The US pet care market is projected to reach $300.8 billion by the mid-decade mark, while the global pet care market grows at a 1.5% CAGR through 2030. AI use cases can still scale because clinical and owner data pipelines already exist. Ninety percent of veterinary clinicians use electronic medical records, and automated radiograph systems report 0.87 sensitivity and 0.90 specificity for abnormality detection.
Key Takeaways
- 1.5% CAGR (2024–2030) projected growth rate for the Global Pet Care Market, indicating slow-to-moderate category growth that can constrain AI spend scaling
- $221.95 billion global pet care market size in 2023, providing the scale where AI-enabled veterinary and pet services can compete
- $300.8 billion projected US pet care market size in 2025, reflecting the revenue pool for AI-driven offerings in the US
- 90% of veterinary clinicians reported using electronic medical records (EMR) in practice settings, increasing the feasibility of AI analytics over clinical data
- 15% of veterinary practices adopted telemedicine in 2022, expanding the demand for AI-supported triage and remote monitoring
- 23% of pet owners reported using tele-vet services in 2023, suggesting adoption readiness for AI-enhanced remote care
- In the US, about 6.1 million households use pet health insurance (2023), which supports AI use cases like claim automation and risk scoring
- 2.2 million veterinary visits per day occur in the US, creating a high-throughput environment for AI triage and documentation
- 27% of surveyed enterprises reported using generative AI for customer service in 2024, mapping directly to pet-owner chat and support use cases
- Accuracy of 94% reported for an automated gait/behavior classification model in dogs using wearable/vision inputs, supporting AI-driven activity monitoring
- Sensitivity of 0.87 and specificity of 0.90 achieved by an ML system for detecting abnormalities in veterinary radiographs, supporting diagnostic support claims
- F1-score of 0.86 reached by an NLP system extracting veterinary clinical information from unstructured notes, enabling automation of documentation
- Training time reduced by 60% using transfer learning in veterinary computer vision tasks, improving cost and speed of deploying models for pet care
- Inference cost reduced by 45% through model quantization in a deep-learning pipeline, helping lower per-usage costs for pet imaging AI
- 92% of organizations say they are concerned about AI model risk, increasing governance and compliance overhead for AI in pet care
AI in pet care is gaining momentum with strong model accuracy, telehealth adoption, and near term ROI drivers despite slow market growth.
Related reading
01 · Category
Market Size6 stats
01
1.5% CAGR (2024–2030) projected growth rate for the Global Pet Care Market, indicating slow-to-moderate category growth that can constrain AI spend scaling[1]
02
$221.95 billion global pet care market size in 2023, providing the scale where AI-enabled veterinary and pet services can compete[1]
03
$300.8 billion projected US pet care market size in 2025, reflecting the revenue pool for AI-driven offerings in the US[2]
04
$1.15 billion global pet wearables market size in 2023, defining a spend envelope for AI features in collars and monitoring devices[3]
05
The global digital therapeutics market is projected to grow from $4.4 billion in 2022 to $27.7 billion by 2030 (market forecast), indicating broader healthcare AI/software budget tailwinds that can extend to animal health[4]
06
The global AI in healthcare market is expected to reach $188.1 billion by 2030 (market forecast), supporting investment momentum that often transfers into vet-tech diagnostics and decision support[5]
Interpretation
Market Size Interpretation
With the global pet care market at $221.95 billion in 2023 and projected to grow just 1.5% CAGR from 2024 to 2030, the market size for AI in pet care looks substantial but slow-growing, meaning AI-enabled veterinary and pet services will likely need to capture share rather than rely on fast category expansion.
02 · Category
User Adoption5 stats
01
90% of veterinary clinicians reported using electronic medical records (EMR) in practice settings, increasing the feasibility of AI analytics over clinical data[6]
02
15% of veterinary practices adopted telemedicine in 2022, expanding the demand for AI-supported triage and remote monitoring[7]
03
23% of pet owners reported using tele-vet services in 2023, suggesting adoption readiness for AI-enhanced remote care[2]
04
35% of dog owners use wearable activity trackers for pets (US survey 2024), supporting AI analytics on motion and health signals[3]
05
4.8 million US households bought pet food online in 2023 (US online penetration), enabling AI product recommender systems[2]
Interpretation
User Adoption Interpretation
With 90% of veterinary clinicians using EMRs and growing consumer uptake such as 23% of pet owners using tele-vet services and 4.8 million US households buying pet food online in 2023, user adoption is clearly expanding fast enough to support AI-driven tools for remote care, smarter triage, and personalized recommendations.
03 · Category
Industry Trends9 stats
01
In the US, about 6.1 million households use pet health insurance (2023), which supports AI use cases like claim automation and risk scoring[8]
02
2.2 million veterinary visits per day occur in the US, creating a high-throughput environment for AI triage and documentation[6]
03
27% of surveyed enterprises reported using generative AI for customer service in 2024, mapping directly to pet-owner chat and support use cases[9]
04
25% of customer service operations are expected to be powered by chatbots by 2027, indicating near-term scaling of conversational AI in pet care support[9]
05
62% of pet owners prefer personalized recommendations for pet products, increasing ROI potential for AI recommendation engines[10]
06
14.4% of veterinarian visits in the US are conducted via telehealth (2021), indicating meaningful existing workflow penetration for AI triage and remote monitoring[7]
07
AI systems in veterinary settings are being adopted for imaging support at multiple stages of care, with peer-reviewed evidence showing AI can improve detection consistency for selected conditions (systematic review, 2023)[11]
08
Veterinary clinical notes are commonly unstructured: structured data availability is limited in routine practice, with prior work reporting that electronic free-text notes contain a majority of clinically relevant observations (review, 2022)[7]
09
Total US pet healthcare spending reached $137 billion in 2023 (APPA), creating a large near-term market for AI-enabled veterinary and wellness services[12]
Interpretation
Industry Trends Interpretation
With 27% of enterprises already using generative AI for customer service in 2024 and 25% of operations expected to be chatbot powered by 2027, the industry trends clearly show that conversational AI is rapidly moving from experimentation to core pet-owner support at scale.
More related reading
04 · Category
Performance Metrics8 stats
01
Accuracy of 94% reported for an automated gait/behavior classification model in dogs using wearable/vision inputs, supporting AI-driven activity monitoring[13]
02
Sensitivity of 0.87 and specificity of 0.90 achieved by an ML system for detecting abnormalities in veterinary radiographs, supporting diagnostic support claims[14]
03
F1-score of 0.86 reached by an NLP system extracting veterinary clinical information from unstructured notes, enabling automation of documentation[11]
04
BLEU score of 32.1 reported for a text-generation model producing veterinary discharge summaries from structured inputs, a measurable language quality metric[15]
05
Lowering veterinary imaging time from 20 minutes to 12 minutes via AI-assisted triage (33% reduction) is reported in a clinical workflow study, improving throughput[11]
06
0.78 Cohen’s kappa agreement reported between AI and expert annotators in animal welfare scoring, indicating reliability for semi-automated pet monitoring[11]
07
A 2021 study reported that NLP extraction of clinical entities from veterinary notes achieved F1-scores between 0.70 and 0.92 depending on entity type, supporting documentation automation potential[16]
08
A 2020 study using ML for veterinary radiograph interpretation reported sensitivity of 0.88 and specificity of 0.91 for selected abnormality detection tasks (peer-reviewed study)[17]
Interpretation
Performance Metrics Interpretation
Across performance metrics, AI for pet care shows consistently strong evaluation results, with accuracy at 94% for gait and behavior classification, F1 at 0.86 for clinical information extraction, and radiograph detection reaching 0.87 sensitivity and 0.90 specificity, alongside workflow gains like cutting veterinary imaging time from 20 minutes to 12 minutes.
05 · Category
Cost Analysis7 stats
01
Training time reduced by 60% using transfer learning in veterinary computer vision tasks, improving cost and speed of deploying models for pet care[18]
02
Inference cost reduced by 45% through model quantization in a deep-learning pipeline, helping lower per-usage costs for pet imaging AI[18]
03
92% of organizations say they are concerned about AI model risk, increasing governance and compliance overhead for AI in pet care[9]
04
48% of organizations report measurable cost savings from AI (McKinsey Global Survey), supporting ROI expectations for pet care implementations[19]
05
$1.8 billion investment in AI-related systems reported by veterinary groups in a 2023 industry survey, indicating capital availability for AI tools[20]
06
38% reduction in fraud losses with AI-based anomaly detection in a retail benchmark study (measurable risk reduction), applicable to pet e-commerce ecosystems[21]
07
A 2021 paper on active learning for veterinary image annotation reduced labeling costs by 35% compared with random sampling while maintaining model accuracy (peer-reviewed study)[16]
Interpretation
Cost Analysis Interpretation
Cost analysis shows that AI can significantly cut deployment and operating expenses, with training time down 60% via transfer learning and inference cost down 45% through quantization, while 48% of organizations already report measurable AI-driven cost savings and 92% remain focused on model risk that can add governance overhead.
report visual · Breakdown
Adoption is rising, and the addressable market is large
High pet-owner and practice adoption signals (tele-vet, EMR use, wearables) pair with major market scale, creating strong demand potential for AI tools in pet care.
62%
62% of pet owners prefer personalized recommendations for pet products, increasing ROI potential for AI recommendation e
38%
38% reduction in fraud losses with AI-based anomaly detection in a retail benchmark study (measurable risk reduction), a
source-verifiedaxios.com · acfe.com
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Nathan Caldwell. (2026, February 13). AI In The Pet Care Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-pet-care-industry-statistics
MLA
Nathan Caldwell. "AI In The Pet Care Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-pet-care-industry-statistics.
Chicago
Nathan Caldwell. 2026. "AI In The Pet Care Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-pet-care-industry-statistics.
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