AI 활용 테스트 시장 규모, 점유율, 성장 및 세계 업계 분석, 지역별 동향과 예측(2026-2034년)

Growth Factors of AI-enabled testing Market

The global AI-enabled testing market was valued at USD 1.01 billion in 2025 and is projected to grow from USD 1.21 billion in 2026 to USD 4.64 billion by 2034, reflecting strong adoption of artificial intelligence across software quality assurance and testing processes. North America accounted for the largest market share of 34.60% in 2025, supported by early adoption of AI technologies, strong digital infrastructure, and the presence of major software testing solution providers.

AI-enabled testing combines artificial intelligence, machine learning, natural language processing, computer vision, and automation technologies to improve software testing efficiency. These solutions automate repetitive testing tasks, generate intelligent test cases, detect defects earlier, reduce testing time, and improve software quality. Organizations across IT & telecom, BFSI, healthcare, manufacturing, and government sectors are increasingly integrating AI-enabled testing into DevOps and CI/CD pipelines to accelerate software delivery while maintaining high-quality standards. Growing demand for faster application releases, digital transformation initiatives, and cloud-native software development continues to fuel market expansion.

Impact of Generative AI

Generative AI is significantly transforming AI-enabled testing by automating test case generation, defect prediction, script creation, and regression testing. Modern AI-powered testing platforms leverage large language models to create self-healing test scripts, identify application changes automatically, and optimize testing workflows with minimal human intervention. These capabilities reduce manual effort, improve testing accuracy, shorten software release cycles, and help enterprises deliver high-quality applications faster. As generative AI technologies continue to evolve, organizations are expected to invest more heavily in intelligent testing platforms capable of supporting complex enterprise applications.

AI-enabled Testing Market Trends

One of the major trends shaping the market is the growing adoption of AI-driven security testing. Enterprises are increasingly embedding security testing into software development lifecycles to identify vulnerabilities before deployment. AI-powered security testing automates vulnerability detection, threat analysis, and compliance validation while improving overall software resilience.

Another notable trend is the increasing popularity of no-code and low-code AI testing platforms. These platforms enable business users and quality assurance professionals with limited programming knowledge to create automated test cases using visual interfaces, thereby expanding AI adoption across organizations.

Market Drivers

The primary factor driving market growth is the rising demand for no-code and codeless testing solutions. Organizations seek faster software delivery while minimizing development costs and manual testing efforts. AI-powered testing platforms allow teams to automate regression testing, performance testing, API testing, and functional testing without extensive coding expertise.

Increasing adoption of Agile development, DevOps methodologies, cloud-native applications, and continuous integration/continuous deployment (CI/CD) pipelines is further accelerating demand for intelligent testing platforms. AI also enables predictive analytics, faster bug detection, improved test coverage, and self-healing automation, making software testing more efficient and reliable.

Market Restraints

Despite rapid growth, the market faces challenges due to the high dependency of AI models on quality datasets. AI-powered testing systems require large volumes of accurate training data to deliver reliable testing outcomes. Poor-quality or biased datasets may lead to inaccurate testing results, missed defects, or false positives.

Additionally, integrating AI-enabled testing into legacy software environments often requires significant investment in infrastructure, skilled professionals, and process transformation, which can limit adoption among smaller organizations.

Market Opportunities

Growing investment in AI-powered autonomous testing platforms presents significant growth opportunities. Enterprises increasingly require intelligent testing tools capable of automatically adapting to application changes, maintaining test scripts, and reducing manual intervention.

The expansion of cloud computing, mobile applications, IoT, enterprise software, and digital transformation projects is creating new opportunities for AI-enabled testing vendors to deliver scalable and cost-efficient testing solutions across multiple industries.

Segmentation Analysis

Based on deployment, the cloud segment is expected to dominate the market with 62.80% share in 2026, driven by lower infrastructure costs, greater flexibility, remote accessibility, and faster testing cycles. On-premise deployment continues to remain relevant among organizations requiring greater data privacy and regulatory compliance.

By application, the web-based segment is projected to account for 70.24% of the market in 2026, owing to widespread adoption of web applications, API testing, regression testing, and browser-based enterprise software. Mobile-based testing is also witnessing steady adoption due to rapid smartphone application development.

