제약 품질관리 분야 AI 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the aI in pharmaceutical quality management market size is expected to grow from USD 0.66 billion in 2025 to USD 0.79 billion in 2026 and is forecast to reach USD 1.87 billion by 2031 at 18.85% CAGR over 2026-2031.

This report is Segmented by Component (Software, Services), Deployment Model (Cloud-Based, On-Premises, Hybrid), AI Capability (Generative AI Copilots & Agents, Predictive Analytics & Risk Scoring, and More), End User (Pharmaceutical Companies, Biotechnology Companies, and More), and by Geography (North America, and More). The Market Forecasts are Provided in Terms of Value (USD).

Global AI In Pharmaceutical Quality Management Market Trends and Insights

AI Automation Of Deviation, CAPA, And Investigation Workflows

Managing deviations and CAPAs still absorbs 4% to 6% of total resources at a life science manufacturing site, and legacy workflows often extend investigations to 45 to 90 days. The AI in pharmaceutical quality management market is gaining momentum here because AI-assisted deviation classification, root-cause research, and draft preparation are cutting documentation time by 40% to 60% and improving triage speed by 15% to 30% in reported deployments. These gains matter beyond speed because more consistent CAPA records make it easier to compare quality signals across sites and detect recurring process failures that isolated site teams can miss. Astellas Pharma's April 2026 deployment of a seven-agent AI setup for deviation processing across its network reported a 67% reduction in investigation workload and an estimated 980 monthly hours recovered at full rollout. Regulatory expectations under 21 CFR Part 11, ICH Q10, and GAMP 5 are keeping human review and audit trails central, which is shaping how these tools are configured and approved.

Rising Demand For Predictive Quality Risk Detection Across GMP Operations

Predictive quality risk detection is becoming one of the clearest reasons companies are investing in the AI in pharmaceutical quality management market, because it helps teams identify batch risk before conventional in-process testing reveals the problem. A 2025 study in Scientific Reports showed that deep learning models predicting critical quality attributes from process parameters achieved R2 values above 0.9 and reduced out-of-specification batches by 18% over 3 production cycles. In sterile fill-finish settings, one deployment cited in the draft produced a 12% reduction in batch rejections and stronger audit traceability, which supports the case for using AI in high-control environments. The FDA's internal Project Elsa, scaled agency-wide by January 2026, is reinforcing this shift because regulators can now scan electronic records rapidly to target high-risk inspections. A 2026 Journal of Pharmaceutical Innovation study that modeled FDA Form 483 data also found data integrity and CAPA effectiveness to be the strongest predictors of repeat regulatory exposure, which ties predictive quality investment directly to inspection risk.

GxP Model Validation, Explainability, And Audit-Trail Requirements

Model explainability still raises deployment cost in the AI in pharmaceutical quality management market because quality teams need traceability, documented controls, and credible human review before AI can be used at scale. The draft notes that EMA's draft Annex 22 limits probabilistic and adaptive models in critical GMP use cases and keeps generative AI in supervised non-critical roles, which is splitting vendor roadmaps into compliant and non-compliant paths. The FDA's April 2026 warning letter to Purolea Cosmetics Lab also showed that AI-generated manufacturing records cannot replace expert review that can identify missing regulatory elements. A 2026 analysis cited in the draft said that AI systems need thorough tracking with complete traceability inside the quality system, which adds model version control, input and output logging, and drift monitoring to the normal validation burden. These requirements are not temporary, so vendors that cannot demonstrate clear auditability will find it harder to compete across the AI in pharmaceutical quality management market.

Other drivers and restraints analyzed in the detailed report include:

1. Need To Reduce Batch Review, Release, And APQR Cycle Times
2. CSA-Led Acceptance Of AI-Enabled Validation Approaches In GxP Systems
3. Poor Data Readiness Across Legacy Quality Records And Disconnected Systems

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software retained 65.2% of AI in pharmaceutical quality management market size in 2025, which reflects the strong position of enterprise eQMS, AI-enhanced LIMS, and deviation management platforms in large pharma accounts. The AI in pharmaceutical quality management market remains software-led because major manufacturers are embedding copilots for CAPA drafting, document summaries, and regulatory gap checks inside long-term platform contracts. These subscriptions are not only about automation, because buyers also want one validated environment that can connect deviation handling, training, document control, and investigation records. That makes software the foundation of daily digital quality work across the AI in pharmaceutical quality management market.

Services are growing faster than the overall market at 19.9% through 2031, because software alone does not solve CSA adoption, data preparation, retraining, or ongoing model governance. The AI in pharmaceutical quality management industry still has a wide knowledge gap around CSA, which supports demand for external implementation and validation help. Managed services are also expanding into drift monitoring, periodic re-validation, and audit-trail maintenance as buyers ask for support beyond the initial launch. This is especially relevant for companies moving from older CSV practices to risk-based assurance models that need new documentation habits and governance routines. As a result, service revenues are scaling alongside software deployments rather than trailing them.

Cloud-based deployment held the largest share in 2025, while on-premises was projected to expand at 20.1% through 2031. in pharmaceutical quality management market continues to rely on cloud infrastructure because vendors can provide compliance documentation and controlled environments that simplify qualification for many buyers. Hybrid deployment remains relevant for organizations that need to connect newer digital tools with legacy plants and older record architectures. Cloud therefore still anchors the mainstream deployment base even as the mix is shifting.

On-premises growth is faster at 20.1% through 2031 because data residency, validation control, and audit-trail ownership carry more weight in high-risk GxP settings. The draft links this shift to European and APAC demand for local inference, especially where GDPR, localized regulatory requirements, or plant-specific governance rules limit comfort with multi-tenant environments. This is creating a more balanced deployment pattern in the AI in pharmaceutical quality management market than a standard enterprise software category would normally show. Buyers are not rejecting cloud, but they are drawing a clearer line between lower-risk quality tasks and the highest-risk workflows that need tighter local control. That distinction is likely to keep hybrid and on-site models relevant for years.

