Artificial Intelligence (AI) In Biotechnology Market Size, Share, and Trends 2025 to 2034

Artificial Intelligence (AI) In Biotechnology Market Size and Forecast 2025 to 2034

The global artificial intelligence (AI) in biotechnology market size was calculated at USD 4.70 billion in 2024 and is predicted to increase from USD 5.60 billion in 2025 to approximately USD 27.43 billion by 2034, expanding at a CAGR of 19.29% from 2025 to 2034.

Artificial Intelligence (AI) In Biotechnology Market Key Takeaways

• North America dominated the global artificial intelligence (AI) in biotechnology market in 2024
• Asia Pacific is projected to expand at the fastest CAGR between 2025 and 2034.
• The software offering segment generated the biggest market share in 2024.
• The drug target identification applications segment is expected to grow at a solid CAGR during the forecast period.
• The predictive modeling applications segment is anticipated to grow at the fastest growth during the forecast period.
• The agriculture biotechnology usage segment captured the biggest market share in 2024.
• The medical biotechnology usage segment is expected to grow at the fastest growth during forecast period. 

Market Overview

The biotechnology industry will pursue artificial intelligence and its related applications in the coming years. According to Melanie Matheu, PhD medium article, the next generation of therapeutics joining drug pipelines will contain targets identified through AI screening, potentially improving the 86% clinical trial rate of failure for small molecules. 

AI in biotech is critical for boosting invention in labs and throughout a medicine's or chemical compound's lifecycle. AI-based tools and applications aid in the development of molecule structures per the target market. Machine learning, a subset of artificial intelligence, aids in estimating combinations and permutations of different compounds to determine the best combination without the need for manual experiments in the lab. 

Artificial intelligence's application in biotech brings about innovations that aid predictive modeling to forecast market demand for a specific drug or chemical. AI in Biotech can also assist in the smart distribution of raw materials needed by the biotech sector via cloud computing.

Innovation Trends in AI in Biotechnology

• Generative AI for Drug Discovery
  
  • AI models like DragonFold and ProGen2 are being used to generate novel protein sequences and molecular structures with therapeutic potential.
  • Companies are accelerating hit identification and lead optimization using deep generative networks.
• Federated Learning for Privacy-Preserving AI
  
  • Enables training AI models across multiple decentralized datasets without transferring sensitive patient data.
  • Used by companies like Owkin to maintain data privacy while improving model accuracy in diagnostics and drug development.
• Digital Twins in Biotechnology
  
  • Creation of high-fidelity, AI-powered virtual models of cells, organs, or patients to simulate biological behavior.
  • Used for optimizing bioprocessing, personalizing treatment plans, and predicting disease progression.
• AI-Powered Biomarker Discovery
  
  • Machine learning algorithms are identifying novel biomarkers from genomic, proteomic, and imaging data.
  • Accelerates early diagnosis and patient stratification in clinical trials.
• Natural Language Processing (NLP) for Literature Mining
  
  • LLMs are being used to analyze scientific literature, clinical data, and patents to identify research gaps and new therapeutic targets.
  • Speeds up hypothesis generation and competitive intelligence gathering.
• Multimodal AI Integration
  
  • Combines data from multiple sources (genomics, imaging, clinical records) to build holistic patient profiles.

Key AI Technologies Used by Biotechnology Companies:

Market Scope

Market Dynamics

Drivers

AI technological advancement

Technological progress is a crucial trend in the biotechnology market for artificial intelligence. Major players in the artificial intelligence market are focusing on developing innovative technologies to strengthen their positions. AI is transforming research and development by utilizing data science and machine learning to large data sets, allowing for the faster discovery of novel molecules. 

To identify therapies, AI in drug development can cross-reference authored scientific literature with additional information sources such as conference abstracts, clinical trial data, unpublished data, and public databases. For instance, in 2023, the FDA approved DiA Imaging Analysis's AI program to assist clinicians in performing cardiac ultrasound exams.

Increasing adoption of AI in the biopharmaceutical industries

Pharmaceutical companies use AI to help with the high-cost and dynamic drug discovery process. AI solutions successfully identify illness patterns within large datasets and assist in determining which drug formulations are better placed for treating various diseases. This aids in the adequate access and analysis of massive amounts of chemistry data, which enhances business outcomes and processes. For instance, Abbott launched its new artificial intelligence-powered coronary imaging system in Europe in April 2021.

Diagnosis of diseases

Machine Learning is generally used in diagnosing disease as it uses real-world results to develop diagnostic procedures, i.e., the more clinical tests performed, the more precise the observations. AI is also aiding in streamlining the radiology and radiation therapy processes, saving time, and improving patient care. According to a survey, the detection rate for actionable lung tumors on chest X-rays seemed higher with AI assistance (0.59%) than without AI (0.25%).

Restraints

Stagnant wages

A robot is an application of artificial intelligence displacing jobs and boosting unemployment rates (in a few cases). As a result, some argue that there is always the possibility of job loss due to chatbots & robots replacing people. Robots, for example, are frequently used to remove human resources in production sectors in technically advanced countries such as Japan.

