AI In Omics Market Size & Share Analysis - Growth Trends and Forecast (2026 - 2031)

Global AI In Omics Market Trends and Insights

Precision Medicine Adoption Reshaping Drug Development Economics

Precision oncology and rare disease workflows are moving molecular profiling closer to the center of therapeutic selection, which is changing how clinical teams decide where to place time, capital, and trial resources in the AI in omics market. That shift matters because multi-omics models can help narrow patient groups earlier, which supports better alignment between biological signal, trial design, and downstream treatment response. Tempus AI's Lens platform shows that this model is scaling commercially, with Data and Applications revenue reaching USD 316.4 million in 2025, up 30.9% year over year. Tempus also reported total 2025 revenue of USD 1.3 billion, which indicates that AI-linked clinical and data services are moving beyond isolated pilot work and into repeatable operating models. As these workflows move earlier in development, disease-specific pathway findings can be reused across related programs, which improves the commercial return on each curated dataset. That is helping the AI in omics market attract spending from drug discovery groups, translational researchers, and clinically focused software vendors at the same time.

Rising Omics Data Volumes Straining Legacy Analytical Infrastructure

Rising data volumes remain a basic growth engine for the AI in omics market because multi-layer biological datasets are becoming too large and too complex for many legacy analytical stacks. Modern projects rarely work with one modality in isolation, so sequence, expression, protein, phenotype, and clinical context often need to be processed together inside the same computational environment. That creates pressure not just on storage, but also on preprocessing, harmonization, inference speed, validation tracking, and retraining capacity as new records continue to arrive. NVIDIA states that Parabricks can deliver more than 100 times faster whole-genome analysis at 50% lower compute cost than CPU workflows, which shows why accelerated and cloud-ready pipelines are gaining favor. Once an institution commits to that stack, migration becomes harder because data models, validation routines, and user habits become tied to the platform architecture. This is why the AI in omics market is moving away from stand-alone tools and toward integrated environments that combine compute, analytics, and workflow management in a single layer.

AI Compressing the Biomarker-to-Clinical-Trial Pipeline

The AI in omics market is also benefiting from a shorter path between biomarker discovery, patient selection, and clinical testing, which has long been one of the hardest bottlenecks in translational medicine. Cancer Cell reported that contrastive learning on multi-omics datasets improved patient selection in a retrospective analysis and reduced survival risk by 15% versus the original Phase III trial population. The same study noted that validated predictive biomarkers are linked to a twofold higher approval likelihood, even as many oncology programs still fail in development. That changes the economics of discovery because companies can direct capital toward better-matched patient subsets earlier, instead of learning those boundaries only after costly late-stage work. It also raises the value of longitudinal proprietary datasets, since repeat measurements improve both biomarker confidence and model generalizability across cohorts. As a result, the AI in omics market is favoring firms that combine unique data access, clinical context, and deployable modeling systems rather than those that only offer general analytics.

Companion Diagnostics Embedding AI Omics Into Regulatory Workflows

The expansion of clinical multiomic assays is turning the AI in omics market into a more regulated and more reimbursable operating space, which changes what kinds of vendors can scale. The U.S. Food and Drug Administration approved Guardant360 CDx as a companion diagnostic for Eli Lilly's Inluriyo in September 2025, which marked the test's sixth companion diagnostic claim^{[1]U.S. Food and Drug Administration, “P200010/S021: Guardant360® CDx,” U.S. Food and Drug Administration, accessdata.fda.gov.}. The agency also cleared the Oncomine Dx Express Test in July 2025 as a companion diagnostic for sunvozertinib. Its Predetermined Change Control Plan is important because it creates a structured route for in-market updates to AI-driven variant-calling software without requiring a full reset of the validation approach. That lowers some operational friction for established vendors that already have quality systems, documentation processes, and post-market monitoring in place. In the AI in omics market, this increasingly favors companies that can pair assay performance with compliant software maintenance and evidence-backed update cycles.

