Long Version Aaron Massecar, Ph.D., MBA, serves as Head of Partnerships at CoVet, where he works at the intersection of artificial intelligence and veterinary care to make daily practice smoother for veterinary teams everywhere. His path into the profession was far from traditional. After earning a Ph.D. in philosophy from the University of Guelph and spending five years teaching ethics, metaphysics, and technology, Aaron felt drawn toward work where philosophical questions could turn into immediate, practical impact. In 2017, he stepped into veterinary medicine—finding a community where thoughtfulness, innovation, and care naturally align. Since then, Aaron has had the privilege of helping shape foundational programs across the industry. At Texas A&M, he contributed to building the Veterinary Entrepreneurship Academy and the Veterinary Innovation Summit, before going on to support broader industry initiatives over two terms as Executive Director of the Veterinary Innovation Council. His journey has also included meaningful leadership roles with the North American Veterinary Community (NAVC), Colorado State University's Translational Medicine Institute, and Veterinary Emergency Group, alongside earning an Executive MBA in strategy from Quantic School of Business and Technology. Today at CoVet, Aaron focuses on cultivating impactful partnerships—such as serving as Colorado State University's official AI partner—that thoughtfully bring modern technology into clinics and classrooms. Passionate about trust, transparency, and collaboration, he writes and speaks about partnership strategy, exploring how shared values lead to lasting progress. Aaron lives in Fort Collins, Colorado, with his wife and their two dogs and two cats. Short Version Aaron Massecar, Ph.D., MBA, is Head of Partnerships at CoVet, dedicated to helping veterinary teams thoughtfully adopt AI tools—including leading CoVet's partnership with Colorado State University. Formerly a philosophy professor, Aaron transitioned into veterinary medicine in 2017 to focus on tangible, practical impact. He has since been fortunate to support key industry initiatives across Texas A&M, the Veterinary Innovation Council, NAVC, CSU, and Veterinary Emergency Group. He holds an Executive MBA from Quantic and shares insights on collaboration and trust (amongst other topics) on substack. Aaron lives in Fort Collins, Colorado, with his wife and four pets. Micro Version Aaron Massecar, Ph.D., MBA, is Head of Partnerships at CoVet, helping bring practical AI solutions to veterinary medicine. A former philosophy professor who turned toward industry innovation in 2017, Aaron has been fortunate to help build transformative programs across Texas A&M, the Veterinary Innovation Council, NAVC, CSU, and Veterinary Emergency Group.
Veterinary teams are stretched thin by administrative work. Documentation, lab reviews, case research. The work around the work has grown, while the number of hours in the day have stayed the same. AI is starting to take some of that load off, but only if it's built for how vets actually work. In this talk, we will explore five places where AI is making a real difference in clinics today.
AI-generated clinical records across small animal, equine, herd health, and referral workflows. Most current tools focus narrowly on companion animal practice, leaving mixed and large-animal vets to either adapt templates that don't fit or keep documenting by hand. The platforms that handle the full breadth of veterinary work will be the ones that actually reach the whole profession.
Capturing and transcribing everything from reception calls to post-consultation follow-ups and telemedicine consultations. A huge portion of client communication happens by phone, yet it's a workflow many ambient AI tools skip entirely, meaning the documentation burden simply shifts rather than disappears.
Integrating lab results alongside consultation recordings and patient history creates a more complete patient record, allowing AI to generate richer case summaries, referrals, and discharge notes grounded in the full clinical context.
Voice and image-assisted charting that makes documentation faster and more consistent. Dental work is routine and documentation-heavy in most clinics, yet it remains underserved by AI tools.
AI-supported differential diagnoses drawing on trusted, referenced veterinary sources rather than generic large language model output. The distinction matters: a tool grounded in real veterinary literature is fundamentally different from one improvising from general training data.
Together, these five point to the same underlying shift: AI in veterinary medicine is moving from novelty to infrastructure. The tools that get the details right will be the ones that give clinicians hours back in the day and records that actually reflect the work being done.
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