Opportunities

Increase in awareness regarding personalized medicine.

The awareness regarding personalized medicine is due to growing demands for innovative drug discovery to resolve the increasing incidence of various diseases, including cancer. In addition, multiple collaborations among researchers and market players are anticipated to fuel the growth. Government policies favorable to personalized medicine add to the opportunity to grow this market. 

Significant innovations in pharmacogenomics are expected to produce an acceptable approach to developing drugs explicitly designed for a particular patient or group of patients. AstraZeneca plc and Renalytix AI plc collaborated in August 2020 to introduce precision medicine methods for various metabolic disorders.

Offering Insights

The software sector contributed the most revenue in 2024 and is expected to be dominant throughout the projected period because of the ongoing software revolution that meets the needs of the healthcare industry. The increasing penetration of software due to the increasing need for storing, managing, evaluating, and sharing data in drug discovery and development and clinical trials led to this segment's dominance.

The rising demand for big data analytics drives the need for AI software in the global AI biotechnology market. Furthermore, the software generates recurring income channels for market participants over time and has thus become the highest revenue factor for AI in the biotech market globally.

Services, on the other hand, are projected to be the fastest-growing sector during the projected period. The need for more understanding among life science experts about AI-based hardware and software activity is raising the need for third-party service providers who supply technically trained personnel to operate costly AI systems. During the forecast period, this aspect is driving prices for the services sector of the global AI in the biotech market.

Applications Insights

The drug target identification is anticipated to expand at the highest revenue share in 2024. Artificial intelligence is an innovative technique for identifying new anticancer targets and discovering novel drugs since biology networks efficiently protect and evaluate the interaction among components of cellular systems affecting human diseases like cancer. Network-based biology analysis algorithms provide various innovative network strategies for identifying cancer targets. 

AI-based nanorobots as drug delivery agents are used to avoid collisions, identify and attach targets, and excrete from the body. Nano/microrobot advancements enable them to navigate to the desired target according to physiological conditions, including pH, improving efficacy and minimizing systemic adverse effects.

The predictive modeling segment is expected to grow at the fastest pace between 2025 to 2034. Machine learning and predictive modeling can aid in predicting the behavior and interactions of various therapeutic candidates, including their biochemical properties, pharmacological efficacy, and safety profiles. It requires an investment, advancement, and a mindset transition, but it will allow better delivery of medicines to patients faster, more efficiently, and with greater personalization. 

AI aids in structure-based drug discovery by forecasting the 3D protein structure, which helps to determine the effect of a substance on the target as well as safety considerations before its synthesis or production. For instance, in 2023, AlphaFold, a Google subsidiary DeepMind algorithm, significantly improved the modeling of 3D protein systems from sequences of amino acids.

Usage Insights

The agriculture biotechnology sector is projected to generate the highest revenue in 2024. Agriculture biotechnology cultivates hereditarily modified plants to increase crop yields or to introduce new qualities to existing plants. It includes traditional plant rearing, tissue culture, micropropagation, sub-atomic reproducing, and plant hereditary design. Biotechnology companies are currently utilizing machine learning and artificial intelligence methods to develop and program autonomous robots that handle significant agricultural tasks such as crop collection at a much faster rate than humans. 

The information captured by the automatons is processed and examined using PC Vision and Deep Learning calculations. These are primarily useful for assessing harvest and soil health. AI calculations aid in tracking and forecasting various ecological changes, such as climate changes that influence harvest yield.

Furthermore, medical biotechnology is anticipated to grow at the fastest pace between 2025 to 2034. Medical biotechnology employs living cells to improve human health by providing medicines and anti-toxins. It also involves examining DNA and hereditarily limiting the cells to generate significant and beneficial qualities. Artificial intelligence awareness and machine learning have been widely used in sedate revelation. 

AI assists in the discovery of small particles that may provide therapeutic benefits if objective structures are realized. AI is commonly used in disease diagnosis because it uses real-world outcomes to enhance analytic tests, i.e., the more definitive tests run, the more precise results are possible. Artificial intelligence is also assisting in shortening the radiotherapy treatment planning process, saving time, and enhancing patient care.

Regional Insights

In 2024, North America dominated the global AI in the biotechnology market. The United States is the worldwide leader in AI in the biotech market. This increase is attributed to increased demand for AI technology across all life sciences applications. Furthermore, the United States is home to several leading pharmaceutical and biotech firms. The massive investments made by US-based pharma firms in drug discovery and clinical trials have resulted in a huge demand for AI in biotechnology. 

U.S. AI in Biotechnology Market Trends: Insights, Trends & Investments

The United States continues to lead the AI in biotechnology market in North America, supported by substantial funding, cutting-edge infrastructure, and a strong collaborative ecosystem. Key players driving innovation in this space include Recursion Pharmaceuticals, which leverages AI and automation to operate one of the most advanced AI-driven experimental biology labs globally.

• NetraMark Holdings Inc. specializes in AI-powered analytics for clinical trials and precision medicine, while AbCellera Biologics Inc. utilizes deep learning and computational modeling to analyze immune responses and identify promising antibodies for drug development. 
• Additionally, in September 2025, President Donald Trump signed an executive order aimed at advancing the use of AI in pediatric cancer research, allocating $50 million in new research grants. 

This initiative builds upon the National Cancer Institute's Childhood Cancer Data Initiative (CCDI), a $500 million, decade-long program launched in 2019 to aggregate and share comprehensive data on childhood cancers.

Asia Pacific is projected to expand at the fastest rate during the projected period due to increased investments by multiple research firms in the deployment of artificial intelligence hardware and software to improve efficiency and productivity. Numerous top research organizations in the region are driving the market growth. 

In AI development and integration, Japan, China, and South Korea have emerged as the most advanced nations. Numerous organizations are researching advanced artificial intelligence software applications in the aircraft industry.

Europe’s AI in Biotechnology Market on Rise:

Europe is emerging as a dynamic hub for AI-driven biotechnology, fueled by innovation, cross-border collaboration, and supportive regulatory frameworks. Across the region, several countries are becoming centers of excellence in this rapidly evolving field.

The United Kingdom stands out as a leader in AI biotech innovation. Startups such as CHARM Therapeutics exemplify the region's momentum; the company recently raised an impressive $80 million in Series B funding to advance a menin inhibitor for leukemia, designed using its proprietary 3D deep learning platform. This reflects the UK’s strong positioning in AI-driven drug discovery and oncology-focused biotech research.

In France, Owkin has emerged as a key player, pioneering the use of federated learning to develop AI models for drug discovery and diagnostics without compromising patient data privacy. The company has formed multiple partnerships with top pharmaceutical firms and research institutions, establishing France as a European leader in privacy-preserving AI for healthcare.

Competitive Landscape: Leading Companies in AI-Driven Biotechnology

The global AI in biotechnology market is shaped by both pharmaceutical giants and tech-driven biotech firms investing heavily in AI platforms for drug discovery, diagnostics, and precision medicine. Below is a detailed overview of key players:

• AstraZeneca
  AstraZeneca has integrated AI into multiple phases of its drug discovery pipeline, especially in oncology and respiratory diseases. The company collaborates with AI firms and academic institutions to accelerate molecule screening, predictive modeling, and biomarker identification.
• Bristol-Myers Squibb
  Bristol-Myers Squibb has formed strategic alliances with AI-focused biotech companies to enhance target identification and immunotherapy development. The company is part of a multi-partner data-sharing initiative launched in 2025 to enable AI-based drug discovery through a shared data ecosystem.
• Gilead Sciences, Inc.
  Gilead employs AI and machine learning for virology and oncology drug development. The company has invested in real-world evidence platforms and is increasingly using AI to optimize clinical trial design and patient recruitment strategies.
• Sanofi
  Sanofi is advancing its AI capabilities through internal platforms and external partnerships. The company has collaborated with tech firms to develop AI algorithms for therapeutic antibody discovery and is leveraging machine learning to improve clinical development outcomes.
• Abbott Laboratories
  While traditionally focused on diagnostics and medical devices, Abbott is using AI for early disease detection, particularly in cardiac and diabetes care. Its AI-powered diagnostics platforms integrate biosensors and real-time data analysis to support precision healthcare.
• Biogen
  Biogen has implemented AI to advance neuroscience research, focusing on neurodegenerative diseases such as Alzheimer’s and ALS. The company partners with AI startups and academic institutions to leverage imaging data and genetic insights for biomarker development.
• Pfizer, Inc.
  Pfizer has adopted AI across its R&D and manufacturing processes. AI is being used for structure-based drug design, patient monitoring, and vaccine development. Notably, Pfizer collaborated with BioNTech on the mRNA COVID-19 vaccine, incorporating AI in development and distribution logistics.
• Novo Nordisk A/S
  Novo Nordisk integrates AI into diabetes and metabolic disorder research. The company uses machine learning models to personalize insulin therapy and improve outcomes through data-driven health management platforms.
• Amgen, Inc.
  Amgen is leveraging AI in areas such as protein engineering and biologic drug development. Its AI platforms help simulate molecular interactions and predict protein folding patterns, accelerating the discovery of therapeutic proteins.
• Merck KGaA
  Germany-based Merck KGaA uses AI in life sciences and materials science research. The company has invested in AI for lab automation, predictive toxicology, and computational chemistry, contributing to faster and safer drug development.

Segments Covered in the Report

By Offering

• Software
• Hardware
• Services

By Applications

• Drug Target Identification
• Drug Screening
• Image Screening
• Predictive Modeling

By Usage

• Agriculture Biotechnology
• Medical Biotechnology
• Animal Biotechnology
• Industrial Biotechnology

By Geography

• North America
• Europe
• Asia-Pacific
• Latin America
• The Middle East and Africa