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Artificial Intelligence in Pharmaceutical Market - Global Forecast 2025-2032

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    Report

  • 195 Pages
  • October 2025
  • Region: Global
  • 360iResearch™
  • ID: 5896366
UP TO OFF until Jan 01st 2026
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Artificial intelligence is reshaping the pharmaceutical sector by driving operational innovation, enhancing regulatory compliance, and unlocking value throughout the development lifecycle. For senior leaders, the adoption of AI represents a strategic pathway to greater efficiency and resilience in a shifting global environment.

Market Snapshot: Current Trajectory and Growth of Artificial Intelligence in Pharmaceuticals

The artificial intelligence in pharmaceutical market has entered an accelerated growth phase, valued at USD 15.79 billion in 2024 and projected to reach USD 20.08 billion in 2025, demonstrating a robust CAGR of 27.61%. By 2032, analysts anticipate this sector could approach USD 111.13 billion. This surge signals a decisive movement towards digital maturity, with pharmaceutical organizations intensifying investments in AI to enable data-driven decision-making, improve discovery pipelines, and implement scalable solutions. As companies deploy AI, drug development, operational modernization, patient-focused initiatives, and therapy innovation are advancing swiftly, enabling better outcomes across the industry’s value chain.

Scope & Strategic Market Segmentation for Artificial Intelligence in Pharmaceuticals

This report offers executive readers comprehensive insights into the evolving landscape of artificial intelligence in the pharmaceutical industry, with a focus on segmentation and key strategic areas. The coverage expands on the drivers and market dynamics that shape AI adoption, illustrating its relevance for innovation, compliance, and competitiveness.

  • Component: Examines the integration of managed services, professional services, clinical trial management platforms, diagnostic and drug discovery software, and compliance solutions crucial to pharmaceutical workflows.
  • Technology: Explores machine learning, computer vision, deep learning, transformers, convolutional and recurrent neural networks, natural language processing, reinforcement learning, text mining, and robotic process automation as enablers for analytics and accelerated research.
  • Therapeutic Area: Assesses how AI is applied across oncology, neurology, immunology, metabolic, cardiovascular, infectious, and respiratory diseases, reflecting its potential to spark advancements in diverse therapeutic pipelines.
  • Applications: Details AI’s use in drug discovery, clinical trial optimization, patient recruitment, risk monitoring, lead and biomarker discovery, precision medicine, and enhancing logistics and supply chain management.
  • Deployment Type: Provides analysis of cloud-based versus on-premises solutions, accounting for data compliance, scalability, and integration with existing IT infrastructures.
  • End User: Profiles usage by pharmaceutical and biotechnology companies, contract research organizations, and academic institutions, highlighting how these entities leverage AI for research, collaboration, and performance gains.
  • Geographies: Offers a breakdown of North and South America, Europe, Middle East and Africa, and Asia-Pacific, with attention to regulatory frameworks, investment patterns, and regional challenges and opportunities.
  • Notable Companies: Showcases approaches taken by AiCure LLC, Atomwise Inc., Exscientia PLC, NVIDIA Corporation, Microsoft Corporation, and Oracle Corporation, underscoring leadership practices in AI integration and development.

Key Takeaways for Senior Stakeholders

  • Artificial intelligence enhances efficiency across all stages of drug development, supporting informed decision-making and advancing process optimization from discovery to launch.
  • Deployment flexibility allows organizations to address regulatory restrictions, supporting secure data handling and scalable growth models based on internal needs.
  • Regional approaches vary: North America leverages advanced digital infrastructure, while Europe and Asia-Pacific focus on harmonized compliance and innovation; emerging markets build foundational digital investments and capabilities.
  • Partnerships between pharmaceutical, technology, and academic organizations accelerate AI maturity, contributing to a richer ecosystem and fostering strategic innovation.
  • Widespread AI use by contract research and biotechnology firms promotes robust validation in clinical studies and strengthens the foundation for evidence-based practice.

