세포 및 유전자 치료 분야 AI 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI in cell and gene therapy market is expected to grow from USD 1.17 billion in 2025 to USD 1.38 billion in 2026 and is forecasted to reach USD 3.68 billion by 2031 at 21.61% CAGR over 2026-2031.

This report is Segmented by Component (Software/AI Platforms, and Services), Deployment (Cloud-Based, and Others), Therapy Type (Cell Therapy and Gene Therapy), Application (Discovery and Pre-Clinical, and Others), End User (Pharmaceutical and Biotechnology Companies, and Others), and Geography (North America, Europe, Asia-Pacific, and Others). The Market Forecasts are Provided in Terms of Value (USD).

Global AI In Cell and Gene Therapy Market Trends and Insights

Exponential Growth in High-Throughput Gene-Editing Datasets Demands AI-Driven Analytics

The AI in cell and gene therapy market is benefiting from a data expansion wave that is now as much an infrastructure issue as a scientific one. Illumina introduced the Billion Cell Atlas in January 2026 as the first release in a planned 5-billion-cell program built to support AI-driven drug discovery workflows.As sequencing throughput rises, the operational bottleneck is moving away from data generation and toward annotation quality, because models still need clinically meaningful labels to separate real therapeutic signals from background biological noise. Recursion stated in 2026 that its biology maps, developed with Roche and Genentech, are built from more than 1 trillion iPSC-derived neuronal cells, which shows how curated multi-modal data can become a durable commercial asset rather than a disposable research input. This is why the AI in cell and gene therapy market is increasingly rewarding companies that control proprietary cellular datasets, not only those that publish stronger model architectures. Over time, the data owners with the broadest and cleanest libraries are likely to hold the strongest pricing power inside the AI in cell and gene therapy market.

Increasing Big-Pharma Alliances with AI Start-Ups to Shorten CGT Development Cycles

The AI in cell and gene therapy market is also gaining from a funding environment where large pharmaceutical companies treat AI as core development infrastructure. NVIDIA and Eli Lilly announced a co-innovation AI lab in January 2026, with a commitment of up to USD 1 billion over 5 years for protein diffusion models, genomics foundation models, and manufacturing digital twins. Roche then expanded its AI factory strategy in March 2026 with more than 3,500 Blackwell GPUs across U.S. and European sites, which signals that large drug makers are building private compute capacity for time-sensitive regulated workflows instead of relying only on public cloud access. These alliances are shortening development cycles, but they are also changing competitive behavior because AI start-ups that become embedded inside sponsor workflows are harder to replace later. In practice, the AI in cell and gene therapy market is starting to resemble enterprise software, where switching costs rise after models, data pipelines, and scientific decisions are tied to one platform. That makes commercial relationships in the AI in cell and gene therapy market more durable than standard project-based outsourcing.

Fragmented, Proprietary Clinical Datasets Limit Model Generalizability

The AI in cell and gene therapy market still faces a hard limit from siloed datasets that do not transfer cleanly across donors, disease settings, or manufacturing sites. A March 2026 review in Pharmaceutics described how AI models in cell and gene therapy often struggle to generalize when they are trained within narrow sponsor-specific data environments. This means architectural progress alone will not solve the performance gap if the training data remain fragmented and proprietary. It also creates a strong first-mover advantage for any organization that can aggregate diverse multi-site and multi-sponsor manufacturing and clinical datasets into one usable framework. Federated learning can reduce some of the sharing barriers, but it also slows execution because sites often work under different governance, privacy, and operational standards. Until the AI in cell and gene therapy market has stronger interoperability rules, model performance in the lab will continue to outpace what can be deployed consistently in commercial programs.

Other drivers and restraints analyzed in the detailed report include:

1. Convergence of Single-Cell Multi-Omics with Generative AI for Potency Prediction
2. AI-Enabled Digital Twins Optimizing Bioreactor Parameters for Cell-Therapy Yields
3. Data-Privacy and Governance Concerns in Patient-Level Genomic Information

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

Segment Analysis

Software/AI platforms accounted for 48.24% of the AI in cell and gene therapy market share in 2025, which made them the largest component category. That position reflects where buyers now see the most value, since model development, workflow orchestration, and predictive analytics are treated as the main deliverables, while underlying data storage and generic compute are becoming more standardized. The AI in cell and gene therapy market is therefore assigning more value to the control layer that links experiments, data, and decision support than to basic implementation work alone. Benchling's May 2026 work with Baseten to bring GPU-scale inference into biotech R&D workflows shows how software vendors are absorbing capabilities that were previously handled by separate infrastructure providers. The same software segment is projected to expand at 22.17% CAGR through 2031, which means the largest component in the AI in cell and gene therapy market is also one of the fastest-moving.

Services still account for a meaningful share of revenue, but their role is shifting toward implementation support, validation, and regulatory advisory work tied to more complex deployments. Standard pre-clinical tasks are becoming more automated, which reduces labor intensity and slows the relative growth of services compared with software-led platforms. The AI in cell and gene therapy industry is also seeing buyers prefer repeatable platform subscriptions over one-time service engagements when they expect frequent model updates and ongoing workflow integration. Post-market surveillance and GMP quality functions still represent a smaller part of software spending than discovery does, yet that balance is likely to change as lifecycle expectations become stricter. Across the AI in cell and gene therapy market, the component mix suggests that durable value is building around integrated platforms that can hold experimental context, model outputs, and decision history in one operating environment.

Cloud-based deployment held 53.26% share in 2025, and is projected to grow at 22.38% CAGR through 2031, which makes it the fastest-growing deployment format in the AI in cell and gene therapy market. This pattern reflects the practical needs of discovery and pre-clinical teams, where access to distributed GPU infrastructure can be expanded faster than local hardware can be installed and validated. Cloud deployment also suits workloads that rise and fall across screening cycles, because organizations can scale compute without carrying all of the capital burden on site. In that sense, the AI in cell and gene therapy market is using cloud more as an operating model than only as a hosting decision. It is widening access for smaller developers that would otherwise struggle to fund advanced training and inference capacity.

Adoption remains uneven, though, because regulated manufacturing environments still require tighter control over data location, audit trails, latency, and system validation. Roche's March 2026 AI factory expansion, with large GPU clusters across U.S. and European sites, is a clear signal that major manufacturers still view private infrastructure as a strategic requirement for certain regulated workflows. On-premises and edge or hybrid models therefore remain important in the AI in cell and gene therapy market even while cloud grows faster overall. Edge and hybrid architectures are currently smaller in revenue terms, but they are well placed for future growth in commercial manufacturing because they combine local governance with selected access to external compute. Over time, the AI in cell and gene therapy market is likely to separate by function, with cloud leading early-stage experimentation and hybrid deployment gaining ground where GMP oversight is strictest.

Complete Report Scope:

• By Component
  • Software / AI platforms
  • Services
• By Deployment Mode
  • Cloud-Based
  • On-Premises
  • Edge / Hybrid
• By Therapy Type
  • Cell Therapy
  • Gene Therapy
• By Application
  • Discovery and Pre-Clinical
  • Clinical Validation
  • Commercial Manufacturing
  • Post-market Surveillance
• By End User
  • Pharmaceutical and Biotechnology Companies
  • Contract Research Organizations (CROs)
  • Contract Development and Manufacturing Organizations (CDMOs)
  • Others
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
    • Rest of Asia-Pacific
  • Middle East and Africa
    • GCC
    • South Africa
    • Rest of Middle East and Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America accounted for 51.62% of the AI in cell and gene therapy market share in 2025, which kept it as the leading regional cluster. The region benefits from strong sponsor concentration, deep venture support, and the FDA's active work on lifecycle management and risk-based validation for AI-related regulated software environments The U.S. remains the central driver because large pharmaceutical headquarters, academic cell therapy hubs, and advanced compute infrastructure are located close to one another. NVIDIA stated in 2026 that LillyPod became the world's first NVIDIA DGX SuperPOD with DGX B300 systems, giving North American developers a major compute advantage for scaled model development and deployment.

Europe remains an established part of the AI in cell and gene therapy market, supported by strong bioprocess engineering capabilities and tighter attention to data compliance. Germany stands out because equipment, process engineering, and manufacturing know-how are closely tied to therapeutic development. Sartorius announced in 2025 that it was working with NVIDIA to advance AI in drug discovery and manufacturing, which fits Europe's strength in linking instrumentation, data capture, and process insight. The United Kingdom, France, Italy, Spain, and the rest of Europe continue to add value through academic-originated AI biotech companies, specialized clinical programs, and EU-level support frameworks.

