AI In Genomics Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component (Hardware, Software, Services), By Technology (Machine Learning {Deep Learning, Supervised Learning, Unsupervised Learning, Others}, Computer Vision), By Functionality (Genome Sequencing, Gene Editing, Others), By Application (Drug Discovery & Development, Precision Medicine, Diagnostics, Others), By End Use (Pharmaceutical and Biotech Companies, Healthcare Providers, Research Centers, Others), By Region and Competition, 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 733.03 Million
CAGR (2026-2031)	10.23%
Fastest Growing Segment	Machine Learning
Largest Market	North America
Market Size (2031)	USD 1314.98 Million

Market Overview

The Global AI In Genomics Market will grow from USD 733.03 Million in 2025 to USD 1314.98 Million by 2031 at a 10.23% CAGR. Global AI in Genomics involves the application of machine learning algorithms and computational intelligence to interpret complex genetic datasets for advancements in drug discovery, clinical diagnostics, and precision medicine. The market is primarily driven by the substantial reduction in DNA sequencing costs and the urgent demand for personalized healthcare solutions that require the rapid analysis of biological information. According to the Global Alliance for Genomics and Health, in 2024, its global partner network stewarded over 3,000,000 genomes, providing the massive volume of standardized data necessary to train robust, high-performance models. This accessibility to large-scale datasets serves as a foundational pillar supporting the sector's accelerated expansion.

However, a significant challenge impeding broader market scalability is the stringent regulatory landscape surrounding data privacy and ethical governance. The inherent sensitivity of genomic information necessitates rigorous compliance with international data protection frameworks, which often results in informational silos that restrict cross-border collaboration. These complex legal and ethical hurdles complicate the data sharing required for algorithmic validation, potentially delaying the commercialization and deployment of AI tools across diverse healthcare systems.

Key Market Drivers

The Need for Accelerated Drug Discovery and Development stands as a primary catalyst for market expansion, driven by the escalating costs and high failure rates associated with traditional pharmacological research. AI technologies are increasingly deployed to streamline target identification and validation, significantly reducing the time required to bring novel therapies to clinical trials. This shift is exemplified by substantial capital inflows into AI-native biotech firms, which validate the commercial viability of these computational approaches. According to Xaira Therapeutics, April 2024, in the 'Launch Announcement', the company debuted with $1 billion in committed capital to revolutionize drug development through generative AI models. Such massive investments underscore the industry's pivot toward computational biology as a standard modality for creating more effective medicines.

Concurrently, Rapid Advancements in AI and Machine Learning Capabilities are dismantling previous technical barriers, enabling the precise interpretation of multimodal genomic datasets. The emergence of generative AI and specialized microservices allows researchers to model complex biological interactions with unprecedented speed and fidelity, moving beyond simple sequence alignment to predictive functional genomics. According to NVIDIA, March 2024, in the 'GTC 2024 Press Release', the company introduced roughly 25 new generative AI microservices specifically designed to accelerate workflows in drug discovery and genomics. Furthermore, algorithmic sophistication is enhancing predictive precision; according to Google DeepMind, May 2024, in the 'AlphaFold 3 Blog Post', the newly released model demonstrates at least a 50% improvement in prediction accuracy for protein-ligand interactions compared to traditional methods. These technological leaps are fundamental to scaling genomic analysis for precision medicine applications.

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Key Market Challenges

The stringent regulatory landscape surrounding data privacy and ethical governance constitutes a formidable barrier to the growth of the Global AI in Genomics Market. While AI algorithms require vast, diverse datasets to identify rare genetic correlations and validate precision medicine models, varying international data protection laws compel organizations to store information in isolated silos. This fragmentation prevents the cross-border data aggregation necessary for training universally applicable models, thereby limiting the diagnostic accuracy of AI tools across different demographics.

Furthermore, the complexity of adhering to these divergent frameworks increases operational costs and extends development timelines, effectively slowing the commercialization of genomic innovations. The fear of non-compliance and data misuse significantly stalls the adoption of these technologies among practitioners who are essential for market expansion. According to the American Medical Association, in 2024, 87% of physicians cited data privacy assurances as a critical requirement before they would integrate AI-driven tools into their clinical practices. This widespread reluctance, driven by regulatory complexities, directly delays the deployment of personalized healthcare solutions and restricts the market's scalability.

Key Market Trends

The Emergence of Large Genomic Foundation Models marks a pivotal shift from task-specific algorithms to generalized architectures capable of interpreting the fundamental code of life across diverse biological domains. Unlike earlier models limited to narrow applications, these "DNA language models" utilize self-supervised learning on vast corpora of unannotated sequences to decipher complex evolutionary patterns and non-coding region functions without explicit labeling. This architectural advancement enables the generative design of novel genomic sequences with unprecedented predictive fidelity, facilitating breakthroughs in synthetic biology and functional genomics. According to the Arc Institute, February 2025, in the 'Evo 2 Launch Announcement', the organization introduced a model trained on 9.3 trillion DNA base pairs from over 128,000 genomes, achieving state-of-the-art accuracy in predicting mutation effects and designing synthetic biological systems.

Concurrently, the Proliferation of AI-Enhanced Precision Oncology Tools is driving the rapid commercialization of clinical diagnostics, transitioning genomic analysis from research laboratories to routine patient care. AI-native diagnostic firms are scaling their operations by integrating multimodal clinical and molecular data, effectively bridging the gap between sequencing capabilities and actionable therapeutic insights. This surge in adoption demonstrates that healthcare providers are increasingly relying on computational intelligence to guide treatment decisions for complex cancer cases, validating the market's move toward scalable clinical utility. According to Tempus AI, August 2025, in the 'Second Quarter 2025 Earnings Report', the company’s genomics revenue increased 115.3% year-over-year to $241.8 million, underscored by accelerating volume growth in its AI-enabled oncology testing portfolio.

Segmental Insights

Machine Learning represents the fastest-growing segment in the Global AI in Genomics Market, primarily driven by its capacity to process vast datasets generated by genomic sequencing. These algorithms efficiently identify complex genetic patterns and biomarkers, significantly advancing drug discovery and precision medicine. This growth is underpinned by the increasing demand for automated research workflows that reduce development timelines. Furthermore, the U.S. Food and Drug Administration (FDA) has actively established regulatory guidelines for AI-enabled medical devices, providing the necessary compliance structure that instills market confidence and accelerates the commercial adoption of these computational technologies.

Regional Insights

North America maintains a leading position in the global AI in genomics market, driven by substantial investment in pharmaceutical research and a sustained focus on precision medicine. This dominance is supported by the presence of major biotechnology corporations and favorable government initiatives that promote computational biology. For example, continuous funding from the National Institutes of Health (NIH) significantly accelerates genomic data analysis and the development of personalized treatments. Furthermore, the region utilizes an established clinical infrastructure to effectively integrate artificial intelligence into genetic sequencing and drug discovery workflows.

Recent Developments

• In January 2025, Illumina and NVIDIA announced a strategic collaboration to integrate artificial intelligence into genomic research and multiomics data analysis. The partnership focused on combining Illumina’s sequencing technologies with NVIDIA’s advanced accelerated computing and AI capabilities to develop biological foundation models. This initiative aimed to make the analysis of the human genome more accessible and efficient for researchers and pharmaceutical companies. By leveraging generative AI, the companies sought to unlock transformative insights from vast amounts of multiomic data, thereby accelerating the development of precision medicine and new therapeutic treatments.
• In October 2024, Microsoft Corporation unveiled a new collection of healthcare artificial intelligence models within its Azure AI Studio to advance precision health. Developed in collaboration with partners such as Providence and Paige.ai, these multimodal foundation models were designed to integrate and analyze diverse data types, including medical imaging, clinical records, and genomics. The initiative aimed to enable healthcare organizations to build and fine-tune AI solutions tailored to specific needs, such as cancer research and disease detection. By processing complex genomic data alongside other medical information, the tools offered deeper insights to improve diagnostic accuracy and patient outcomes.
• In August 2024, Recursion Pharmaceuticals and Exscientia entered into a definitive agreement to merge, aiming to create a global leader in technology-enabled drug discovery within the AI in genomics market. The all-stock transaction was designed to integrate Recursion's scaled biological exploration capabilities with Exscientia's precision chemistry and automated synthesis platform. By combining their massive proprietary datasets and artificial intelligence tools, the companies sought to industrialize the drug discovery process and improve efficiency significantly. The collaboration was expected to deliver approximately $100 million in annual synergies and strengthen a pipeline consisting of over ten clinical readouts anticipated in the following 18 months.
• In May 2024, Google DeepMind and Isomorphic Labs introduced AlphaFold 3, a revolutionary artificial intelligence model designed to predict the structure and interactions of life's molecules with high precision. This advanced system expanded beyond protein prediction to accurately model DNA, RNA, and ligand interactions, achieving a significant improvement in accuracy over previous methods. The release aimed to accelerate drug discovery and genomics research by allowing scientists to visualize cellular systems in high definition. The organizations also launched the AlphaFold Server, a free tool for the scientific community to access the model's capabilities for non-commercial research, facilitating breakthroughs in understanding biological mechanisms.

Key Market Players

• IBM Corp.
• Deep Genomics Inc.
• Nvidia Corporation
• Data4Cure, Inc.
• Illumina, Inc.
• Thermo Fisher Scientific Inc.
• Sophia Genetics S.A.
• Freenome Holdings, Inc.
• BenevolentAI Ltd.
• Genentech, Inc.

By Component	By Technology	By Functionality	By Application	By End Use	By Region
Hardware Software Services	Machine Learning {Deep Learning Supervised Learning Unsupervised Learning Others} Computer Vision	Genome Sequencing Gene Editing Others	Drug Discovery & Development Precision Medicine Diagnostics Others	Pharmaceutical and Biotech Companies Healthcare Providers Research Centers Others	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global AI In Genomics Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• AI In Genomics Market, By Component:

• Hardware
• Software
• Services

• AI In Genomics Market, By Technology:

• Machine Learning {Deep Learning
• Supervised Learning
• Unsupervised Learning
• Others}
• Computer Vision

• AI In Genomics Market, By Functionality:

• Genome Sequencing
• Gene Editing
• Others

• AI In Genomics Market, By Application:

• Drug Discovery & Development
• Precision Medicine
• Diagnostics
• Others

• AI In Genomics Market, By End Use:

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

• AI In Genomics Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE