Veterinary AI Brief

Robert Sanchez, Adam Little, and Aaron Massecar explore new possibilities for client education, computer work, and clinical decision support, alongside the questions of trust and oversight that come with them.

Robert shares an AI-assisted visual explainer and what it could mean for helping pet owners understand care. The hosts debate where to start with computer-use agents, how to keep people in control, and why better clinical answers still need a connection to real patient outcomes.

In this episode:

• How the hosts are thinking about AI risk and safety.
• Computer use and visual design: what new capabilities could change for veterinary teams.
• Personalized client education and interactive learning.
• Choosing a useful first task, setting boundaries, and watching the results.
• Clinical decision support, trusted sources, and the missing outcomes loop.

Try the client-education exercise discussed in the episode:

"Help me identify six topics I repeatedly explain to pet owners. Ask me about the questions clients find hardest to understand. For the most useful topic, propose a short visual explanation with one clear takeaway, a simple storyboard, and a list of medical claims for a veterinarian to check. Use generic examples and no identifiable patient information. Draft the concept first, then wait for my feedback."

Not sure where to start with AI? Try Robert's interview approach:

Ask your AI: "Interview me, one question at a time, until you understand what matters most in my work, what repeatedly gets in the way, and what a useful result would look like. Then ask what must never happen, what information is off limits, and which actions need my approval. Summarize what you learned and suggest three small tasks I can review myself. Recommend one and explain how we'll know it helped. Start with information I provide here; ask before accessing other systems."

Choose a task you have authority to do, review its output, and keep a veterinary professional responsible for clinical content.

What is one task you would like AI to help your team with? Tell us in the episode comments, and follow Veterinary AI Brief for the next conversation.

Recorded September 14, 2026.

Chapters: 00:00 Welcome 01:13 AI risk and safety 15:33 Astra and computer use 19:05 Visual client education 29:24 Where should a practice start? 37:35 Have AI interview you 39:13 Clinical decision support 45:04 The missing outcomes loop 46:07 Novel context and the bitter lesson

This episode looks at three connected shifts in veterinary AI: tools moving from reactive chat to proactive agents, practical clinic use cases like follow-ups and meeting notes, and the growing security and compliance concerns that come with deeper integrations. Mark McDonald is joined by Dr. Adam Little and Aaron Massecar to map where the industry is already experimenting and where the biggest risks are emerging.We discuss how agentic tools like Grokbot are changing the way work gets done, how veterinarians and students are using AI to learn and improve, and why security, vendor vetting, and data handling are becoming non-negotiable.

• In this episode, Mark McDonald, Dr. Adam Little, and Aaron Massikar discuss the shift from reactive AI to proactive AI that actually picks up tasks.
• Grokbot is framed as a productized, agent-based workflow system where users assign bots specific jobs like revenue tracking, commitment follow-up, and research monitoring.
• Adam explains why the onboarding experience matters: the value of AI is much easier to access when the system can proactively prompt users and connect to tools with less setup friction.
• A major theme is the move from thread-based AI use to identity-based digital workers that repeatedly handle a defined role or workflow.
• The hosts explore veterinary use cases like no-show outreach, post-surgical follow-ups, invoice generation, estimate drafting, and client summaries.
• Aaron describes how AI meeting notes are evolving from simple transcription to analysis of communication quality, compliance, tone, and response patterns in real clinical conversations.
• The conversation expands into multiplayer AI, where veterinarians could opt in to share examples, learn from each other, and collaborate through AI-mediated peer learning.
• Aaron shares what he is seeing in veterinary schools: students are now building their own tutors, study guides, and practice tools for the NAVLE and sharing them with classmates.
• The security segment focuses on the risks of deeply connected AI systems, including training-data exposure, unintended actions, model misbehavior, and broader enterprise attack surfaces.
• The discussion closes with a warning that organizations need policies, vendor vetting, and security scaffolding now, because employees will use these tools with or without formal approval.

