Guide
How many companies use AI, which sectors lead, what holds firms back and a practical path from a first pilot to daily use. Written for owners and managers.
Updated 7 min read
The short answer
AI adoption is the point where a company moves from trying AI tools to using them in everyday work. US adoption is rising but uneven: it depends on sector, firm size and skills. Firms that start with one process and measure it get further than those that spread thin.
Key points
AI adoption is not buying a tool. It is when AI becomes part of how a team does its work, week after week.
A trial with one person is a pilot. Adoption starts when a process runs on AI and people rely on it.
This guide covers where US firms are, what slows them and how to move from pilot to daily use.
18%
of firms had adopted AI by year-end 2025
5.4%
bi-weekly AI use rate in 2024
Numbers depend on who is asked and how the question is worded. Compare them with care.
The Federal Reserve Board reported in 2026 that about 18 percent of firms had adopted AI as of year-end 2025.
In an earlier Census Bureau round, just 3.8% of businesses reported AI in producing goods or services, with big gaps between sectors.
NBER research using Census data found bi-weekly estimates of AI use rising from 3.7% to 5.4%, with about 6.6% expected by early fall 2024.
These figures measure different things and different years. Read them as a direction of travel, not one number.
Any set of AI adoption statistics mixes questions. Some ask whether staff ever tried a chatbot. Others ask whether the firm uses AI to make its products.
Look at four things: who ran the survey, which firms it covered, the date and the exact wording.
Vendor surveys often ask only their own customers. Government and central bank data cover more firms and are usually safer to quote.
63%
work-related generative AI use in finance
62%
in professional services
Use is not spread evenly. Finance leads work-related generative AI use at 63 percent, with professional services close at 62 percent, per the Federal Reserve Board.
Sector matters just as much in regulated fields. If you work in healthcare, our guide to AI in healthcare covers where it is used and the rules that apply.
Insurers face a similar picture. Our guide to AI in insurance explains the common uses and risks.
Size matters too. Large companies have data teams and budgets. Small firms have less time and fewer specialists, so their path looks different. See AI in small business.
Enterprise AI adoption brings its own pattern. Large firms often run many pilots at once, in different departments, with different tools.
The challenge there is rarely curiosity. It is moving a pilot into production across teams, with shared data, ownership and rules.
Companies of this size usually need governance first: who approves a use case, which data a tool may touch and who answers if it fails.
AI adoption in healthcare splits in two. Clinical uses carry patient-safety and privacy duties. Front-office uses, such as scheduling and calls, are lighter to start.
Adoption of AI in healthcare therefore tends to begin with administrative work. Examples are in AI use cases in healthcare.
AI adoption in insurance follows the same logic. Claims intake, document reading and underwriting support come first because they are repetitive and measurable.
In both sectors, a person stays in the loop for decisions that affect people.
49%
cite a lack of internal expertise
The biggest brake is usually people, not technology. In the PEX Report 2025/26, lack of internal expertise was cited by 49 percent of respondents.
Data is the next problem: scattered, messy or locked in old systems. Cost and trust follow. Staff worry about errors, and leaders worry about risk.
None of these blocks adoption on its own. Each has a practical fix, starting with a narrow first project.
A common question is how a business should get started with AI. A short, ordered path works better than a big plan.
Pick one process that is repetitive, has clear steps and costs real hours. Measure how long it takes now, before you change anything.
Add it to your current tools, prove it on real cases first and teach the team that will use it.
Review the result against your starting numbers. If it pays back, repeat with the next process. For the full method, see how to implement AI.
Our AI consulting work follows this path: map the manual work, rank it by payback, build on your current tools and measure the result.
Our data
We asked ChatGPT, Gemini and Perplexity 10 buyer questions about AI consulting. They named 95 companies, and 81% appeared in only one of the three.
| Criterion | ChatGPT | Gemini | Perplexity |
|---|---|---|---|
| Companies named | 34 | 25 | 60 |
| Sites cited most | slalom.com (4 of 10) | effectivesoft.com (6 of 10) | phosailabs.com and wiserbrand.com (3 of 10) |
Nexum measurement, October 20, 2026, via DataForSEO: 10 buyer questions to each AI, US English, 30 answers in total. 6 companies appeared in all three. Google showed an AI Overview for 7 of the 10 questions.
FAQ
Final step
You leave knowing which processes to automate first and what each one costs you today, whether we work together or not.
You can keep paying for manual work, or find out this week exactly what it costs you.
What happens on the call
Before
You answer 3 quick questions when you book, so we come prepared.
On the call
We find the manual work that costs you most and put a number on it, live.
Decide
An honest read on whether we're the right fit. If we're not, you'll hear it on the call.
Once you book, we look at your site and tools before the call, so the 30 minutes go to your numbers, not introductions.
Prefer to write first? Email us directly: contact@nexumautomations.com
