AI in Biotechnology Market By Product Type (Hardware, Software, and Services), By Applications (Drug Target Identification, Image Screening, Drug Screening, and Predictive Modeling), By End-use (Agriculture Biotechnology, Animal Biotechnology, Medical Biotechnology, and Industrial Biotechnology), Region and Companies – Industry Segment Outlook, Market Assessment, Competition Scenario, Trends and Forecast 2024-2033

Report Overview

The Global AI in Biotechnology Market size is expected to be worth around US$ 26.3 Billion by 2033, from US$ 4.5 Billion in 2023, growing at a CAGR of 19.3% during the forecast period from 2024 to 2033. North America held a dominant market position, capturing more than a 40.8% share and holds US$ 1.8 Billion market value for the year.

Increasing demand for precision medicine, enhanced drug discovery, and personalized healthcare solutions is driving the growth of AI in the biotechnology market. AI technologies, such as machine learning, natural language processing, and data analytics, are transforming drug discovery, genomics, diagnostics, and personalized treatment planning by enabling faster and more accurate predictions.

Rising investments in biotechnology and AI-driven research have spurred innovations in gene editing, molecular modeling, and predictive analytics. In March 2023, Predictive Oncology (US) partnered with Integra Therapeutics (Spain) to advance gene editing technologies, combining expertise in protein expression with gene editing capabilities to develop targeted cancer therapies.

This collaboration exemplifies the growing role of AI in accelerating the development of next-generation cancer treatments. Similarly, in January 2023, Green Mountain Biotech and MeNow announced a partnership to integrate AI with skincare solutions based on traditional Chinese medicine, highlighting AI’s potential to revolutionize applications in consumer health products.

The ability of AI to analyze vast amounts of biological data also creates significant opportunities for optimizing clinical trials, identifying biomarkers, and improving diagnostic tools. AI-powered platforms can assist researchers in simulating complex biological processes, reducing the time and cost of developing new therapies.

The increasing focus on AI-driven innovations in personalized medicine and healthcare is expected to further propel market growth, with AI technologies offering scalable solutions for creating targeted treatments. As AI continues to evolve, its applications in biotechnology will expand, offering the potential to address complex challenges in drug development, disease prevention, and patient care.

Key Takeaways

• In 2023, the market for AI in biotechnology generated a revenue of US$ 4.5 billion, with a CAGR of 19.3%, and is expected to reach US$ 26.3 billion by the year 2033.
• The product type segment is divided into hardware, software, and services, with software taking the lead in 2023 with a market share of 52.3%.
• Considering applications, the market is divided into drug target identification, image screening, drug screening, and predictive modeling. Among these, drug target identification held a significant share of 39.7%.
• Furthermore, concerning the end-use segment, the agriculture biotechnology sector stands out as the dominant player, holding the largest revenue share of 40.6% in the AI in biotechnology market.
• North America led the market by securing a market share of 40.8% in 2023.

Industrial Advantages For Market Key Players

AI in biotechnology provides significant advantages, particularly in enhancing drug discovery and development. Market leaders use AI to predict interactions between chemicals, accelerating the identification and optimization of potential drug candidates. This technology drastically reduces the time and cost associated with these processes, allowing for more efficient resource utilization.

Integrating AI with diagnostic tools improves the accuracy and efficiency of medical diagnostics. These advanced algorithms can analyze extensive biomedical data swiftly, leading to precise diagnoses and tailored treatment plans. Such capabilities support personalized medicine, enabling treatments that are specifically designed for individual genetic profiles, thereby improving outcomes and reducing side effects.

AI automates routine tasks within research and production environments, minimizing human error and freeing up valuable human resources for more complex challenges. Applications range from genetic sequencing to data analysis and lab inventory management, streamlining operations and enhancing productivity across the board.

Predictive capabilities of AI are revolutionizing preventative medicine by forecasting drug responses and potential outbreaks. This foresight assists healthcare providers and biotechnology firms in making proactive decisions that can mitigate healthcare crises and manage public health more effectively, leading to significant cost savings.

AI not only helps in managing vast datasets but also assists biotech companies in staying compliant with stringent regulatory standards. By ensuring operations meet all legal requirements, AI reduces the risk of non-compliance and fosters the creation of new intellectual property, which can open up new markets and provide a competitive edge.

Product Type Analysis

The software segment led in 2023, claiming a market share of 52.3% owing to the increasing adoption of artificial intelligence technologies in research and development. AI-powered software applications are expected to revolutionize data analysis, enhance predictive modeling, and streamline drug discovery processes.

The growing demand for personalized medicine, coupled with the increasing complexity of biological data, is likely to drive the need for more advanced software solutions. AI software that can process large datasets, recognize patterns, and provide actionable insights is anticipated to be in high demand in genomics, proteomics, and other life sciences applications. Moreover, advancements in machine learning and deep learning algorithms are expected to further fuel the adoption of AI-driven software, positioning it as a critical tool for innovation and efficiency in biotechnology.

Applications Analysis

The drug target identification held a significant share of 39.7% due to as pharmaceutical companies focus on improving drug discovery and development processes. AI technologies are anticipated to significantly enhance the efficiency of identifying potential drug targets by analyzing vast biological datasets, including genomic, proteomic, and transcriptomic data.

AI’s ability to detect complex patterns and relationships within biological systems is likely to speed up the identification of promising therapeutic targets. As drug development timelines shorten and the need for precision medicine increases, AI-driven tools for drug target identification are projected to become a key component in biotechnology research. Furthermore, the rising incidence of chronic diseases and the growing emphasis on tailored treatments are likely to drive continued investment in AI applications for target identification.

End-use Analysis

The agriculture biotechnology segment has seen significant growth, with a 40.6% revenue share. This growth is driven by the rising demand for sustainable agricultural practices and the global need for food security. AI technologies are enhancing capabilities in crop yield prediction, pest management, and soil health monitoring. They achieve this by analyzing extensive datasets collected from sensors, satellites, and other technological sources.

AI is revolutionizing breeding programs and identifying gene traits that enhance crop resilience. These advancements are set to propel the application of AI within agriculture biotechnology further. As the agricultural sector undergoes a digital transformation, the integration of AI is crucial for enhancing productivity and sustainability.

The continued adoption of AI-driven solutions in precision agriculture is expected to sustain the growth of the agriculture biotechnology segment. This segment remains a pivotal area of innovation in the wider biotechnology market. With AI’s expanding role, it is poised to significantly improve efficiency and environmental sustainability in agriculture, marking it as a central focus for future developments.

Key Market Segments

By Product Type

• Hardware
• Software
• Services

By Applications

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

By End-use

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

Drivers

Increasing Innovation Driving the AI in Biotechnology Market

The growing innovation in biotechnology is significantly driving the AI market within this sector. As AI technology advances, its applications in drug discovery, genomics, and personalized medicine are becoming increasingly prominent. In 2022, Sanofi partnered with Exscientia, contributing US$ 100 million to support the development of 15 innovative small molecule candidates targeting cancer and immunological diseases.

This collaboration highlights the expanding role of AI in accelerating the discovery and commercialization of new therapies. The ability to analyze vast datasets, predict molecular interactions, and optimize drug design through AI is expected to shorten development timelines and improve the precision of treatments.

Additionally, AI algorithms can enhance the design of clinical trials, reducing costs and increasing success rates. As innovation in AI-powered solutions continues, the market for AI in biotechnology will likely experience substantial growth, offering significant potential for improving outcomes in drug development and other biotechnological advancements.

Restraints

High Cost of AI Implementation Restraining the AI in Biotechnology Market

The high cost of AI implementation represents a major restraint on the growth of the AI in biotechnology market. Integrating advanced AI solutions requires substantial investments in infrastructure, skilled labor, and technology development, which can be prohibitive for smaller biotech firms. High initial costs for AI software, hardware, and data processing capabilities can impede widespread adoption, especially among emerging companies with limited resources.

The complexity of deploying AI systems, particularly in regulated industries like biotechnology, also increases operational costs due to the need for compliance with stringent regulations such as FDA guidelines and data protection laws.

Furthermore, the requirement for constant updates and maintenance of AI algorithms, alongside the need for specialized talent, adds to the financial burden. As a result, while large pharmaceutical companies may afford to implement these technologies, smaller biotech firms could face significant challenges in adopting AI-driven solutions, limiting overall market growth.

Opportunities

Rising Application in Genomic Disease Therapies

The rising application of AI in genomic disease therapies presents a significant opportunity for growth within the AI in biotechnology market. With breakthroughs in gene editing technologies, such as CRISPR, and the increasing understanding of genomic diseases, AI has the potential to revolutionize the treatment landscape for complex genetic disorders.

In 2023, Zinc fingers, an advanced AI-powered biotechnology technology, emerged as a promising alternative to CRISPR, offering unique advantages in precise genetic modifications. This technology enables more accurate targeting of specific genes, improving the precision of therapies for a range of genetic diseases.

As the need for personalized treatments grows, AI-driven advancements in genomics will likely accelerate the development of innovative therapies for diseases such as sickle cell anemia, cystic fibrosis, and certain cancers. The ability to analyze vast genomic datasets and identify actionable insights will further enhance the capabilities of researchers in developing targeted treatments, offering substantial growth potential for AI-driven solutions in genomics.

Impact of Macroeconomic / Geopolitical Factors

Macroeconomic and geopolitical factors play a significant role in shaping the growth and development of the AI in biotechnology market. Economic downturns often lead to budget constraints for both private and public sector investments in healthcare and technology, potentially slowing the adoption of AI-driven solutions.

Conversely, economic recoveries tend to boost investment in innovative technologies, driving demand for advanced biotechnology applications. Geopolitical tensions, such as trade wars or regulatory changes in key markets, may disrupt the supply chains for biotech companies and hinder international collaborations.

However, on the positive side, rising healthcare needs and global efforts to address pressing challenges, such as pandemics, fuel the demand for cutting-edge AI technologies to streamline drug development, diagnostics, and personalized treatments. The continued focus on improving healthcare efficiency and outcomes through AI will likely offset the challenges posed by macroeconomic uncertainties, fostering steady growth in the biotechnology sector.

Trends

Growing Surge in Partnerships and Collaborations Driving the AI in Biotechnology Market

The growing surge in partnerships and collaborations is driving the AI in biotechnology market as companies seek to leverage each other’s strengths in drug discovery, diagnostics, and precision medicine. Increasingly, organizations recognize the potential of combining AI and biotechnology to accelerate innovation and enhance research outcomes.

In August 2024, Exscientia plc and Recursion Pharmaceuticals, Inc. announced a strategic agreement to merge their expertise in AI and drug discovery, positioning themselves as leaders in technology-enabled pharmaceutical development. This collaboration, which blends Recursion’s extensive biology exploration capabilities with Exscientia’s precision chemistry expertise, is expected to significantly expedite the development of novel therapies.

The rise in similar partnerships reflects an industry-wide trend toward leveraging AI to enhance drug discovery pipelines, with companies anticipating improved therapeutic development timelines and greater precision in addressing complex diseases. This trend is projected to fuel further market expansion, as AI and biotechnology become increasingly intertwined.

Regional Analysis

North America is leading the AI in Biotechnology Market

North America dominated the market with the highest revenue share of 40.8% owing to the increasing adoption of artificial intelligence technologies across the pharmaceutical and biotechnology sectors. The demand for more efficient drug discovery processes, personalized medicine, and advanced diagnostics has propelled the integration of AI tools, allowing companies to streamline operations and make data-driven decisions faster.

A notable example is the January 2024 collaboration between NVIDIA Corporation and Amgen, where they partnered to utilize the NVIDIA DGX SuperPOD in developing AI-driven drug discovery models. This partnership aims to leverage one of the largest human datasets available to accelerate pharmaceutical research, refine drug development, and optimize clinical trial processes.

