Global AI in Testing Market by Component (Software, Services) by Deployment mode (Cloud-based, On-premise), By Testing Type (Functional Testing, Performance Testing, Compatibility Testing, Security Testing) by Industry Vertical (BFSI, Healthcare, Retail, Telecom, Automotive, Other Industry Verticals) Region and Companies – Industry Segment Outlook, Market Assessment, Competition Scenario, Trends and Forecast 2024-2033

Report Overview

The global AI in testing market is estimated to be USD 26.8 billion in the year 2033 with a CAGR of 31.8% during the forecast period and was valued at USD 1.7 billion in the year 2023.

AI in testing refers to applying Artificial intelligence (AI) and machine learning (ML) approaches in testing processes to enhance automation. AI has the potential to improve testing’s effectiveness, accuracy, and cost-effectiveness significantly. Testing is a crucial component of the software development cycle.

The market is anticipated to develop due to various key factors including ongoing integration and deployment of advanced technology in testing, rising demand for AI and ML applications, and an increase in transformation from manual to automated testing. AI ensures the automation of several testing procedures, such as the development, execution, and analysis of test cases.

According to Stanford University research, the proportion of companies adopting AI in 2022 has more than doubled since 2017.

According to the AI Safety Institute of the UK, the country has onboarded 23 AI technical researchers and aims to grow to a team of 50-60 by the end of the year.

AI is transforming test automation by enabling intelligent test generation, predictive analysis, and self-healing test scripts. By 2025, it is estimated that over 50% of testing processes in large enterprises will incorporate AI, allowing teams to automate repetitive tasks while focusing on more complex testing scenarios. AI-driven tools such as visual testing, defect prediction, and autonomous testing are becoming essential to meet the demands of continuous testing in agile and DevOps environments.

Another key factor contributing to the rapid adoption of AI in testing is the growing complexity of software systems, especially in areas like IoT, cloud computing, and AI-driven applications. In 2024, nearly 40% of companies developing IoT and AI-based applications are expected to integrate AI into their testing frameworks to ensure robust performance and security. The predictive capabilities of AI are proving valuable in identifying potential issues early, reducing downtime, and improving the overall quality of software releases.

However, challenges such as high implementation costs and the complexity of integrating AI into legacy systems continue to hinder widespread adoption. Despite this, the opportunities are significant. The growing demand for AI in testing is particularly high in sectors such as healthcare, finance, and e-commerce, where application failures can result in major financial and reputational damage. By 2025, the healthcare and finance industries are expected to account for over 25% of the AI in the testing market, due to their critical need for highly secure, reliable software systems.

Key Takeaways:

• The global AI in testing market was valued at USD 1.7 billion in the year 2023 and is estimated to be USD 26.8 billion in the year 2033 with a CAGR of 31.8% during the forecast period.
• Based on the component, the software segment has the largest market share of 63.1% in the year 2023.
• The global AI in the testing market is segmented into cloud-based and on-premise segments based on the deployment mode, where the cloud-based segment is leading the market with a share of 69.8% in the year 2023.
• Based on the testing type functional testing dominates the market with a share of 36.5% in 2023.
• Based on the industry vertical the BFSI segment is leading the market with a share of 25.7% in the year 2023.

Component Analysis

Based on the component, the AI in the testing market is segmented into software and services segment. Among these, the software segment has the largest market share of 63.1% in the year 2023. The software solutions are highly scalable and flexible, enabling the testing of intricate AI systems on a range of contexts and platforms. By automating repeated testing processes, software solutions increase productivity and reduce human error. AI algorithms are developing so quickly that testing their dependability and performance requires specialist software.

Furthermore, software solutions frequently offer extensive reporting and analytics features, which aid in locating and fixing issues with AI systems. The need for continuous testing is also highlighted by the increasing use of DevOps and Agile approaches, which increases demand for software-based AI testing solutions. Thus, the software solutions ensure the reliability and quality of AI applications across a range of industries.

Deployment Analysis

The global AI in the testing market is segmented into cloud-based and on-premise segments based on the deployment mode, where the cloud-based segment dominated the market with a share of 69.8% in the year 2023. As cloud platforms are scalable, businesses can easily expand their testing and AI infrastructure as required without making significant investments in hardware. cloud-based solutions have increased accessibility, thus enabling testing tools and AI to be used remotely from any location with an internet connection, encouraging flexibility in work.

AI capabilities are integrated into cloud platforms to streamline the development and deployment procedures. Furthermore, cloud services offer robust security protocols, relieving issues about data security and compliance. Lastly, the pay-as-you-go cloud computing model reduces the financial hurdles to entry, enabling businesses (from start-ups to huge enterprises) to have access to cutting-edge AI technologies.