Asia-Pacific is projected to expand at 23.62% CAGR through 2031, which makes it the fastest-growing regional segment in the AI in cell and gene therapy market. China, Japan, and South Korea are the main growth centers, with stronger policy support, rising trial activity, and expanding local development ecosystems. Japan is contributing through company and academic work on manufacturing optimization and cell reprogramming, including Hitachi's platform development for high-throughput cell design. South Korea and Australia are adding regional volume through CRO growth and clinical trial activity, which broadens the operating base for the AI in cell and gene therapy market beyond the largest national players. Middle East and Africa, especially the GCC and South Africa, and South America, including Brazil and Argentina, remain smaller markets, but they are starting to build relevance through selective government-backed genomic medicine and advanced therapy programs.

1. 10x Genomics
2. Atara Biotherapeutics
3. Benchling
4. Cellarity
5. Cytiva
6. FUJIFILM
7. Ginkgo Bioworks
8. Google DeepMind
9. IBM
10. Illumina
11. Insitro
12. Lonza Group
13. Microsoft
14. NVIDIA
15. Owkin
16. PerkinElmer
17. Recursion Pharmaceuticals
18. Sartorius
19. Strateos
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 and 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 Exponential Growth in High-Throughput Gene-Editing Datasets Demands AI-Driven Analytics
  • 4.2.2 Increasing Big-Pharma Alliances with AI Start-Ups to Shorten CGT Development Cycles
  • 4.2.3 Falling Cost of Cloud GPUs Accelerating AI Adoption Across the CGT Value Chain
  • 4.2.4 Convergence of Single-Cell Multi-Omics with Generative AI For Potency Prediction
  • 4.2.5 AI-Enabled Digital Twins Optimizing Bioreactor Parameters for Cell-Therapy Yields
  • 4.2.6 Enterprise AI Platforms and GPU Access Enable Bundled, Scaled Deployments
• 4.3 Market Restraints
  • 4.3.1 Fragmented, Proprietary Clinical Datasets Limit Model Generalizability
  • 4.3.2 Data-Privacy & Governance Concerns in Patient-Level Genomic Information
  • 4.3.3 Regulatory Ambiguity in Software-as-a-Medical-Device (SAMD) AI Validation in CGT
  • 4.3.4 Supply-Chain Bottlenecks for GMP-Grade GPU Clusters
• 4.4 Value-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 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 Component
  • 5.1.1 Software / AI platforms
  • 5.1.2 Services
• 5.2 By Deployment Mode
  • 5.2.1 Cloud-Based
  • 5.2.2 On-Premises
  • 5.2.3 Edge / Hybrid
• 5.3 By Therapy Type
  • 5.3.1 Cell Therapy
  • 5.3.2 Gene Therapy
• 5.4 By Application
  • 5.4.1 Discovery and Pre-Clinical
  • 5.4.2 Clinical Validation
  • 5.4.3 Commercial Manufacturing
  • 5.4.4 Post-market Surveillance
• 5.5 By End User
  • 5.5.1 Pharmaceutical and Biotechnology Companies
  • 5.5.2 Contract Research Organizations (CROs)
  • 5.5.3 Contract Development and Manufacturing Organizations (CDMOs)
  • 5.5.4 Others
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Spain
    • 5.6.2.6 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 Japan
    • 5.6.3.3 India
    • 5.6.3.4 Australia
    • 5.6.3.5 South Korea
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 Middle East and Africa
    • 5.6.4.1 GCC
    • 5.6.4.2 South Africa
    • 5.6.4.3 Rest of Middle East and Africa
  • 5.6.5 South America
    • 5.6.5.1 Brazil
    • 5.6.5.2 Argentina
    • 5.6.5.3 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, Recent Developments)
  • 6.3.1 10x Genomics
  • 6.3.2 Atara Biotherapeutics
  • 6.3.3 Benchling
  • 6.3.4 Cellarity
  • 6.3.5 Cytiva (Danaher)
  • 6.3.6 Fujifilm Diosynth Biotechnologies
  • 6.3.7 Ginkgo Bioworks
  • 6.3.8 Google DeepMind
  • 6.3.9 IBM
  • 6.3.10 Illumina, Inc.
  • 6.3.11 Insitro
  • 6.3.12 Lonza
  • 6.3.13 Microsoft
  • 6.3.14 NVIDIA
  • 6.3.15 Owkin
  • 6.3.16 PerkinElmer, Inc.
  • 6.3.17 Recursion Pharmaceuticals
  • 6.3.18 Sartorius AG
  • 6.3.19 Strateos
  • 6.3.20 Thermo Fisher Scientific, Inc.

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment