Artificial intelligence documentation tools are arriving in veterinary practice faster than the governance frameworks designed to evaluate them. Search interest in veterinary AI grew by more than 1,680 per cent year-on-year between 2024 and 2025, and a study by Digitail and the American Animal Hospital Association found that nearly 40 per cent of veterinary professionals were already using AI tools in their practice setting. For practice managers, the question is no longer whether to engage with these tools, but how to evaluate them rigorously before committing. That evaluation is more demanding than a standard software procurement. AI documentation tools touch clinical records, client data, regulatory obligations, and professional liability simultaneously. This article sets out the key areas any practice manager should work through before signing a contract, not as a barrier to adoption, but as the foundation for a confident one.

What an AI documentation tool actually does in a veterinary context

The term "AI documentation tool" covers a wide range of products with meaningfully different capabilities. At the simpler end, a tool may function primarily as a real-time transcription service, converting spoken consultation audio into a text record that the clinician then edits. At the more sophisticated end, an AI assistant actively interprets what is said, structures the output into a clinical note format (separating history, examination findings, assessment, and plan), and may suggest clinical codes or flag items for follow-up.

The distinction matters for several reasons. A passive transcription tool carries fewer regulatory implications than one that interprets clinical content or supports clinical decisions. The Veterinary Innovation Council notes that practice managers and clinicians should understand exactly which category a product falls into before assessing its risks. A tool that generates a structured note from ambient audio is doing something qualitatively different from one that simply produces a raw transcript, and the governance questions that follow differ accordingly.

Practice managers should also be aware that the accuracy of these tools with veterinary-specific content is not guaranteed. The Veterinary Innovation Council specifically recommends testing systems with species- and condition-specific terminology and pharmacological nomenclature before deployment, rather than relying on vendor demonstrations using curated examples. A 2026 buyer's guide from VetGeni recommends testing with complex, multi-problem cases rather than simple wellness visits, to expose how the tool handles ambiguity.

Data residency: where is consultation data being processed and stored?

Data residency refers to the physical and jurisdictional location where data is processed and stored. In this context, that means audio recordings of consultations, transcripts, and the clinical notes generated from them. For European practices, this is not a technical detail; it is a compliance question.

Under the General Data Protection Regulation (GDPR), personal data relating to EU residents must be handled in accordance with the regulation's requirements, regardless of where the vendor is headquartered. If a vendor routes audio or text data through servers located outside the European Economic Area, for example in the United States, that transfer must be governed by an appropriate legal mechanism, such as Standard Contractual Clauses. Some EU member states impose additional national data protection requirements on top of the GDPR baseline, which may affect which transfer mechanisms are acceptable.

Practice managers should ask vendors specifically:

• Whether audio recordings are processed on-device, within the EU, or on servers in third-party countries

• Which legal mechanism governs any international data transfers

• Whether the vendor can provide documentation confirming the location of all processing infrastructure

The AAVSB's 2025 regulatory white paper on AI in veterinary medicine identifies data storage and confidentiality as a core area of regulatory concern, noting that licensees must actively safeguard client data privacy, an obligation that cannot be delegated to a vendor by assumption.

GDPR and data protection obligations specific to veterinary practice

A common misconception is that GDPR does not apply to veterinary records because they concern animals rather than human patients. In practice, veterinary consultation records routinely contain client personal data, including names, contact details, address information, and payment records, and GDPR applies to all of this.

Before deploying any AI documentation tool, practices need to:

• Identify the lawful basis for processing client personal data through the tool. In most cases this will be the performance of a contract (the veterinary services agreement with the client) or legitimate interests, but practices should assess and document this explicitly.

• Execute a Data Processing Agreement with the AI vendor. Where a vendor processes personal data on behalf of the practice, they act as a data processor under GDPR, and a Data Processing Agreement is a legal requirement, not an optional formality. It should specify what data is processed, for what purpose, for how long it is retained, and what happens to it on contract termination.

• Not assume compliance because a vendor markets the product as "GDPR-ready." As the AAVSB white paper notes, responsibility for compliance remains with the practice, not the vendor.

Practices should also consider whether their existing privacy notice adequately describes the use of AI tools to process client data. If it does not, an update is required before deployment.

How client consent should be handled when AI is active during a consultation

The question of what to tell clients before using an AI documentation tool during a consultation involves both legal and practical dimensions. Under GDPR, transparency is a core principle: individuals whose data is being processed should be informed of that fact, the purpose, and who is involved. Practices typically meet this obligation through a privacy notice rather than by seeking explicit consent for each consultation.

Transparency and consent are distinct concepts. The AAVSB guidance recommends that practices obtain informed consent where appropriate, and that they maintain full transparency regarding AI use. In practice, this means:

• Informing clients that an AI tool is active during the consultation, either verbally at the start of the appointment or through a notice displayed in the waiting area or on a consent form

• Offering clients a clear and easy way to opt out if they do not wish their consultation to be processed through the tool

• Documenting the practice's chosen approach within its data protection policy so that the decision is auditable

Whether verbal notice is sufficient or written consent is preferable will depend on the nature of the data being processed, the sensitivity of the consultation, and any specific requirements under national law. Practices operating across multiple jurisdictions, or those that handle particularly sensitive cases, should take legal advice on their specific position. The approach should be deliberate and documented, not improvised.

Compatibility with your existing veterinary practice management system

An AI documentation tool that cannot connect reliably to the practice's existing practice management software creates more administrative work, not less. Before evaluating any product, practice managers should establish a clear picture of their current medical record system environment and what integration would actually require.

Key questions to put to vendors include:

• Which veterinary practice management systems does the tool natively integrate with, and what does "integration" mean in practice: a direct application programming interface connection, a copy-paste workflow, or something in between?

• Does the tool produce structured data outputs (such as clinical codes) in a format the existing system can ingest, or does it generate free-text notes that require manual re-entry?

• What happens to data generated in the AI tool if the integration fails or the contract ends?

A 2025 review published in the Journal of the American Veterinary Medical Association found that the availability of accurate data in the form of curated medical records is a major limiting factor for AI implementation in veterinary practice. This cuts in both directions: poor data quality limits what AI tools can do, and AI tools that do not integrate cleanly with existing systems risk degrading the quality of the records they are supposed to improve.

Data standardisation is a related concern. Research into the Vertebrate Breed Ontology illustrates that limited universal data standards in veterinary medicine continue to hinder interoperability. Practice managers should ask whether the tool's outputs are structured in a way that supports long-term data usability, not just immediate note generation.

Medical device classification: is the tool regulated, and should it be?

In the European Union, the Medical Device Regulation (MDR) applies to software intended for a medical purpose, including software that supports clinical decision-making. AI tools that move beyond transcription into interpreting clinical content, suggesting diagnoses, or flagging abnormal findings may fall within MDR scope, depending on their intended purpose and how vendors market them.

The American Association of Veterinary State Boards (AAVSB) notes that federal premarket approval is rare for veterinary AI tools, and that many commonly used products do not undergo the same regulatory scrutiny as human medical devices. The same pattern applies in the EU context: the absence of MDR classification does not necessarily mean a product is exempt. It may simply mean the question has not been formally addressed.

Practice managers should ask vendors directly:

• Has the product been assessed for MDR classification in the EU?

• If it has been classified as a medical device, what evidence of conformity exists and where is the CE marking documentation?

• If it has been assessed and determined to be out of scope, on what basis was that determination made?

A 2026 peer-reviewed audit in Frontiers in Veterinary Science found widespread gaps in transparency and validation disclosure among commercial veterinary AI vendors, using a 25-point scorecard adapted from established regulatory frameworks. Practice managers should not assume vendors have conducted this analysis. They should ask for evidence.

Data security: what certifications and controls should you expect from a vendor?

Data security is consistently among the top concerns for veterinary professionals considering AI adoption. A survey cited by CompanAIn found that 53.9 per cent of veterinary professionals identified data security and privacy as a significant concern when evaluating AI tools. Requesting a vendor's security documentation is a routine and reasonable step in any procurement process.

A reputable AI documentation vendor should be able to provide evidence of:

• ISO 27001 certification, the internationally recognised standard for information security management systems

• Encryption standards for data both in transit (between the practice and the vendor's servers) and at rest (where data is stored)

• Access controls specifying who within the vendor organisation can access practice data, and under what conditions

• Breach notification procedures that comply with GDPR's 72-hour notification requirement to the relevant supervisory authority

• Penetration testing results or third-party security audit reports

Practices should also ask whether the vendor uses client data to train or improve its AI models, and if so, whether clients and practices have the right to opt out of this use. This is a data protection question as much as a security one, and the answer should be clearly set out in the Data Processing Agreement.

Staff training requirements and adoption realities

The CompanAIn survey data indicates that 42.9 per cent of veterinary professionals identify a training deficit as a barrier to AI adoption. This reflects a real implementation challenge: even well-designed tools require time and structured support before they become embedded in clinical workflows.

