A comprehensive analysis of recent research reveals that practice management software architecture, not feature sets, has become the primary determinant of innovation capacity in veterinary medicine.
Introduction
The companion animal veterinary sector faces converging challenges that threaten practice viability. Veterinary visits have declined for four consecutive years while prices for veterinary services have increased 45% over the past five years, substantially outpacing the 26% cumulative increase in the Consumer Price Index over the same period. Recent research published in January 2026 by industry advisors Jon Ayers, Jeff Dixon, Adam Little, and Adam Wysocki documents that an estimated 15% of pet owners now use artificial intelligence to assess their pets’ medical conditions, a figure that was effectively zero 18 months prior. This represents a fundamental shift in client behavior that practice management strategies have not yet addressed.
The launch of ChatGPT Health on January 7, 2026, represents a watershed moment for healthcare delivery, including veterinary medicine. OpenAI reports that more than 5% of all ChatGPT messages globally concern healthcare topics, with one in four weekly active users engaging with healthcare-related prompts each week. The platform integrates health data from multiple consumer devices and services, supports medical record integration, and allows users to customize their own healthcare assistance. This consumer-facing AI infrastructure is reshaping the relationship between pet owners and veterinary care providers.
The strategic question facing veterinary practices is not whether to adopt AI-enabled tools, but whether their existing software infrastructure can support integration with these rapidly advancing applications. Analysis of the practice management software marketplace reveals that integration architecture, rather than switching platforms, represents the critical decision point for most practices seeking to modernize operations and maintain competitiveness.
The Software Integration Bottleneck
Practice Information Management Systems serve as the operational backbone of veterinary clinics, maintaining systems of record for appointments, client and patient demographics, medical histories, invoices, payments, and communications. Every practice operates on a single PIMS to avoid data fragmentation and workflow conflicts. Industry data suggests that approximately 4% to 5% of the estimated 30,000 companion animal practices in the United States change their PIMS annually, representing 1,200 to 1,500 practices. This low transition rate reflects the substantial disruption associated with PIMS migration, including data conversion complexity, workflow retraining requirements, and potential loss of medical record fidelity.
The proliferation of AI-enabled applications over the past 24 months has created significant value-addition opportunities for practices. These applications span multiple categories including AI scribes for clinical documentation, predictive health analytics, client communication automation, diagnostic interpretation assistance, and online appointment scheduling. The effectiveness of these applications depends critically on their ability to access PIMS databases through application program interfaces. For cloud-based Software as a Service PIMS, API access typically requires vendor support and formal integration partnerships. Industry observers report substantial variation in vendor responsiveness to integration requests.
Research conducted for the January 2026 analysis included direct engagement with PIMS vendors regarding integration timelines. One established SaaS PIMS vendor responded to an integration inquiry from an innovative AI application company with the following statement: “We have a very large queue of AI companies interested in integration and a limited set of resources supporting this area. You will need patience to work through our process.” This response pattern, documented across multiple vendors, indicates that resource allocation to API development and third-party integration support varies significantly across the PIMS marketplace. Several newer entrants to the PIMS market have positioned open API access as a competitive differentiator, explicitly welcoming third-party integrations through documented APIs and sandbox environments.
The Write-Back Requirement for AI Effectiveness
The distinction between read-only and read-write API access has emerged as a critical determinant of whether AI applications reduce workload or simply redistribute it. AI scribes, which use speech recognition and natural language processing to document clinical encounters, represent a case study in this dynamic. When a scribe application can only read data from the PIMS but cannot write documentation back to the medical record, veterinary staff must manually copy and paste AI-generated content into the patient record. This manual transfer step eliminates much of the time savings that motivated the initial adoption.
Practices utilizing AI scribes with full read-write integration report documentation time reductions of 40% or more per appointment. The same AI technology without write-back capability may reduce documentation time by only 10% to 15% due to the residual manual transfer requirement. This differential compounds across hundreds or thousands of appointments annually. For a practice conducting 5,000 appointments per year with an average documentation time of 10 minutes per appointment, the difference between integrated and non-integrated AI scribes represents approximately 200 hours of annual staff time.