Based on technology, the machine learning segment is expected to hold 37.48% market share in 2026, as ML algorithms improve test automation, optimize test coverage, and enable predictive defect analysis. Computer vision is anticipated to register the fastest growth due to increasing use in UI validation, autonomous systems, and automotive software testing.

By industry, the IT & telecom segment is projected to dominate with 36.24% market share in 2026, supported by continuous software deployment, cloud adoption, and DevOps implementation. The healthcare industry is expected to record the fastest growth as AI-enabled testing supports medical software validation, compliance, predictive analytics, and digital healthcare innovation.

Regional Outlook

North America remained the leading regional market with a valuation of USD 0.35 billion in 2025 and is projected to reach USD 0.41 billion in 2026. Strong investment in AI technologies, widespread DevOps adoption, and the presence of major software vendors continue to strengthen regional growth.

Europe generated USD 0.26 billion in 2025 and is expected to reach USD 0.31 billion in 2026. Government initiatives promoting artificial intelligence, increasing enterprise automation, and strong cybersecurity regulations are encouraging wider deployment of AI-enabled testing platforms.

Asia Pacific accounted for USD 0.23 billion in 2025 and is forecast to reach USD 0.29 billion in 2026. Rapid digitalization, expanding IT services, cloud adoption, and increasing investments in AI technologies across China, India, Japan, and South Korea are driving significant regional growth.

The Middle East & Africa and Latin America are steadily expanding due to growing investments in artificial intelligence, cloud infrastructure, and enterprise software modernization. Latin America generated USD 0.05 billion in 2025 and is expected to reach USD 0.07 billion in 2026.

Competitive Landscape

The AI-enabled testing market is highly competitive, with companies focusing on AI innovation, strategic partnerships, product launches, and cloud-native testing platforms. Leading vendors continue to integrate machine learning, generative AI, computer vision, and automation capabilities into their testing solutions to improve software quality and accelerate digital transformation.

Major companies operating in the market include Functionize, Sauce Labs, Tricentis, Diffblue, Applitools, Mabl, UBS Hainer GmbH, Testim, Perforce Software, and OpenText (Micro Focus).

Recent industry developments include Mabl's GitLab integration for AI-powered DevSecOps testing, Perforce's generative AI enhancements for automated test data generation, Katalon's AI-based regression testing capabilities, UiPath's AI-driven SAP testing platform, and Applitools' Execution Cloud for AI-powered cloud testing.

Conclusion

The AI-enabled testing market is poised for remarkable growth as organizations increasingly adopt artificial intelligence to modernize software quality assurance. Rising demand for automated testing, no-code platforms, DevOps integration, cloud-native development, and AI-driven security testing is accelerating market expansion worldwide. Continuous technological innovation and growing enterprise investments in intelligent testing platforms are expected to strengthen market growth through 2034.

Segmentation By Deployment

• Cloud
• On-premise

By Application

• Web-based
• Mobile-based

By Technology

• Machine Learning
• NLP (Natural Language Processing)
• Computer Vision
• MBTA (Model-based Test Automation)
• Others (RPA)

By Industry

• IT & Telecom
• BFSI
• Healthcare
• Energy & Utilities
• Others (Government, Education, and Manufacturing)

By Region

• North America (By Deployment, Application, Technology, Industry, and Country)
  • U.S. (By Industry)
  • Canada (By Industry)
  • Mexico (By Industry)
• Europe (By Deployment, Application, Technology, Industry, and Country)
  • U.K. (By Industry)
  • Germany (By Industry)
  • France (By Industry)
  • Italy (By Industry)
  • Spain (By Industry)
  • Russia (By Industry)
  • Benelux (By Industry)
  • Nordics (By Industry)
  • Rest of Europe
• Asia Pacific (By Deployment, Application, Technology, Industry, and Country)
  • China (By Industry)
  • Japan (By Industry)
  • India (By Industry)
  • South Korea (By Industry)
  • ASEAN (By Industry)
  • Oceania (By Industry)
  • Rest of the Asia Pacific
• Middle East & Africa (By Deployment, Application, Technology, Industry, and Country)
  • Turkey (By Industry)
  • Israel (By Industry)
  • GCC (By Industry)
  • North Africa (By Industry)
  • South Africa (By Industry)
  • Rest of the Middle East & Africa
• South America (By Deployment, Application, Technology, Industry, and Country)
  • Brazil (By Industry)
  • Argentina (By Industry)
  • Rest of South America

