AI Test Automation Market

The AI test automation market stood at USD 8.81 billion in 2025 and is projected to reach USD 35.96 billion by 2032, at a CAGR of 22.3%. The market is expanding as enterprise software environments grow more complex and change frequently. Traditional scripted testing is increasingly difficult to sustain in agile and DevOps-led development models, especially at scale. AI test automation tools address this by improving software testing for large test suites at scale. Organizations managing broad application portfolios with frequent UI and API updates have shown the highest adoption. AI is used to strengthen existing testing tools rather than replace them directly. There is greater emphasis on solutions that demonstrate measurable improvements in efficiency and reliability. Closer integration between testing and operational teams has also led to rising demand.

Vendors in the AI test automation market are devising GTM strategies based on their core strengths and customer base. Large vendors position AI-driven testing as part of broader application engineering or automation offerings. These players are building AI capabilities directly into established test automation products. The focus is on improving test creation, stabilizing scripts, and reducing maintenance effort. Vendors are refining their strategies around practical adoption rather than pushing AI as a headline feature. Most are embedding AI more deeply into everyday testing workflows so it helps teams deal with unstable tests and frequent application changes. Vendor partnerships with cloud providers and DevOps platforms are becoming more common to facilitate ease of deployment. There is also steady investment in governance and reporting to support enterprise-scale use. Overall, strategies are less about rapid experimentation and more about delivering dependable results that can be sustained over time.

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In September 2025, SmartBear entered into a strategic collaboration with Amazon Web Services (AWS). The agreement is designed to make SmartBear’s solutions more accessible in AWS environments and easier to integrate into cloud-native development workflows.

In June 2025, OpenText released enhancements to its AI-assisted functional test automation solutions for desktop, mobile, and web applications. These updates aim to reduce test maintenance effort and extend AI-driven resilience in complex enterprise application landscapes.

Tricentis

Tricentis focuses on applying AI to improve the reliability and efficiency of enterprise test automation. Its strengths are most evident in large organizations running complex business applications, especially ERP, CRM, and other core systems. These environments change frequently and require extensive regression testing. Tricentis uses machine learning to support test design, prioritization, and ongoing maintenance, reducing manual effort across large test suites. AI is positioned as a practical enabler rather than a replacement for existing testing practices. This appeals to enterprises that operate under structured release processes and risk controls. As a result, adoption is strongest among organizations with mature QA teams that value stability, coverage, and predictability over experimentation.

UiPath

UiPath enters the AI test automation market through its broader automation platform rather than from a testing-first background. Its capabilities are typically used in environments where software testing intersects with business process automation and operational workflows. In these cases, testing activities are closely linked to data movement, system validation, and process execution. UiPath applies AI to support tasks such as test execution, environment setup, and validation of automated processes. This approach works best for enterprises that already use UiPath at scale and want to extend automation into testing without introducing separate tools. The company’s role in this market is therefore shaped by integration and platform reach. Adoption tends to be selective and tied to specific automation-driven use cases rather than a full replacement of dedicated testing platforms.

Market Ranking Analysis

The AI test automation market is shaped by a small set of vendors that lead for distinct, well-defined reasons rather than a single dominant approach. Tricentis is widely adopted in large enterprises with complex, frequently changing application landscapes, where AI is primarily used to stabilize regression suites and reduce ongoing maintenance overhead. UiPath approaches testing from a broader automation perspective, embedding AI-driven testing into end-to-end workflows and business processes to scale testing beyond isolated QA teams. Keysight’s position is anchored in its long-standing strength in performance and protocol testing, making it particularly relevant for network-centric and infrastructure-heavy environments where system reliability is non-negotiable. SmartBear remains favored by development teams for API and UI testing, using AI in a targeted way to improve test accuracy and limit rework rather than overhaul workflows. OpenText remains relevant in enterprise accounts for its broad application lifecycle coverage and emphasis on control, governance, and compliance, especially in regulated settings.

Related Reports:

Al Test Automation Market by Offering (Autonomous Testing Tools, Test Data Generation Tools), Testing Type (API & Backend Testing, LLM Evaluation, Regression Testing), Application (Test Execution, Test Script Maintenance) - Global Forecast to 2032

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