The write-back requirement extends beyond clinical documentation to encompass client communications, diagnostic results, treatment recommendations, and task management. AI-powered client communication systems can generate personalized follow-up messages, treatment plan explanations, and preventive care reminders based on the clinical encounter. These systems require write-back capability to log communications in the client record, update reminder schedules, and track engagement metrics. Without this integration, practices maintain parallel communication tracking systems that fragment the client relationship view.
Consumer AI and the Transformation of Client Expectations
Pet owners increasingly arrive at veterinary appointments having already consulted AI about their pet’s symptoms. Research from CATalyst indicates that 15% of pet owners use AI to understand their pets’ medical needs, while 50% use traditional search engines for the same purpose. This behavioral shift represents a qualitative change from the traditional model where pet owners arrived with symptoms and relied entirely on the veterinarian for diagnosis and treatment planning. The emerging model features pet owners arriving with preliminary hypotheses, differential diagnoses, and specific questions derived from AI consultation.
Practicing veterinarians report mixed experiences with AI-informed clients. Ken Lambrecht, veterinarian and CEO of Healthy Pet Connect, stated in response to the research findings: “I love when clients come in informed. I always get a nice heads up from my assistants and can go check ChatGPT/Gemini myself.” He emphasized urgency regarding professional adaptation: “If we don’t keep up as practitioners and as a profession we will lose pet parents’ trust and that is everything.” This perspective frames AI-informed clients as an opportunity for enhanced engagement rather than a challenge to professional authority.
The communication gap between veterinary professionals and clients creates the conditions for consumer AI adoption. Pet owners who do not fully understand explanations provided during appointments, who receive laboratory results without adequate interpretation, or who seek more detailed information about diagnoses frequently turn to AI tools for clarification. Stacee Santi, a veterinarian-entrepreneur-author, shared an example from human medicine involving her 79-year-old mother’s medical appointments. After finding physician explanations inadequate for her mother’s comprehension, Santi loaded the medical chart into ChatGPT for translation into accessible language. Her reflection on the experience was notable: “I don’t know which is more shocking, that ChatGPT is so good at this or that human doctors are so bad at this.”
Veterinary practices can address this communication gap through proactive use of AI tools for client engagement. AI-enhanced communication systems can generate appointment summaries in plain language, provide detailed explanations of laboratory results with trend visualization, and deliver personalized preventive care recommendations. These communications leverage the clinical context from the appointment record, including physical examination findings and diagnostic results, to create tailored content that reinforces rather than replaces the veterinary consultation. Practices that implement these communication strategies position themselves as partners in the AI-informed healthcare model rather than as obstacles to information access.
AI Radiology as a Case Study in Professional Augmentation
The veterinary AI radiology market provides empirical evidence for how AI enhances rather than displaces professional expertise. Initial market hypotheses suggested that AI interpretation of radiographic images would reduce demand for board-certified radiologists by handling routine normal findings autonomously. The actual market outcome contradicts this assumption. Practices utilizing AI radiology interpretation platforms report increases in both total radiology utilization and specialist consultation requests.
This counterintuitive finding reflects three mechanisms. First, AI-integrated radiology platforms reduce the friction associated with obtaining radiographic assessments. Traditional teleradiology required manual image packaging, upload through separate interfaces, and detailed form completion. AI-integrated systems auto-upload images from Picture Archiving and Communication Systems, pull patient history from the PIMS, and provide preliminary risk assessments within minutes. When obtaining a radiographic interpretation becomes effortless, practices use radiology more frequently as a diagnostic tool. Second, AI platforms include visual aids such as heat maps and highlighted regions that make radiographic findings comprehensible to pet owners. This visualization capability increases client acceptance of recommended diagnostic imaging and compliance with follow-up studies. Third, AI provides a confidence multiplier for general practitioners. Having an AI risk assessment available during image review allows veterinarians to triage findings more effectively and refer appropriate cases to specialists with better positioning and more complete clinical questions.
