실험실 정보 관리 시스템(LIMS)용 AI 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the aI in Laboratory Information Management Systems (LIMS) Market size is expected to grow from USD 350 million in 2025 to USD 406.88 million in 2026 and is forecast to reach USD 863.83 million by 2031 at 16.25% CAGR over 2026-2031.

This report is Segmented by AI Capability (Predictive Analytics, Anomaly Detection, Workflow Automation, and More), Component (Platform Software, AI Modules, Services), Deployment (On-Premise, Private Cloud, Saas, Hybrid), Lab Type (Pharma/Biotech, CRO/CDMO, Diagnostics, Biobanks, Academic), and Geography (North America, Europe, and More). Forecasts are in Value (USD).

Global AI In Laboratory Information Management Systems (LIMS) Market Trends and Insights

Embedded AI Copilots for Review-by-Exception and Analyst Productivity

The AI in LIMS market is seeing its fastest near-term pull from copilots that move scientists and analysts away from repetitive review and toward exception handling. That matters because high-volume quality control laboratories still spend large amounts of time searching records, checking deviations, and moving across separate informatics tools. Sapio Sciences integrated Anthropic's Claude Cowork into the Sapio Platform in April 2026, which gave scientists one conversational interface to query, analyze, and act on LIMS and ELN data with traceability and user attribution.

LigoLab also states that its AI agent roadmap supports natural language interaction for laboratory operations, which shows that plain-language access is becoming part of the product baseline rather than a premium add-on. As the copilot layer spreads across the AI in LIMS market, the advantage is likely to shift toward vendors that pair language interfaces with structured laboratory context, auditability, and actionability inside validated workflows. This is also changing buyer expectations, because laboratories no longer want AI that only summarizes records, they want AI that can retrieve the right context and support compliant action inside routine work.

Growing Multi-Omics and High-Throughput Data Complexity

The market is also being pushed forward by the growing volume and diversity of multi-omics and high-throughput laboratory data. Classical LIMS architectures were built to track samples and methods, but they were not designed to harmonize transcriptomic, proteomic, epigenomic, and sequencing outputs at the scale now being generated by national and enterprise research programs. Illumina launched Connected Multiomics v1.1 in February 2026 with AI-guided workflow suggestions learned from expert-created pipelines, which reduced the need for deep bioinformatics knowledge when designing integrated analyses.

Sapio Sciences and Ultima Genomics formed a partnership in September 2025 to combine ultra-high-throughput sequencing with AI-driven LIMS workflows, which reflects growing demand for scalable and traceable multi-omics execution. A 2025 paper in Quantitative Biology found that normalization, imputation, and cross-modality harmonization remain the central barriers in multi-omics integration, which aligns well with the parts of the AI in LIMS market that focus on embedded analytical assistance. Singapore's PRECISE-SG100K program also reported nearly 50,000 whole-genome sequences through a LIMS-mediated cloud pipeline, with AI-driven quality control helping reduce turnaround time by more than 3-fold after workflow optimization.

GxP Validation Burden for AI-Enabled Workflows

The market continues to face resistance from the validation burden tied to regulated laboratory software. Laboratories in pharmaceutical manufacturing and related settings cannot treat AI functions like ordinary user-interface upgrades because these functions can influence review, exception handling, stability interpretation, and release support. The main difficulty is that adaptive or model-driven behavior does not fit as neatly into legacy validation routines that were built for static and deterministic software. That creates slower approval cycles, more documentation work, and more caution from quality teams that do not yet have mature internal governance for AI oversight. The result is that the AI in LIMS market often advances first in lower-risk workflows, while more critical use cases move more slowly into production. This effect is strongest in risk-averse buyers, especially where compliance teams want clearer rules on model monitoring, revalidation triggers, and sustained human review before scaling beyond limited deployments.

Other drivers and restraints analyzed in the detailed report include:

1. Smart Lab Automation and Closed-Loop Workflow Orchestration
2. Predictive Quality Monitoring and Compliance Automation
3. Legacy LIMS, LIS, and Instrument Integration Debt

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Predictive Analytics and Forecasting held 35.16% of the AI in LIMS market share in 2025, which made it the largest capability segment by a clear margin. Its lead reflects the fact that quality control trending, stability modeling, and instrument failure prediction already map to measurable laboratory outcomes such as fewer disruptions and faster review. The AI in LIMS market has favored this capability because supervised models perform well when data histories are large and prediction targets are clearly defined. Waters showed in 2025 that telemetry-based forecasting can shift LC-MS maintenance from reactive interventions to planned action, which supports both uptime and resource efficiency. That makes predictive use cases easier to justify than more open-ended generative use cases, especially in regulated settings where credibility and traceability matter.

