Used well, artificial intelligence (AI) can reduce administrative burden without moving the veterinarian away from the centre of care. Not as a distant concept or a headline about the future, but as practical support for the work clinics are already carrying.

The most immediate example is documentation. A tool that helps turn what is said aloud during an appointment into a draft of a medical record does not change the veterinarian’s responsibility; rather, it changes their starting point. Instead of beginning from a blank page at the end of a long day or rushing between visits, the veterinarian can finalize the record while the details are still fresh.

It is a simple but meaningful use case, as veterinary medicine is burdened by small tasks that can add up. A few minutes after every appointment becomes hours at the end of the week. A delayed record becomes a delayed follow-up or referral.

For many veterinary teams, the value of AI is practical. It helps reduce everyday friction in already stretched work.

When communication breaks down, clinical care can suffer

Documentation is inextricable from care in veterinary medicine. It preserves the reasoning behind a decision so care can continue after the appointment ends.

That is why AI documentation tools have become an early and practical use case. When a veterinarian speaks observations aloud during an appointment, the tool can help produce a draft for review. The veterinarian remains wholly responsible for the record, but the work shifts from recreating the visit later to refining a draft while the appointment is still fresh.

Used well, these AI tools can save time. They can also make the visit clearer for everyone in the room. When veterinarians speak more of their observations aloud, clients and support staff have more context as the visit unfolds.

The interesting part is that the same habit can improve both sides of the appointment. The client hears more of the doctor’s thinking in real time, and the AI has better context to draft the record. That is where the tool starts to feel less like administrative software and more like support for the actual clinical encounter.

This is a practical starting point for AI, as it addresses a problem many clinics already face. It does not ask the veterinarian to defer their expertise. The technology gives them a better way to manage work that already needs to be done.

Context matters more than capability

The next stage of AI in veterinary medicine will depend less on what a general-purpose system can produce and more on what it understands about the clinical setting. A chatbot may sound capable, but it will not necessarily understand how the clinic works.

A general-purpose chatbot can be useful, but it does not know the practice. It does not know the patient’s history, the clinic’s documentation preferences, or what was discussed in the exam room that morning. That context is where the real opportunity sits.

In veterinary practice, context is what turns AI from a novelty into a source of support. A system that understands more of the workflow can help organize information in a way that is easier to review. It may help bring attention to a relevant detail before or after an appointment.

This is also where AI can become a useful second set of eyes. It may flag a medication prescription that falls outside a typical dosage range, a documented patient allergy, or another prescription that may not pair well with the medication being considered today. The veterinarian still makes the decision. The value is that the system can help bring the right details to the surface at the right time.

The veterinarian must always remain the decision-maker. AI works best when it helps prepare information for review, and at no point should it supplant clinical judgment.

That distinction is central to trust. Clinical accountability must remain with trained professionals. AI outputs should be reviewed before they are used. No system should be treated as an infallible authority, especially when patient well-being is involved. The Canadian Veterinary Medical Association has recognized that AI tools are being used by veterinary professionals, while also emphasizing the need for scientific rigour to help reduce risk to patients and liability for veterinarians.¹

The same caution applies as AI moves closer to diagnosis. A tool that helps draft a record carries a different level of risk than one that may influence medical decisions. The closer a system gets to clinical decision-making, the higher the standard should be.

Where AI may have the biggest operational impact

Beyond the exam room, AI may have an even broader impact on practice management.

Many clinics are burdened by administrative tasks that are frequently underestimated. Between appointments, the team manages a steady stream of client needs that compete with patient care.

AI may be especially useful here by helping the clinic handle repetitive work more efficiently.

Client communication is one area to watch closely. Many clinics struggle to answer every call quickly, which may cause clients to worry. AI-enabled front desk services may help clinics respond faster to routine questions and escalate to a team member when the situation is sensitive.

That distinction is important. An AI system should not guess its way through uncertainty and should “know” when to stop. When a situation becomes urgent, emotional, or clinically uncertain, the tool should revert to a human being.

That is the difference between useful support and risky automation.

Start with low-risk, high-value workflows

For teams unsure where to begin, the safest first step is usually a familiar workflow where the output can be easily reviewed.

Documentation is a common starting point because the burden is familiar and the output is reviewable. This tangibly reduces the time skilled people spend recreating information that was previously communicated.

A clinic should define success before it adopts a tool. Otherwise, it becomes easy to mistake novelty for progress. The measure might be less after-hours charting. It might be faster follow-up communication. The right metric depends on the problem the team is trying to solve.

Training matters as much as the software

Teams need to understand what the tool is good at and where it can fail. They also need time to build new habits. A documentation tool, for example, often works best when the veterinarian verbalizes more of the appointment. That can feel awkward at first. Over time, it can become a communication advantage because clients hear the doctor’s thinking in real time.

Ask hard questions about data

Data questions belong at the start of the buying process. Veterinary teams are right to ask how data is handled when using AI documentation tools. That includes where data goes, who can access it, whether it is used for model training, and what happens when someone leaves the practice. They should also understand what managers or corporate groups can see.

This is personal for me. My mom is a veterinarian, my co-founder is a veterinarian, and many members of our team have worked in veterinary practice. That changes how we think about these systems. Data can be incredibly useful, but it must be handled with discipline. Teams should benefit from broader patterns without turning individual consultations into a surveillance tool.

A clinic may benefit from identifying where communication slows or where workflow repeatedly creates pressure, but there is a difference between learning from patterns and exposing individual consultation details too widely.

Strong privacy controls matter because trust matters. Teams need to know the tool is there to support their work.

Clients are adopting AI, too

Veterinary teams should also consider the client side of AI.

Clients will use these tools whether clinics like it or not. If a pet owner goes home worried, there is a good chance they will ask a general AI tool to explain what they heard. Practices are at the forefront of providing clearer, safer, more contextual communication during visits, rather than leaving clients in the dark.

When evaluating AI tools, practical criteria are more useful than hype. A vendor should be able to explain what data the system uses, how outputs are reviewed, how clinic standards are reflected, and how data is protected. If those answers are vague, the clinic should be cautious.

The best tools will make sense to the people using them between appointments, phone calls, and late-day records. They will fit the workflow instead of forcing the team to work around the technology.

There will be missteps as AI becomes more common in veterinary medicine, but the strongest use cases will come from matching the technology to problems the team already recognizes.

Veterinary medicine needs better support because the work has become too heavy in too many places. AI can help reduce administrative workload, free up time for client communication, improve record-keeping, and help teams catch details that might otherwise be missed.

The goal is not to make veterinary medicine less human. It is to give veterinary teams more room for the human parts of medicine: judgment, explanation, trust, and care. If AI can take pressure off the administrative side while strengthening communication, that is not a future-facing novelty. It is a practical tool for a profession that needs support now.

Mike Parent is a co-founder of CoVet, an AI copilot for veterinary teams focused on documentation, workflow support, and client communication. He has spent his career building technology for healthcare and veterinary practice, with a focus on tools that fit naturally into clinical workflows. Parent’s perspective on veterinary medicine is also personal: his mother and co-founders are veterinarians, and CoVet’s team includes veterinarians and former practice staff. He can be reached at mike@co.vet.