Testing Type Analysis

Based on the testing type the market is segmented into Functional Testing, Performance Testing, Compatibility Testing, and Security Testing segments. Among these functional testing dominates the market with a share of 36.5% in 2023. Functional testing is crucial to ensure that AI systems operate effectively, and are capable of meeting functional criteria and delivering anticipated outcomes. Functional testing plays a crucial role in validating AI algorithms and models, thus robust tools and frameworks that focus on this area are in higher demand.

Extensive functional testing is necessary to find and fix errors or inconsistencies in AI systems due to their intricate algorithms and interactions. Functional testing methodologies are a crucial component of the AI development lifecycle. Additionally, the requirement for functional testing solutions is in high demand as AI applications thrive across different businesses and sectors.

Industry Vertical Analysis

Based on the industry vertical, the market is segmented into BFSI, Healthcare, Retail, Telecom, Automotive, and other segments. Among these, the BFSI segment dominates the market with a share of 25.7% in the year 2023. Several important aspects contribute to the banking, financial services, and insurance (BFSI) segment’s dominance in the AI testing market. As the BFSI industry relies on data-driven decision-making, AI technology is essential for processes like risk assessment, fraud detection, and automated customer care.

Due to the strict regulations that the BFSI sector complies with, AI-based compliance and governance solutions are becoming more popular. Due to the intense competition in the BFSI industry, companies are focusing on using AI to streamline operations, enhance customer experiences, and optimize workflows. Furthermore, the BFSI sector handles huge amounts of sensitive data, necessitating the use of thorough testing procedures to guarantee the precision, dependability, and security of AI systems.

Key Market Segments

By Component

• Software
• Services

By Deployment Mode

• Cloud-based
• On-premises

By Testing Type

• Functional Testing
• Performance Testing
• Compatibility Testing
• Security Testing

By Industry Vertical

• BFSI
• Healthcare
• Retail
• Telecom
• Automotive
• Other Industry Verticals

Driving Factors

Rising demand for no-code or no-code testing solutions

The rising demand for no-code or no-code testing solutions to make software testing more efficient and accessible is the main factor driving AI in the testing market. The complexity of coding in traditional testing techniques reduces the software development lifecycle’s flexibility.

However, AI technologies facilitate cross-functional cooperation with the help of no-code testing. It also reduces time by allowing users to use a variety of skill sets to write and run tests without the need for high coding knowledge. AI-powered testing solutions increase test coverage, anticipate possible flaws, and automate the production of test cases using machine learning techniques.

Organizations may lower risk, assure efficient software quality, and allocate resources effectively by utilizing AI capabilities. The growing need for no-code testing solutions and the incorporation of AI technologies are interdependent, indicating the effectiveness of AI in the testing industry. Adopting AI-powered testing solutions is becoming strategically necessary as firms prioritize efficiency, scalability, and flexibility in their software development thus maintaining competitive advantage and satisfying rising customer demands.

Restraining Factors

Increasing reliance on AI-based testing apps

One major barrier restraining AI in the testing market is the increasing reliance on AI-based testing apps. Although AI-based testing tools are accurate and efficient, using them excessively might present several problems. Reliance on AI-driven testing tools may result in a loss of human judgment and oversight. AI algorithms might not have the contextual awareness and creativity that human testers possess.

Testing professionals may have a skills gap as a result of their reliance on AI testing tools. There can be gaps in essential testing abilities, such as exploratory testing and risk analysis. Furthermore, an excessive dependence on AI-based testing tools may lead to a false sense of security. As testers might believe AI algorithms are perfect, they might be careless in closely examining test findings and might overlook significant errors.

Growth Opportunities

Increasing modernization, industrialization, and increasing investments by businesses in advanced technologies

AI in the testing market has different opportunities including the convergence of increasing modernization, industrialization, and increasing investments by businesses in advanced technologies. Testing technologies powered by AI provide different advantages in the ever-changing environment. They make it possible to thoroughly analyze huge data sets and extract important connections and patterns that conventional approaches could have overlooked. Furthermore, AI algorithms can quickly adjust to shifting market conditions, guaranteeing that firms receive timely and useful insights.

Businesses may increase return on investment, get a competitive edge, and optimize resource allocation by utilizing AI technology. In conclusion, a combination of modernization, industrialization, and increasing investment in advanced technologies by businesses creates a favorable atmosphere for artificial intelligence (AI) innovation in the market. Businesses may negotiate this complicated landscape with agility, precision, and strategic vision by implementing AI-powered testing solutions, which promote sustainable growth and market leadership.