Practice managers should set realistic expectations about the onboarding period. The staff roles most directly affected by an AI documentation tool include:

• Veterinarians and veterinary surgeons, who need to understand how to interact with the tool during consultations, how to review and edit generated notes, and how to recognise errors in AI output

• Veterinary nurses, who may be involved in documentation workflows and need to understand what the tool does and does not capture

• Receptionists and administrative staff, who may need to manage client-facing communications about the tool's use

A framework published in the Journal of the American Veterinary Medical Association identifies staff training as a critical component of safe AI deployment, alongside workflow integration and performance monitoring. Practice managers should ask vendors what onboarding support is included in the contract, specifically whether training is provided by the vendor or expected to be self-directed, and whether ongoing support is available as staff turnover occurs.

UC Davis School of Veterinary Medicine offers a useful model for structured evaluation: when it adopted an AI assistant platform in late 2025, it formed a formal task force to assess its use over several months. Smaller practices may not have the same resources, but a defined pilot period with clear evaluation criteria is a proportionate equivalent.

Accuracy, clinical oversight, and who is responsible for the final note

The governance question most frequently overlooked in early AI adoption discussions is also the most consequential: who is responsible for the accuracy of an AI-generated clinical note?

The answer is unambiguous. As Veterinary Business Advisors states, the veterinarian remains responsible for reviewing, editing, and approving all documentation before it is finalised, and for the diagnoses and treatment plans it contains. The AAVSB white paper reinforces this: licensees must understand the risks and limitations of AI to protect the standard of patient care, and responsibility remains with the clinician regardless of how the note was generated.

This has practical implications for how practices structure their workflows:

• A clear review and sign-off step should be built into the documentation process before any AI-generated note becomes part of the official record

• Clinicians should treat AI-generated notes as a draft requiring verification, not a finished product requiring only a signature

• The risk of automation bias, where clinicians over-trust automated outputs without adequate scrutiny, should be explicitly addressed in training

The Veterinary Innovation Council notes that most AI products have no independent scientific evaluation, and that discrepancies between promotional claims and real-world performance are common. This does not mean the tools are without value, but it does mean that clinical oversight is not a formality. It is the mechanism by which errors are caught before they affect patient care or create legal exposure.

Poor documentation carries its own risks. As Veterinary Business Advisors notes, when veterinarians rely on memory or improperly recorded notes, crucial details can be missed, affecting continued care and creating legal risk. AI tools can reduce that risk, but only if the notes they generate are reviewed rather than accepted uncritically.

Questions to ask a vendor before signing a contract

The following questions represent the minimum a practice manager should put to any AI documentation vendor during evaluation.

Data handling and storage

• Where is consultation data processed and stored: on-device, within the EU, or in third-party countries?

• What legal mechanism governs any international data transfers?

• Is client or practice data used to train or improve the AI model, and can this be opted out of?

• What is the data retention period, and what happens to data when the contract ends?

Security

• Does the vendor hold ISO 27001 certification, and can documentation be provided?

• What encryption standards apply to data in transit and at rest?

• What are the vendor's breach notification procedures?

Integration

• Which veterinary practice management systems does the tool integrate with natively?

• What format do structured outputs take, and are they compatible with the practice's existing system?

• What happens to data generated in the tool if the integration fails?

Regulatory status

• Has the product been assessed for MDR classification in the EU? What was the outcome and the basis for it?

• Is a Data Processing Agreement available, and what does it cover?

Training and support

• What onboarding is included in the contract?

• Is ongoing support available, and at what cost?

Contractual terms

• What audit trail access does the practice have to its own data?

• What are the terms for data deletion on contract termination?

• What liability does the vendor accept if the tool generates an inaccurate clinical note?

A considered adoption is a safer adoption

The pressure to adopt AI documentation tools quickly is real. Administrative burden remains a significant concern in veterinary practice, and the case for tools that reduce documentation burden is well-founded. The speed of the market does not reduce the complexity of the evaluation.

A structured due diligence process, covering data residency, GDPR compliance, client transparency, practice management system integration, regulatory classification, security standards, staff training, and clinical oversight, protects the practice, its clients, and the clinicians who will use the tool every day. It also puts practice managers in a stronger negotiating position with vendors, and provides a documented basis for the decisions made.

The AAVSB frames this clearly: as AI becomes more widely adopted in veterinary medicine, the need for clear guidance on responsible use grows, and responsibility for that responsible use sits with the practice, not the platform. Treating vendor due diligence as an investment in long-term confidence, rather than an obstacle to progress, is the approach most likely to result in adoption that works.

Frequently asked questions

▶ What does an AI documentation tool actually do in a veterinary practice?

AI documentation tools range from basic real-time transcription services, which convert spoken consultation audio into text for a clinician to edit, to more sophisticated AI assistants that interpret what is said, structure the output into a clinical note, and may suggest clinical codes or flag follow-up items. The distinction matters because a tool that generates a structured note from ambient audio carries different regulatory and governance implications than one that simply produces a raw transcript. Practice managers should establish exactly which category a product falls into before assessing its risks.

▶ Does GDPR apply to veterinary practices using AI documentation tools?

Yes. A common misconception is that GDPR doesn't apply to veterinary records because they concern animals rather than human patients. In practice, veterinary consultation records routinely contain client personal data, including names, contact details, address information, and payment records, and GDPR applies to all of it. Practices must identify a lawful basis for processing that data through an AI tool, execute a Data Processing Agreement with the vendor, and ensure their existing privacy notice describes the use of AI tools to process client data. Responsibility for compliance remains with the practice, not the vendor.

▶ Where should consultation data be processed and stored, and why does it matter?

Data residency refers to the physical and jurisdictional location where data is processed and stored. For European practices, this is a compliance question, not a technical detail. Under GDPR, if a vendor routes audio or text data through servers outside the European Economic Area, that transfer must be governed by an appropriate legal mechanism, such as Standard Contractual Clauses. Practice managers should ask vendors whether audio is processed on-device, within the EU, or on servers in third-party countries, and request documentation confirming the location of all processing infrastructure.

▶ What should practices tell clients when an AI tool is active during a consultation?

Transparency is a core principle under GDPR. Practices should inform clients that an AI tool is active during the consultation, either verbally at the start of the appointment or through a notice in the waiting area or on a consent form. Clients should also have a clear and easy way to opt out if they don't wish their consultation to be processed through the tool. The approach should be deliberate and documented within the practice's data protection policy, not improvised. Whether verbal notice is sufficient or written consent is preferable will depend on the nature of the data and any specific national law requirements.

▶ How should practice managers evaluate whether an AI tool integrates with their existing practice management software?

Practice managers should establish a clear picture of their current medical record system environment before evaluating any product. Key questions for vendors include which practice management systems the tool natively integrates with, what "integration" means in practice (a direct application programming interface connection, a copy-paste workflow, or something in between), and whether the tool produces structured data outputs in a format the existing system can ingest. Practices should also ask what happens to data generated in the AI tool if the integration fails or the contract ends.

▶ Could an AI documentation tool be classified as a medical device under EU law?

Possibly. Under the Medical Device Regulation, software intended for a medical purpose, including software that supports clinical decision-making, may fall within its scope. AI tools that move beyond transcription into interpreting clinical content, suggesting diagnoses, or flagging abnormal findings may require classification, depending on their intended purpose and how vendors market them. The absence of a classification doesn't mean a product is exempt. Practice managers should ask vendors whether the product has been assessed for Medical Device Regulation classification, and if so, what the outcome was and on what basis that determination was made.

▶ What security certifications and controls should a reputable AI documentation vendor provide?

A reputable vendor should be able to provide evidence of ISO 27001 certification (the internationally recognised standard for information security management systems), encryption standards for data both in transit and at rest, access controls specifying who within the vendor organisation can access practice data, breach notification procedures that comply with GDPR's 72-hour notification requirement, and penetration testing results or third-party security audit reports. Practices should also ask whether the vendor uses client data to train or improve its AI models, and whether practices and clients have the right to opt out of that use.

▶ Who is responsible for the accuracy of an AI-generated clinical note?

The veterinarian remains responsible for reviewing, editing, and approving all documentation before it's finalised, and for the diagnoses and treatment plans it contains. The American Association of Veterinary State Boards confirms that responsibility remains with the clinician regardless of how the note was generated. Clinicians should treat AI-generated notes as a draft requiring verification, not a finished product requiring only a signature. Practices should also address the risk of automation bias, where clinicians over-trust automated outputs without adequate scrutiny, explicitly in staff training.

▶ What training do staff need before a practice deploys an AI documentation tool?

Survey data indicates that 42.9 per cent of veterinary professionals identify a training deficit as a barrier to AI adoption. Veterinarians and veterinary surgeons need to understand how to interact with the tool during consultations, how to review and edit generated notes, and how to recognise errors in AI output. Veterinary nurses involved in documentation workflows need to understand what the tool does and doesn't capture. Receptionists and administrative staff may need to manage client-facing communications about the tool's use. Practice managers should ask vendors what onboarding support is included in the contract and whether ongoing support is available as staff turnover occurs.

▶ How should practice managers test an AI documentation tool before committing to it?

The Veterinary Innovation Council recommends testing systems with species- and condition-specific terminology and pharmacological nomenclature before deployment, rather than relying on vendor demonstrations using curated examples. A 2026 buyer's guide recommends testing with complex, multi-problem cases rather than simple wellness visits, to expose how the tool handles ambiguity. A defined pilot period with clear evaluation criteria is a proportionate approach for most practices. UC Davis School of Veterinary Medicine, when it adopted an AI assistant platform in late 2025, formed a formal task force to assess its use over several months.