00:00 - Episode setup: proactive AI, security, and vet use cases 01:13 - Grokbot and the shift from chat to task-doing agents 02:12 - How users assign bots specific jobs and workflows 03:32 - Connecting agents to email, Salesforce, and other context 04:25 - Why easier onboarding matters for mainstream adoption 05:24 - From threads to ongoing digital workers with identity 06:18 - SpaceX, Cursor, and the compute advantage behind Grokbot 07:39 - The emerging "super app" for knowledge workers 08:30 - Scheduling, task coordination, and performance feedback loops 10:43 - High-value veterinary jobs these agents could pick up 11:38 - Meeting notes as the path from transcription to automation 12:38 - When AI starts completing tasks instead of just identifying them 13:06 - Using AI as a communication coach across clinical conversations 15:03 - Clinics using recorded conversations to improve together 16:01 - Applying feedback to medical explanations and HR meetings 17:27 - The idea of multiplayer, opt-in veterinary learning networks 19:19 - AI embedded in collaborative tools like Slack and chat 20:46 - Aaron on how vet student use has matured over the past year 21:42 - Data hygiene, training concerns, and privacy awareness 22:36 - Students building their own NAVLE tutors and software 24:02 - Why personalized tutors may matter more than static study notes 25:26 - Hyper-personalized learning pathways and student excitement 26:54 - Embedding critical life context into agentic systems 27:28 - Why deeper integration creates a catch-22 for security 28:56 - Example of an agent taking action without intended permission 30:21 - Unprompted data exposure risks and frontier model concerns 31:44 - Why employees will keep using AI even if organizations lag 33:04 - Open-weight models, hacks, and the likelihood of more attacks 34:53 - Cyber defense programs and hardening enterprise systems 35:35 - Compliance maturity: SOC 2, ISO, HIPAA, GDPR, and DPAs 37:27 - Why veterinary is not the first target, but still needs readiness 38:26 - Vector database risks and reverse engineering concerns 39:25 - Security innovation as AI pressure-testing continues 40:48 - Why the answer cannot be to avoid connecting critical systems 42:14 - WordPress, vendor vetting, and reducing attack surfaces 43:36 - Closing thoughts and trying Grokbot in practice

Dr. Adam Little, Robert Sanchez, and Dr. Aaron Massecar return with a fast-moving roundup of the biggest veterinary AI stories. The episode centers on a lawsuit involving Zoetis and a misdiagnosed cancer case, then expands into the practical realities of AI receptionists, scribe integrations, and what comes next for governance and security.In this episode, the hosts focus on how the profession should judge AI systems: by perfection, or by relative risk compared to current human-led workflows. They also dig into how voice AI, integrations, and agentic tools are changing daily practice, while warning that security and validation are now urgent priorities.

Key topics

• The Columbia Veterinary Hospital lawsuit against Zoetis, where an AI-assisted screening result allegedly contributed to a missed cancer diagnosis, repeat surgery, and the death of an 11-year-old dog.
• Whether the legal and ethical blame sits with the AI company, the veterinarian, the hospital, or the broader workflow around the case.
• Why the hosts keep returning to relative risk, not perfect performance, when evaluating AI in healthcare and veterinary medicine.
• The concern that companies may market AI with "shock and awe" claims while downplaying limits, failure modes, and validation data.
• How this case could influence veterinarian adoption by increasing caution, liability concerns, or skepticism toward AI-assisted pathology and radiology.
• The rise of AI receptionists and why a 25-minute failed booking experience for a Bordetella vaccine appointment matters as a client-experience case study.
• The idea that bad outcomes often come from workflow failures, not just model failures, especially when latency, context, or agency breaks down.
• Recent veterinary software movement toward deeper integrations, including two-way connections between scribes and PIMS systems, plus MCP-style access to platform data.
• Why integrations matter beyond copy-paste convenience: fewer windows, less friction, better focus, and more of the practice happening in one place.
• The next big concern after integration: security, including sandbox escape risks, hardened validation, and compliance language like ISO 27001 and SOC 2 Type II.