Tariff Impact: Navigating New Trade and Supply Chain Realities

United States tariff adjustments are impacting pharmaceutical supply chain expenses, motivating stakeholders to revisit and reconfigure sourcing strategies. As a result, there is a trend towards establishing regional supply hubs, investing in partial reshoring, and adopting decentralized distribution models. AI-driven supply chain platforms and strategic collaborations play a pivotal role in reducing disruption risks, bolstering resilience, and improving access to essential enabling technologies.

Methodology & Data Sources

The analytical approach combines first-hand interviews with industry executives and experts, supported by secondary research from top-tier journals, regulatory databases, and patent filings. Expert workshops are conducted to validate data and offer clarity for executive decision-making.

Why This Report Matters

  • Equips senior leaders with focused intelligence for steering AI investments and guiding innovation strategies through in-depth segmentation and regional context.
  • Clarifies operational models and risk mitigation methods, enabling organizations to better adapt to changes in the pharmaceutical industry landscape.
  • Identifies technology integration and partnership trends that offer opportunities for differentiation and strategic positioning in an evolving market.

Conclusion

Committing to AI enables pharmaceutical organizations to streamline operations, optimize talent allocation, and implement rigorous data governance. Leveraging these insights supports executive leadership in navigating industry transformation and strengthening organizational outcomes.

 

Additional Product Information:

  • Purchase of this report includes 1 year online access with quarterly updates.
  • This report can be updated on request. Please contact our Customer Experience team using the Ask a Question widget on our website.

