세계의 생명공학분야 인공지능(AI) 시장(2025-2030년) : 컴포넌트, 기술, 조직 규모, 용도 분야, 최종 사용자, 배포 형태별 예측

The Artificial Intelligence in Biotechnology Market was valued at USD 3.18 billion in 2024 and is projected to grow to USD 3.65 billion in 2025, with a CAGR of 16.17%, reaching USD 7.82 billion by 2030.

Artificial Intelligence (AI) is rapidly emerging as a cornerstone of transformative change in the biotechnology sector. In recent years, AI-powered tools have evolved from experimental technologies into essential drivers of innovation, enabling researchers and companies alike to explore complex biological processes with unprecedented precision. This fusion of technology and biology is not only accelerating drug discovery and improving diagnostic accuracy, but it is also streamlining research methodologies and enhancing operational efficiencies.

The AI revolution in biotechnology is marked by the seamless integration of complex algorithms with massive datasets derived from genomics, proteomics, and clinical records. Organizations now leverage advanced machine learning models and deep learning techniques to predict outcomes, optimize processes, and reduce the traditional barriers imposed by manual research. As the industry evolves, decision-makers must contend with both the exciting potential and the inherent challenges posed by rapid technological advancements. This report offers a detailed exploration of the current landscape, highlighting the pivotal role that AI plays in redefining research, development, and commercialization within the biotech realm.

Through continuous investment in new solutions and the proactive application of AI technologies, the sector is poised to witness radical shifts in both clinical and operational paradigms. As we navigate this new era of innovation, it becomes increasingly clear that the effective integration of AI and biotechnology is no longer optional but rather a critical imperative for sustained growth and competitive advantage.

Transformative Shifts: Redefining Biotechnology with AI

Over the past few years, the biotechnology landscape has undergone dramatic and transformative shifts driven by AI advancements. Companies and research institutions are seeing a significant departure from traditional methodologies, embracing digital tools that manage and interpret large-scale biological data. AI's role spans from accelerating drug discovery processes to enhancing clinical decision-making, enabling faster turnaround times and improved patient outcomes.

This transformation can be largely attributed to breakthroughs in algorithmic approaches, where innovations in neural networks, computer vision, deep learning, and robotic process automation have redefined the analytical capabilities of biotech firms. The infusion of AI into research and development unleashes new dimensions of data intelligence, turning vast repositories of genetic, molecular, and clinical data into actionable insights. The evolution in technology is not just making processes more efficient-it is fundamentally redrawing the blueprint of biotechnology operations, from early-stage research through to post-sales service delivery.

Furthermore, the need for agility in addressing complex challenges in healthcare has accelerated the adoption of AI. Companies are now integrating multifaceted platforms that support predictive analytics, automated diagnostics, and personalized medicine strategies. Each advancement with AI represents a leap forward, paving the way for novel application areas and paradigms that were once considered speculative. This shift is redefining competitive boundaries and setting the stage for groundbreaking advancements that have the potential to transform patient care, research accuracy, and overall industry efficacy.

Key Segmentation Insights: Decoding Market Dynamics and Opportunities

The market for AI in biotechnology is dissected through a multidimensional segmentation framework that reflects the broad spectrum of technological and operational dimensions. An analysis based on component reveals that the market is studied across two primary divisions: services and solutions. In this context, the services include specialized functions such as consulting, implementation, post-sales and maintenance services, and training and education, while the solutions segment is analyzed further with an emphasis on platforms and software that drive innovative applications.

A closer look at the market through the lens of technology indicates a robust performance in areas including computer vision, deep learning, machine learning, natural language processing, neural networks, and robotic process automation. This technological diversity is complemented by segmentation based on organization size, which distinguishes between the dynamic environment of large enterprises and the agile operations of small and medium enterprises. Such segmentation further extends to application areas where biotechnology is benefiting from AI-driven improvements in agricultural biotechnology, clinical trials, diagnostics, genomics analysis, personalized medicine, precision medicine, and research and development.

Additional insights are uncovered by examining the end-user segmentation, which spans agricultural institutes, biotechnology firms, healthcare providers, pharmaceutical companies, as well as research and academic institutions that are pioneering innovation. Moreover, the deployment mode segmentation highlights the strategic importance of cloud, hybrid, and on-premises solutions. This comprehensive and nuanced segmentation framework not only elucidates underlying market trends but also helps identify areas ripe for future investment and collaboration.