Table of Content

1. Introduction

• 1.1. Definition, By Segment
• 1.2. Research Methodology/Approach
• 1.3. Data Sources

2. Executive Summary

3. Market Dynamics

• 3.1. Macro and Micro Economic Indicators
• 3.2. Drivers, Restraints, Opportunities and Trends
• 3.3. Impact of COVID-19

4. Competition Landscape

• 4.1. Business Strategies Adopted by Key Players
• 4.2. Consolidated SWOT Analysis of Key Players
• 4.3. Global AI-enabled Testing Key Players Market Share/Ranking, 2025

5. Global AI-enabled Testing Market Size Estimates and Forecasts, By Segments, 2021-2034

• 5.1. Key Findings
• 5.2. By Deployment (USD)
  • 5.2.1. Cloud
  • 5.2.2. On-premise
• 5.3. By Application (USD)
  • 5.3.1. Web-based
  • 5.3.2. Mobile-based
• 5.4. By Technology (USD)
  • 5.4.1. Machine Learning
  • 5.4.2. NLP (Natural Language Processing)
  • 5.4.3. Computer Vision
  • 5.4.4. MBTA (Model-based test automation)
  • 5.4.5. Others (RPA, etc.)
• 5.5. By Industry (USD)
  • 5.5.1. IT & Telecom
  • 5.5.2. BFSI
  • 5.5.3. Healthcare
  • 5.5.4. Energy & Utilities
  • 5.5.5. Others(Government, Education, Manufacturing, etc.)
• 5.6. By Region (USD)
  • 5.6.1. North America
  • 5.6.2. Europe
  • 5.6.3. Asia Pacific
  • 5.6.4. Middle East & Africa
  • 5.6.5. South America

6. North America AI-enabled Testing Market Size Estimates and Forecasts, By Segments, 2021-2034

• 6.1. Key Findings
• 6.2. By Deployment (USD)
  • 6.2.1. Cloud
  • 6.2.2. On-premise
• 6.3. By Application (USD)
  • 6.3.1. Web-based
  • 6.3.2. Mobile-based
• 6.4. By Technology (USD)
  • 6.4.1. Machine Learning
  • 6.4.2. NLP (Natural Language Processing)
  • 6.4.3. Computer Vision
  • 6.4.4. MBTA (Model-based test automation)
  • 6.4.5. Others
• 6.5. By Industry (USD)
  • 6.5.1. IT & Telecom
  • 6.5.2. BFSI
  • 6.5.3. Healthcare
  • 6.5.4. Energy & Utilities
  • 6.5.5. Others
• 6.6. By Country (USD)
  • 6.6.1. United States
    • 6.6.1.1. By Industry
  • 6.6.2. Canada
    • 6.6.2.1. By Industry
  • 6.6.3. Mexico
    • 6.6.3.1. By Industry

7. Europe AI-enabled Testing Market Size Estimates and Forecasts, By Segments, 2021-2034

• 7.1. Key Findings
• 7.2. By Deployment (USD)
  • 7.2.1. Cloud
  • 7.2.2. On-premise
• 7.3. By Application (USD)
  • 7.3.1. Web-based
  • 7.3.2. Mobile-based
• 7.4. By Technology (USD)
  • 7.4.1. Machine Learning
  • 7.4.2. NLP (Natural Language Processing)
  • 7.4.3. Computer Vision
  • 7.4.4. MBTA (Model-based test automation)
  • 7.4.5. Others
• 7.5. By Industry (USD)
  • 7.5.1. IT & Telecom
  • 7.5.2. BFSI
  • 7.5.3. Healthcare
  • 7.5.4. Energy & Utilities
  • 7.5.5. Others
• 7.6. By Country (USD)
  • 7.6.1. United Kingdom
    • 7.6.1.1. By Industry
  • 7.6.2. Germany
    • 7.6.2.1. By Industry
  • 7.6.3. France
    • 7.6.3.1. By Industry
  • 7.6.4. Italy
    • 7.6.4.1. By Industry
  • 7.6.5. Spain
    • 7.6.5.1. By Industry
  • 7.6.6. Russia
    • 7.6.6.1. By Industry
  • 7.6.7. Benelux
    • 7.6.7.1. By Industry
  • 7.6.8. Nordics
    • 7.6.8.1. By Industry
  • 7.6.9. Rest of Europe