Challenging Factors

Shortage of experienced professionals and technical errors

The AI in the testing market faced various obstacles due to a shortage of experienced professionals, technical errors, and others. The complexity of testing grows as AI is progressively incorporated into more complex systems and procedures. Technical problems and mistakes can weaken the efficacy and dependability of AI-based testing instruments, undermining trust in the outcomes and impeding their uptake. These difficulties are further compounded by a lack of skilled workers who are knowledgeable about testing procedures as well as AI technologies.

A comprehensive understanding of various domains, methods, and quality assurance procedures is necessary for the efficient deployment and optimization of AI testing solutions. Organizations may find it difficult to use AI for experimentation without an adequate skill set, which could result in inefficiencies and less accurate outcomes.

Furthermore, the rapid advancement of AI technology adds to the complexity of the situation, necessitating ongoing modification and enhancement. To maintain relevance, correctness, and dependability, testing procedures must keep up with the advancement of AI frameworks and algorithms. These issues can be addressed by funding training and education initiatives and encouraging industry collaboration. Furthermore, continuous R&D is essential to enhancing AI testing instruments and making them more resilient, adjustable, and user-friendly to handle these difficulties.

Latest Trends

Emergence of advanced digital technology

The emergence of digital technology and the rapid expansion of data has made enterprises more susceptible to cybersecurity risks. Thus, safeguarding digital assets from potential breaches is receiving more attention, which makes security testing a crucial component of the software development lifecycle. Furthermore, the complexity of contemporary software systems and the ever-changing threat landscape have been a challenging aspect of traditional security testing techniques.

Security testing solutions driven by AI provide a more dynamic and flexible nature as they can identify abnormalities and vulnerabilities with more accuracy and efficiency. Furthermore, security testing tools can now automatically detect and fix security errors in a variety of application scenarios due to the developments in AI and machine learning techniques. Organizations are also adhering to strict security measures mandated by regulatory requirements and compliance standards, which include extensive testing protocols.

Security testing driven by AI makes compliance efforts easier by offering a thorough, ongoing evaluation of security controls. Thus, the rise in the use of AI-based security testing indicates that businesses are becoming more aware of how successful it is at securing digital assets and reducing cybersecurity risks, which is fuelling the growth of the AI testing industry.

Regional Analysis

North America region is leading the market

North America region dominates the market with a share of 35% in the year 2023 and is expected to hold the largest market share of USD 0.59 Billion. a robust ecosystem of technological businesses, research institutes, and start-ups in North America, propels innovation and market expansion in the region. The region has substantial government funding and support for AI research and development, which fosters the creation of new technologies.

Numerous top businesses in various sectors, including technology, finance, healthcare, and automotive, are situated in North America. To obtain a competitive edge, these businesses are making significant investments in AI-based solutions. Furthermore, the availability of a trained labor force comprising data scientists, engineers, and testers would facilitate the uptake and application of AI technologies in the area.

Key Regions and Countries

North America

• US
• Canada

Europe

• Germany
• France
• The UK
• Spain
• Italy
• Russia
• Netherlands
• Rest of Europe

Asia Pacific

• China
• Japan
• South Korea
• India
• Australia
• Singapore
• Rest of APAC

Latin America

• Brazil
• Mexico
• Rest of Latin America

Middle East & Africa

• South Africa
• Saudi Arabia
• UAE
• Rest of MEA

Market Share and Key Players Analysis

Several companies are operating in the highly fragmented AI testing market. These companies have a strong commitment to developing innovative products, services, and strategies to thrive in the competitive market. These tactics consist of increased R&D spending, joint ventures, acquisitions, mergers, and strategic alliances. This has made it difficult for new competitors to enter the very competitive sector.

Key players in the AI in-testing market include IBM, which offers its Watson AI-powered testing solutions for automation and defect prediction, and Microsoft, with Azure AI tools that enhance automated testing processes. Tricentis leads with AI-driven continuous testing platforms, enabling end-to-end automation.

Applitools is known for its AI-based visual testing solutions, while Parasoft provides AI-enhanced tools for test automation and risk-based testing. These companies are driving innovation in AI testing, offering advanced tools that improve accuracy, speed, and scalability in software testing processes.

Top Key Players in the Market

• IBM Corporation
• Infosys Limited
• Cognizant Technology Solutions Corp
• Capgemini SE
• Wipro Limited
• Accenture plc
• DXC Technology Company
• TCS (Tata Consultancy Services) Limited
• HCL Technologies Limited
• Atos SE
• Tech Mahindra Limited
• Hexaware Technologies Limited
• Other Key Players

Recent Developments

• In April 2024, Invicti, one of the leading providers of application security testing solutions launched its first AI-enabled predictive risk scoring for application security testing.
• In December 2023, Renesas launched a cloud-based environment to accelerate automotive AI software development and evaluation.

Report Scope