ARTIFICIAL INTELLIGENCE (AI) IN BIOTECHNOLOGY MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)
Global AI in biotechnology market is estimated to be valued at USD 2.10 Bn in 2024 and is expected to reach USD 7.11 Bn by 2031, exhibiting a compound annual growth rate (CAGR) of 19% from 2024 to 2031. AI has the potential to revolutionize various processes in biotechnology such as drug discovery and development. It is used across many biotechnology domains like agriculture, healthcare, forensics and environmental protection due to low-cost genome sequencing and biological research.
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Increasing funding from private and public organizations for AI in biotechnology can drive the market growth during the forecast period. Declining hardware and processing costs coupled with the development of more advanced algorithms can boost the adoption of AI technologies in biotechnology applications. Rising need to eliminate expensive and time-consuming lab tests and trials can also drive the market growth.
Rising Drug discovery and precision medicine
The application of artificial intelligence in drug discovery and precision medicine accelerate the progress of biotechnology research and development. With AI, researchers can now screen millions of potential drug compounds in silico in a fraction of the time it would take humans. Powerful machine learning algorithms are being trained on vast datasets containing genetic information, molecular structures, electronic health records and clinical trial outcomes. This allows AI to identify novel drug targets, propose new molecule designs and predict how patients may respond to different therapies based on their unique biological profiles. AI technologies have a transformative impact across the entire drug development pipeline. Companies are using deep learning to analyze biomolecular datasets and discover insightful biomarkers or disease subtypes that would otherwise be impossible to detect manually. Startups like Benevolent AI have discovered new drug candidates for hard-to-treat diseases by systematically screening billions of potential molecules. Pharmaceutical giants are also investing heavily in AI to address bottlenecks in preclinical and clinical testing. For instance, in November 2022, XtalPi Inc. partnered with CK Life Sciences. This partnership leveraged their respective areas of expertise to create a cutting-edge AI tumor vaccine research and development platform, which will improve the ability to discover and design tumor vaccines and expedite the development of new vaccine types. Amgen's R&D and The Scientist established a relationship in June 2022. The partnership looks at novel approaches to drug discovery and development that leverage AI and machine learning to generate novel protein treatments.
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Clinical trial recruitment and retention
Increasing complexity of clinical trials can drive the AI in biotechnology market growth. Recruiting patients and retaining them through the entire clinical trial process is challenging for biotech companies. AI can help address these issues by identifying more eligible candidates through advanced analytics of patient datasets. It allows targeting personalized outreach by leveraging parameters like medical history, demographics, habits and proximity to the hospital. This focused recruitment using AI improves participant diversity and reduces screening failure rates. Once enrolled, keeping patients engaged throughout the trial requires significant manual effort. Non-compliance to treatment protocols or dropping out prematurely impacts trial timelines and results. Thus, AI continuously monitor participants through digital technologies. Technologies like mobile apps, wearables and remote monitoring devices provide vital signs, medication intake details, and others on a real-time basis. AI tools can identify early signs of disengagement or non-adherence by spotting anomalies in the data patterns. Timely interventions like counselling sessions or extra care can be initiated to boost retention. The use of AI for predictive modelling also helps estimate at-risk patients.
Key Takeaways from Analyst:
Global AI in biotechnology market can witness growth in the next few years. Declining cost of gene sequencing and rise of digital biology enables more life science labs to adopt AI and machine learning techniques. Researchers also use deep learning to accelerate drug discovery, better understand diseases, and develop personalized treatments. This has transformed the biotech industry.
Data privacy and regulation can hamper the market growth. Biomedical data contains sensitive personal information, thus, companies must find ways to utilize AI responsibly and uphold patient privacy. Regulatory approvals will also be critical as more AI-based medical technologies enter clinical trials and clinical use.
North America currently dominates the market due to heavy investments in AI by pharmaceutical firms and research institutes in the U.S. and Canada. However, Asia Pacific is expected to witness fastest growth. Countries like China and India are ramping up initiatives to apply advanced computing to health and commit funding towards AI for biotech. Biomanufacturing base expanding in places like Singapore and South Korea makes these regions top targets for providers of AI solutions.
Market Challenges: High manufacturing cost
The high manufacturing costs associated with developing advanced AI technologies for biotechnology applications poses a major challenge for growth in this sector. Developing sophisticated neural networks, complex algorithms and specialized computing hardware required for medical imaging analysis, drug discovery, precision agriculture and other areas takes massive investments in research and development. It often involves the work of highly skilled engineers, data scientists and researchers for several years. This significant upfront capital expenditure makes it difficult for startups and smaller companies to enter the market and commercialize their innovations. Even large corporations find it challenging to justify such high development costs unless there is potential for substantial returns. The costs are further exacerbated by the need for specialized expertise and infrastructure. Advanced AI solutions for tasks like genomic sequencing, protein structure prediction and epidemic modeling rely on domain-specific knowledge that is difficult to find. They also require high-performance computing facilities with cutting-edge processors, large storage capacities and fast networking capabilities. Maintaining such resources involves recurring operational expenses. According to data from the United Nations Educational, Scientific and Cultural Organization (UNESCO), almost 60% of countries worldwide face severe shortages of biotechnologists and data science professionals. The limited availability of human capital drives up salaries and training costs. Overall, the combination of extensive R&D investments and scarcity of key resources leads to very high entry barriers for organizations.
Market Opportunities: Growing focus on personalized medicine
Growing focus on personalized medicine can offer growth opportunities for global AI in biotechnology market. Personalized medicine involves tailoring medical treatment to the individual characteristics of each patient. With the help of artificial intelligence and machine learning technologies, the prediction, detection and prognosis of diseases can be far more precise taking into individual patient's clinical, genomic and molecular characteristics. AI and machine learning algorithms allow analysis of huge amounts of molecular, clinical and imaging data to better understand disease at a personal level. These technologies can help identify genomic variations that make a disease more or less severe for particular individuals. By leveraging large datasets containing genomes, health records and outcomes of tens of thousands of patients, AI enables precision in disease prediction, early detection, treatment selection and monitoring of treatment response at an individual level. For example, AI tools are being used to analyze whole genome sequencing data to predict cancer risk and recommend targeted prevention for high-risk individuals based on their genes.
In 2021, according to the report published by the World Health Organization, the number of national precision medicine initiatives has doubled from 2020-2021 with over 60 countries developing policies to implement genomic medicine and promote data sharing.
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By Component - Rising Need for Customization Drives Software Segment
In terms of component, software segment is estimated to contribute the highest market share of 49.1 % in 2024, owing to rising need for customization across organizations. Pharmaceutical and biotechnology companies operate in a complex and highly-regulated industry. Software solutions allow for a level of personalization that other components cannot match. Companies can tweak algorithms, customize interfaces, and adapt workflows to their precise needs and challenges. This saves valuable time and resources as compared to less malleable options. Software also provides opportunities for improvement through regular updates. As new data emerges and methods evolve, software facilitates non-disruptive integration of enhancements.
By Application - Targeted Technology Unlocks Precision in Drug Discovery and Development
In terms of application, drug discovery & development segment is estimated to contribute the highest market share of 35.12% in 2024, by aptly applying new AI tools. Leveraging vast amounts of scientific literature and clinical trial data, targeted AI technologies can rapidly screen compound libraries to predict efficacy and toxicity. This accelerates all phases from target identification to lead optimization. Machine learning further allows modeling of disease pathways to pinpoint the most promising targets. Augmenting human experts, AI streamlines preclinical research and clinical trial design. By improving both efficiency and effectiveness, AI transforms how new drugs are conceived and tested to better serve patients. As the space increasingly demands precision, AI will continue powering more precise approaches throughout discovery and development.
By End User - A New Breed of Partnerships Propel Pharmaceutical Leadership
In terms of end user, pharmaceutical companies segment is estimated to contribute the highest market share of 40.1% in 2024, owing to novel collaborations. While biotech birth innovative science, pharma houses champion large-scale commercialization. Now, traversing the valley of death between research and real-world impact requires unprecedented cooperation. Pharmaceutical giants increasingly offer not just funding but integrated AI solutions and data-based consultancy. This enables biotech to focus resources on their expertise of hypothesis generation. In return, pharma’s gain a pipeline of cutting-edge programs aligned with their business objectives. As multi-party partnerships become the norm, pharmaceutical sponsors maintain leadership by brokering dynamic alliances across industry and academia.
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North America dominates the global AI in biotechnology market with an estimated market share of 41.2% in 2024. The region boasts robust pharmaceutical and biotech industry with presence of leading players like Pfizer, Merck, and Johnson & Johnson having their headquarters in the U.S. Government funding into R&D through organizations like NIH has created a highly conducive environment for innovation and adoption of emerging technologies like AI. Many startups working on applications of AI for drug discovery and development are based in America, attracting significant venture capital funding. Leading universities like Harvard and MIT are at the forefront of research involving machine and deep learning techniques for areas like protein modeling and genomic analysis.
Asia Pacific region has emerged as the fastest growing market for AI in biotechnology. Countries like China and India are witnessing exponential growth in their biotech industry with government initiatives promoting domestic innovation. While Western pharmaceutical companies have large manufacturing bases, Asian firms are increasingly investing more in basic research. This has boosted internal developmental activities and integration of AI tools. For example, Alibaba and Baidu, China-based companies, are investing heavily in AI assistants for predicting disease progression. Reduced operating costs and availability of programming talent attracts global biotech players to set up AI divisions in Asia to enhance research productivity. The region also serves as an important hub for contract research and manufacturing, utilizing AI for better analytics.
Artificial Intelligence (AI) in Biotechnology Market Report Coverage
| Report Coverage | Details | ||
|---|---|---|---|
| Base Year: | 2023 | Market Size in 2024: | US$ 2.10 Bn |
| Historical Data for: | 2019 to 2023 | Forecast Period: | 2024 to 2031 |
| Forecast Period 2024 to 2031 CAGR: | 19% | 2031 Value Projection: | US$ 7.11 Bn |
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| Companies covered: |
AstraZeneca, Bristol-Myers Squibb, Gilead Sciences, Inc., Sanofi, Abbott Laboratories, Biogen, Pfizer, Inc., Novo Nordisk A/S, Amgen, Inc., Merck KGaA, Johnson & Johnson Services, Inc., F. Hoffmann-La Roche Ltd., Novartis AG, Deep Genomics, NVIDIA Corporation, Verge Genomics, Recursion Pharmaceuticals |
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*Definition: Global AI in Biotechnology Market refers to the use of artificial intelligence technologies and techniques in biotechnology applications across various industries such as pharmaceutical and biotechnology companies, academic and research institutions, clinical research organizations, and bioscience startups around the world. AI helps accelerate drug discovery and personalized medicine by analyzing complex biological and chemical interactions and enormous amounts of data to identify new drug candidates and therapeutic mechanisms.
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About Author
Manisha Vibhute is a consultant with over 5 years of experience in market research and consulting. With a strong understanding of market dynamics, Manisha assists clients in developing effective market access strategies. She helps medical device companies navigate pricing, reimbursement, and regulatory pathways to ensure successful product launches.
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