Global Artificial Intelligence in Genomics Market: Growth Analysis, Trends, and Forecast (2026-2036)

The global artificial intelligence in genomics market is witnessing unprecedented growth, reflecting the transformative potential of AI technologies within the field of genomics. In 2025, the market was valued at USD 1.48 billion, and it is expected to reach USD 1.90 billion in 2026. Looking further ahead, the market is projected to expand substantially, reaching an estimated USD 19.96 billion by 2036, representing a robust compound annual growth rate of 26.5% from 2026 to 2036. This remarkable growth can be attributed to several converging factors that position AI as an essential tool in the interpretation and application of genomic data across healthcare, life sciences, and other research-intensive sectors.

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Role of AI in Genomics

Artificial intelligence encompasses a set of technologies that enable machines to perform tasks traditionally associated with human intelligence, such as learning, reasoning, problem-solving, and pattern recognition. When applied to genomics, AI leverages advanced algorithms, including machine learning, deep learning, and natural language processing, to process and analyze complex genomic datasets. These tools enable researchers and healthcare practitioners to extract meaningful insights from genomic sequences, identify disease-associated genetic variants, and accelerate drug discovery and precision medicine applications. Traditional bioinformatics tools often struggle to handle the vast quantities of data generated by modern sequencing technologies, and AI provides a scalable solution capable of analyzing multi-terabyte datasets efficiently. By automating analysis pipelines and improving the speed and accuracy of genomic interpretation, AI is helping to unlock the full potential of genomic information in both clinical and research contexts.

Key Drivers of Market Growth

The growth of the AI in genomics market is being driven by several key trends. One major factor is the increasing adoption of AI technologies across the healthcare and life sciences sectors. As pharmaceutical research becomes more data-intensive and personalized medicine gains traction, the ability to rapidly analyze genomic data has become a competitive advantage. AI algorithms reduce the time and cost associated with traditional drug discovery and development, allowing researchers to identify promising therapeutic targets and optimize clinical trial design more effectively. Additionally, the rising generation of genomic data, fueled by the widespread use of next-generation sequencing platforms, has created a pressing need for advanced analytical tools capable of extracting actionable insights from massive datasets. This surge in data availability, combined with the limitations of conventional analysis methods, has positioned AI as an indispensable component of modern genomics research.
Another driver of market growth is the increasing focus on precision medicine, which relies heavily on understanding an individual's genetic profile to guide treatment decisions. AI-powered analysis of genomic data enables clinicians to predict patient responses to specific therapies, identify potential adverse reactions, and tailor interventions to the genetic makeup of each patient. This approach is particularly valuable in oncology, rare disease management, and pharmacogenomics, where personalized strategies can significantly improve outcomes. In addition, the integration of AI with genomic research facilitates advances in other areas, such as biomarker discovery, population genomics, and agricultural genomics, broadening the impact of AI beyond conventional human healthcare applications.

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Market Segmentation by Offering

The artificial intelligence in genomics market is characterized by diverse offerings that include both software and services. Software solutions dominate the market, primarily due to their recurring revenue potential and essential role in analyzing genomic datasets. AI genomics software captures and processes sequencing data, applies advanced algorithms to interpret complex patterns, and delivers clinically or scientifically actionable outputs. The software's ability to generate high-value insights from large datasets has led to widespread adoption among pharmaceutical companies, academic research institutions, and clinical laboratories. Meanwhile, services are witnessing the fastest growth within the market. The rapid expansion of services can be attributed to increasing demand from organizations that lack in-house AI capabilities for genomics analysis. Outsourced services offer flexible, scalable solutions for data interpretation, annotation, and management, enabling organizations to leverage AI's capabilities without significant infrastructure investments. The growth of cloud-based genomics services, which provide secure, scalable, and accessible platforms for data storage and analysis, further accelerates adoption.

Market Segmentation by Functionality

Functionality-wise, genome sequencing remains the largest segment of the AI in genomics market. AI-driven sequencing tools improve the accuracy and efficiency of identifying genetic variants, understanding pathogen structures, and characterizing diseases. This capability supports applications such as rare disease diagnosis, newborn screening, and cancer profiling, which require precise genomic interpretation. In parallel, pharmacogenomics is emerging as a high-growth segment. AI algorithms analyze how genetic differences affect drug responses, enabling the development of targeted therapies and reducing the likelihood of adverse drug reactions. As pharmaceutical research increasingly embraces genome-guided drug development, the demand for AI solutions in pharmacogenomics continues to expand.