Table of Contents

1. Preface
1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders
2. Research Methodology
3. Executive Summary
4. Market Overview
5. Market Insights
5.1. Integration of generative AI for accelerated drug candidate structure optimization and synthesis planning
5.2. Application of federated learning frameworks for secure multi-center pharmaceutical data collaboration
5.3. Deployment of AI-driven digital twin models for personalized pharmacokinetic and dynamic simulations in trials
5.4. Development of explainable AI algorithms to ensure regulatory compliance in complex drug approval workflows
5.5. Adoption of deep learning models for high-throughput in silico screening of biologics targeting protein-protein interactions
5.6. Utilization of AI-guided robotic platforms for automated high-content cell-based assay development and analysis
5.7. Implementation of real-time AI-enabled pharmacovigilance systems leveraging social media and EHR data streams
6. Cumulative Impact of United States Tariffs 2025
7. Cumulative Impact of Artificial Intelligence 2025
8. Artificial Intelligence in Pharmaceutical Market, by Component
8.1. Services
8.1.1. Managed Services
8.1.2. Professional Services
8.2. Software
8.2.1. Clinical Trial Management Software
8.2.2. Diagnostic Software
8.2.3. Drug Discovery Platforms
8.2.4. Regulatory Compliance Tools
8.2.5. Supply Chain Management Software
9. Artificial Intelligence in Pharmaceutical Market, by Technology
9.1. Computer Vision
9.1.1. Image Segmentation
9.1.2. Medical Imaging
9.1.3. Object Detection
9.2. Deep Learning
9.2.1. Convolutional Neural Networks
9.2.2. Generative Adversarial Networks
9.2.3. Recurrent Neural Networks
9.2.4. Transformers
9.3. Machine Learning
9.3.1. Reinforcement Learning
9.3.2. Supervised Learning
9.3.3. Unsupervised Learning
9.4. Natural Language Processing
9.4.1. Sentiment Analysis
9.4.2. Speech Recognition
9.4.3. Text Mining
9.5. Robotic Process Automation
10. Artificial Intelligence in Pharmaceutical Market, by Therapeutic Area
10.1. Cardiovascular Diseases
10.2. Immunology
10.3. Infectious Diseases
10.4. Metabolic Diseases
10.5. Neurology
10.6. Oncology
10.7. Respiratory Diseases
11. Artificial Intelligence in Pharmaceutical Market, by Applications
11.1. Clinical Trials
11.1.1. Clinical Data Management
11.1.2. Patient Recruitment
11.1.3. Predictive Analytics
11.1.4. Risk-Based Monitoring
11.2. Drug Discovery
11.2.1. Drug Design
11.2.2. End-Model Validation
11.2.3. Lead Optimization
11.2.4. Target Selection
11.3. Personalized Healthcare
11.3.1. Biomarker Discovery
11.3.2. Genomic Profiling
11.3.3. Precision Medicine Development
11.4. Supply Chain Management
11.4.1. Demand Forecasting
11.4.2. Inventory Management
11.4.3. Logistics Optimization
12. Artificial Intelligence in Pharmaceutical Market, by Deployment Type
12.1. Cloud-Based
12.2. On-Premises
13. Artificial Intelligence in Pharmaceutical Market, by End User
13.1. Academic and Research Institutions
13.2. Contract Research Organizations (CROs)
13.3. Pharmaceutical & Biotechnology Companies
14. Artificial Intelligence in Pharmaceutical Market, by Region
14.1. Americas
14.1.1. North America
14.1.2. Latin America
14.2. Europe, Middle East & Africa
14.2.1. Europe
14.2.2. Middle East
14.2.3. Africa
14.3. Asia-Pacific
15. Artificial Intelligence in Pharmaceutical Market, by Group
15.1. ASEAN
15.2. GCC
15.3. European Union
15.4. BRICS
15.5. G7
15.6. NATO
16. Artificial Intelligence in Pharmaceutical Market, by Country
16.1. United States
16.2. Canada
16.3. Mexico
16.4. Brazil
16.5. United Kingdom
16.6. Germany
16.7. France
16.8. Russia
16.9. Italy
16.10. Spain
16.11. China
16.12. India
16.13. Japan
16.14. Australia
16.15. South Korea
17. Competitive Landscape
17.1. Market Share Analysis, 2024
17.2. FPNV Positioning Matrix, 2024
17.3. Competitive Analysis
17.3.1. AiCure, LLC
17.3.2. Aspen Technology Inc.
17.3.3. Atomwise Inc.
17.3.4. BenevolentAI SA
17.3.5. BioSymetrics Inc.
17.3.6. BPGbio Inc.
17.3.7. Butterfly Network, Inc.
17.3.8. Cloud Pharmaceuticals, Inc.
17.3.9. Cyclica by Recursion Pharmaceuticals, Inc.
17.3.10. Deargen Inc.
17.3.11. Deep Genomics Incorporated
17.3.12. Deloitte Touche Tohmatsu Limited
17.3.13. Euretos Services BV
17.3.14. Exscientia PLC
17.3.15. Insilico Medicine
17.3.16. Intel Corporation
17.3.17. International Business Machines Corporation
17.3.18. InveniAI LLC
17.3.19. Isomorphic Labs Limited
17.3.20. Microsoft Corporation
17.3.21. Novo Nordisk A/S
17.3.22. NVIDIA Corporation
17.3.23. Oracle Corporation
17.3.24. SANOFI WINTHROP INDUSTRIE
17.3.25. Turbine Ltd.
17.3.26. Viseven Europe OU
17.3.27. XtalPi Inc.

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Companies Mentioned

The key companies profiled in this Artificial Intelligence in Pharmaceutical market report include:
  • AiCure, LLC
  • Aspen Technology Inc.
  • Atomwise Inc.
  • BenevolentAI SA
  • BioSymetrics Inc.
  • BPGbio Inc.
  • Butterfly Network, Inc.
  • Cloud Pharmaceuticals, Inc.
  • Cyclica by Recursion Pharmaceuticals, Inc.
  • Deargen Inc.
  • Deep Genomics Incorporated
  • Deloitte Touche Tohmatsu Limited
  • Euretos Services BV
  • Exscientia PLC
  • Insilico Medicine
  • Intel Corporation
  • International Business Machines Corporation
  • InveniAI LLC
  • Isomorphic Labs Limited
  • Microsoft Corporation
  • Novo Nordisk A/S
  • NVIDIA Corporation
  • Oracle Corporation
  • SANOFI WINTHROP INDUSTRIE
  • Turbine Ltd.
  • Viseven Europe OU
  • XtalPi Inc.

Table Information