Timestamps

00:00 - Welcome back and what the veterinary AI news cycle has been missing 01:03 - Columbia Veterinary Hospital lawsuit and the Zoetis AI misdiagnosis claim 02:55 - Who is responsible when an AI-assisted decision goes wrong 04:21 - The veterinarian's responsibility versus specialist or lab guidance 06:13 - Was the product positioned clearly as a screening tool 07:35 - Why the hosts reject "is it perfect?" and focus on relative risk 09:14 - Why this lawsuit feels like a broader blame-diffusion problem 10:29 - Why this case is messy compared with a more direct AI failure 12:22 - Could this change veterinarian adoption of AI tools 14:19 - Why edge cases will multiply as more practices use AI daily 16:42 - Why AI literacy and fluency matter more than blind trust 19:01 - Workflow failures and the bridge to AI receptionists 20:27 - The Bordetella booking story and why client friction drives clinic switching 22:26 - Human call friction data and why status quo is not great either 24:20 - Why older AI phone experiences created lasting skepticism 25:41 - AI receptionists, client AI calls, and the demand for transactional workflows 28:53 - GPT Live and the leap in real-time voice interaction 31:43 - Three ingredients of a good phone experience: latency, context, agency 35:59 - New integrations with PIMS and AI tools, including two-way and MCP-style connections 38:46 - Why integration reduces app-switching and improves focus 41:27 - "Tool use" and the future of agents acting on behalf of the practice 44:38 - Why the first scribe workflows had a useful human-in-the-loop bug 46:32 - Validation as building the road, not just the car 47:28 - Security threats, sandbox escape, and why this is the next urgent infrastructure problem 51:35 - How much AI has already replaced in everyday work, from setup to form filling 53:30 - Final takeaways on adoption, implementation, and staying close to the frontier

Join us in this episode of the Veterinary AI Brief as we explore how veterinary professionals are actively transforming their practices with AI tools. From building custom workflows to improving communication and patient care, our guests share their journeys, projects, and practical tips for adopting AI in veterinary medicine.

• Real-world AI applications in veterinary clinics
• Building and customizing AI tools without coding expertise
• Improving client communication and team workflows
• Practical steps for beginners to start experimenting with AI
• Future opportunities in proactive AI-driven patient management

• How Dr. William Lane leveraged platform tools like Notebook LM and Node-based workflows to streamline clinical tasks
• Dr. Carly Little's innovative communication mentorship program using AI transcripts and feedback
• Dr. Sam Lewis's development of automated patient records and hospital dashboards
• Insights on navigating the learning curve and building impactful tools without extensive coding
• Strategies for starting small, iterating, and tackling problems unique to your practice

00:00 - Introduction to the episode and guest backgrounds 00:29 - The importance of structured AI workflows in veterinary practice 00:52 - Practical AI projects: From journal searching to patient record automation 01:33 - The impact of custom dashboards and communication tools in clinics 02:11 - Platforms and tools: Notebook LM, Node workflows, Replit, Cursor, Base44 02:36 - Building AI solutions without programming skills: Learning and iteration 03:27 - Developing team-wide and client-facing AI integrations 04:43 - Practical benefits: Patient care, staff efficiency, and client satisfaction 05:42 - Transitioning from reactive to proactive AI management 06:40 - How non-technical vets can start their AI journey 07:09 - Building and customizing AI tools to fit practice needs 08:11 - Project examples: Urgent care boards, pet portals, communication scoring 09:24 - The evolving role of veterinarian as builder of tailored solutions 10:02 - Overcoming the technical learning curve and resource strategies 11:23 - The power of starting small and iterating progressively 12:56 - How AI enhances team collaboration and client transparency 14:10 - Key insights: Cost-effective, flexible, and domain-specific AI tools 16:18 - Future outlook: Proactive, real-time AI interventions in veterinary care 17:45 - Adopting a problem-centric mindset to drive meaningful AI solutions 19:00 - Practical advice for non-technical vets: Use AI as a partner, not a coder 20:18 - The importance of iteration and patience in AI projects 21:23 - Building trust and custom solutions that fit your practice 22:45 - Making AI tools accessible and scalable for veterinary teams 24:10 - The role of AI in improving hospital workflows, diagnostics, and client communication 26:04 - Final takeaways: Ask what you need, start small, and build iteratively

Unlock the future of veterinary medicine with AI-driven breakthroughs proven to enhance diagnostic accuracy, reduce workloads, and transform clinical workflows. In this episode, we explore how cutting-edge research from Lancet's MASAI trials reveals AI's game-changing potential—catching 29% more cancers without increasing false positives and slashing radiologist workloads by nearly half. But what does this look like for vets? Is there a realistic path to integrating these advances into everyday practice?Join us as we break down the implications of AI in diagnostics, from human health to animal care. We delve into how machine learning models are helping detect early metastasis, streamline report writing, and elevate clinical decision-making—all while highlighting the critical importance of vet responsibility and trust. Our conversation with Dr. Tam of Colorado State University reveals why these innovations aren't just scalable for human health—they could redefine how we approach veterinary cancer screening, imaging, and workflow automation.