Geography Analysis

North America held 38.2% of AI in pharmaceutical quality management market share in 2025, which kept it in the leading regional position. The AI in pharmaceutical quality management market is strongest in this region because FDA activity is dense and manufacturers face a more visible enforcement and governance environment. The draft points to Project Elsa and the FDA's broader digital inspection posture as signals that U.S. companies need stronger pre-inspection readiness across electronic records. The White House direction on strategic active pharmaceutical ingredient reserves also supports additional quality system spending by facilities expecting closer scrutiny and greater supply assurance obligations. Canada and Mexico add support through MDSAP-linked compliance demand and manufacturing expansion, but the core regional advantage still comes from the United States and its risk-based assurance framework.

Europe was the second-largest regional market, and its demand profile is being shaped by a more structured governance-first approach. The AI in pharmaceutical quality management market in Europe is being influenced by draft Annex 22, the revised Annex 11 discussion, and the EU AI Act, all of which raise the compliance threshold for deployable systems. Germany stands out through the Qua2ntum project led by Fraunhofer IPT with Sartorius, Groninger, and OCTUM, which is building QMS frameworks for AI in regulated settings. Spain, Italy, and France are also moving forward through generics clusters that compete heavily on operational quality performance.

Asia-Pacific was projected to expand at 19.4% through 2031, which made it the fastest-growing region in the draft. The AI in pharmaceutical quality management market is benefiting here from China's April 2026 AI plus drug regulation roadmap, which set out targets for intelligent inspection, AI-assisted dossier review, and multi-source risk aggregation through 2030. India is also moving from reactive data integrity control toward predictive quality models under its Pharma 4.0 push, while Japan is building a credibility assessment framework for AI and is improving structured data availability through eCTD 4.0 adoption from 2026. South Korea's move toward AI-based drug approval review in 2026 adds another regional tailwind, while MEA and South America remain earlier-stage markets led mainly by multinational standardization efforts and selective geographic expansion.

1. AmpleLogic
2. ComplianceQuest
3. Dassault Systemes (BIOVIA)
4. Dot Compliance
5. Honeywell / Sparta Systems
6. Ideagen
7. Intellect
8. IQVIA
9. Korber Pharma
10. Leucine
11. MasterControl
12. Octave (ETQ Reliance)
13. Oracle
14. Qualio
15. Qualityze
16. Scilife
17. SimplerQMS
18. Veeva Systems

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions & Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 AI Automation of Deviation, CAPA, and Investigation Workflows
  • 4.2.2 Rising Demand for Predictive Quality Risk Detection Across GMP Operations
  • 4.2.3 Need to Reduce Batch Review, Release, and APQR Cycle Times
  • 4.2.4 CSA-Led Acceptance of AI-Enabled Validation Approaches in GxP Systems
  • 4.2.5 Multi-Site Quality Signal Mining Across QMS, MES, LIMS, and ERP Data
• 4.3 Market Restraints
  • 4.3.1 GxP Model Validation, Explainability, and Audit-Trail Requirements
  • 4.3.2 Poor Data Readiness Across Legacy Quality Records and Disconnected Systems
  • 4.3.3 QA User Trust, Adoption, And Human-In-The-Loop Governance Challenges
• 4.4 Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 Market Size & Growth Forecasts (Value, USD)

• 5.1 By Component
  • 5.1.1 Software
  • 5.1.2 Services
• 5.2 By Deployment Model
  • 5.2.1 Cloud-Based
  • 5.2.2 On-Premises
  • 5.2.3 Hybrid
• 5.3 By AI Capability
  • 5.3.1 Generative AI Copilots & Agents
  • 5.3.2 Predictive Analytics & Risk Scoring
  • 5.3.3 NLP / Document Intelligence
  • 5.3.4 Anomaly Detection & Pattern Recognition
  • 5.3.5 Recommendation Engines & Root-Cause Guidance
  • 5.3.6 Computer Vision for Quality Inspection
• 5.4 By End User
  • 5.4.1 Pharmaceutical Companies
  • 5.4.2 Biotechnology Companies
  • 5.4.3 CDMOs / CMOs
  • 5.4.4 Other End Users
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Spain
    • 5.5.2.6 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 India
    • 5.5.3.3 Japan
    • 5.5.3.4 South Korea
    • 5.5.3.5 Australia
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 Middle East and Africa
    • 5.5.4.1 GCC
    • 5.5.4.2 South Africa
    • 5.5.4.3 Rest of Middle East and Africa
  • 5.5.5 South America
    • 5.5.5.1 Brazil
    • 5.5.5.2 Argentina
    • 5.5.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles {(includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products & Services, and Recent Developments)}
  • 6.3.1 AmpleLogic
  • 6.3.2 ComplianceQuest
  • 6.3.3 Dassault Systemes (BIOVIA)
  • 6.3.4 Dot Compliance
  • 6.3.5 Honeywell / Sparta Systems
  • 6.3.6 Ideagen
  • 6.3.7 Intellect
  • 6.3.8 IQVIA
  • 6.3.9 Korber Pharma
  • 6.3.10 Leucine
  • 6.3.11 MasterControl
  • 6.3.12 Octave (ETQ Reliance)
  • 6.3.13 Oracle
  • 6.3.14 Qualio
  • 6.3.15 Qualityze
  • 6.3.16 Scilife
  • 6.3.17 SimplerQMS
  • 6.3.18 Veeva Systems

7 Market Opportunities & Future Outlook

• 7.1 White-space & unmet-need assessment