Artificial Intelligence (AI) in Genomics Market By Component (Software, and Services), By Technology, By Functionality, By Application, By End User - Global Industry Outlook, Key Companies (IBM Watson Health, Google DeepMind, NVIDIA Corporation, and others), Trends and Forecast 2025-2034

The Global Artificial Intelligence (AI) in genomics market represents a transformative shift in the life sciences sector, where artificial intelligence technologies are becoming integral to decoding and understanding the human genome. As the volume of genomic data continues to expand exponentially, AI has emerged as a key enabler in managing and interpreting this information. 

Traditional genomic workflows that once required extensive manual efforts are now being accelerated by AI models that can quickly identify genetic patterns, mutations, and potential biomarkers. The growing adoption of AI in genomics is reshaping how researchers approach precision medicine, disease risk prediction, and therapeutic development across various industries.

The market is driven by the convergence of genomics with AI technologies such as machine learning, deep learning, natural language processing, and computer vision. These AI tools enhance capabilities in gene sequencing, annotation, variant calling, and data integration, providing actionable insights from complex genomic datasets. For instance, machine learning models are widely used to predict disease-associated genes and to streamline large-scale genomic data analysis in drug discovery pipelines. AI’s capacity to improve accuracy while reducing time and operational costs makes it highly profitable to pharmaceutical and biotechnology companies.

The US AI in Genomics Market

The US AI in Genomics Market is projected to be valued at USD 357.4 million in 2025. It is further expected to witness subsequent growth in the upcoming period, holding USD 9,491.4 million in 2034 at a CAGR of 44.0%.

 

 
In the US, AI in the genomics market is evolving rapidly as the country sits at the forefront of both artificial intelligence and genomic research advancements. The US benefits from a highly developed ecosystem that integrates world-class research institutions, cutting-edge biotech companies, and established AI technology providers. 

Federal initiatives like the National Human Genome Research Institute (NHGRI) and private sector investments are fueling the fusion of AI with genomics to tackle critical healthcare challenges, such as identifying genetic predispositions to chronic diseases and streamlining drug development. As a result, AI-driven genomics platforms are becoming essential tools for hospitals, research labs, and pharmaceutical companies across the nation.

A unique factor driving the US market is its emphasis on personalized medicine and precision healthcare, supported by AI technologies. AI systems are widely utilized to analyze genetic variations and tailor treatment plans to individual patients, particularly in oncology, rare disease management, and pharmacogenomics. 

The integration of AI with next-generation sequencing (NGS) technologies has accelerated the pace at which genetic data can be processed, allowing US healthcare providers to deliver more targeted therapies. Additionally, government-backed programs, such as the All of US Research Program, further promote the use of AI in genomic studies aimed at creating diverse, population-scale datasets for precision medicine initiatives.

Global Artificial Intelligence (AI) in Genomics Market: Key Takeaways

• Market Value: The Global Artificial Intelligence (AI) in genomics market size is expected to reach a value of USD 44,929.0 million by 2034 from a base value of USD 1,397.9 million in 2025 at a CAGR of 47%.
• By Component Type Segment Analysis: Software components are poised to consolidate their dominance in the component type segment capturing 56.1% of the total market share in 2025.
• By Technology Type Segment Analysis: Machine Learning technologies are anticipated to maintain their dominance in the technology type segment capturing 61.5% of the total market share in 2025.
• By Functionality Type Segment Analysis: Genome Sequencing is poised to consolidate its market position in the functionality type segment capturing 44.9% of the total market share in 2025.
• By Application Type Segment Analysis: Drug Discovery & Development applications are expected to maintain their dominance in the application type segment capturing 29.7% of the total market share in 2025.
• By End-User Type Segment Analysis: Pharmaceutical and Biotech Companies are anticipated to consolidate their market position in the end-user type segment capturing 31.2% of the total market share in 2025.
• Regional Analysis: North America is anticipated to lead the Global Artificial Intelligence (AI) in the genomics market landscape with 30.4% of total global market revenue in 2025.
• Key Players: Some key players in the Global Artificial Intelligence (AI) in the genomics market are IBM Watson Health, Google DeepMind, NVIDIA Corporation, Microsoft Genomics, Illumina Inc., Thermo Fisher Scientific, F. Hoffmann-La Roche Ltd., DNAnexus, Sophia Genetics, Tempus Labs, BenevolentAI, Atomwise, PathAI, Fabric Genomics, Sema4 (now part of GeneDx), Genuity Science, BioAge Labs, Verge Genomics, PrecisionLife, Insitro, and Other Key Players.

Global Artificial Intelligence (AI) in Genomics Market: Use Cases

• AI-Powered Precision Oncology: By integrating AI with large-scale tumor genomic datasets, oncologists can identify specific genetic mutations and alterations that drive cancer progression in individual patients. AI models assist in correlating these mutations with potential targeted therapies, immunotherapies, or experimental drugs, thus rising the success rate of treatment plans. Companies and hospitals globally are now leveraging AI-powered genomics platforms to design customized cancer treatment regimens, monitor resistance patterns, and predict tumor recurrence.
• AI-Driven Rare Disease Diagnosis: Rare diseases often present with complex symptoms and may take years to diagnose using conventional methods. AI models trained on genomic, phenotypic, and clinical data are helping shorten the diagnostic journey by matching patient-specific genetic variations with known disease-causing mutations. Deep learning algorithms can also detect previously unknown variants of uncertain significance and correlate them with rare disease pathways using global reference databases.
• Genomics-Enabled Drug Repurposing Using AI: AI algorithms can mine vast genomic and transcriptomic datasets, looking for patterns that reveal how specific drugs interact with gene expression profiles or genetic pathways involved in various diseases. Pharmaceutical companies globally are integrating AI in genomics workflows to uncover new applications for existing compounds, thereby reducing R&D costs and speeding up the drug development cycle.
• AI in Agricultural Genomics for Climate-Resilient Crops: Genomic data from plants is now being analyzed using AI models to identify genetic traits linked to drought tolerance, pest resistance, and enhanced nutrient profiles. By combining AI with genomic selection techniques, agritech companies, and research institutes are accelerating plant breeding programs aimed at producing crop varieties capable of withstanding changing environmental conditions.

Global Artificial Intelligence (AI) in Genomics Market: Stats & Facts

• According to the United Arab Emirates government, the Emirati Genome Programme has sequenced 802,000 genomes, including 702,000 from Emiratis, aiming to create a comprehensive genetic dataset to drive medical breakthroughs and attract pharmaceutical companies.
• As per the US Department of State, the US Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence outlines a comprehensive strategy to promote Global Artificial Intelligence (AI) practices through multilateral fora and strengthen partnerships to address shared AI challenges.
• The U.S. government highlights that a USD 500 billion "Stargate" AI infrastructure initiative was announced in January 2025 to leverage AI in tackling major health challenges, including cancer and heart disease, while generating new job opportunities.
• According to the Thai Ministry of Public Health, in 2019, the Thai government authorized a five-year Genomics Initiative worth USD 150 million to characterize the genomes of 50,000 citizens, integrating AI to enhance genomic research and healthcare outcomes.
• The US National Conference of State Legislatures (NCSL) states the AI Leadership to Enable Accountable Deployment Act proposes the creation of a Chief AI Officer Council to direct AI practices across federal agencies, ensuring interagency coordination and responsible AI use.
• As per the U.S. Chief Information Officers Council, about 46% of AI use cases across federal agencies are categorized as mission-enabling, with healthcare and medical AI applications, including genomics, ranking as one of the largest focus areas.
• According to the US Office of Management and Budget (OMB), new guidance issued in September 2024 directs federal agencies to manage risks, promote competition and innovation, and ensure interagency collaboration in acquiring and using AI technologies.
• According to the World Health Organization (WHO), new guidelines identify key areas for governments and regulatory bodies to consider when integrating AI into healthcare, including transparency, data validation, and stakeholder collaboration.
• According to the Australian Government, Genomics Australia has been established as a national body to drive the integration of genomic medicine into clinical practice, starting with personalized cancer care, aiming to revolutionize healthcare through genomic advancements.
• As per the European Union, the General Data Protection Regulation (GDPR) enforces strict guidelines on data privacy and security, impacting how genomic data is handled within AI applications across EU member states.
• The Health Insurance Portability and Accountability Act (HIPAA) sets national standards for the protection of health information, influencing the integration of AI in genomics within the healthcare sector as stated by the US Department of Health & Human Services,
• According to the National AI Initiative Act of 2020, the U.S. government tasked the NSF and OSTP to create a National AI Research Resource (NAIRR) to broaden access to computational resources and data for AI research, including genomics applications.
• According to Genomics England, the 100,000 Genomes Project successfully sequenced 100,000 whole genomes from NHS patients, integrating AI to identify causes of rare diseases and develop personalized treatments.
• Following Japan’s Tohoku Medical Megabank Organization, the biobank has genomic and health data from over 150,000 individuals, utilizing AI to advance personalized medicine and disaster-preparedness research.
• As per the China National GeneBank (CNGB), genetic data from millions of individuals and species has been collected and analyzed using AI for healthcare and biodiversity studies.
• According to Genome Canada, the Canadian government has invested USD 81 million in the Canadian Precision Health Initiative, aiming to gather genomic data from over 100,000 Canadians to enhance precision health and drive economic growth.
• According to Singapore’s National Precision Medicine program, the initiative will analyze the genomes of 100,000 Singaporeans, leveraging AI to tailor medical treatments and improve population health outcomes. Additionally, the Singaporean government has committed USD 200 million over five years to support public healthcare institutions in utilizing artificial intelligence (AI) tools and genomics data to enhance preventive care.
• As per to the Korean Genome Project, genomic data from over 80,000 individuals has been collected, with AI applications facilitating research in disease prevention and personalized medicine.
• France aims to sequence the genomes of 235,000 patients annually, incorporating AI to enhance diagnostics and therapeutic strategies which was highlighted by the French Plan for Genomic Medicine 2025.
• According to the German National Cohort (NAKO), genomic data from 200,000 participants is being used with AI applications to understand disease mechanisms and improve public health interventions.
• According to the Israeli Precision Medicine Partnership, investments in genomic and AI research are developing personalized healthcare solutions, focusing on cancer and rare genetic disorders.
• As per the Brazilian Initiative on Precision Medicine (BIPMed), open access to genomic and clinical data is provided, with AI being used to advance research in population-specific genetic diseases.
• According to the Global Alliance for Genomics and Health (GA4GH) and the Global Genomic Medicine Collaborative (G2MC), collaborative efforts are being made to ensure that genomic medicine benefits are equitably distributed worldwide, especially in underserved regions.
• The Japan's Cabinet Office states that the country released an interim report on February 4, 2025, advocating a "light-touch" approach to AI regulation to encourage innovation, including in genomics.

Global Artificial Intelligence (AI) in Genomics Market: Market Dynamic

Global Artificial Intelligence (AI) in Genomics Market: Driving Factors

Exponential Growth of Genomic Data and the Demand for Advanced Analytics
Projects such as the Human Genome Project, the 100,000 Genomes Project in the UK, and various national biobank programs have generated petabytes of raw genetic data that require highly sophisticated tools for meaningful analysis. Traditional bioinformatics methods are being outpaced by the volume and complexity of this data. AI, particularly machine learning and deep learning algorithms, has emerged as the critical enabler to process, interpret, and visualize this data efficiently. 

The need to uncover hidden patterns, biomarkers, and genetic interactions in massive datasets is driving organizations to integrate AI into their genomics workflows. From drug discovery to clinical genomics, the demand for faster, automated, and more scalable analytics platforms is pushing AI adoption globally, transforming how genomic insights are extracted and applied across industries.

Rising Demand for Personalized and Precision Medicine Globally
Personalized medicine relies heavily on understanding individual genetic variations that influence disease susceptibility, drug responses, and treatment outcomes. AI facilitates this shift by rapidly analyzing genomic profiles to match patients with the most effective therapies, particularly in areas like oncology, cardiology, and rare diseases. 

In clinical settings, AI-enhanced genomic tools are helping physicians deliver precision medicine by combining genetic data with clinical and lifestyle information to inform diagnosis and treatment planning. Moreover, governments and healthcare systems globally are investing in national precision medicine initiatives, further boosting the market. The rise in consumer genetic testing and direct-to-consumer genomics companies also underscores how patients themselves are driving this trend.

Global Artificial Intelligence (AI) in Genomics Market: Restraints

Data Privacy and Ethical Concerns Surrounding Genomic Information
Genomic information is inherently personal and, if mishandled, could lead to issues such as genetic discrimination or unauthorized use by third parties, including insurers or employers. As AI-powered platforms collect and process vast volumes of individual genetic data, questions regarding consent, data ownership, and the anonymization of patient records are becoming more critical. 

Variations in regulatory frameworks across regions, such as the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the U.S., create complex compliance challenges for companies operating in multiple markets. The lack of universal ethical standards and data governance practices for AI in genomics can deter some healthcare providers and research organizations from fully adopting these technologies, slowing down the overall market expansion.

High Implementation Costs and Limited Access in Low-Resource Settings
The costs associated with building or licensing AI algorithms, accessing high-performance computing infrastructure, and securing cloud-based genomics platforms can be prohibitive. Moreover, the successful deployment of AI in genomics depends on access to skilled data scientists, bioinformaticians, and genomic experts, which adds to operational costs. 

In regions with limited technological infrastructure or funding for genomics research, such as parts of Latin America, Africa, and Southeast Asia, the adoption of AI-driven genomics solutions remains slow. This creates a digital divide, where only well-resourced organizations in developed countries can fully leverage the benefits of AI in genomic research and clinical practice, thereby limiting the global reach and potential of the market.

Global Artificial Intelligence (AI) in Genomics Market: Opportunities

Expansion of AI-Driven Multi-Omics Integration for Comprehensive Insights
While genomics focuses on DNA, other “omics” layers provide equally critical information about how genes are expressed and function in biological systems. AI has the potential to unify and analyze these diverse datasets simultaneously, offering a holistic view of disease mechanisms, cellular processes, and treatment responses. 

By leveraging AI’s pattern recognition and predictive capabilities, researchers and clinicians can derive more comprehensive biological insights that were previously unattainable using genomics data alone. This multi-omics integration will unlock new avenues for biomarker discovery, personalized medicine, and systems biology approaches in drug development and diagnostics. As pharmaceutical companies and research institutes seek end-to-end biological insights, AI-powered multi-omics platforms will become a vital component of advanced healthcare and research strategies globally.

Growing Adoption of AI in Genomics for Population Health and Public Health Genomics
AI can accelerate this process by analyzing genomic data at a population level to identify at-risk groups, track disease outbreaks, and predict future health trends. AI's scalability allows it to process millions of genomic records efficiently, providing actionable insights to policymakers and healthcare providers for preventive care and resource allocation. 

Initiatives like precision public health, where AI-driven genomics guides public health decisions tailored to specific populations or regions, are gaining momentum. As more countries invest in national biobank programs and personalized population health projects, AI in genomics will play a pivotal role in transforming public health models globally, creating new market opportunities in both developed and emerging economies.

Global Artificial Intelligence (AI) in Genomics Market: Trends

Shift toward Explainable AI (XAI) in Genomic Decision-Making
As AI algorithms are progressively embedded into clinical genomics and drug discovery pipelines, stakeholders including clinicians, regulatory bodies, and patients are demanding more transparency into how AI arrives at specific genomic interpretations or predictions. Traditional "black-box" AI models, while highly accurate, often lack interpretability, which raises concerns in medical and regulatory settings where understanding decision logic is critical for trust and compliance. 

In response, AI developers are focusing on building models that can not only deliver actionable genomic insights but also explain the rationale behind their outputs in human-readable formats. This trend is accelerating particularly in clinical genomics applications like oncology and rare disease diagnostics, where the ability to justify treatment recommendations or risk assessments based on genetic data is essential.

Cloud-Native and Federated Learning Models for Genomics Data Collaboration
With genomic data volumes rising exponentially and privacy regulations tightening, organizations are shifting towards decentralized AI models that allow collaborative learning across institutions without centralizing sensitive data. Federated learning enables AI algorithms to be trained across multiple genomic datasets stored in different locations such as hospitals, research labs, or biobanks, while maintaining data privacy and compliance with local regulations. 

Combined with cloud-native infrastructure, this approach is revolutionizing how AI models are developed, tested, and deployed for genomics-based applications. It facilitates secure cross-border collaborations, particularly in international genomics consortia, where data sovereignty concerns previously limited data sharing.

Research Scope and Analysis

By Component

The software segment is set to maintain its commanding position within AI in the genomics market, accounting for approximately 56.1% of the total market share by 2025. This dominance is largely driven by the surging demand for advanced AI-powered platforms that can handle complex genomic datasets, automate data interpretation, and provide actionable insights across research and clinical settings. The proliferation of cloud-based genomics platforms and AI-driven bioinformatics tools is transforming how organizations process raw genomic data, from gene sequencing to variant analysis. 

Software solutions are being deployed to integrate machine learning, deep learning, and natural language processing (NLP) models into genomics workflows, enhancing the speed and accuracy of disease gene identification, biomarker discovery, and personalized medicine strategies. Furthermore, the rise of AI-enabled multi-omics platforms designed to combine genomics with proteomics and metabolomics data is creating new opportunities for software vendors to deliver more comprehensive analytical solutions.

The services segment is also emerging as a key complementary component, offering critical support to maximize the value of AI-enabled genomics software. The services segment includes professional services such as consulting, data annotation, algorithm customization, model training, validation, system integration, and ongoing support. 

Many end-users, particularly small- and medium-sized enterprises (SMEs) and research institutions with limited internal AI expertise rely heavily on third-party service providers to implement and optimize AI in genomics platforms. 

Additionally, managed services that oversee continuous updates, system maintenance, and security for AI-powered genomics software are growing in demand, especially in the context of evolving regulatory requirements for genomic data handling. Service providers also play a crucial role in offering training programs and technical guidance to clinicians and researchers to ensure the successful adoption of AI solutions.

By Technology

Machine Learning technologies are projected to solidify their leadership in the technology type segment, capturing approximately 61.5% of the total market share by 2025. The prominence of machine learning (ML) stems from its versatility and proven efficiency in handling diverse genomics tasks, ranging from data classification to pattern recognition in large-scale genomic datasets. 

ML algorithms such as supervised and unsupervised learning models are extensively deployed to streamline genomic data interpretation, automate variant calling, and improve the detection of complex gene-disease associations. Pharmaceutical companies and research labs are leveraging ML for predictive modeling in drug discovery, pharmacogenomics, and clinical genomics, enabling faster identification of potential drug targets and patient-specific treatment responses. The flexibility of ML tools to adapt to a variety of use cases whether in risk prediction models, population genomics, or personalized therapy recommendations has fueled their widespread adoption across both research and healthcare settings.

Deep Learning technologies are also playing a transformative role in advancing Global Artificial Intelligence (AI) in the genomics landscape. Deep learning (DL), with its ability to process unstructured and highly complex datasets, is gaining significant traction for solving intricate genomics challenges that traditional ML algorithms struggle with. 

Deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) are being utilized to uncover deeper genomic patterns, such as epigenetic modifications, gene regulatory networks, and rare variant impacts that are often hidden in vast sequencing datasets. One of the key advantages of deep learning is its superior capacity for feature extraction from raw genomic data, enabling the development of highly accurate models for tasks such as tumor classification, genetic variant prioritization, and anomaly detection in genomic sequences.

By Functionality

Genome Sequencing is expected to solidify its dominance within the functionality type segment, accounting for approximately 44.9% of the total market share by 2025. The growing demand for rapid, high-throughput genomic analysis is fueling this segment, as genome sequencing continues to serve as the foundational process in genomics research and clinical diagnostics. AI technologies are integrated into sequencing workflows to enhance the speed, accuracy, and scalability of sequencing processes while reducing operational costs. 

AI-powered algorithms help automate base calling, detect rare mutations, and manage the overwhelming data output generated by next-generation sequencing (NGS) platforms. In clinical applications, AI-enhanced genome sequencing is streamlining early disease detection, particularly in cancer genomics, rare diseases, and hereditary conditions, allowing healthcare providers to tailor treatments to individual patient profiles. The global shift toward population-scale genomics programs, including national biobank initiatives and large-scale cancer genomics projects, is further propelling this segment.

Gene Editing functionalities are also gaining prominence, with AI playing a key role in accelerating gene modification research and applications. AI is being utilized to refine and enhance gene editing technologies such as CRISPR-Cas9 by predicting on-target and off-target effects with high precision. Deep learning and machine learning models are employed to optimize guide RNA (gRNA) design, improve editing efficiency, and minimize the risk of unintended mutations, which is critical for clinical translation. 

AI’s predictive capabilities enable researchers to navigate complex genetic interactions, identify optimal gene targets, and simulate editing outcomes before laboratory experimentation, significantly reducing research timelines and costs. Beyond research, AI-enhanced gene editing is driving advancements in therapeutic development, including gene therapies for inherited disorders, cancer immunotherapies, and regenerative medicine.

By Application

Drug Discovery & Development is anticipated to retain its dominant position within the application type segment, securing approximately 29.7% of the total market share by 2025. The integration of AI with genomics has revolutionized the drug discovery pipeline by significantly reducing the time, complexity, and cost associated with identifying viable drug targets. AI algorithms are now routinely deployed to sift through vast genomic datasets to detect disease-associated genes, predict drug-target interactions, and model molecular responses at unprecedented speed and accuracy. 

Pharmaceutical and biotech companies are leveraging AI-driven genomics to prioritize targets with a higher likelihood of clinical success, reduce attrition rates in preclinical stages, and personalize drug design according to patient-specific genetic profiles. Moreover, AI-enabled virtual screening and molecular modeling have accelerated the identification of lead compounds and biomarkers, particularly in oncology, rare diseases, and infectious diseases.

 
Diagnostics is emerging as one of the most rapidly expanding applications, significantly shaping the global market landscape. AI-enhanced genomics is transforming diagnostics by enabling the identification of genetic mutations, structural variations, and epigenetic alterations with higher precision and at earlier stages of disease progression. 

AI algorithms, particularly deep learning models, are used to process and interpret raw genomic data to provide clinicians with actionable diagnostic insights for complex conditions such as cancer, cardiovascular diseases, neurodegenerative disorders, and rare genetic syndromes. AI-powered genomics tools have significantly improved the sensitivity and specificity of diagnostic tests, reducing false negatives and improving patient outcomes.

By End-User

Pharmaceutical and Biotech Companies are projected to strengthen their foothold in the end-user segment, accounting for approximately 31.2% of the total market share by 2025. The integration of AI-powered genomic tools within the pharmaceutical and biotech industries is driving a paradigm shift in how new therapeutics and biologics are discovered, developed, and brought to market. These companies are leveraging AI-driven genomics to decode complex genetic data, identify novel druggable targets, and design more precise, effective compounds tailored to individual genetic profiles. 

AI is helping pharmaceutical firms expedite the traditionally time-consuming R&D processes by automating gene-disease association studies, optimizing compound screening, and supporting patient stratification in clinical trials. Additionally, biotech companies are using AI algorithms to model gene expression patterns, predict off-target drug effects, and accelerate the discovery of next-generation therapies, such as gene therapies and RNA-based treatments.

Healthcare Providers are playing a critical role in advancing the adoption of AI in genomics, particularly at the point-of-care level. Hospitals, clinics, and diagnostic centers are utilizing AI-enhanced genomics platforms to deliver personalized healthcare solutions, improving disease diagnostics, risk stratification, and treatment decision-making for patients. 

AI is empowering healthcare providers to move beyond traditional "one-size-fits-all" models of care by integrating genomic data into patient workflows, enabling precision diagnostics and tailored treatment regimens, especially in oncology, cardiology, and rare genetic disorders. Furthermore, AI-driven genomic tools are streamlining clinical workflows by automating complex tasks such as genetic variant interpretation and clinical reporting, reducing the burden on healthcare professionals while improving diagnostic turnaround times.

The AI in Genomics Market Report is segmented on the basis of the following:

By Component

• Software
• Services

By Technology

• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Others

By Functionality

• Genome Sequencing
• Gene Editing
• Clinical Workflow
• Predictive Gene Testing

By Application

• Drug Discovery & Development
• Diagnostics
• Precision Medicine
• Agriculture & Animal Research
• Other

By End User

• Pharmaceutical and Biotech Companies
• Healthcare Providers
• Research Centers
• Others

Regional Analysis

Region with the Largest Revenue Share
North America is expected to maintain its leadership position in the Global Artificial Intelligence (AI) in genomics market, securing approximately 30.4% of total global market revenue by 2025. This dominance is underpinned by a robust ecosystem of established pharmaceutical companies, biotech firms, and AI technology providers, particularly in the US and Canada. The region benefits from early adoption of advanced technologies, well-funded research initiatives, and a strong regulatory infrastructure that supports genomic innovation. 

North America is home to several key players pioneering AI in genomics, including major tech companies integrating machine learning and deep learning into genomics research, drug discovery, and precision medicine. 

Additionally, initiatives like the NIH’s "All of Us" Research Program and other population-scale genomics projects are fueling demand for AI-powered platforms that can manage and interpret vast genomic datasets efficiently. The region’s growing focus on personalized medicine, integrated with a rise in healthcare investments and collaborations between academia and industry, is creating a dynamic landscape for the expansion of AI applications in genomics.

Asia-Pacific (APAC) is projected to exhibit the fastest Compound Annual Growth Rate (CAGR) in the AI in genomics market over the forecast period, positioning itself as a key engine of market expansion globally. Countries such as China, Japan, India, South Korea, and Australia are driving this surge, propelled by rising investments in genomics research, growing biotechnology sectors, and government support for precision healthcare initiatives. 

The rapid expansion of APAC's healthcare infrastructure, integrated with a rising patient population and rising incidence of genetic disorders and chronic diseases, is creating a massive demand for AI-driven genomic applications. Moreover, regional governments and private organizations are launching large-scale genome sequencing projects, such as China's "China Precision Medicine Initiative" and India's "GenomeIndia" project, aimed at building national genetic databases and integrating AI to enhance clinical outcomes.

• The U.S.
• Canada

• Germany
• The U.K.
• France
• Italy
• Russia
• Spain
• Benelux
• Nordic
• Rest of Europe

• China
• Japan
• South Korea
• India
• ANZ
• ASEAN
• Rest of Asia-Pacific

• Brazil
• Mexico
• Argentina
• Colombia
• Rest of Latin America

• Saudi Arabia
• UAE
• South Africa
• Israel
• Egypt
• Rest of MEA

The global competitive landscape of AI in the genomics market is highly dynamic, marked by a blend of established technology giants, specialized biotech firms, emerging AI-driven startups, and research organizations. A defining characteristic of this market is the convergence of diverse industries healthcare, biotechnology, information technology, and life sciences each contributing unique capabilities to shape the evolution of AI applications in genomics. 

Large tech companies such as IBM Watson Health, Google DeepMind, and Microsoft Genomics are leveraging their advanced AI infrastructures, cloud platforms, and machine learning expertise to develop scalable genomics solutions that can handle the complexities of multi-omics datasets and accelerate breakthroughs in drug discovery and personalized medicine. 

These players dominate key partnerships with pharmaceutical firms and healthcare providers to integrate AI-based genomic platforms into clinical and research workflows globally. Simultaneously, specialized biotech and genomics firms such as Illumina Inc., Thermo Fisher Scientific, and Roche are embedding AI into their sequencing and analytics offerings, focusing on improving accuracy, speed, and automation across genomics processes. These companies are investing heavily in AI-powered tools to support genome sequencing, biomarker discovery, and genetic variant interpretation, thus maintaining a competitive edge in both research and clinical settings. 

• IBM Watson Health
• Google DeepMind
• NVIDIA Corporation
• Microsoft Genomics
• Illumina Inc.
• Thermo Fisher Scientific
• F. Hoffmann-La Roche Ltd.
• DNAnexus
• Sophia Genetics
• Tempus Labs
• BenevolentAI
• Atomwise
• PathAI
• Fabric Genomics
• Sema4 (now part of GeneDx)
• Genuity Science
• BioAge Labs
• Verge Genomics
• PrecisionLife
• Insitro
• Other Key Players

• February 2025: Deerfield Management Company acquired Singular Genomics Systems, Inc., a publicly-held gene sequencing company, for USD 20 per share in cash. This strategic move aims to enhance Deerfield's capabilities in genomic sequencing and personalized medicine.
• February 2025: IBM acquired HashiCorp Inc., a company known for its multi-cloud infrastructure automation software. This acquisition aims to strengthen IBM's hybrid cloud and AI strategy.
• February 2025: Deerfield Management Company made a bid to acquire Singular Genomics Systems Inc., a gene sequencing company, for USD 12 per share. This move underscores Deerfield's interest in expanding its genomics portfolio.
• January 2025: Strategic Value Partners LLC acquired Revelyst Inc., aiming to enhance its investment portfolio in the technology sector.
• December 2024: Novo Nordisk expanded its AI drug development agreement with Valo Health to a value of up to USD 4.6 billion, focusing on treatments for obesity, type 2 diabetes, and cardiovascular disease.
• January 2022: Sema4 acquired GeneDx, a genomic testing and Analysis Company, for USD 623 million. This acquisition aims to enhance Sema4's capabilities in genomic diagnostics and personalized medicine.