Data Privacy and Consent Constraints Fragmenting Training Datasets

Data privacy and consent rules keep the AI in omics market from using all of the data it generates, even when the scientific value of those records is high. Whole-genome sequences cannot be de-identified as easily as many other clinical records, so institutions still face strict governance expectations around access, storage, linkage, and secondary use. The European Commission states that the EHDS Regulation entered into force in March 2026 and covers secondary use of genomic, proteomic, transcriptomic, metabolomic, and epigenomic data, but national Health Data Access Bodies will only become operational over time^{[2]European Commission, “European Health Data Space Regulation,” European Commission, europa.eu.}. That means near-term access remains uneven, with local consent rules, legal reviews, and security requirements still slowing multi-site dataset assembly. The hardest-to-access material often includes underrepresented ancestry groups, which can deepen bias when models are trained on narrower and less representative cohorts. Federated learning can help the AI in omics market reduce central data transfer, but it still depends on shared governance, compatible workflows, and reliable coordination across institutions.

Multiomics Data Standardization Constraining Cross-Platform Interoperability

Standardization gaps are another drag on the AI in omics market because multi-omics products depend on data that can move cleanly across laboratories, platforms, and analytical settings. Scientific Reports found that an assessment of 10,109 public omics datasets across 16 data types showed biological meta-information remained highly heterogeneous and non-standardized. The same fragmentation is especially difficult in proteomics and metabolomics, where platform choices, sample preparation methods, and normalization practices create strong batch effects that can weaken model transferability. Vendors therefore spend heavily on internal harmonization layers before they can train, benchmark, and validate models at scale across different cohorts. Those steps increase development cost and extend timelines, which is a heavier burden for smaller suppliers that do not have broad informatics teams or long implementation cycles. In the AI in omics market, interoperability is therefore a direct limit on commercialization speed rather than only a technical inconvenience.

*Our updated forecasts treat driver/restraint impacts as directional, not additive. The revised impact forecasts reflect baseline growth, mix effects, and variable interactions.

Segment Analysis

By Component: Services Capturing Value as AI Complexity Scales

Software accounted for 55.2% of 2025 revenue, which kept it as the largest component in the AI in omics market and reflected the strength of platform-led commercialization. That lead came from cloud-based environments that combine prebuilt pipelines, curated knowledge layers, compute access, and visualization tools into one operating system for omics analysis. For many users, this reduces the need to build internal infrastructure before beginning multi-omics work, which makes adoption easier for teams that have data but limited engineering capacity. It also shortens time to use because analysts can move from raw files to interpretation inside a single workflow instead of stitching together multiple tools. Hardware remained the smallest component because more analytical workloads are now being separated from local instrumentation and shifted into scalable remote environments.

Services are projected to grow at 34.9% CAGR through 2031, which makes them the fastest-expanding component of the AI in omics market size and shows where incremental value creation is moving. Demand is shifting toward custom model development, bioinformatics consulting, workflow management, and long-cycle data integration support as projects become harder to standardize across institutions and use cases. QIAGEN said its Digital Insights business delivered double-digit constant-exchange-rate growth in FY2025 and that the company plans at least 14 AI-enabled applications by 2028. That matters because the AI in omics industry is creating recurring service demand around software cores, especially where users need validation, retraining, regulatory documentation, and workflow tuning after initial deployment. As AI tools move deeper into regulated and clinically linked settings, external support becomes less optional and more embedded in the day-to-day operating model.

By Omics Type: Genomics Anchoring the Stack With Expanding Multi-Layer Depth

Genomics held 35.2% of revenue in 2025 and is forecast to expand at 40.1% CAGR through 2031, which means it leads both scale and growth in the AI in omics market. That position reflects its role as the base layer for most multi-omics workflows, since genomic sequence often serves as the starting frame for linking downstream expression, protein, and phenotype signals. Population-scale sequencing and the wider use of biobank-linked research continue to enlarge the training base available for model development, which reinforces genomics as the most commercially central omics layer. Transcriptomics and proteomics are gaining ground as complementary layers because they add functional context that sequence data alone cannot provide in clinical and drug development settings. Metabolomics and epigenomics remain smaller in revenue, but research interest keeps rising because both layers help explain phenotypic variability that sequence changes do not fully capture on their own.

