바이오제조 AI 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the aI in biomanufacturing market size was valued at USD 22.40 billion in 2025 and is estimated to grow from USD 25.13 billion in 2026 to reach USD 44.68 billion by 2031, at a CAGR of 12.20% during the forecast period (2026-2031).

This report is Segmented by AI Technology (ML, NLP, Computer Vision, Digital Twin, Rand More), Offering (Software, Hardware, Services), Application (Process Optimization, QC, Pand More), Deployment Mode (Cloud-Based, On-Premise, Hybrid), End User (Pharma/Biopharma, and More), and Geography (North America, Europe, Asia-Pacific, MEA, South America). The Market Forecasts are Provided in Terms of Value (USD).

Global AI In Biomanufacturing Market Trends and Insights

Biologics and Biosimilars Demand Reshaping AI Investment Priorities

The biomanufacturing industry is responding to the increasing demand for biologics, biosimilars, and advanced therapies. These products require higher process sensitivity, complex scale-up methods, and stricter quality standards, driving the need for continuous process intelligence. Manufacturers are addressing challenges such as batch variability, tighter release timelines, and process transfers across facilities while maintaining labor efficiency. Additionally, patent expirations for biologics are pressuring biosimilar producers to achieve cost efficiency and high yields from the start. This shift has led to AI applications focusing on cell culture control, fill-finish operations, and quality predictions, with service-based adoption rising as producers seek AI solutions without building extensive internal teams.

Bioprocess Data Surge Enabling Production-Grade AI

The availability of abundant, frequent, and connected process data is accelerating AI adoption in biomanufacturing. Advanced platforms now monitor numerous process attributes and generate significantly higher data density compared to traditional methods, enabling real-time quality assessments and reducing delays from offline testing. Early adopters gain a competitive edge by leveraging proprietary process histories for model training, which late entrants cannot easily replicate. Machine learning advancements are optimizing culture conditions, improving decision-making in complex manufacturing environments. The market is increasingly shaped by the ownership of high-quality, transferable datasets alongside algorithm sophistication.

Fragmented Data Infrastructure as the Primary Adoption Ceiling

Biomanufacturing facilities face challenges due to data generated in incompatible formats across process development, manufacturing, and quality systems. Years of isolated equipment purchases have resulted in disconnected instruments, inconsistent metadata, and insufficient context for training cross-functional models. This slows deployment as teams spend more time aligning records than validating AI use cases. Additionally, models trained at one site often underperform at others due to differences in naming conventions, sensor data, and workflows. As a result, digitally advanced sites progress faster, while legacy sites remain in pilot stages, delaying enterprise scaling and extending ROI timelines.

Other drivers and restraints analyzed in the detailed report include:

1. Cell and Gene Therapy Pipeline Driving AI-Enabled Scale-Up
2. Regulatory Emphasis on PAT and Quality by Design Accelerating AI Adoption
3. Talent Shortage Constraining AI Governance Depth and Model Lifecycle Management

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

Segment Analysis

In 2025, Machine Learning and Deep Learning held a 34.55% share of the AI in biomanufacturing market, establishing their role as core technologies for control, prediction, and optimization in bioprocessing. Their dominance stems from applications in process monitoring, predictive maintenance, and quality forecasting, particularly where structured data and defined variables like yield or impurity trends exist. Machine learning offers a practical entry point for measurable value without factory redesigns, with supervised and hybrid approaches attracting the largest budgets. Computer Vision is projected to grow at a 13.23% CAGR through 2031, driven by its ability to automate visual inspections, container integrity checks, and equipment monitoring. This technology converts visual data into actionable insights, improving compliance and efficiency in sterile environments.

In 2025, software accounted for 62.45% of the market, reflecting its role in integrating platforms, analytics, and applications with existing infrastructure. Manufacturers favored software for its ability to connect systems like LIMS and quality workflows while enabling incremental adoption. This flexibility allowed companies to test use cases without major hardware investments. Services are expected to grow at a 14.15% CAGR through 2031, as buyers increasingly seek outcomes over licenses. Services address critical needs like process mapping, model validation, and compliance, making them essential for operationalizing AI tools in regulated environments.

Geography Analysis

In 2025, North America secured 39.67% of the AI in biomanufacturing market share, making it the leading regional contributor. The region benefits from a high concentration of pharmaceutical headquarters, specialist manufacturers, CDMOs, and digital infrastructure providers. Its early-mover advantage in biologics production ensures many sites already have the necessary data history and automation foundation for AI deployment. Regulatory clarity provided by the FDA further strengthens North America's position, aligning capital availability, regulatory engagement, and data maturity.

Europe plays a pivotal role in the AI in biomanufacturing market, combining established biologics capacity with a strong regulatory and industrial base. Countries like Germany, the United Kingdom, France, and Switzerland drive regional activity with extensive manufacturing networks and advanced quality systems.

Asia-Pacific will be the fastest-growing regional segment, with a projected 15.45% CAGR through 2031. The region's rapid growth is fueled by increasing capacity, supportive policies, and digital advancements. China's strategic focus on biomanufacturing and Japan's collaborative efforts in process design and advanced manufacturing further enhance the region's potential for AI-driven innovation and infrastructure development.

1. ABB Ltd.
2. Aizon Inc.
3. Aspen Technology Inc.
4. Benchling Inc.
5. BioXcelerate Ltd.
6. Culture Biosciences Inc.
7. Danaher
8. Dassault Systemes SE (BIOVIA)
9. DataHow AG
10. Emerson Electric
11. Ginkgo Bioworks Holdings Inc.
12. Honeywell International
13. IBM
14. Insilico Medicine
15. Invert Inc.
16. Lonza Group
17. Merck KGaA (MilliporeSigma)
18. Microsoft Corporation (Azure Life Sciences)
19. NVIDIA
20. Recursion Pharmaceuticals
21. Sartorius
22. Siemens Healthineers
23. Synthace Ltd.
24. TetraScience Inc.
25. 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 Biologics, Biosimilars, and Advanced Therapies Drive Rising Demand and Process Complexity
  • 4.2.2 Biopharmaceutical Manufacturing Faces Pressure to Cut Cost of Goods Sold (COGS)
  • 4.2.3 Biopharma Embraces Industry 4.0 and Smart Manufacturing Principles
  • 4.2.4 Bioprocess Data Surge Fuels AI Model Training and Optimization
  • 4.2.5 Cell and Gene Therapy Pipeline Boom Calls for AI-Driven Scale-Up Solutions
  • 4.2.6 Regulatory Emphasis on Process Analytical Technology (PAT) and Quality by Design (QbD)
• 4.3 Market Restraints
  • 4.3.1 AI Implementation Hindered by Fragmented and Siloed Bioprocess Data Infrastructure
  • 4.3.2 Legacy GMP Environments Face High Upfront Capital Costs and Integration Complexity
  • 4.3.3 Talent Shortage in Bioprocess Engineering and AI/ML Expertise
  • 4.3.4 Uncertainty in Regulatory and Compliance
• 4.4 Value / Supply-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 AI Technology
  • 5.1.1 Machine Learning (ML) and Deep Learning
  • 5.1.2 Natural Language Processing (NLP)
  • 5.1.3 Computer Vision
  • 5.1.4 Digital Twin and Simulation AI
  • 5.1.5 Reinforcement Learning
  • 5.1.6 Generative AI
  • 5.1.7 Other AI Technologies (Federated Learning, Physics-Informed Neural Networks, Hybrid AI)
• 5.2 By Offering
  • 5.2.1 Software
  • 5.2.2 Hardware
  • 5.2.3 Services
• 5.3 By Application
  • 5.3.1 Process Optimization and Control
  • 5.3.2 Quality Control and Assurance
  • 5.3.3 Predictive Maintenance
  • 5.3.4 Drug Discovery and Development Support
  • 5.3.5 Manufacturing Execution and Automation
  • 5.3.6 Supply Chain Optimization and Demand Forecasting
  • 5.3.7 Regulatory Compliance and Documentation
  • 5.3.8 Other Applications
• 5.4 By Deployment Mode
  • 5.4.1 Cloud-Based
  • 5.4.2 On-Premise
  • 5.4.3 Hybrid
• 5.5 By End User
  • 5.5.1 Pharmaceutical and Biopharmaceutical Companies
  • 5.5.2 Contract Development and Manufacturing Organizations (CDMOs/CMOs)
  • 5.5.3 Biotechnology Companies
  • 5.5.4 Academic and Research Institutes
  • 5.5.5 Food and Beverage Manufacturers
  • 5.5.6 Other End Users
• 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 India
    • 5.6.3.3 Japan
    • 5.6.3.4 South Korea
    • 5.6.3.5 Australia
    • 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, Strategic Information, Market Rank/Share, Products & Services, Recent Developments)
  • 6.3.1 ABB Ltd.
  • 6.3.2 Aizon Inc.
  • 6.3.3 Aspen Technology Inc.
  • 6.3.4 Benchling Inc.
  • 6.3.5 BioXcelerate Ltd.
  • 6.3.6 Culture Biosciences Inc.
  • 6.3.7 Danaher Corporation (Cytiva)
  • 6.3.8 Dassault Systemes SE (BIOVIA)
  • 6.3.9 DataHow AG
  • 6.3.10 Emerson Electric Co.
  • 6.3.11 Ginkgo Bioworks Holdings Inc.
  • 6.3.12 Honeywell International Inc.
  • 6.3.13 IBM Corporation
  • 6.3.14 Insilico Medicine
  • 6.3.15 Invert Inc.
  • 6.3.16 Lonza Group AG
  • 6.3.17 Merck KGaA (MilliporeSigma)
  • 6.3.18 Microsoft Corporation (Azure Life Sciences)
  • 6.3.19 NVIDIA Corporation
  • 6.3.20 Recursion Pharmaceuticals
  • 6.3.21 Sartorius AG
  • 6.3.22 Siemens AG
  • 6.3.23 Synthace Ltd.
  • 6.3.24 TetraScience Inc.
  • 6.3.25 Thermo Fisher Scientific Inc.

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment