By Tom Walsh, DVM
Co-written with the team at Shepherd Veterinary Solutions

Executive Overview

Step into any modern veterinary practice today, and you will likely hear the quiet hum of innovation. Artificial intelligence (AI) has raced past the phase of speculative novelty to embed itself deeply into the daily infrastructure of animal healthcare. From ambient listening devices drafting medical records in real-time to predictive diagnostic assistants flagging anomalies in routine bloodwork, the technology promises to revolutionize how veterinary professionals manage their clinics, their patients, and their own well-being.

Yet, ask ten veterinary practice owners whether their AI investments have truly paid for themselves, and you will elicit ten starkly disparate responses. One practitioner might enthusiastically declare, "It has completely transformed our documentation and given me my evenings back!" while another admits, "We signed up six months ago, and I couldn’t honestly tell you if anyone on the team even uses it."

Both experiences are remarkably common across the industry. However, the chasm separating these two outcomes has remarkably little to do with the software itself. Instead, it exposes a deeper operational truth: the success of artificial intelligence in veterinary medicine is determined not by the sophistication of the algorithm, but by how the practice measures, rolls out, and sustains the adoption of the tool.

Recent industry data underscores this rapid cultural shift. A landmark 2024 survey of 3,968 veterinary professionals, conducted by Digitail in collaboration with the American Animal Hospital Association (AAHA), revealed that 39.2 percent of respondents were already integrating AI tools or software into their daily workflows. Among those who had taken the plunge, nearly 70 percent reported utilizing these solutions on a daily or weekly basis. Furthermore, an additional 38.7 percent expressed a clear intention to adopt AI in the near future.

Clearly, artificial intelligence is no longer a fringe experiment; it is fast becoming standard clinical equipment. However, high adoption rates and genuine return on investment (ROI) are not synonymous. True financial and operational recovery requires moving beyond marketing promises and getting granular about where real value manifests, how to establish verifiable baselines, and whether the technology is genuinely being embraced by the clinical team.

Detailed Chronology: The Rapid Evolution of Veterinary AI

To understand where artificial intelligence stands today, it is instructive to examine how rapidly the landscape has evolved within the veterinary sector over the past decade.

Phase 1: The Era of Basic Digitization (Pre-2020)

For decades, veterinary clinics operated on paper charts or early-generation, rigid Practice Management Software (PMS). Data entry was entirely manual, administrative friction was high, and the end of the workday invariably meant hours spent hunched over keyboard terminals, typing up handwritten notes from memory. Technology was viewed strictly as a system of record rather than a tool for cognitive relief.

Phase 2: The Automation Wave (2020–2023)

As cloud-based practice software matured, basic automation began to infiltrate the front desk. Automated email reminders, digital intake forms, and rudimentary text-message confirmations started replacing traditional phone tag. While these tools reduced some administrative overhead, they were rule-based, static, and largely divorced from the clinical exam room itself. Veterinarians still faced the same immense burden of clinical documentation.

Phase 3: The Generative AI Boom (2023–Present)

The public explosion of large language models (LLMs) and generative artificial intelligence fundamentally shifted the paradigm. Software developers quickly recognized that veterinary medicine—a field drowning in unstructured clinical notes, compliance documentation, and client communication—was prime for disruption.

Suddenly, ambient AI scribes emerged, capable of listening to a natural conversation between a veterinarian and a pet owner, filtering out extraneous chatter, and structuring a professional SOAP (Subjective, Objective, Assessment, Plan) note within seconds of the exam concluding. Concurrently, diagnostic decision-support tools and advanced client communication algorithms entered the market, transforming AI from a passive record-keeper into an active clinical partner.

Supporting Context & Metrics: Where AI Delivers Tangible Value

When evaluating the financial viability of an AI subscription, practice owners must dissect the technology into three distinct functional pillars: clinical documentation, diagnostic support, and client communication. Each pillar generates value in fundamentally different ways.

1. Clinical Documentation & Ambient Scribing

Ambient transcription and scribe tools listen in during patient examinations, converting spoken dialogue into structured medical records. The return on investment here is concrete and immediate: time.

When medical charting is shifted out of the post-closing, after-hours pile and executed dynamically during the appointment, practitioners reclaim their personal lives. Furthermore, notes are completed while the details of the visit are still fresh in the clinician’s mind, drastically reducing memory-fading errors. Downstream benefits include cleaner records that facilitate seamless handoffs between primary care doctors, relief veterinarians, technicians, and front-desk personnel.

Practices evaluating these tools should track two straightforward metrics:

• Charting completion time (the minutes elapsed between exam conclusion and record finalization).
• Same-day record completion percentage.

"At my office, clinical documentation gave us our first real win," shares Tom Walsh, DVM, owner of PrimePet Rehabilitation Therapy in Myrtle Beach, S.C. "Before AI, I spent too much time after clinic reconstructing detailed mobility and therapy notes for patients. Once the tool listened during visits, notes wrote themselves while we talked with owners, creating cleaner handoffs and fewer missed details from busy physical therapy days. In the AI world, documentation often is the entry point because everyone feels the drag of falling behind on records, and fixing it improves both daily life and record quality immediately."

2. Diagnostic and Decision Support

The second major category involves clinical intelligence platforms that analyze a patient’s historical records, lab results, and presenting signs to surface relevant differential diagnoses or treatment considerations. These tools are explicitly not designed to make clinical decisions; rather, they serve as a digital safety net to ensure nothing obvious slips through the cracks during a high-volume, high-stress day.

Trust is paramount in this domain. These systems earn institutional confidence when they function like a well-prepared, highly read colleague who offers subtle context. Conversely, they lose credibility rapidly if they overpromise or present incorrect assumptions with absolute confidence. For complex or chronic cases, clinical accuracy trumps processing speed every time, and any responsible implementation keeps the licensed veterinarian firmly at the helm.

The measurable value here lies in reduced cognitive fatigue and fewer diagnostic oversights. While harder to quantify on a strict balance sheet, the mitigation of liability and the enhancement of patient care are profoundly real.

3. Client Communication & Operational Efficiency

Often considered the least glamorous application of artificial intelligence, automated client communication frequently yields the highest direct return on investment. Reminders, vaccination updates, post-op check-ins, appointment confirmations, and discharge summaries consume staggering amounts of front-desk labor when managed manually.

AI tools capable of drafting empathetic, client-ready summaries, or triage algorithms that mitigate endless phone tag, free administrative staff to focus on high-value, in-clinic client interactions. When deployed effectively, these tools simultaneously elevate the client experience.

• Key Metrics to Monitor: No-show rates, automated reminder compliance percentages, and staff hours dedicated to outbound telephone calls.

Establishing the Baseline: To Know ROI, You Must Know the ‘Before’

The most uncomfortable truth regarding the measurement of artificial intelligence returns is this: the vast majority of veterinary practices never recorded their operational metrics before purchasing the software.

Return on investment is, by definition, a comparative calculation. It demands a baseline. If a clinic owner does not know their average charting completion time, their baseline no-show percentage, their preventive care reminder compliance rate, or the exact number of hours administrative staff spend tied to the telephone prior to software adoption, they have no empirical foundation for comparison.

What typically happens in the absence of data is a cycle of emotional estimation. A practice adopts a new tool, notices that clinic operations feel momentarily smoother, and credits the software with a wholesale transformation. Alternatively, when the initial "honeymoon phase" fades and minor software glitches occur, leadership quietly concludes that the technology "did not do much." Neither conclusion is anchored in objective reality.

The corrective action is unglamorous, yet essential. Before implementing any artificial intelligence solution, practice management must dedicate two to four weeks to capturing baseline numbers tied directly to the targeted outcome:

• Time spent on medical records per doctor, per day.
• Client communication response times.
• Monthly client attrition and retention rates.

The Adoption Problem: Why Tools Fail

The single most prevalent reason artificial intelligence investments underperform has virtually nothing to do with the underlying technology. Rather, it stems from top-down mandates imposed on an unprepared, unconsulted team.

Executive leadership evaluates a software platform, visualizes the promised efficiency gains, signs a multi-year contract, and abruptly announces a clinic-wide rollout. The veterinary technicians, receptionists, and associate veterinarians—who were never consulted, given only a rushed lunch-and-learn training session, and never asked about their daily operational pain points—view the software as just one more administrative burden heaped onto an already exhausting workday.