Market Segmentation by Application

Applications of AI in genomics are wide-ranging, but drug discovery and development currently account for the largest market share. AI accelerates target identification, optimizes lead compounds, and informs clinical trial strategies, reducing the time and cost of bringing new therapies to market. By contrast, precision medicine is expected to experience the fastest growth. The integration of AI in precision medicine allows researchers to analyze multi-omics datasets, identify patient subgroups, and design personalized treatment strategies. The rapid evolution of AI-driven tools in this area, coupled with increasing clinical adoption and reimbursement support for genomic testing, drives strong growth prospects.

Market Segmentation by Delivery Mode

The delivery mode of AI solutions is also evolving, with cloud and web-based platforms projected to register the fastest growth. Cloud-based models provide several advantages, including scalability, cost-effectiveness, secure data storage, and ease of collaboration across geographically distributed teams. They enable real-time analysis of large genomic datasets without the need for extensive on-premises infrastructure, making AI-driven genomics more accessible to a broader range of users. Organizations increasingly prefer cloud-based platforms for their flexibility and ability to integrate seamlessly with other digital tools, further reinforcing their adoption.

Regional Analysis

From a regional perspective, North America currently dominates the AI in genomics market due to its established healthcare infrastructure, extensive investment in research, and high adoption of innovative technologies. The region benefits from a mature ecosystem that includes research institutions, hospitals, and pharmaceutical companies, all of which contribute to the high uptake of AI-powered genomics solutions. Conversely, Asia-Pacific is projected to be the fastest-growing region over the forecast period. Growth in this region is driven by increasing government support for genomics research, emerging startups specializing in AI-driven genomic technologies, and growing clinical adoption of precision medicine. Countries such as China, India, Japan, and South Korea are investing heavily in both AI infrastructure and genomic research initiatives, fueling market expansion.

Emerging Trends in AI for Genomics

Several trends are shaping the evolution of the AI in genomics market. The integration of large language models and generative AI in genomic analysis is revolutionizing the field by enabling predictive modeling of protein structures, interpretation of biological sequences, and identification of novel therapeutic targets. Generative AI algorithms can rapidly generate hypotheses, design new genetic sequences, and simulate biological processes at a scale previously unattainable, accelerating research in both human health and agricultural applications. Additionally, AI is increasingly applied in clinical diagnostics, particularly in the identification of rare diseases. AI-enabled interpretation of whole genome and exome sequencing data allows for rapid diagnosis, reducing the time required to detect rare genetic disorders from weeks to mere hours. This capability enhances patient care, supports clinical decision-making, and improves the efficiency of healthcare systems.

Investment and Infrastructure Support

Investment in AI infrastructure and genomics research also underpins market growth. Public and private funding supports the development of advanced computational platforms, the curation of large genomic datasets, and the expansion of AI capabilities in healthcare and research. This financial support not only facilitates technology development but also promotes adoption across different segments of the market. Academic institutions, research laboratories, and healthcare organizations are increasingly leveraging AI to analyze genomic data, supporting drug discovery, biomarker identification, and precision medicine applications.

End-User Insights

The end-user landscape of the AI in genomics market is diverse. Pharmaceutical and biopharmaceutical companies lead in market share due to their reliance on AI for drug discovery, development, and precision medicine initiatives. Academic and research institutions also play a significant role, using AI to advance genomic research, analyze complex datasets, and generate insights for scientific discovery. Hospitals and clinical laboratories are expanding the adoption of AI in diagnostics, particularly in genomic sequencing and rare disease identification. Collectively, these end users drive the widespread application of AI technologies in genomics, fostering innovation and expanding the market.

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Conclusion

The artificial intelligence in genomics market is on a trajectory of rapid growth, driven by technological advancements, increasing genomic data availability, and the rising demand for precision medicine and faster drug discovery. AI's ability to process, analyze, and interpret large-scale genomic data has transformed the landscape of healthcare and life sciences research, enabling more efficient, accurate, and cost-effective applications. The market is characterized by software dominance, rapid growth in service offerings, strong regional expansion, and the emergence of generative AI and large language models as pivotal tools in genomic analysis. With continued investment, innovation, and adoption, the market is projected to reach nearly USD 20 billion by 2036, establishing AI as an essential driver of progress in genomics.

Key Questions Answered in the Report

What is the expected compound annual growth rate (CAGR) of the AI in genomics market from 2026 to 2036?
Which region currently dominates the artificial intelligence in genomics market?
How does AI help reduce the time and cost of drug discovery and development?
Why is the integration of AI in precision medicine considered a market growth driver?
Which segment-software or services-accounts for the largest share of the AI in genomics market?
Which segment is projected to grow at the fastest CAGR during the forecast period?
Which functionality segment dominates the AI in genomics market in 2026?
Why is pharmacogenomics expected to witness significant growth during the forecast period?
Which application holds the largest share of the AI in genomics market?
Why is the cloud and web-based delivery model expected to grow faster than on-premises solutions?

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