Based on Component, market is studied across Services and Solutions. The Services is further studied across Consulting, Implementation, Post-Sales & Maintenance Services, and Training & Education Services. The Solutions is further studied across Platform and Software.

Based on Technology, market is studied across Computer Vision, Deep Learning, Machine Learning, Natural Language Processing, Neural Networks, and Robotic Process Automation.

Based on Organization Size, market is studied across Large Enterprises and Small & Medium Enterprises.

Based on Application Area, market is studied across Agriculture Biotechnology, Clinical Trials, Diagnostics, Genomics Analysis, Personalized Medicine, Precision Medicine, and Research & Development.

Based on End-User, market is studied across Agricultural Institutes, Biotechnology Firms, Healthcare Providers, Pharmaceutical Companies, and Research & Academic Institutions.

Based on Deployment Mode, market is studied across Cloud, Hybrid, and On-Premises.

Regional Dynamics: Spotlight on Americas, EMEA, and Asia-Pacific Markets

Regional trends play a pivotal role in shaping the landscape of AI in biotechnology. Across the Americas, innovation is driven by a mature ecosystem of research institutions and an active venture capital presence that collectively foster rapid technological adoption. Robust regulatory frameworks and intensive investments in health technology further bolster the integration of AI into biological research and healthcare applications.

The region encompassing Europe, the Middle East, and Africa (EMEA) is also witnessing significant momentum. Here, the collaborative focus on public-private partnerships and comprehensive policy initiatives supports a broad array of research and development activities, facilitating the deployment of AI solutions. Initiatives in these regions embrace a fusion of historical expertise and cutting-edge technology, thereby enabling a proactive response to both clinical and agricultural challenges.

In the Asia-Pacific region, the explosive growth of digital health innovations coupled with increased healthcare infrastructure investments is carving out a distinct competitive edge. This region is rapidly becoming a hub for technological advancements and research-driven collaborations, as governments and private players invest heavily in modernizing their biotechnology sectors. Collectively, these regional insights illustrate the varying degrees of maturity and potential across global markets, underscoring the importance of a tailored approach to capitalizing on regional strengths and addressing local challenges.

Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

Industry Leaders Shaping AI-Driven Biotechnology Innovation

A host of pioneering companies are at the forefront of leveraging AI to revolutionize biotechnology. Among these, notable innovators include Atomwise, Inc., which utilizes sophisticated algorithms for drug discovery, and BenevolentAI, Ltd., known for its groundbreaking approaches in harnessing big data. BERG Health, LLC has made significant strides in integrating AI with metabolic insights, while Cytel, Inc. and CytoReason, Ltd. are focusing on refining the analytics and modeling aspects of clinical trials.

Deep Genomics, Inc. and Envisagenics, Inc. are recognized for their contributions to genomics analysis and biomarker discovery, and Exscientia, plc is redefining drug design through precise algorithmic modeling. Genialis, Inc. and GNS Healthcare, Inc. continue to set the benchmark for computational tools that drive personalized healthcare solutions. Equally influential are Insitro, Inc. and NuMedii, Inc., whose innovative approaches are accelerating the roadmap toward precision medicine through the integration of AI in research.

Other key players such as Owkin, Inc., PathAI, Inc., and Recursion Pharmaceuticals, Inc. are expanding the capabilities of AI in diagnostics and treatment optimization. Tempus Labs, Inc. and TwoXAR Pharmaceuticals, Inc. stand out for their adept use of AI in clinical research, while Valo Health, LLC, Verge Genomics, Inc., and Zymergen, Inc. have successfully blended innovative technological platforms with traditional biotechnological processes. These companies not only exemplify leadership in their respective niches but also reflect the dynamic, competitive landscape that is increasingly driven by AI-centric innovations.

