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The Exam RoomBy The VeterinaryPracticeNow Desk5 min readOctober 5, 2026
AI adoption in veterinary clinics rose from 39 percent to 84 percent in two years
A survey of 1,730 veterinary professionals found that 88 percent of AI users engage with the tools daily or weekly, with clinical documentation leading adoption.
AI adoption among veterinary professionals in the United States and Canada reached 83.7 percent in 2026, up from 39.2 percent in 2024, according to a study released by Digitail and the American Animal Hospital Association.
The 1,730-respondent survey and what it measured
The 2026 Veterinary AI Study gathered self-reported responses from 1,730 veterinary professionals between July 27 and August 12, 2026. The survey used a maturity index that assessed "strategy, governance, vendor evaluation, workflow integration, adoption and readiness," according to the study, scoring practices on a four-point scale. The average AI maturity score was 2.67, "with three-quarters of AI-using practices classified as emerging or established rather than mature," the study found.
Among the 83.7 percent who reported using AI, 88.3 percent engage with the tools daily or weekly. Clinical documentation "is the leading application, used by 62.8 percent of AI users," according to the study. The category includes AI scribes and SOAP note generators. "Nearly four in five respondents expect AI to become part of most clinic workflows within five years," the study reported.
The governance gap the numbers reveal
The study found that "more than 65 percent of practices added AI without meaningfully changing workflows." "Nearly half lack an AI strategy or designated lead, and almost one-third have no AI governance," according to the survey. The speed of adoption has outpaced the development of policies and safeguards at most practices.
The study found that practices with higher maturity scores reported stronger business results. According to the study, "the most mature practices cited six times the outcomes of the least mature, while the proportion reporting no measurable impact fell as maturity increased." Digitail CEO and co-founder Sebastian Gabor said in the report that "a strategic approach can support more patient time, client retention, and revenue."
What the survey does not tell you
The study does not define what constitutes meaningful workflow change, nor does it specify what governance structures the one-third of practices without any have in place. The maturity index is Digitail's own framework, not a validated clinical or business metric, and the six-times-outcomes figure is self-reported rather than audited. The survey does not break out adoption rates by practice size, ownership structure, or geographic region, so we cannot tell whether the 84 percent figure holds equally across independent single-doctor practices and corporate multi-site groups.
The study also does not address liability. If an AI scribe generates a SOAP note that omits a clinical finding and that omission contributes to a missed diagnosis, the survey does not tell us who is responsible under current malpractice frameworks or whether practices have updated their policies to address that.
Where professional concerns have shifted
According to the study, fear of job displacement among veterinary professionals "declined from 37.2 percent in 2024 to 20 percent in 2026, while concerns about data security and insufficient training rose." The study found that "38.9 percent of AI users have tried building an AI agent, and another 29.9 percent are considering it." The profession has moved past whether AI will be used and is now focused on how to use it safely and effectively, the survey suggests.
If you are an associate DVM using an AI scribe or documentation tool daily, ask your practice manager or owner whether the practice has a policy that defines what you are responsible for verifying before you sign the note. If it does not, you are operating without a clear liability boundary, and that is a problem to raise before a documentation error forces the conversation.
If you own the practice, 84 percent adoption means your staff is likely already using AI tools whether you have approved them or not. The one-third of practices with no governance are not outliers, they are the modal case. We would start with three items: What tools are being used? What data are they accessing? Who is responsible for verifying output before it becomes part of the medical record? A policy does not need to be long, but it does need to exist and be written down.
If you are a practice manager, the gap between adoption speed and governance is your problem to close. The study suggests that practices with higher maturity scores report better business outcomes, but the causal direction is not clear from the survey. What is clear is that operating without policies creates malpractice and data security exposure that the practice owner will bear. Implement usage policies and training before a breach or a documentation error forces the issue.
Our read
The 84 percent figure is real, and it represents a faster adoption curve than most veterinary technologies have seen. The governance gap is also real, and it is the more important number. The study does not tell us what percentage of practices have experienced a clinical error or data breach attributable to AI use, but the fact that nearly half lack a strategy or designated lead suggests that most practices are not prepared to handle one when it happens.
The six-times-outcomes claim is a number that gets quoted in vendor decks, and it should be read with that in mind. Self-reported business results from a survey fielded by a company that sells AI tools to veterinary practices are not the same as audited revenue data, and the study does not control for practice size, case mix, or baseline profitability. The maturity index is a useful framework for thinking about adoption stages, but it is not a validated predictor of clinical or financial performance.
The shift in professional concerns from job displacement to data security and training is a healthy sign. It suggests that the profession has moved past whether AI will be used and is now focused on how to use it safely, how to verify its output, and who is responsible when it gets something wrong. Those are matters that policies and governance structures are designed to answer, and the fact that most practices do not have them yet is the gap this study reveals.
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