Code generation with AI tools is rapidly expanding in the software development world. The software testing and QA activities are also impacted by this trend. What does it mean for suppliers of test automation tools? In this article, Maz Daly explains how they should transition from turnkey solutions to providing a platform that allows customers to build their own software testing platform with confidence and scale.
Author: Maz Daly, Chief Marketing Officer at Leapwork.
Much of the testing market is selling yesterday’s platform with tomorrow’s AI language while customers are already experimenting with AI, code, and other tools to build what their applications and workflows require.
The testing market has entered a sea of sameness.
Across the category, vendors map features to analyst criteria, assemble Frankenstein platforms from acquired components, and wrap familiar functionality in agentic claims. Buyers encounter increasingly similar stories about what AI makes possible and increasingly long lists of capabilities designed to satisfy a market report. Meanwhile, the source of innovation is shifting.
Development teams can use AI, code, Selenium, Playwright, and other tools to create testing capabilities tailored to their applications and workflows. They can build functional tests, expand regression coverage, validate business processes, and automate quality practices with a level of specificity that a standard product roadmap cannot anticipate.
Creation, however, is only the first step. As those capabilities scales across an enterprise, they need a system around them: expertise to design and maintain coverage, standards and guardrails to keep teams aligned, evidence leaders can trust, and connections across applications and systems. This gives the QA platform a different job. It must help organizations build what fits their needs, then provide the governance and control to connect, maintain, and trust those capabilities as teams adopt them across the enterprise.
It also fundamentally changes how buyers should evaluate QA platforms. The question is not only what a platform can do out of the box, but how well it works with the code, tools, and testing assets teams have already built and maintained. Buyers should ask whether it can incorporate customer built components, protect existing development investments, and add governance without forcing teams to start over. The best platform will help organizations connect, govern, maintain, and trust those capabilities across the enterprise.
Testing automation has seen this cycle before. AI puts it on steroids.
The current AI cycle repeats a pattern the testing market has seen before. Selenium gave developers a foundation for browser automation, and teams built frameworks, libraries, integrations, reporting systems, and processes around it. Its value extended beyond browser automation through the frameworks, libraries, integrations, and processes customers built around it.
As adoption grew, enterprise teams needed more around that foundation: visual testing, no code workflows, reusable governance, centralized reporting, orchestration, broader application coverage, and the expertise to maintain quality across the business.
Playwright carries the developer-first tradition forward with a modern approach to browser automation. Teams can use it directly, combine it with their own code, and adapt it to their development practices. Like Selenium, Playwright provides an important foundation. Enterprise quality, however, requires the capabilities around it.
Teams need to bring developer created automation together with visual and no code workflows, business process validation, performance testing, and the evidence required across the enterprise. A shared platform gives these approaches a common operating model. Teams can build in the ways that suit them while quality leaders and technology executives retain visibility and control.
AI changes the speed and range of this work. Customers can create more capabilities, tailor them more closely to their environments, and adapt them as needs change. That also increases the need for testing expertise, governance, and maintenance. The enterprise platform must turn this distributed customer work into reliable practice.
The platform becomes the governance layer
The future of testing will include commercial platforms, customer built code, AI tools, Selenium, Playwright, and capabilities that have yet to emerge. The enterprise platform should connect these elements through a common model for control, evidence, and accountability.
Developers need room to experiment. Quality leaders need consistency. Technology executives need visibility into risk, coverage, and performance. Organizations need to understand what ran, how it ran, who can review the result, and whether the evidence supports a business decision.
A platform creates value by bringing those needs together. It gives customers the freedom to build what fits their environment while providing the expertise and guardrails required to maintain and scale it. It supports customer innovation without turning every team into an isolated owner of its own quality system.
This shift is changing how organizations evaluate QA platforms. Customers want to use AI their way, bring their own tools and code into the process, and preserve the investments that already support their development practices.
They also need centralized governance, human oversight, and deterministic outcomes where the business requires certainty. A modern platform should connect developer created automation, including Playwright, with the orchestration, evidence, and enterprise coverage required across the organization.
The value of the platform comes from making customer innovation durable. It gives teams room to create while providing the expertise, guardrails, and shared operating model required to maintain quality at scale.
Evaluation must move with the market
When customers become a source of innovation, the evaluation process has to evolve with them.
Take analyst reports, for example. Vendors may spend months aligning product roadmaps with inclusion criteria, accelerating features into general availability, and packaging those releases for the next report. That turns product development into a race for report eligibility while customer needs to move faster than the criteria can capture.
A position in the upper right is not a verdict on current capability. It is a subjective snapshot based on an analyst’s methodology, inclusion criteria, and research cutoff. In an AI market, six months is a lifetime. During that period, a company can launch a platform, reach general availability, ship multiple releases, and materially change what customers can buy. By publication, the report may exclude platforms that emerged after the cutoff while assessing older versions of the products it included.
Platforms that lead in the real world use every point in that timeline to listen, learn, and release. They draw on customer feedback, deployment data, and new technical possibilities to improve the product as needs change. Report inclusion may be a marketing milestone, but customer value is the operating measure.
The art of the possible now advances in shorter sprints. Buyers should evaluate whether a platform can keep pace with those sprints, support the tools and code their teams already use, and continue to deliver value as their needs change. The relevant measure is customer driven value over time.
The new standard for QA platforms
The next testing platform will be built by customers because customers now have more power to create their own capabilities. The platform’s role is to help them do that with confidence and scale.
The most important question is no longer which vendor offers the longest list of features. It is which platform can help an organization build what comes next, maintain it as conditions change, govern it across the enterprise, and turn it into a quality practice people can trust. That is the standard the testing market should measure now.
About the Author
Maz Daly is the Chief Marketing Officer at Leapwork. She is a strategic marketing executive who helps high-growth technology companies drive revenue, expand brand leadership and turn marketing into a core business function. Daly has deep experience across product marketing, demand generation, partner marketing, communications and go-to-market strategy.
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