Intelligent Workflow Automation and Orchestration is the fastest-growing capability in the AI in LIMS market, with an 18.88% CAGR projected through 2031. Growth is being driven by rising interest in agentic systems that can coordinate steps across LIMS, ELN, instruments, and connected lab devices rather than only generate alerts or summaries. LabVantage Cortex illustrates this direction by embedding agentic AI into the LIMS operating layer so that tasks such as worksheet support, sample management, stability study coordination, and automated monitoring can happen within one platform context. The broader AI in LIMS market is also seeing continued growth in anomaly detection, knowledge retrieval, semantic search, and copilot functions because scientific teams want faster access to context across expanding data estates. Over time, capability selection is likely to favor tools that can combine interpretable prediction, workflow action, and governed deployment rather than offering isolated AI features without operational depth.

AI-Enabled LIMS Platform Software accounted for 65.17% of the AI in LIMS market size in 2025, which shows that buyers still place the highest value on upgrading the core informatics layer. This pattern suggests that AI is being purchased less as a separate bolt-on and more as part of a broader platform refresh that expands contract value and vendor lock-in. In the AI in LIMS industry, that favors suppliers that already control sample records, audit trails, workflow logic, and user permissions because those assets shape how effectively AI can be embedded. Sapio Sciences positioned its platform around conversational interaction across laboratory data, while LabVantage introduced a cloud-native platform that embeds agentic functions into core laboratory operations. Those moves reinforce the idea that platform ownership remains central to long-term value capture.

Services is forecast to expand at 17.12% CAGR through 2031, which makes it the fastest-growing component in the AI in LIMS market. That growth reflects the practical reality that implementation, validation, workflow redesign, and ongoing model governance still require specialist support that many laboratories do not have internally. Service demand is especially strong when organizations are deploying across multiple sites, linking instruments and upstream systems, or trying to maintain clear compliance evidence during rollout. The AI in LIMS market therefore does not behave like a simple software scaling story, because operational success still depends on data preparation, qualification work, user training, and post-deployment oversight. Models, copilots, and analytics modules are gaining traction, but they remain constrained when customers lack clean data structures, validated integration patterns, or clear rules for where AI may influence regulated actions.

Geography Analysis

North America held 38.18% of the AI in LIMS market share in 2025, which kept it as the largest regional contributor. The region benefits from a dense concentration of pharmaceutical manufacturers, CROs, large genomics programs, and established laboratory informatics suppliers. The AI in LIMS market is especially deep in the United States because buyers there combine strong spending capacity with strict expectations around quality, auditability, and workflow control. Large multi-site laboratory networks in the United States and Canada are also pushing standardization projects that make AI-driven harmonization more valuable across distributed testing environments. These factors continue to support the region's lead even as deployment still moves carefully in the most regulated use cases.

Europe remained a significant part of the AI in LIMS market, led by Germany, the United Kingdom, and France. Regional demand is shaped by the need to satisfy both regulated pharmaceutical data controls and strong data protection requirements, which favors architectures with clear residency and governance options. LDB Labordatenbank highlights EU-resident AI model choices, while dialog EDV promotes laboratory software aligned with secure and structured deployment needs, which reflects how local compliance preferences influence vendor positioning. The AI in LIMS market in Europe therefore rewards suppliers that can balance AI functionality with infrastructure confidence, documentation discipline, and lower-friction validation. That balance should keep the region commercially important even if adoption remains more measured than in less regulated research settings.

Asia-Pacific is the fastest-growing geography in the AI in LIMS market, with a 17.36% CAGR projected through 2031. Growth is being supported by pharmaceutical expansion in India, genomics investment in China and South Korea, and precision medicine and automation initiatives in Japan and Australia. Shimadzu's Autonomous Labo work in Japan shows how the region is not only adopting AI-enabled laboratory software but also building tighter links among instruments, robotics, and optimization workflows. South America and the Middle East and Africa are still earlier in the adoption cycle, yet both regions are seeing incremental demand from pharmaceutical manufacturing growth, clinical trial expansion, and healthcare digitalization programs. Even with a smaller current base, these regions add long-term opportunity for the AI in LIMS market where laboratory modernization agendas align with stronger digital infrastructure.

1. Agaram Technologies
2. Agilent Technologies
3. Benchling
4. Clinisys
5. CloudLIMS
6. Dassault Systemes BIOVIA
7. Dotmatics
8. eLabNext
9. Illumina
10. L7 Informatics
11. Labguru
12. LabLynx
13. LabVantage Solutions
14. LabWare
15. QBench
16. Revvity Signals
17. Sapio Sciences
18. Scispot
19. STARLIMS Corporation
20. Thermo Fisher Scientific

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions & Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Embedded AI Copilots for Review-By-Exception and Analyst Productivity
  • 4.2.2 Cloud-Native LIMS Modernization for AI-Ready Data Workflows
  • 4.2.3 Growing Multi-Omics and High-Throughput Data Complexity
  • 4.2.4 Smart Lab Automation and Closed-Loop Workflow Orchestration
  • 4.2.5 Predictive Quality Monitoring and Compliance Automation
  • 4.2.6 Multi-Site Standardization Across Pharma, CRO, and Diagnostics Networks
• 4.3 Market Restraints
  • 4.3.1 GxP Validation Burden for AI-Enabled Workflows
  • 4.3.2 Legacy LIMS, LIS, and Instrument Integration Debt
  • 4.3.3 Weak Metadata Provenance and Model Drift Risk
  • 4.3.4 Restricted Use of Adaptive and Generative AI in Regulated Decisions
• 4.4 Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 Market Size & Growth Forecasts (Value, USD)

• 5.1 By AI Capability
  • 5.1.1 Predictive Analytics and Forecasting
  • 5.1.2 Anomaly Detection and Review-by-Exception
  • 5.1.3 Generative AI Copilots and Natural Language Assistance
  • 5.1.4 Intelligent Workflow Automation and Orchestration
  • 5.1.5 Knowledge Retrieval and Semantic Search
  • 5.1.6 Quality Signal Detection and Risk Scoring
• 5.2 By Component
  • 5.2.1 AI-Enabled LIMS Platform Software
  • 5.2.2 AI Models, Copilots, and Analytics Modules
  • 5.2.3 Services
• 5.3 By Deployment Model
  • 5.3.1 On-Premise
  • 5.3.2 Private Cloud / Single-Tenant
  • 5.3.3 Public Cloud / Multi-Tenant SaaS
  • 5.3.4 Hybrid
• 5.4 By Laboratory Type
  • 5.4.1 Pharmaceutical and Biotechnology Laboratories
  • 5.4.2 CROs and CDMOs
  • 5.4.3 Clinical Diagnostics and Molecular Laboratories
  • 5.4.4 Biobanks and Genomics Laboratories
  • 5.4.5 Academic and Translational Research Laboratories
  • 5.4.6 Other Laboratories
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Spain
    • 5.5.2.6 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 India
    • 5.5.3.3 Japan
    • 5.5.3.4 South Korea
    • 5.5.3.5 Australia
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 Middle East and Africa
    • 5.5.4.1 GCC
    • 5.5.4.2 South Africa
    • 5.5.4.3 Rest of Middle East and Africa
  • 5.5.5 South America
    • 5.5.5.1 Brazil
    • 5.5.5.2 Argentina
    • 5.5.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles {(includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products & Services, and Recent Developments)}
  • 6.3.1 Agaram Technologies
  • 6.3.2 Agilent Technologies
  • 6.3.3 Benchling
  • 6.3.4 Clinisys
  • 6.3.5 CloudLIMS
  • 6.3.6 Dassault Systemes BIOVIA
  • 6.3.7 Dotmatics
  • 6.3.8 eLabNext
  • 6.3.9 Illumina
  • 6.3.10 L7 Informatics
  • 6.3.11 Labguru
  • 6.3.12 LabLynx
  • 6.3.13 LabVantage Solutions
  • 6.3.14 LabWare
  • 6.3.15 QBench
  • 6.3.16 Revvity Signals
  • 6.3.17 Sapio Sciences
  • 6.3.18 Scispot
  • 6.3.19 STARLIMS Corporation
  • 6.3.20 Thermo Fisher Scientific

7 Market Opportunities & Future Outlook

• 7.1 White-space & unmet-need assessment