A 2025 study published in Frontiers in Veterinary Science compared AI risk interpretation against interpretations from 11 board-certified radiologists. The research demonstrated that AI exhibited higher specificity than human radiologists, reducing false positive findings, while human radiologists maintained advantages in sensitivity for ambiguous presentations. Notably, AI demonstrated lower variation across cases than the human radiologist cohort. The researchers concluded that “AI will likely complement rather than replace human experts.” This finding aligns with evidence from human radiology, where predictions of radiologist obsolescence have proven incorrect. Despite Geoffrey Hinton’s 2016 prediction that radiologists would be obsolete within five years, radiology residency positions in human medicine reached record highs in 2025, radiologist salaries increased 48% since 2015, and workforce projections indicate 25% to 40% growth by 2055.
The marginal cost structure of digital radiology enables this expansion. Once digital radiography hardware is installed, the incremental cost of conducting a study consists primarily of technician time, typically 10 minutes for two technicians. AI interpretation adds approximately $5 to $10 per study. This low marginal cost structure means practices can offer radiology more liberally as a diagnostic tool. Some practices do not charge for initial radiographic studies and AI interpretation, only charging when specialist referral is indicated. This pricing strategy lowers the barrier to diagnostic imaging and expands the addressable market.
Jevons Paradox and the Economics of AI Adoption
The expansion of radiology utilization despite AI-enabled cost reduction exemplifies Jevons Paradox, an economic principle stating that technological improvements that increase efficiency of resource use often lead to increased consumption of that resource rather than decreased consumption. The paradox occurs because efficiency gains reduce cost per unit, making the resource accessible to previously priced-out users and enabling new use cases. Applied to veterinary AI, Jevons Paradox suggests that AI tools that reduce the cost of diagnostic interpretation, clinical documentation, or client communication will expand utilization of those services rather than simply reducing costs.
Industry observers including Aaron Levie, CEO of Box, have postulated that Jevons Paradox will apply broadly to knowledge work as AI reduces the cost of producing analysis, writing, and decision support. The veterinary radiology evidence provides empirical support for this thesis in a healthcare context. The implications extend to practice pricing strategy. If AI reduces the cost of delivering certain veterinary services by 20% to 30%, practices face a strategic choice: capture the efficiency gain as margin expansion, or pass some or all of the savings to clients to expand market access.
The research analysis presents a specific scenario to illustrate this dynamic. Consider a diagnostic test with a $33 direct cost and a typical 3X markup to $100 charged to clients. A 20% price reduction to $80 would reduce margin per test from $67 to $47. However, if Jevons Paradox applies and volume increases by 40% due to expanded accessibility, the practice would achieve the same total margin on the diagnostic category while serving more patients. The volume increase derives from two sources: market share gain from competitors maintaining higher prices, and expansion of the addressable market as price-sensitive pet owners who previously avoided diagnostics due to cost now access care.
The veterinary profession’s historical markup practices for diagnostics, typically 3X to 4X cost, may represent the most vulnerable aspect of current pricing structures. This vulnerability increases as AI-assisted diagnostic interpretation becomes available. The parallel to AI-assisted radiographic interpretation is direct. Practices that recognize this dynamic and strategically adjust diagnostic pricing to expand market access rather than maximize per-unit margins may achieve competitive advantages that compound over time through increased client loyalty and visit frequency.
The Path Forward for Practice Management Software Vendors
The analysis by Ayers, Dixon, Little, and Wysocki includes a strategic framework for understanding how PIMS vendors reached the current integration bottleneck. The authors characterize the situation as a failure of strategy rather than deliberate exclusion, although exclusionary practices are documented. PIMS vendors failed to anticipate the pace of AI innovation and the resulting integration demands. They under-resourced API development and integration support, prioritizing feature additions over ecosystem enablement. They deployed expensive, opaque, and time-consuming integration processes that created trust deficits with the developer community.
The consequences of these strategic failures are now apparent. Third-party integration platforms have emerged to route around restrictive PIMS vendors. BitWerx and GreyWind provide integration services that enable AI applications to access PIMS data without direct vendor support, though with varying approaches and limitations. BitWerx operates as an unsanctioned integrator prioritizing speed and breadth of connectivity. GreyWind functions as a sanctioned integrator for specific PIMS but requires case-by-case approval for each application integration. The emergence of these middleware solutions indicates substantial unmet demand for integration capability.
The authors argue that the future architecture of veterinary practice software is a network of applications with AI coordinating between them, rather than a hub-and-spoke model with the PIMS at the center. The PIMS remains the most valuable node as the system of record, but its value depends on enabling rather than controlling the ecosystem. PIMS vendors that recognize this shift and invest in transparent, well-documented APIs with predictable authentication and permissions will maintain market leadership. Those that continue gatekeeping approaches will face customer defection as practices select PIMS based on ecosystem compatibility rather than feature checklists.
The distinction between veterinarian as customer and veterinarian as builder will increasingly blur as AI-assisted development tools enable non-technical users to create custom software. Corporate group practices are already developing proprietary PIMS and communication systems for internal use when vendor solutions do not meet requirements. This trend will accelerate as development friction decreases. PIMS vendors that prevent their own customers from building adjacent tools will face not only third-party developer frustration but direct customer dissatisfaction.
Implications for Practice Decision-Making
For individual practices and small groups, the research suggests that PIMS switching is rarely necessary to achieve modernization objectives. The switching costs, data conversion risks, and staff disruption typically outweigh benefits unless specific conditions apply: vendor abandonment of product support, fundamental operational requirement failures, or persistent blocking of critical integrations. Most practices can achieve meaningful capability gains by adding complementary applications to their existing PIMS rather than replacing the core system.
The critical evaluation criterion is not the PIMS feature set but the vendor’s integration posture. Practices should assess whether their current PIMS vendor actively supports third-party integrations, provides documented APIs with write-back capability for relevant systems of record, and demonstrates willingness to enable innovation rather than control it. For practices operating on cloud-based SaaS PIMS, this assessment is particularly important because data access typically requires vendor-provided APIs rather than direct database connectivity available with on-premises systems.
For corporate groups and enterprise veterinary organizations, the standardization question requires different analysis. While standardizing on a single PIMS across acquired practices simplifies reporting and inventory management, it consumes substantial change management capacity and may contribute to staff turnover and burnout. The rapid advancement of AI-powered analytics tools is reducing the technical necessity of PIMS standardization for enterprise reporting. Advanced analytics platforms can aggregate data across heterogeneous PIMS environments, obviating the need for forced migrations to achieve reporting consistency.
The selection criteria for practices evaluating PIMS options should emphasize two dimensions equally: workflow fit for daily operations and ecosystem readiness for modernization. Workflow fit encompasses scheduling capabilities, note creation efficiency, estimate and checkout processes, and practice-type specific requirements. Ecosystem readiness encompasses API accessibility and quality, existing integration partnerships in relevant categories, realistic time-to-integrate for new applications, data export accessibility, and vendor posture toward third-party access. Practices that prioritize only workflow fit risk selecting systems with excellent user interfaces that subsequently block strategic initiatives through integration restrictions.
Conclusion
The veterinary software landscape is experiencing a fundamental architectural shift. The proliferation of AI-enabled applications over the past 24 months has created unprecedented opportunities to address practice efficiency challenges, reduce staff burnout, enhance client engagement, and expand care delivery. The realization of these opportunities depends critically on software integration architecture rather than individual application capabilities.
Practices seeking to modernize operations should focus evaluation efforts on integration readiness rather than PIMS replacement unless specific failure conditions apply. The questions that determine long-term competitiveness concern API access, write-back capability, vendor partnership approach, and ecosystem openness. Software vendors that recognize their role as enablers of innovation rather than gatekeepers of systems of record will maintain market leadership through the AI transformation of veterinary medicine.