The AI in omics market still centers on genomics because new multimodal models usually begin with sequence data and then attach other biological layers as the context widens. NVIDIA said Evo 2 is a 40-billion parameter genomic foundation model trained on 8.85 trillion nucleotides from more than 128,000 genomes. That scale supports the view that genomic sequence is becoming the primary substrate for larger biological foundation models that can later absorb transcriptomic, proteomic, and clinical context. Within the AI in omics industry, this keeps genomics at the center of product design, long-horizon model training, and data licensing priorities across the value chain. It also means suppliers that already control high-quality genomic workflows hold an important starting advantage as broader multi-omics platforms continue to evolve.

By AI Technology: Machine Learning Dominates, NLP Emerging as Strategic Differentiator

Machine learning accounted for 39.3% of 2025 revenue, making it the largest AI technology segment in the AI in omics market and underscoring how established these methods already are in operational use. Its lead came from variant calling, biomarker discovery, patient stratification, and related tasks where production workflows have already been validated and adopted by laboratories and research teams. Deep learning has strengthened that position in settings where pattern recognition matters more than manually engineered features, especially as datasets become larger and less tractable for simpler approaches. Computer vision and data mining remain smaller revenue pools, but spatial omics is steadily making image-linked analysis more important in both research and translational use. As the mix of data types broadens, technology leadership is becoming less about one algorithm class and more about how well each method fits the evidence structure of a given workflow.

Natural language processing is projected to grow at 32.2% CAGR through 2031, the fastest rate among AI technology groups in the AI in omics market and a sign that text-heavy evidence layers are becoming more commercially relevant. A large share of usable context on variants, drug-gene interactions, disease mechanisms, and trial eligibility still sits inside unstructured literature, regulatory material, and clinical notes rather than neat analytical tables. DBCLS reported in May 2026 that Japan's ChatTogoVar outperformed GPT-4o for genomic variant interpretation in a study published in Human Genome Variation. That matters because stronger retrieval and interpretation of text can expand the evidence available to downstream molecular models without waiting for entirely new wet-lab data generation. In practical terms, NLP is becoming a differentiator for vendors that want to offer interpretable, clinically useful outputs instead of only predictive scores.

By Application: Drug Discovery Leads, Precision Medicine Accelerating

Drug discovery and development represented 35.4% of 2025 revenue, which made it the largest application in the AI in omics market and kept pharmaceutical R&D at the center of spending. The segment remains large because drug developers were early adopters of multi-omics analytics, and they continue to use these tools to improve target selection, biomarker strategy, and trial design. Tempus reported USD 316.4 million in 2025 Data and Applications revenue, which shows the scale of demand for data-linked analytical platforms that support research and development programs. Clinical diagnostics, biomarker discovery, and target identification make up the rest of the application mix, and each benefits when patient stratification becomes more precise and easier to operationalize. As long as R&D productivity remains a central concern, discovery programs are likely to keep absorbing a large share of commercial spending across platform and services vendors.

Precision medicine is forecast to expand at 36.3% CAGR through 2031, the fastest pace among applications in the AI in omics market and a sign that molecular profiling is moving into wider clinical use. This reflects a move beyond late-stage oncology use toward earlier and broader workflows in oncology and other disease areas where molecular context can guide care more directly. Tempus said in early 2026 that its pan-cancer HRD algorithm uses RNA expression data to identify patients who may benefit from PARP inhibitors beyond traditional DNA-only testing. That kind of approach raises the value of existing datasets because it broadens patient selection without requiring entirely new rounds of sample collection. Over time, this should keep pushing clinical demand toward tools that can combine molecular detail, explainability, and deployment-ready software inside the same offering.