The AI market in biotechnology has also benefitted from the rising emphasis on precision medicine, with machine learning algorithms being used to analyze complex biological data, predict patient outcomes, and create customized treatment plans. Additionally, regulatory agencies in North America have begun to recognize the value of AI in accelerating medical research, further supporting its adoption across the industry. As AI technologies continue to advance, the biotechnology sector is expected to benefit from more precise, cost-effective, and faster drug development processes.

The Asia Pacific region is expected to experience the highest CAGR during the forecast period

Asia Pacific is poised for rapid growth in the AI biotechnology sector, driven by increasing healthcare investments and advancements in genomics. The region is embracing precision medicine, with a strong focus on AI for drug discovery and diagnostics. This surge in interest is fueled by countries like China, India, and Japan, which are at the forefront of biotechnology innovation.

In June 2024, SOPHiA GENETICS from Switzerland and India’s Strand Life Sciences announced a strategic partnership. This collaboration aims to enhance genomics, bioinformatics, and diagnostics through advanced data analysis capabilities. It exemplifies the commitment to integrating AI in healthcare, aiming to improve patient care and treatment outcomes.

The integration of AI in biotechnology across Asia Pacific is set to expand. Governments, research institutions, and private sectors are collaborating to harness AI for better healthcare delivery, cost reduction, and faster drug development. With robust investments and a rising demand for AI-powered diagnostics and treatment optimization, the region is becoming a pivotal area for growth in AI-driven biotechnology solutions.

Key Regions and Countries

• North America
  • US
  • Canada
• Europe
  • Germany
  • France
  • The UK
  • Spain
  • Italy
  • Russia
  • Netherland
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • South Korea
  • India
  • Australia
  • New Zealand
  • Singapore
  • Thailand
  • Vietnam
  • Rest of APAC
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE
  • Rest of MEA

Key Players Analysis

The major players in the AI in biotechnology market are actively engaged in the development and introduction of innovative products, as well as implementing strategic initiatives aimed at enhancing their competitive positioning. Key players in the AI in biotechnology market focus on strategies such as innovation in drug discovery, collaboration with research institutions, and integration of machine learning algorithms to accelerate the development of personalized therapies.

Companies prioritize investments in AI-powered platforms that can analyze complex biological data, optimize clinical trials, and predict therapeutic outcomes more efficiently. Strategic partnerships with pharmaceutical companies and healthcare providers are integral to expanding AI applications across various biotechnology segments. Players also concentrate on enhancing data security and regulatory compliance to meet industry standards while improving the scalability of their solutions. Additionally, expanding AI capabilities to address areas like genomics and molecular diagnostics is a key growth strategy.

One of the leading companies in the market is IBM, which uses its AI platform Watson to transform various biotechnology processes. IBM’s growth strategy revolves around integrating Watson’s cognitive computing capabilities with biotechnology research to help researchers make faster, more accurate decisions in drug discovery and personalized medicine.

The company collaborates with pharmaceutical firms, academic institutions, and hospitals to advance AI-driven solutions in genomics, oncology, and immunology. IBM’s commitment to continuous innovation and investment in AI technologies strengthens its position as a leader in the biotechnology sector.

Top Key Players in the AI in Biotechnology Market

• Sanofi
• Recursion
• Insilico Medicine
• IBM
• Biogen
• Bayer
• AstraZeneca
• Amgen
• Abbott Laboratories

Recent Developments

• In 2023: Insilico Medicine plans to establish a large, AI-powered biotech laboratory in Abu Dhabi. This new facility will serve as a hub for advancing biotechnology research through the use of artificial intelligence to accelerate drug discovery and development.
• In 2022: Recursion, an AI-driven biotech company based in Utah, entered into a partnership with Roche and Genentech to utilize AI for discovering molecules aimed at treating cancer and neurological diseases. This collaboration seeks to enhance the speed and accuracy of drug discovery through artificial intelligence.
• In January 2023: Bayer and Google Cloud announced a strategic collaboration to advance Bayer’s quantum chemistry equations, utilizing machine learning to accelerate the early stages of drug discovery. This partnership aims to enhance computational chemistry capabilities and expedite the identification of promising drug candidates.

Report Scope