AI use is growing across legal workplaces, but the claim that “millions of paralegals” are adopting it is not supported by the available figures. Surveys measure attorneys, legal professionals, offices, or firms—not paralegals as a distinct group. In the United States, the Bureau of Labor Statistics estimated 376,200 paralegals and legal assistants were employed in 2024; that is a workforce estimate, not a count of AI users.
Are paralegals adopting AI right now?
Some legal workplaces and professionals are using AI, and paralegals work within those environments. But the published figures below do not establish what share of paralegals personally use it. They count different populations and kinds of adoption, so they cannot be combined into a paralegal adoption rate—or converted into a number of paralegal users.
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| Source and measure | Reported figure | What it measures |
|---|---|---|
| ABA 2024 Artificial Intelligence TechReport, published 2025 | 30.2% | Attorneys saying their offices currently used AI-based tools. The Online Research volume surveyed 512 respondents; the survey was administered October–December 2024. |
| ABA Legal Industry Report 2025 | 31%, up from 27% | Respondents reporting that they personally used generative AI at work, compared with the prior year. This is not the same measure as office deployment. |
| Clio Legal Trends Report 2025 | 79% in both 2024 and 2025 | Surveyed legal professionals reporting AI use in their firms. The group is not limited to paralegals. Clio separately reports use of legal-specific AI solutions at 40% in 2025, down from 58% in 2024. |
| Thomson Reuters 2025 GenAI report | 26%, up from 14% | Surveyed legal professionals reporting they were already using generative AI. The report also says 28% of law firms and 23% of corporate legal departments reported use. |
These are not directly comparable time-series measures: personal experimentation, use somewhere in a firm, and office or organizational deployment have different denominators and meanings. Thomson Reuters also reports that 45% of law-firm respondents either currently used generative AI or expected it to become central to workflow within a year; that combines current use with an expectation, not just current adoption.
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Why legal workplaces are interested in AI
Efficiency is a prominent stated motivation. In the ABA’s account of its 2024 survey, 54.4% of respondents selected saving time or increasing efficiency as a perceived benefit of AI-based tools. Document management or review was selected by 9.1%, and reducing costs by 4.5%. These are respondents’ perceptions, not measurements showing that AI actually saves time, reduces costs, or improves work quality.
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Tasks that legal teams may evaluate for AI assistance include research, document review, summarization, drafting support, and repetitive information handling. That describes possible use cases, not proof that a tool can safely complete them without human review. An answer that sounds plausible can still be incomplete, inaccurate, or unsuitable for a matter.
Adoption varies by workplace and tool
Office size matters
The ABA’s 2024 survey reported office use of AI-based tools at 47.8% for firms with 500 or more lawyers, 29.5% for firms with 10–49 lawyers, 24.1% for firms with 2–9 lawyers, and 17.7% for solo practitioners. These are office-use figures reported by attorneys, not individual paralegal usage rates. The pattern also cautions against assuming that a large firm’s adoption experience describes a small office.
“AI” can mean different tools and stages of use
General-purpose generative AI and legal-specific research or document tools are different categories, but the category alone does not establish reliability or safety. The ABA survey discusses tools including ChatGPT and legal services such as CoCounsel and Lexis+ AI. A firm considering a tool still needs to assess its permitted uses, data handling, accuracy, and fit with its procedures.
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- Personal use: an individual reports using generative AI at work.
- Firm or office use: a respondent says the organization uses AI somewhere; that does not mean every employee has access or uses it.
- Central workflow: a stronger organizational commitment than occasional experimentation. A forecast that AI will become central is not evidence it has already happened.
What the evidence does—and does not—say about paralegal jobs
The BLS estimated 376,200 U.S. paralegals and legal assistants in 2024 and projected 376,800 in 2034, an increase of 600 jobs, or 0.2%. It identifies AI-related efficiency gains as a possible constraint on occupational growth. Its Occupational Outlook Handbook describes little or no employment change projected from 2025 to 2035. These are projections, not observed job losses and not evidence that AI has already replaced paralegals.
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The practical implication is narrower than either “AI will replace paralegals” or “AI will not affect the role”: the cited employment outlook anticipates little net change, while allowing that technology may limit demand. The available surveys do not show how many paralegals use AI or quantify AI’s effect on paralegal employment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to read AI adoption claims as a paralegal
- Check who was counted. A survey of attorneys, all legal professionals, firms, or offices is not a paralegal-only survey.
- Check what “use” means. A personal trial, organization-wide deployment, and planned centrality are distinct stages.
- Separate interest from results. A reported expectation of efficiency is not measured time saved, accuracy, or quality improvement.
- Keep verification in the workflow. The ABA identifies accuracy, reliability, and data privacy among concerns. Treat AI output as material to check, not as a substitute for professional judgment or established review.
The ABA’s 2024 survey defines AI as “intelligence displayed by machines such as when a machine mimics human cognitive functions like reasoning, learning, or natural language processing.” For workplace decisions, however, the key question is more concrete: which task is being assisted, what information is entered, and who checks the result?
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