By End User: Pharma Anchors Revenue, Academia Drives Model Development

Pharmaceutical and biotechnology companies captured 45.8% of 2025 revenue, which gave them the largest place in the AI in omics market and reflected their role as the main buyers of data-rich analytical solutions. Their lead comes from the pressure to shorten development timelines, improve target quality, reduce failed trial spending, and build stronger biomarker strategies earlier in the pipeline. These buyers also have the budgets to license multi-omics data, deploy integrated platforms, and validate outputs in settings that often require higher standards of traceability and documentation. Hospitals and clinical laboratories form the next layer of demand because growing genomic testing volumes are linking day-to-day care more closely with the data pipelines that feed model training and refinement. That clinical link matters because routine sequencing and liquid biopsy workflows can keep refreshing real-world evidence for both commercial and academic users.

Academic and research institutes are projected to grow at 35.5% CAGR through 2031, the fastest pace among end users in the AI in omics market and a sign that model development capacity is spreading beyond commercial buyers. Germany's MoReHealth Niedersachsen initiative launched in 2025 as a EUR 3 million (USD 3.49 million) multi-omics program integrating AI-supported tools across 4 institutions. This shows that public research funding is building coordinated data-generation networks instead of isolated laboratory projects, which supports larger and more reusable datasets over time. In the AI in omics industry, that academic growth strengthens the supply of training data, validation cohorts, and translational partnerships that commercial vendors later depend on. Contract research organizations remained the smallest end-user segment, but their role is still increasing where sponsors want model development, validation, and assay support bundled into one external engagement.

Geography Analysis

North America held 38.2% of the AI in omics market share in 2025, which made it the largest regional revenue pool and reflected the concentration of sequencing infrastructure, AI talent, and pharmaceutical research spending in one ecosystem. The United States drives that lead because clinical genomics networks, data-oriented software firms, academic medical centers, and large drug developers are already deeply connected. Tempus reported USD 1.3 billion in 2025 revenue, up 83.4% year over year, which illustrates the commercialization scale that the U.S. environment can support when clinical data, analytics, and payer familiarity line up. The U.S. Food and Drug Administration's 2025 companion diagnostic decisions also showed that AI-linked molecular tests can move through recognizable regulatory paths when evidence packages are strong. That combination of infrastructure depth, reimbursement familiarity, and regulatory experience keeps North America ahead in deployment speed across the AI in omics market.

Europe remains a major region in the AI in omics market, even as its policy structure both enables longer-term data use and raises near-term compliance costs for many participants. The European Commission states that the EHDS Regulation entered into force in March 2026 and covers secondary use of genomic, proteomic, transcriptomic, metabolomic, and epigenomic data. Germany's genomDE model project had integrated more than 5,000 patients by autumn 2024 and targets 100,000 whole-genome sequences over 5 years^{[3]Deutsches Ärzteblatt, “Genomsequenzierung Auf Dem Weg in Die Regelversorgung,” Deutsches Ärzteblatt, aerzteblatt.de.}. These programs should expand the region's usable data base over time, but the operational burden of the EU AI Act and related governance steps still favors better-established vendors.

Asia-Pacific is projected to grow at 37.4% CAGR, which gives it the fastest regional expansion in the AI in omics market size through 2031 and points to rising momentum in government-backed genomics and digital health programs. That pace reflects efforts across major Asian economies to build local sequencing capacity, strengthen clinical data systems, and reduce dependence on Western ancestry-biased training sets. Japan also shows active translation of language models into genomics, with DBCLS reporting in 2026 that ChatTogoVar outperformed GPT-4o in genomic variant interpretation. South America and the Middle East and Africa remain smaller today, but better healthcare infrastructure and growing startup activity should gradually widen future adoption opportunities.