 250 or more, the US Chamber of Commerce counts firms under 250 employees, the Federal Reserve's survey covers 1 to 499 and Eurostat's "small" is 10 to 49. We use each source's own line.

What drives each segment

Enterprises are pulled in by capacity and consistency, small firms by getting more done with the people they already have. A large company wants to absorb volume without a matching rise in headcount, standardize output across locations and automate work that crosses departments. In KPMG's Q1 2026 pulse of US firms with $1 billion or more in revenue, operations (79%) and technology (78%) departments led agentic deployment, and 73% of firms used agents on workflows spanning multiple functions.

Cost is part of that decision from the start. PwC's April 2025 survey of 308 US executives ranked cost joint top among AI agent challenges (34%, level with cybersecurity), and Gartner's June 2025 forecast that over 40% of agentic AI projects will be canceled by the end of 2027 lists "escalating costs" first among the causes. In KPMG's Q3 2026 pulse, 74% of organizations include cost reviews in AI approvals, up from 61% a quarter earlier, and 43% have set usage or token budgets.

Small firms start somewhere else. Among small-business AI users in the Federal Reserve's 2025 Small Business Credit Survey, the most common uses are writing or marketing (83%), individual productivity (61%) and planning or analysis (51%), and 87% of owners in Goldman Sachs's small-business program "say AI augments rather than replaces employees". Price rarely decides it: few small firms cite cost as a reason to stay out (see below), and JPMorganChase Institute's bank data puts the median entry spend of small businesses that adopted AI in 2024 at about $20 a month, down from about $50 for 2019 and 2021 adopters. Our overview of AI agents for every profession shows how widely that pull spreads across roles.

What blocks each segment

Enterprises are blocked by security and risk, cost, integration and skills. The smallest firms are blocked mostly by doubt that AI applies to them and by not knowing what it can do, with privacy close behind. Budget, the blocker most often assumed for small businesses, is a minority reason at every size in the Census data, and large firms cite it slightly more often than small ones.

Small firms: relevance and know-how come first

Table 3. Why businesses do not plan to use AI in the next six months, by number of employees. Source: US Census Bureau, BTOS AI supplement, responses pooled 17 November 2025 to 8 February 2026, published 23 April 2026. Asked only of businesses not planning to use AI; more than one answer allowed. Covers AI in general; agents are not reported separately.

For the smallest firms the problem is relevance and understanding: nearly two-thirds say AI is not applicable to their business, and about one in five lack knowledge of what it can do, a gap about understanding capabilities (21.2%) far more than hiring skilled staff (6.4%). Large non-adopters worry more about privacy, security and maturity. Cost sits near the bottom for everyone.

Small firms that use AI, or plan to, name practical hurdles (Federal Reserve survey unless noted):

• Finding the right tool: named by 54% of firms planning to use AI in the next 12 months.
• Time to implement or train employees: 37% of the same planners.
• Accuracy and fit: accuracy (46%) and adapting tools to business needs (43%) top the list for current users.
• Expertise and support: Goldman Sachs's participants cite lack of technical expertise, difficulty choosing tools and data privacy, and 73% say more training and implementation resources would help.

Cost does come up. The US Chamber of Commerce says non-users report "growing concerns around the quality of tools, cost of implementation, and potential legal or compliance issues". Still, in the one probability sample that ranks reasons, fewer than 1 in 10 of the smallest non-adopters call AI too expensive.

Enterprises: security, cost, integration and skills

Before an agent touches production at a large company, it typically passes security and privacy review, integration work, a cost review and procurement. That is how a large, regulated organization protects itself, and it means the best technical fit can lose to the option that clears review most cleanly. The data puts weight behind each gate:

• Security and risk. Nearly two-thirds of respondents to McKinsey's 2026 AI trust survey (about 500 organizations) cite security and risk concerns as the top barrier to fully scaling agentic AI. In LangChain's survey, security is the second concern at companies with 2,000 or more employees (24.9%), after quality.
• Cost. Gartner expects AI inference costs per agentic workflow to rise more than fivefold through 2028, noting that routing a task to an agentic reasoning model raises provider inference costs at least fivefold compared with a basic chatbot interaction.
• Integration. Gartner warns that "integrating agents into legacy systems can be technically complex, often disrupting workflows and requiring costly modifications," though PwC's executives ranked connecting agents across applications and workflows among their lowest concerns (19%).
• Skills. KPMG's Q1 2026 pulse found resistance "driven primarily by human readiness", led by skills gaps (76%) and job security concerns (67%). Among EU enterprises of all sizes that had considered AI, lack of relevant expertise (70.89%) was the top reason for not using it (Eurostat).
• Governance. 49% of KPMG's Q3 2026 respondents have defined high-risk use cases where agents may not make decisions autonomously.

An earlier version of this guide said process slows enterprises while skills slow small firms. Skills gaps are a top barrier inside large organizations too. Both segments benefit from a disciplined selection process, which is why we wrote a buyer's framework for how to evaluate AI agent platforms that works at either scale.

Leading use cases

Sales and marketing and R&D are where small and large firms are closest; back-office work, from finance and administration to HR and IT, is where large firms pull far ahead.

Table 4. Share of all US businesses using AI in each function in the last six months, by number of employees. Source: US Census Bureau, BTOS AI supplement, fielded 17 November 2025 to 8 February 2026, published 23 April 2026. Shares of all firms, including non-users. Covers AI in general; agents are not reported separately.

The widest gaps are in IT (11.2% against 25.9%) and finance and accounting (5.9% against 20.5%), with HR and administration close behind. Customer service roughly doubles, research and development is level at 11.9% at both ends, and sales and marketing tops the list for the smallest firms.

Enterprises lead with cross-system workflows, as the KPMG figures above show. In LangChain's survey, organizations with 10,000 or more employees named internal productivity as the top agent use case (26.8%), with customer service (24.7%) and research and data analysis (22.2%) close behind. Our roundup of the best AI agents for customer support covers the support side in practice.

Small firms lead with writing, marketing and individual productivity, the uses the Federal Reserve figures above put first among small-business AI users. In the Census data, administrative work and customer support reach only 6.9% and 7.1% of all 1 to 4 employee firms, users and non-users. For specific tools, see our guide to the best AI agents for small business.

Europe shows the same back-office gap. Eurostat's biggest small-versus-large differences are in AI for ICT security (14.51% of small enterprises against 47.51% of large) and for business administration and management (28.49% against 43.38%). Across both data sets, large firms use AI to run and protect the organization, while small firms mostly use it to write, market and plan.

Budgets and buying process

At US firms with $1 billion or more in revenue, planned AI budgets average about $207 million; the median small business that began paying for AI in 2024 started at about $20 a month. KPMG's Q1 2026 pulse found that figure for the following 12 months ($186 million in its global survey). JPMorganChase Institute measured the small-business figure from actual payments for AI services across 4.6 million firms with Chase business accounts.

In a large company, the decision usually passes a committee of the function owner, security, legal, procurement and finance, with the cost reviews and no-autonomy rules above as formal gates. In a small business it usually sits with the owner or one or two people. Both descriptions are our judgment; no survey in our research measures who signs off.

After adopting, the two behave differently. Census asked AI users what they changed in order to use it (1 to 4 employees against 250 or more):

• Trained current staff to use AI: 13.4% against 31.4%.
• Hired staff trained in AI: 1.0% against 6.8%.
• Used vendors or consulting services to install or integrate AI: 3.1% against 10.3%.
• Did not make any changes to use AI: 65.5% against 48.5%.

Small adopters mostly plug a tool in and carry on, and their use stays shallow: 7% of small-business AI users in the Federal Reserve survey had fully integrated AI into their business, and 14% of Goldman Sachs participants said AI was fully embedded in their core operations. With no security team or integrator, a small firm leans on whatever the tool's maker provides.

That settles build versus buy for most small firms. The US Chamber of Commerce found 8% of small businesses taking a homegrown approach to developing their own AI and 63% mostly or entirely relying on externally developed tools (AI in general; agents are not broken out). Large companies keep both options open and weigh compliance and control. We work through the decision axis by axis in build vs buy an AI agent.

Enterprise vs SMB at a glance

Each row pairs a dimension with the best available figure. The enterprise column mixes two populations (firms with 250 or more employees in Census data, firms with $1 billion or more in revenue in KPMG's), so treat it as directional; the buying-process row and the enterprise build-vs-buy cell are our judgment.

Which pattern are you

With hundreds of employees and a formal approval process, your work is mostly governance; with a small team, it is mostly getting a first useful result with little setup. The data above points to a different first move for each.

If you recognize the enterprise pattern, your constraints are trust, cost control and skills:

1. Bring security and risk into the pilot from day one; it is the barrier McKinsey's trust survey found most often named for scaling agentic AI.
2. Give each pilot a cost ceiling and a usage or token budget, and track inference cost per workflow alongside license fees.
3. Define where agents may not decide on their own before you deploy.
4. Budget for training alongside the tool, given KPMG's finding that skills gaps drive internal resistance.
5. Tie the pilot to a measurable outcome so the cost review has something to weigh.

If you recognize the small-business pattern, your constraints are knowing where AI fits and finding time to set it up:

1. Pick one task you already do every week, such as a newsletter, a quote or a weekly report; supporting existing work is the most common small-firm use.
2. Check the software you already pay for. Gartner predicted in August 2025 that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025, so an agent may already sit inside a tool you use.
3. Choose a tool you can set up without a project, and be picky: Gartner estimates only about 130 of the thousands of agentic AI vendors are real.
4. Check outputs before relying on them; accuracy is the top challenge small-business AI users report.
5. Keep customer and financial data out until you are comfortable with the provider's privacy terms.

A managed platform fits the small-business pattern because it removes setup work: no agent to build, no stack of tools to connect and no team to train before the first result. A free entry point lets you test the first question most small firms face, whether AI applies to your work, before you spend anything. Given how few small firms name cost as their barrier, the low effort matters more than the low price.

That is the model Gravity is built around. Gravity is in public beta: you describe an outcome in plain words, and an expert-built agent runs it and hands back the finished result in about 60 seconds. The Free plan costs nothing and needs no card, Mini Pilot is $15 a month and Auto Pilot is $20 a month, and you can buy more usage if you run out. For a small business, that is a way to answer "does this apply to us?" on a real task. For an enterprise, a managed platform still has to clear the security, cost and governance reviews described above, and it should.

Frequently asked questions

How is AI agent adoption different for enterprise versus SMB in 2026?

Large companies are mostly deciding how to deploy agents, while the smallest firms are often still deciding whether AI applies to them. KPMG found 62% of US organizations with $1 billion or more in revenue building, deploying or developing agents in Q3 2026, while Census has 23.9% of US businesses with 1 to 4 employees using any AI. Enterprises are slowed by security, cost, integration and skills; small firms mostly by doubts about relevance and know-how, and rarely by budget.

What drives enterprises to adopt AI agents?

Capacity and consistency: more volume without a matching rise in headcount, standardized output and automation of work that crosses departments. In KPMG's Q1 2026 pulse, operations (79%) and technology (78%) departments led agentic deployment at large US firms. Cost is weighed from the start, and 74% of KPMG's Q3 2026 respondents include cost reviews in AI approvals.

What drives small businesses to adopt AI agents?

Mostly getting more done with the people they already have. Among the smallest US firms using AI, 42.8% use it to supplement an employee's existing task and 11.1% for a task nobody did before (Census). Writing or marketing is the top use, cited by 83% of small-business AI users in the Federal Reserve's survey. These figures cover AI in general; agents are not reported separately.

What are the biggest blockers to AI agent adoption?

They differ by size. Large firms cite security and risk, cost, integration and skills, and KPMG's Q1 2026 pulse found skills gaps (76%) the top driver of internal resistance. Among the smallest US firms not planning to use AI, the top reasons are that AI is not applicable (63.3%), lack of knowledge of its capabilities (21.2%) and privacy or security (20.1%); only 6.7% call it too expensive.

Do enterprises and SMBs use AI agents for the same tasks?

Partly. Sales and marketing and R&D are where firm sizes are closest (sales and marketing: 13.1% of firms with 1 to 4 employees, 19.0% of firms with 250 or more), but large firms pull far ahead in finance and accounting (20.5% against 5.9%), IT and HR. These Census figures cover AI in general; agents are not reported separately.

Why are managed platforms with free tiers easier for SMBs to adopt?

Because they address the barriers small firms actually report: not knowing what AI can do, finding a tool that fits and the time to set it up. A free tier lets you test whether AI applies to your work before spending anything, while cost itself is cited by only 6.7% of the smallest non-adopting firms. On Gravity, which is in public beta, Free is $0 with no card, Mini Pilot is $15 a month, Auto Pilot is $20 a month, and you can buy more usage if you run out.

Are large companies more likely to use AI than small businesses?

Yes, and the gap opens at about 50 employees. In Census data published 8 October 2026, US firms with 1 to 49 employees used AI at 21.5% to 24.8%, against about 36% for firms with 100 or more. Eurostat's 2025 EU figures are steeper: 17% of small, 30.36% of medium and 55.03% of large enterprises.

The short version

• Same technology, different questions. Large firms are deciding how to deploy agents; many of the smallest firms are still deciding whether AI applies to them.
• The blockers are not the ones usually assumed. Cost and security weigh on enterprises; small firms are held back more by relevance and know-how than by budget.
• Place yourself, then pick the playbook. Enterprises govern cost, risk and skills; small firms start with one weekly task and a tool they can set up without a project.

Sources

1. US Census Bureau, Business Trends and Outlook Survey, Employment Size Class data file: AI use and expected AI use by employment size, cycle 202620 (published 8 October 2026; accessed 12 October 2026)
2. US Census Bureau, Business Trends and Outlook Survey, National data file (accessed 12 October 2026)
3. US Census Bureau, Business Trends and Outlook Survey data page: survey description, population and sample (accessed 12 October 2026)
4. US Census Bureau, BTOS AI question wording updates: the 17 November 2025 change and new time series (accessed 12 October 2026)
5. US Census Bureau, BTOS AI Supplement tables by employment size: reasons for not using AI, functions, uses and changes made (published 23 April 2026; responses 17 November 2025 to 8 February 2026)
6. US Census Bureau, "Large Firms With at Least 20 Employees Biggest AI Users" (published 26 May 2026)
7. Eurostat, "Use of artificial intelligence in enterprises" (data extracted December 2025; page last edited 2 June 2026; accessed 12 October 2026)
8. KPMG, AI Pulse, Q3 2026: 314 US leaders at $1B+ revenue firms, fielded 24 July to 25 August 2026 (published 24 September 2026)
9. KPMG, AI Pulse, Q1 2026: 237 US leaders at $1B+ revenue firms, fielded 17 February to 17 March 2026 (published 31 March 2026)
10. PwC, AI Agent Survey: 308 US executives, surveyed 22 to 28 April 2025 (accessed 12 October 2026)
11. Gartner, over 40% of agentic AI projects will be canceled by end of 2027 (published 25 June 2025)
12. Gartner, 40% of enterprise apps will feature task-specific AI agents by 2026 (published 26 August 2025, updated 5 September 2025)
13. Gartner, AI inference costs per agentic workflow will increase more than fivefold through 2028 (published 17 August 2026)
14. McKinsey, "State of AI trust in 2026: Shifting to the agentic era": about 500 organizations, December 2025 to January 2026 (published 25 March 2026)
15. LangChain, State of Agent Engineering: 1,340 responses, 18 November to 2 December 2025 (accessed 12 October 2026)
16. LangChain, State of AI Agents, 2024 edition (accessed 12 October 2026)
17. Federal Reserve Banks, 2026 Report on Employer Firms: Findings from the 2025 Small Business Credit Survey, 6,525 firms with 1 to 499 employees, fielded 3 September to 14 November 2025 (accessed 12 October 2026)
18. Goldman Sachs 10,000 Small Businesses Voices, "Small Businesses Embrace AI, But Need Training and Support to Fully Harness It": 1,256 program participants, 27 January to 4 February 2026 (published 17 March 2026)
19. JPMorganChase Institute, "Understanding the use of AI among small businesses": Chase Business Banking data on 4.6 million firms, 2019 to 2025 (published 14 April 2026)
20. JPMorganChase Institute, small business in the age of AI: employer vs nonemployer adoption (published 28 May 2026)
21. US Chamber of Commerce, "Empowering Small Business: The Impact of Technology on U.S. Small Business": 3,870 businesses under 250 employees, surveyed 6 to 26 June 2025 by Teneo Research (published 18 August 2025)