The report delves into recent significant developments in the Artificial Intelligence in Biotechnology Market, highlighting leading vendors and their innovative profiles. These include Atomwise, Inc., BenevolentAI, Ltd., BERG Health, LLC, Cytel, Inc., CytoReason, Ltd., Deep Genomics, Inc., Envisagenics, Inc., Exscientia, plc, Genialis, Inc., GNS Healthcare, Inc., Insitro, Inc., NuMedii, Inc., Owkin, Inc., PathAI, Inc., Recursion Pharmaceuticals, Inc., Tempus Labs, Inc., TwoXAR Pharmaceuticals, Inc., Valo Health, LLC, Verge Genomics, Inc., and Zymergen, Inc.. Strategic Action Plan: Recommendations for a Competitive Edge

For those seeking to secure a position at the forefront of biotechnology innovation, it is imperative to conceive a strategic action plan grounded in actionable insights and clear objectives. Industry leaders are encouraged to invest robustly in emerging AI technologies, prioritizing the enhancement of research and operational capacities. Strategic partnerships and collaborations with technology providers can further accelerate the adoption of sophisticated AI models, ensuring that organizations remain agile in a highly competitive market.

Further expansion should involve the integration of scalable digital platforms that can harness the full potential of data analytics. Embracing cloud, hybrid, or on-premises solutions depending on organizational needs ensures resilience and customized performance. Proactivity in adopting comprehensive training and education programs is crucial; these initiatives not only elevate internal capabilities but also facilitate smoother transitions during the digital transformation journey.

Organizations must also focus on developing a responsive and adaptive corporate culture that readily embraces change. This involves nurturing a cross-disciplinary workforce that can bridge traditional biotechnology expertise with advanced computational techniques. Monitoring and adapting to regulatory developments is equally vital, helping to mitigate potential compliance challenges while capitalizing on emerging market opportunities. Ultimately, a well-orchestrated strategy will enable firms to harness AI's transformative power, driving both innovation and sustainable competitive advantage.

Conclusion: Advancing Biotechnology Through AI Excellence

In summary, the fusion of artificial intelligence with biotechnology is catalyzing a paradigm shift that promises to redefine research methodologies, clinical practices, and overall operational frameworks in the industry. The insights derived from detailed segmentation and regional analysis underscore the multifaceted benefits of integrating sophisticated AI solutions across all layers of operation. The ever-expanding capabilities of technologies such as deep learning, neural networks, and robotic process automation continue to propel the industry forward, delivering robust improvements in efficacy, accuracy, and speed.

As the market evolves, the strategic alignment between technological innovation and biotechnological expertise becomes increasingly critical. Organizations now face a competitive landscape that rewards agility, foresight, and strategic investment in state-of-the-art AI-driven tools. By embracing these insights and integrating best practices, industry players can foster an environment ripe for innovation and sustainable advancement. The continuous evolution of AI technologies represents not just a trend, but a fundamental shift in the way biotechnology will operate in the years to come.

The strategic synthesis of technology and tradition offers organizations a unique opportunity to redefine their operations and achieve new heights in research and clinical excellence. This transformation, marked by a relentless pursuit of innovation, will undoubtedly carve the path to breakthroughs that have the potential to save lives and elevate global health standards.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Increased adoption of AI-driven platforms for efficient biotech workflow optimization
    • 5.1.1.2. Rapid genomics data generation necessitating AI for efficient data management and analysis
    • 5.1.1.3. Increasing demand for personalized medicine fueling AI integration in biotechnology labs
  • 5.1.2. Restraints
    • 5.1.2.1. Cost and complexity concerns in implementing AI systems within biotech infrastructures
  • 5.1.3. Opportunities
    • 5.1.3.1. Deploying AI in clinical trial management to improve patient recruitment and trial efficiency
    • 5.1.3.2. Introducing AI technologies for efficient identification and classification of genetic mutations impacting health
  • 5.1.4. Challenges
    • 5.1.4.1. Ensuring data privacy and handling significant amounts of sensitive biomolecular data securely
• 5.2. Market Segmentation Analysis
  • 5.2.1. Component: Rising preference of software solutions for improved data analytics
  • 5.2.2. End-User: Increased adoption of artificial intelligence in biotechnology in agricultural institutes
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Artificial Intelligence in Biotechnology Market, by Component

• 6.1. Introduction
• 6.2. Services
  • 6.2.1. Consulting
  • 6.2.2. Implementation
  • 6.2.3. Post-Sales & Maintenance Services
  • 6.2.4. Training & Education Services
• 6.3. Solutions
  • 6.3.1. Platform
  • 6.3.2. Software

7. Artificial Intelligence in Biotechnology Market, by Technology

• 7.1. Introduction
• 7.2. Computer Vision
• 7.3. Deep Learning
• 7.4. Machine Learning
• 7.5. Natural Language Processing
• 7.6. Neural Networks
• 7.7. Robotic Process Automation

8. Artificial Intelligence in Biotechnology Market, by Organization Size

• 8.1. Introduction
• 8.2. Large Enterprises
• 8.3. Small & Medium Enterprises

9. Artificial Intelligence in Biotechnology Market, by Application Area

• 9.1. Introduction
• 9.2. Agriculture Biotechnology
• 9.3. Clinical Trials
• 9.4. Diagnostics
• 9.5. Genomics Analysis
• 9.6. Personalized Medicine
• 9.7. Precision Medicine
• 9.8. Research & Development

10. Artificial Intelligence in Biotechnology Market, by End-User

• 10.1. Introduction
• 10.2. Agricultural Institutes
• 10.3. Biotechnology Firms
• 10.4. Healthcare Providers
• 10.5. Pharmaceutical Companies
• 10.6. Research & Academic Institutions

11. Artificial Intelligence in Biotechnology Market, by Deployment Mode

• 11.1. Introduction
• 11.2. Cloud
• 11.3. Hybrid
• 11.4. On-Premises

12. Americas Artificial Intelligence in Biotechnology Market

• 12.1. Introduction
• 12.2. Argentina
• 12.3. Brazil
• 12.4. Canada
• 12.5. Mexico
• 12.6. United States

13. Asia-Pacific Artificial Intelligence in Biotechnology Market

• 13.1. Introduction
• 13.2. Australia
• 13.3. China
• 13.4. India
• 13.5. Indonesia
• 13.6. Japan
• 13.7. Malaysia
• 13.8. Philippines
• 13.9. Singapore
• 13.10. South Korea
• 13.11. Taiwan
• 13.12. Thailand
• 13.13. Vietnam

14. Europe, Middle East & Africa Artificial Intelligence in Biotechnology Market

• 14.1. Introduction
• 14.2. Denmark
• 14.3. Egypt
• 14.4. Finland
• 14.5. France
• 14.6. Germany
• 14.7. Israel
• 14.8. Italy
• 14.9. Netherlands
• 14.10. Nigeria
• 14.11. Norway
• 14.12. Poland
• 14.13. Qatar
• 14.14. Russia
• 14.15. Saudi Arabia
• 14.16. South Africa
• 14.17. Spain
• 14.18. Sweden
• 14.19. Switzerland
• 14.20. Turkey
• 14.21. United Arab Emirates
• 14.22. United Kingdom

15. Competitive Landscape

• 15.1. Market Share Analysis, 2024
• 15.2. FPNV Positioning Matrix, 2024
• 15.3. Competitive Scenario Analysis
  • 15.3.1. Insilico Medicine and Sanofi's collaboration marked a significant achievement in AI-driven drug discovery
  • 15.3.2. Terray Therapeutics secures USD 120M for AI-driven biotech innovations and clinical trial advancements
  • 15.3.3. Iambic Therapeutics secures USD 150 million in Series B funding to advance AI-driven drug discovery innovations
• 15.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. Atomwise, Inc.
• 2. BenevolentAI, Ltd.
• 3. BERG Health, LLC
• 4. Cytel, Inc.
• 5. CytoReason, Ltd.
• 6. Deep Genomics, Inc.
• 7. Envisagenics, Inc.
• 8. Exscientia, plc
• 9. Genialis, Inc.
• 10. GNS Healthcare, Inc.
• 11. Insitro, Inc.
• 12. NuMedii, Inc.
• 13. Owkin, Inc.
• 14. PathAI, Inc.
• 15. Recursion Pharmaceuticals, Inc.
• 16. Tempus Labs, Inc.
• 17. TwoXAR Pharmaceuticals, Inc.
• 18. Valo Health, LLC
• 19. Verge Genomics, Inc.
• 20. Zymergen, Inc.