First, VMX takeaways: Instinct's acquisition of ScribbleVet, what it means for the scribing landscape, and why the "bolt-on AI" approach to practice management systems may already be outdated. Aaron gives an insider's perspective from Covet on why dedicated AI copilots are pulling away from PIMS-native tools.

Then, the big one: new frontier AI models from Anthropic and OpenAI have unlocked something called agentic engineering — and it's collapsing the barrier between idea and execution. Robert shares how he built a personal AI assistant over the holiday break that handles email triage, Salesforce data entry, meeting follow-ups, and more. The implications for practice owners, veterinarians, and the entire SaaS ecosystem are massive.

Finally, the rise of OpenClaw — autonomous AI agents that work 24/7, interact with each other, and are already generating real revenue. It's weird, it's wild, and it might be the closest thing to AGI we've tasted yet.

Whether you're a practice owner wondering how AI reshapes your business model, a veterinarian curious about building your own tools, or an industry leader trying to stay ahead — this one's essential listening.

It's our holiday "step-back" episode: what actually mattered in veterinary AI in 2025—and what's most likely to hit practices hard in 2026. With special guest Jon Ayers, Robert, Adam and Aaron map the rapidly changing landscape: the breakout adoption of AI scribes, the "app explosion" happening around (not inside) PIMS platforms, and why the next year will be less about "tech toys" and more about AI as labor—tools that behave like employees and move spend from software budgets into staffing/COGS. We also get blunt about the reality in clinics: you only have so much change-management budget. So the question isn't "What's the coolest new tool?"—it's "What 1–3 changes will measurably improve throughput, capture more calls, and upgrade the pet owner experience without blowing up your workflow?" In this episode: Why many practices should not switch PIMS in 2026—and what to do instead How scribes became the fastest "new tech" adoption in vet med (and what that signals next) The PIMS platform dilemma: be the ecosystem enabler or get labeled the bottleneck The coming wave: AI receptionists, online booking, and next-gen client communications A practical way to "pick your shots" in 2026 so change doesn't stall everything The bigger stakes: pet-owner economics, access to care, and why regulation may need to evolve If you want a clear, actionable lens for evaluating the flood of tools—and choosing the moves that actually change outcomes—this is the one.

In this episode of the Veterinary AI Brief, co-hosts Robert Sanchez, Dr. Adam Little, and Aaron Massecar dive into the latest advancements in AI technology and its implications for the veterinary industry. The discussion kicks off with a focus on Google's release of Gemini 3, a groundbreaking AI model that promises to revolutionize the field with its multimodal capabilities. The hosts explore how these developments are set to transform veterinary practices, from enhancing diagnostic tools to improving client communication.

Robert Sanchez, Adam Little, Aaron Massecar

1. Google's Gemini 3 is a game-changer in AI, offering state-of-the-art capabilities that surpass previous models.
2. The integration of AI in everyday applications like Gmail and Google Docs is set to enhance efficiency and user experience.
3. AI's role in veterinary medicine is expanding, with potential applications in diagnostics, client communication, and practice management.
4. The rapid adoption of AI tools in the veterinary industry highlights the demand for innovative solutions that improve workflow and patient care.

• [00:00] Introduction and Welcome
• [05:12] The Impact of Google's Gemini 3
• [15:45] AI in Veterinary Diagnostics
• [25:30] Enhancing Client Communication with AI
• [35:50] The Future of AI in Veterinary Practice Management

Welcome to the first episode of The Veterinary AI Brief! In this kickoff conversation, Robert, Adam, and Aaron take you inside the AI explosion and what it means for veterinary professionals right now. We cover: - What AI agents really are, and how they'll soon handle everything from estimate follow-ups to workflow automation. - The launch of ChatGPT's Learning Mode and its implications for personalized CE and mentorship. - What's likely coming with GPT-5, and why this next leap could be a game-changer for everyday workflows. Whether you're a curious vet or an ambitious builder, this episode gives you the clarity and confidence to start using AI with purpose.