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AI-enabled Testing Market Size, Share, Growth and Global Industry Analysis By Type & Application, Regional Insights and Forecast to 2026-2034

Published Mar 01, 2026
Length 149 Pages
SKU # FOB21037958

Description

Growth Factors of AI-enabled testing Market

The global AI-enabled testing market is witnessing significant growth due to increasing adoption of artificial intelligence, machine learning, and related technologies in software quality assurance. AI-enabled testing automates test case creation, execution, and defect identification, improving software performance and enhancing user experience. These solutions enable testers to save time, increase test coverage, and develop self-curative and reusable test cases, thereby improving efficiency and accuracy.

According to Fortune Business Insights, the market was valued at USD 1.01 billion in 2025, projected to reach USD 1.21 billion in 2026, and expand to USD 4.64 billion by 2034, with a CAGR of 18.30%. North America dominated the market in 2025 with a 34.60% share, driven by strong IT infrastructure, high adoption of emerging technologies, and presence of major software testing vendors.

COVID-19 Impact

The COVID-19 pandemic accelerated the adoption of AI-enabled testing solutions, as enterprises shifted operations online and faced heightened risks from software failures. AI-enabled testing provided faster bug detection, minimized QA costs, and improved time-to-market. During the pandemic, major collaborations, such as Appvance with PwC Australia in 2020, introduced AI-powered combined test automation systems, further fueling market growth. These solutions ensured continuity in operations, prevented data loss, and improved testing efficiency across industries.

Market Trends

One of the key trends driving the market is the implementation of AI-driven security testing. AI enables identification of vulnerabilities, threat prevention, and integration of security testing into every phase of the Software Development Life Cycle (SDLC). In November 2023, GitHub launched AI-driven application security testing, featuring autofix for code scanning, secret scanning for leaked passwords, and custom expression generators, strengthening security capabilities and accelerating market growth.

Growth Factors

The rising demand for no-code or codeless AI testing platforms is boosting market expansion. Low-code platforms allow stakeholders with limited coding knowledge to build AI-driven test cases, streamlining test automation and making it accessible to a wider audience. By 2025, nearly 70% of newly developed enterprise solutions are expected to use low-code or no-code technologies. For instance, SofySense (April 2023) combines generative AI with no-code mobile application testing, enhancing efficiency, QA assistance, and automation capabilities.

Restraining Factors

AI-enabled testing is highly data-dependent, and biased or insufficient datasets can produce inaccurate or unfair results. Limited data can reduce the model’s ability to detect edge cases, creating potential gaps in software testing. Developers must prioritize diverse and representative datasets to minimize bias and enhance testing accuracy.

Market Segmentation Analysis

By Deployment:

Cloud-based solutions dominate with 62.80% market share in 2026, providing cost efficiency, global accessibility, faster testing cycles, and real-time collaboration.

On-premise solutions offer enhanced privacy and customizable testing environments, holding a substantial share.

By Application:

Web-based applications held 70.24% market share in 2026, due to ease of use, scalability, and broad adoption across API testing, regression testing, and automated web testing.

Mobile-based testing provides flexibility to test apps directly on smartphones and tablets, gaining gradual adoption.

By Technology:

Machine learning leads with 37.48% share in 2026, optimizing test sets and automating scripts efficiently.

Computer vision is growing rapidly, applied in IoT, automotive, marketing, and mobile testing. Collaborations like Anyverse with Tech Mahindra (2023) highlight growth in computer vision testing solutions.

By Industry:

IT & telecom holds 36.24% market share in 2026, leveraging AI testing for network, server, and application performance optimization.

Healthcare is expected to grow fastest, using AI for real-time analysis, predictive modeling, bias detection, and data pattern recognition in medical applications.

Regional Insights

North America: Market size USD 0.35B in 2025, projected USD 0.21B in 2026, driven by U.S. investments in AI-enabled testing and presence of top vendors like Functionize, Tricentis, and Mabl.

Asia Pacific: Highest growth potential; Japan USD 0.07B, China USD 0.09B, India USD 0.05B in 2026, supported by investments in AI, machine learning, and NLP technologies.

Europe: Rapid adoption in Germany, Spain, Italy, France; UK USD 0.1B, Germany USD 0.07B in 2026, driven by government initiatives and innovation in AI testing.

Middle East & Africa, South America: Moderate growth fueled by AI and robotics adoption, digital economy development, and enterprise demand.

Key Industry Players & Developments

Top companies include Functionize, Sauce Labs, Tricentis, Diffblue, Applitools, Mabl, UBS Hainer, Testim, Perforce Software, and Open Text (MicroFocus). Notable developments:

Nov 2023: Mabl integrated with GitLab for AI-powered DevSecOps testing.

Sep 2023: Perforce Software added generative AI to BlazeMeter Test Data Pro.

Oct 2023: Katalon launched TrueTest with AI-based testing capabilities.

May 2023: UiPath introduced AI-driven automation for SAP testing.

Conclusion

In conclusion, the AI-enabled testing market is projected to grow from USD 1.01 billion in 2025 to USD 4.64 billion by 2034, at a CAGR of 18.30%, driven by AI integration, no-code solutions, cloud adoption, and demand in IT & telecom and healthcare sectors. Despite challenges such as data dependency and bias, emerging technologies, strategic partnerships, and innovations by key players will continue to drive global market expansion and improve software testing efficiency across industries.

ATTRIBUTE DETAILS

Study Period 2021-2034

Base Year 2025

Forecast Period 2026-2034

Historical Period 2021-2024

Growth Rate CAGR of 18.30% from 2026 to 2034

Unit Value (USD Million)

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


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Table of Contents

149 Pages
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
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