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Global AI-enabled Testing Market to reach USD 1601.58 million by the end of 2030

Global AI-enabled Testing Market Size study & Forecast, by Component (Solution, Services), by Deployment (Cloud, On-premises), by Technology (Machine Learning and Pattern Recognition, Natural Language Processing (NLP), Computer Vision and Image Processing), by Application (Test Automation, Infrastructure Optimization, Others), by End-Use Industry (Healthcare, IT & Telecommunication, Energy & Utilities, BFSI, Government, Others) and Regional Analysis, 2023-2030

Product Code: ICTNGT-62951327
Publish Date: 5-12-2023
Page: 200

Global AI-enabled Testing Market is valued at approximately USD 414.7 Million in 2022 and is anticipated to grow with a healthy growth rate of more than 18.4% over the forecast period 2023-2030. AI-enabled testing, also known as artificial intelligence in testing, refers to the use of artificial intelligence (AI) and machine learning (ML) techniques to enhance and automate various aspects of software testing. Software testing is a critical part of the software development life cycle, and AI can play a significant role in making it more efficient, accurate, and cost-effective. The key factors driving the market growth is continuous integration and continuous deployment, rising demand for AI and ML applications, and rising shift from manual to automated testing that anticipated to support the market growth.

Moreover, the rising adoption of artificial intelligence (AI) is indeed supporting the growth of the AI-enabled testing market. AI enables the automation of various testing processes, including test case generation, execution, and analysis. This reduces the need for manual testing efforts, leading to faster and more efficient testing processes. Automated testing can be performed around the clock, accelerating the development cycle. According to Statista, in 2018, the global artificial intelligence (AI) software market was valued USD 10.1 billion and it is anticipated to reach USD 126 billion by 2025. In addition, improved efficiency and accuracy and innovative testing methodologies a major opportunities for the market. However, the non-deterministic nature of AI systems is a major challenge for the market.

The key regions considered for the Global AI-enabled Testing Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to the presence of key market players, growing adoption of AI and ML, growing industrialization and cohesive government initiative and policies. Whereas, the Asia Pacific is expected to grow with the highest CAGR during the forecast period, owing to factors such as the increasing digital transformation, rising need for efficient testing, and growing ai and machine learning expertise.

Major market player included in this report are:
Micro Focus International Plc
Capgemini SE
Diffblue Ltd.
Functionize Inc.
D2L Corp.
ReTest Gmbh
Sauce Labs Inc.

Recent Developments in the Market:
Ø In Feb, 2023, Micro Focus, a cybersecurity expert, was acquired by OpenText With this purchase, OpenText’s corporate goal broadens to support business professionals in securing their operations, gaining more visibility into their data, and managing a digital fabric that is becoming more hybrid and complicated.

Global AI-enabled Testing Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Component, Deployment, Technology, Application, End-Use, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component:

By Deployment:

By Technology:
Machine Learning and pattern Recognition
Natural Language Processing (NLP)
Computer Vision and Image Processing

By Application:
Test Automation
Infrastructure Optimization

By End-Use:
IT & Telecommunication
Energy & Utilities

By Region:

North America


Asia Pacific
South Korea

Latin America

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Million)
1.2.1. AI-enabled Testing Market, by Region, 2020-2030 (USD Million)
1.2.2. AI-enabled Testing Market, by Component, 2020-2030 (USD Million)
1.2.3. AI-enabled Testing Market, by Deployment, 2020-2030 (USD Million)
1.2.4. AI-enabled Testing Market, by Technology, 2020-2030 (USD Million)
1.2.5. AI-enabled Testing Market, by Application, 2020-2030 (USD Million)
1.2.6. AI-enabled Testing Market, by End-Use, 2020-2030 (USD Million)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global AI-enabled Testing Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global AI-enabled Testing Market Dynamics
3.1. AI-enabled Testing Market Impact Analysis (2020-2030)
3.1.1. Market Drivers Increase in Demand of Artificial Intelligence (AI) Improved efficiency and productivity Ease of Maintenance
3.1.2. Market Challenges Non-Deterministic Nature of AI Systems
3.1.3. Market Opportunities Improved Efficiency and Accuracy Innovative Testing Methodologies
Chapter 4. Global AI-enabled Testing Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global AI-enabled Testing Market, by Component
5.1. Market Snapshot
5.2. Global AI-enabled Testing Market by Component, Performance – Potential Analysis
5.3. Global AI-enabled Testing Market Estimates & Forecasts by Component 2020-2030 (USD Million)
5.4. AI-enabled Testing Market, Sub Segment Analysis
5.4.1. Solution
5.4.2. Services
Chapter 6. Global AI-enabled Testing Market, by Deployment
6.1. Market Snapshot
6.2. Global AI-enabled Testing Market by Deployment, Performance – Potential Analysis
6.3. Global AI-enabled Testing Market Estimates & Forecasts by Deployment 2020-2030 (USD Million)
6.4. AI-enabled Testing Market, Sub Segment Analysis
6.4.1. Cloud
6.4.2. On-premises
Chapter 7. Global AI-enabled Testing Market, by Technology
7.1. Market Snapshot
7.2. Global AI-enabled Testing Market by Technology, Performance – Potential Analysis
7.3. Global AI-enabled Testing Market Estimates & Forecasts by Technology 2020-2030 (USD Million)
7.4. AI-enabled Testing Market, Sub Segment Analysis
7.4.1. Machine Learning and Pattern Recognition
7.4.2. Natural Language Processing (NLP)
7.4.3. Computer Vision and Image Processing
Chapter 8. Global AI-enabled Testing Market, by Application
8.1. Market Snapshot
8.2. Global AI-enabled Testing Market by Application, Performance – Potential Analysis
8.3. Global AI-enabled Testing Market Estimates & Forecasts by Application 2020-2030 (USD Million)
8.4. AI-enabled Testing Market, Sub Segment Analysis
8.4.1. Test Automation
8.4.2. Infrastructure Optimization
8.4.3. Others
Chapter 9. Global AI-enabled Testing Market, by End-Use
9.1. Market Snapshot
9.2. Global AI-enabled Testing Market by End-Use, Performance – Potential Analysis
9.3. Global AI-enabled Testing Market Estimates & Forecasts by End-Use 2020-2030 (USD Million)
9.4. AI-enabled Testing Market, Sub Segment Analysis
9.4.1. Healthcare
9.4.2. IT & Telecommunication
9.4.3. Energy & Utilities
9.4.4. BFSI
9.4.5. Government
9.4.6. Others
Chapter 10. Global AI-enabled Testing Market, Regional Analysis
10.1. Top Leading Countries
10.2. Top Emerging Countries
10.3. AI-enabled Testing Market, Regional Market Snapshot
10.4. North America AI-enabled Testing Market
10.4.1. U.S. AI-enabled Testing Market Component breakdown estimates & forecasts, 2020-2030 Deployment breakdown estimates & forecasts, 2020-2030 Technology breakdown estimates & forecasts, 2020-2030 Application breakdown estimates & forecasts, 2020-2030 End-Use breakdown estimates & forecasts, 2020-2030
10.4.2. Canada AI-enabled Testing Market
10.5. Europe AI-enabled Testing Market Snapshot
10.5.1. U.K. AI-enabled Testing Market
10.5.2. Germany AI-enabled Testing Market
10.5.3. France AI-enabled Testing Market
10.5.4. Spain AI-enabled Testing Market
10.5.5. Italy AI-enabled Testing Market
10.5.6. Rest of Europe AI-enabled Testing Market
10.6. Asia-Pacific AI-enabled Testing Market Snapshot
10.6.1. China AI-enabled Testing Market
10.6.2. India AI-enabled Testing Market
10.6.3. Japan AI-enabled Testing Market
10.6.4. Australia AI-enabled Testing Market
10.6.5. South Korea AI-enabled Testing Market
10.6.6. Rest of Asia Pacific AI-enabled Testing Market
10.7. Latin America AI-enabled Testing Market Snapshot
10.7.1. Brazil AI-enabled Testing Market
10.7.2. Mexico AI-enabled Testing Market
10.8. Middle East & Africa AI-enabled Testing Market
10.8.1. Saudi Arabia AI-enabled Testing Market
10.8.2. South Africa AI-enabled Testing Market
10.8.3. Rest of Middle East & Africa AI-enabled Testing Market

Chapter 11. Competitive Intelligence
11.1. Key Company SWOT Analysis
11.1.1. Company 1
11.1.2. Company 2
11.1.3. Company 3
11.2. Top Market Strategies
11.3. Company Profiles
11.3.1. Tricentis Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
11.3.2. Micro Focus International Plc
11.3.3. testRigor
11.3.4. Capgemini SE
11.3.5. Applitools
11.3.6. Diffblue Ltd.
11.3.7. Functionize Inc.
11.3.8. D2L Corp.
11.3.9. ReTest Gmbh
11.3.10. Sauce Labs Inc.
Chapter 12. Research Process
12.1. Research Process
12.1.1. Data Mining
12.1.2. Analysis
12.1.3. Market Estimation
12.1.4. Validation
12.1.5. Publishing
12.2. Research Attributes
12.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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