Within weeks, staff quietly revert to legacy habits. Yet, the corporate subscription renews automatically.

This phenomenon is not born of stubborn technophobia. Clinical teams are fiercely protective of their time, their professional autonomy, and their clinical judgment. They are inherently skeptical of external solutions promising to "transform" workflows they have spent years mastering. Documentation tools directly impact the legal medical record—a document for which the attending veterinarian bears ultimate professional and legal liability. If a clinician does not fundamentally trust the AI-generated output, or feels that the software is being forced upon them rather than engineered around their workflow, they simply will not use it.

Clinics achieving genuine, sustainable ROI approach deployment through collaboration. They involve the end-users in the software selection process. They initiate soft launches with one or two willing, highly respected team members—often selecting the clinician who is notoriously picky about workflow efficiency, operating on the foundational theory that if the office skeptic embraces the tool, the rest of the staff will follow.

Crucially, these practices frame artificial intelligence as an optional, doctor-controlled accelerator rather than an automated replacement for human clinical judgment. They tie the software directly to an operational bottleneck that the entire team has agreed is worth solving.

"Team buy-in matters more than any feature set when it comes to real ROI," emphasizes Dr. Walsh. "In my practice, I brought our front-desk lead and a senior rehab tech into the process before we rolled out client communication tools. They helped customize discharge summaries and reminders around home exercise plans and mobility progress for our patients. Because the team shaped it from the beginning, adoption was high, and we saw better compliance with less time spent on follow-up calls. It really was a painless experience."

The Metric No One Tracks: Actual Adoption Rate

Among all the analytics available to modern clinic managers, adoption rate remains the most revealing metric in the conversation surrounding artificial intelligence—and the one practices most consistently fail to monitor.

Adoption rate is elegantly simple to define: Out of all the eligible staff members or patient encounters that could potentially utilize the tool, what percentage actually do?

Consider a practical scenario: If a practice employs five full-time veterinarians, all of whom have access to an ambient medical scribe, but only two utilize the software on a regular basis, the clinic’s adoption rate sits at 40 percent. Consequently, the actual per-user software cost is more than double what is reflected on the monthly invoice.

Fortunately, most modern software platforms surface this data natively through usage dashboards, feature-level reporting logs, and individual login activity metrics. Reviewing these reports is the fastest diagnostic method available to practice managers. A low adoption rate three months post-implementation is not necessarily an indictment of the software; rather, it serves as a flashing diagnostic signal indicating the need to revisit staff training, evaluate workflow friction, or repair team buy-in.

It is worth recalling the findings of the aforementioned AAHA/Digitail survey: veterinary professionals who successfully navigated the initial adoption curve overwhelmingly remained committed to the technology, with nearly 70 percent utilizing AI tools on a daily or weekly basis. Adoption, once firmly established, exhibits immense staying power. The primary challenge lies entirely in crossing that initial operational bridge.

When tracked honestly, the adoption metric completely reframes the ROI equation. An expensive software tool utilized by every clinician that saves each doctor thirty minutes every single day represents an extraordinary financial bargain. Conversely, that exact same software platform, left idle by an alienated staff, is merely a bloated line item destined for cancellation.

Future Outlook: The Strategic Audit Ahead

As artificial intelligence continues its rapid maturation, the technological capabilities available to veterinary practices will only grow more advanced, predictive, and nuanced. However, whether these emerging tools ultimately pay off within an individual clinic will continue to rely on foundational human disciplines: establishing clear operational baselines, actively engaging front-line teams in technology integration, and maintaining an unvarnished audit trail of actual software usage.

The most successful veterinary practices of tomorrow will not necessarily be those that purchase the most expensive or feature-heavy artificial intelligence suites on the market. Rather, they will be the clinics whose leadership took the time to measure where they started, brought their teams along for the journey, and retained the intellectual honesty to evaluate whether the technology was truly working for them.

As clinic owners look toward the upcoming quarter, the most valuable audit to run is not a technical assessment of whether artificial intelligence works in theory, but a rigorous operational inquiry: Is it working for your practice today?

Tom Walsh, DVM, is the owner of PrimePet Rehabilitation Therapy. He co-wrote this article in collaboration with the editorial and veterinary solutions team at Shepherd Veterinary Solutions.