8. Asia Pacific AI-enabled Testing Market Size Estimates and Forecasts, By Segments, 2021-2034

• 8.1. Key Findings
• 8.2. By Deployment (USD)
  • 8.2.1. Cloud
  • 8.2.2. On-premise
• 8.3. By Application (USD)
  • 8.3.1. Web-based
  • 8.3.2. Mobile-based
• 8.4. By Technology (USD)
  • 8.4.1. Machine Learning
  • 8.4.2. NLP (Natural Language Processing)
  • 8.4.3. Computer Vision
  • 8.4.4. MBTA (Model-based test automation)
  • 8.4.5. Others
• 8.5. By Industry (USD)
  • 8.5.1. IT & Telecom
  • 8.5.2. BFSI
  • 8.5.3. Healthcare
  • 8.5.4. Energy & Utilities
  • 8.5.5. Others
• 8.6. By Country (USD)
  • 8.6.1. China
    • 8.6.1.1. By Industry
  • 8.6.2. India
    • 8.6.2.1. By Industry
  • 8.6.3. Japan
    • 8.6.3.1. By Industry
  • 8.6.4. South Korea
    • 8.6.4.1. By Industry
  • 8.6.5. ASEAN
    • 8.6.5.1. By Industry
  • 8.6.6. Oceania
    • 8.6.6.1. By Industry
  • 8.6.7. Rest of Asia Pacific

9. Middle East & Africa AI-enabled Testing Market Size Estimates and Forecasts, By Segments, 2021-2034

• 9.1. Key Findings
• 9.2. By Deployment (USD)
  • 9.2.1. Cloud
  • 9.2.2. On-premise
• 9.3. By Application (USD)
  • 9.3.1. Web-based
  • 9.3.2. Mobile-based
• 9.4. By Technology (USD)
  • 9.4.1. Machine Learning
  • 9.4.2. NLP (Natural Language Processing)
  • 9.4.3. Computer Vision
  • 9.4.4. MBTA (Model-based test automation)
  • 9.4.5. Others
• 9.5. By Industry (USD)
  • 9.5.1. IT & Telecom
  • 9.5.2. BFSI
  • 9.5.3. Healthcare
  • 9.5.4. Energy & Utilities
  • 9.5.5. Others
• 9.6. By Country (USD)
  • 9.6.1. Turkey
    • 9.6.1.1. By Industry
  • 9.6.2. Israel
    • 9.6.2.1. By Industry
  • 9.6.3. GCC
    • 9.6.3.1. By Industry
  • 9.6.4. North Africa
    • 9.6.4.1. By Industry
  • 9.6.5. South Africa
    • 9.6.5.1. By Industry
  • 9.6.6. Rest of MEA

10. South America AI-enabled Testing Market Size Estimates and Forecasts, By Segments, 2021-2034

• 10.1. Key Findings
• 10.2. By Deployment (USD)
  • 10.2.1. Cloud
  • 10.2.2. On-premise
• 10.3. By Application (USD)
  • 10.3.1. Web-based
  • 10.3.2. Mobile-based
• 10.4. By Technology (USD)
  • 10.4.1. Machine Learning
  • 10.4.2. NLP (Natural Language Processing)
  • 10.4.3. Computer Vision
  • 10.4.4. MBTA (Model-based test automation)
  • 10.4.5. Others
• 10.5. By Industry (USD)
  • 10.5.1. IT & Telecom
  • 10.5.2. BFSI
  • 10.5.3. Healthcare
  • 10.5.4. Energy & Utilities
  • 10.5.5. Others
• 10.6. By Country (USD)
  • 10.6.1. Brazil
    • 10.6.1.1. By Industry
  • 10.6.2. Argentina
    • 10.6.2.1. By Industry
  • 10.6.3. Rest of South America

11. Company Profiles for Top 10 Players (Based on data availability in public domain and/or on paid databases)

• 11.1. Functionize, Inc.
  • 11.1.1. Overview
    • 11.1.1.1. Key Management
    • 11.1.1.2. Headquarters
    • 11.1.1.3. Offerings/Business Segments
  • 11.1.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.1.2.1. Employee Size
    • 11.1.2.2. Past and Current Revenue
    • 11.1.2.3. Geographical Share
    • 11.1.2.4. Business Segment Share
    • 11.1.2.5. Recent Developments
• 11.2. Sauce Labs Inc.
  • 11.2.1. Overview
    • 11.2.1.1. Key Management
    • 11.2.1.2. Headquarters
    • 11.2.1.3. Offerings/Business Segments
  • 11.2.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.2.2.1. Employee Size
    • 11.2.2.2. Past and Current Revenue
    • 11.2.2.3. Geographical Share
    • 11.2.2.4. Business Segment Share
    • 11.2.2.5. Recent Developments
• 11.3. Tricentis
  • 11.3.1. Overview
    • 11.3.1.1. Key Management
    • 11.3.1.2. Headquarters
    • 11.3.1.3. Offerings/Business Segments
  • 11.3.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.3.2.1. Employee Size
    • 11.3.2.2. Past and Current Revenue
    • 11.3.2.3. Geographical Share
    • 11.3.2.4. Business Segment Share
    • 11.3.2.5. Recent Developments
• 11.4. Diffblue Ltd.
  • 11.4.1. Overview
    • 11.4.1.1. Key Management
    • 11.4.1.2. Headquarters
    • 11.4.1.3. Offerings/Business Segments
  • 11.4.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.4.2.1. Employee Size
    • 11.4.2.2. Past and Current Revenue
    • 11.4.2.3. Geographical Share
    • 11.4.2.4. Business Segment Share
    • 11.4.2.5. Recent Developments
• 11.5. Applitools
  • 11.5.1. Overview
    • 11.5.1.1. Key Management
    • 11.5.1.2. Headquarters
    • 11.5.1.3. Offerings/Business Segments
  • 11.5.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.5.2.1. Employee Size
    • 11.5.2.2. Past and Current Revenue
    • 11.5.2.3. Geographical Share
    • 11.5.2.4. Business Segment Share
    • 11.5.2.5. Recent Developments
• 11.6. mabl Inc.
  • 11.6.1. Overview
    • 11.6.1.1. Key Management
    • 11.6.1.2. Headquarters
    • 11.6.1.3. Offerings/Business Segments
  • 11.6.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.6.2.1. Employee Size
    • 11.6.2.2. Past and Current Revenue
    • 11.6.2.3. Geographical Share
    • 11.6.2.4. Business Segment Share
    • 11.6.2.5. Recent Developments
• 11.7. UBS Hainer GmbH
  • 11.7.1. Overview
    • 11.7.1.1. Key Management
    • 11.7.1.2. Headquarters
    • 11.7.1.3. Offerings/Business Segments
  • 11.7.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.7.2.1. Employee Size
    • 11.7.2.2. Past and Current Revenue
    • 11.7.2.3. Geographical Share
    • 11.7.2.4. Business Segment Share
    • 11.7.2.5. Recent Developments
• 11.8. testim
  • 11.8.1. Overview
    • 11.8.1.1. Key Management
    • 11.8.1.2. Headquarters
    • 11.8.1.3. Offerings/Business Segments
  • 11.8.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.8.2.1. Employee Size
    • 11.8.2.2. Past and Current Revenue
    • 11.8.2.3. Geographical Share
    • 11.8.2.4. Business Segment Share
    • 11.8.2.5. Recent Developments
• 11.9. Perforce Software, Inc.
  • 11.9.1. Overview
    • 11.9.1.1. Key Management
    • 11.9.1.2. Headquarters
    • 11.9.1.3. Offerings/Business Segments
  • 11.9.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.9.2.1. Employee Size
    • 11.9.2.2. Past and Current Revenue
    • 11.9.2.3. Geographical Share
    • 11.9.2.4. Business Segment Share
    • 11.9.2.5. Recent Developments
• 11.10. Open Text
  • 11.10.1. Overview
    • 11.10.1.1. Key Management
    • 11.10.1.2. Headquarters
    • 11.10.1.3. Offerings/Business Segments
  • 11.10.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.10.2.1. Employee Size
    • 11.10.2.2. Past and Current Revenue
    • 11.10.2.3. Geographical Share
    • 11.10.2.4. Business Segment Share
    • 11.10.2.5. Recent Developments

12. Key Takeaways