The counterintuitive finding from AI radiology research, that AI expands professional expertise rather than replaces it, provides a template for understanding broader AI adoption patterns in veterinary medicine. When technology reduces friction and cost while maintaining or enhancing quality, utilization expands to serve previously unmet demand. This dynamic creates opportunities for practices to strategically adjust pricing in select service categories to expand market access and client loyalty while maintaining overall profitability. The practices that recognize and operationalize these economic principles will achieve competitive advantages that compound over time through increased visit frequency and expanded client bases.
The veterinary profession faces legitimate challenges including sustained visit declines and substantial cost inflation. The evidence suggests that AI-enabled software applications, when properly integrated within practice systems, represent viable tools to address these challenges. The path forward requires practices to demand integration openness from vendors and vendors to recognize that their long-term success depends on enabling the innovation ecosystem rather than controlling it.
About viggoVet: viggoVet is a clinical AI-driven, cloud-based veterinary practice management platform built by veterinarians, for veterinarians. Our Veterinary Business Intelligence Platform strengthens practice operations, enhances financial visibility, and improves medical precision through open integration architecture and evidence-based AI. We are committed to the principle that innovation requires open systems, not walled gardens.
References and Sources
Primary Research Documents
1. Ayers J, Dixon J, Little A, Wysocki A. Companion Animal Veterinary Software: Part I – Navigating Practice Challenges with Support of Technology and AI. Independent Industry Analysis. January 16, 2026.
2. Ayers J, Little A, et al. AI in Companion Animal Medicine: Transformation Ahead! Independent Industry Analysis. September 22, 2025.
Government and Statistical Data
3. Bureau of Labor Statistics, United States Department of Labor. Consumer Price Index data, 2020-2025. Accessed January 2026.
4. Bureau of Labor Statistics, United States Department of Labor. Veterinary Services Price Index data, 2020-2025. Accessed January 2026.
Market Research and Industry Data
5. CATalyst Council. Pet Owner AI Usage Survey. 2025. Research indicating 15% of pet owners use AI to understand pet medical needs and 50% use traditional search engines for the same purpose.
6. Luna A, Hound.vet. Veterinary Practice Management System Usage Data. Dataset of over 11,000 veterinary practice profiles, 2020-2025. Hound.vet is America’s leading marketplace for veterinary careers.
7. Kynetec Market Research. Veterinary PIMS Market Analysis and Churn Rate Study. Funded by Jon Ayers, publication expected March 2026. Estimates 30,000 companion animal practices in the United States with 4-5% annual PIMS transition rate.
Technology and AI Platform Data
8. OpenAI. ChatGPT Health Launch Announcement. January 7, 2026. Platform statistics indicate more than 5% of all ChatGPT messages globally concern healthcare topics, with one in four weekly active users engaging with healthcare-related prompts.
9. Vetology AI. Classifier Performance Metrics Release. 2025. Available at: https://vetology.net/vetology-ai-releases-classifier-performance-metrics/
10. BitWerx. PIMS Integration Platform Technical Documentation. Lexington, Kentucky. Serves over 5,000 veterinary practices with tiered connectivity model and real-time read/write capabilities release scheduled Q1 2026.
11. GreyWind Healthcare Integration Services. Partnership with Antech Diagnostics (Mars Petcare). Miami, Florida. Over 4,000 AVImark connections according to Antech sources.
Peer-Reviewed Research
12. Author names not specified in source document. Comparison of AI Risk Interpretation Against 11 Board-Certified Radiologists. Frontiers in Veterinary Science. 2025. Key findings: AI demonstrated higher specificity and lower variation than human radiologists; conclusion that “AI will likely complement rather than replace human experts.”
Professional Organization Statements
13. American College of Veterinary Radiology. Statement on AI Products for Veterinary Diagnostic Imaging. Spring 2025. Stated that “currently, no commercially available AI products for veterinary diagnostic imaging meet the required standards for transparency, validation, or safety.”
Expert Interviews and Public Statements
14. Lambrecht K, DVM. CEO, Healthy Pet Connect; Chair, The Veterinary Cooperative; Owner, West Towne Veterinary Center; CEO, Fit Pets for Rescues. LinkedIn response to AI adoption post, January 2026. Former board member of AAHA, Feline VMA, AAVN, PNA, and Marketlink.
15. Santi S, DVM. Veterinarian-entrepreneur-author. LinkedIn post regarding ChatGPT use for medical chart interpretation, January 2026. Shared with permission.
16. Wysocki A. Founder, VetSoftwareHub. Independent veterinary software consultant. Quotes from published analysis and LinkedIn commentary, January 2026.
17. Little A. Independent veterinary technology consultant. Strategic analysis contributions and LinkedIn commentary, January 2026.
Media and Industry Commentary
18. Huang J. CEO, NVIDIA. The Joe Rogan Experience #2422. Discussion of AI impact on radiology profession and Jevons Paradox application to knowledge work.
19. Hinton G. Computer scientist and AI researcher. 2016 prediction regarding AI impact on radiology profession. Widely cited in technology and healthcare literature.
20. Levie A. CEO, Box. Social media commentary on Jevons Paradox application to AI and knowledge work, December 29, 2025. LinkedIn post discussing how AI making tasks cheaper leads to increased consumption rather than decreased workforce.
Employment and Workforce Data
21. Human Medicine Radiology Workforce Statistics. Multiple sources cited in industry analysis: radiology residency positions at record highs in 2025, radiologist salaries increased 48% since 2015, workforce projected to grow 25-40% by 2055. Specific source publications not detailed in primary document.
22. Board-Certified Veterinary Radiologists Population Data. Fewer than 2,000 board-certified veterinary radiologists serve all of North America and Europe. Source: Industry estimates cited in Ayers et al. analysis.
Third-Party Integration Platforms
23. VetSoftwareHub. Comprehensive veterinary software directory and evaluation platform. Founded by Adam Wysocki. Available at: vetsoftwarehub.com. Lists 42+ different PIMS systems in use in the US market.
Software Vendors Referenced
24. Multiple PIMS Vendors: Avimark, Cornerstone, ezyVet, Pulse, Impromed/Infinity, Neo, Shepard, Vetspire, Instinct, Digitail, DaySmart Vet, Provet, VetCove, NectarVet, Lupa. Market presence and capabilities as documented in Ayers et al. analysis and VetSoftwareHub directory.
25. Mars Veterinary Health Proprietary Systems: Woofware (VCA), Petware and Voyager (Banfield). Internal PIMS systems referenced in market analysis.
26. AI Application Vendors: Multiple vendors referenced in categories including scribes (Covet), online booking (Vetstoria, Weave, Covetrus Comms, DaySmart, Chckvet, AVA), and other value-added applications. Specific capabilities as documented in market analysis.
Economic Theory
27. Jevons WS. The Coal Question; An Inquiry Concerning the Progress of the Nation, and the Probable Exhaustion of Our Coal Mines. London: Macmillan and Co., 1865. Original formulation of the efficiency paradox applied to resource consumption, subsequently termed Jevons Paradox.
Notes on Data Sources
Market Estimates: Practice counts, churn rates, and market share estimates represent best available industry intelligence compiled from multiple proprietary sources, vendor discussions, and third-party data. Exact figures are subject to validation through ongoing market research.
Professional Quotes: All direct quotes from named professionals were either published publicly on professional platforms (LinkedIn) or shared with explicit permission for attribution as noted.
Pricing and Cost Data: Diagnostic markup practices (3X to 4X), AI interpretation costs ($5-$10 per study), and marginal cost calculations represent industry-standard estimates based on practice operations analysis.
Integration Technical Details: API capabilities, write-back functionality, and integration timelines reflect reported experiences from practices, developers, and direct vendor communications as documented in the primary source analysis.
This article synthesizes research and data from multiple authoritative sources in veterinary medicine, technology, and economics. All statistical claims and attributed statements are traceable to documented sources. Where industry estimates are used, this is explicitly noted. Readers seeking additional detail on specific data points should refer to the primary source documents listed above.