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AI is no longer on the horizon. It is already reshaping how software is built, tested, and released, and it is moving faster than most quality processes were designed to absorb. Developers are shipping AI-written code at speed. Test teams are generating test cases, maintaining suites, and triaging defects with AI.
But speed cuts both ways. AI magnifies the process it enters. Strong quality practices can become faster and more scalable. Weak coverage, poor assumptions, fragmented ownership, and insufficient oversight can scale just as quickly.
More AI-generated tests do not automatically mean more confidence in a release. So the question is no longer whether AI will change software quality. It already has. The more important question is whether our testing practices, team structures, skills, and accountability models can evolve fast enough to keep pace.
And while a growing market of tools promises increasingly autonomous testing, technology alone cannot answer the harder questions of judgment, ownership, and accountability. Those questions sit at the center of how AI is changing quality assurance for every team.
That is the conversation at the heart of The Future of Software Testing: a series bringing together different perspectives on how AI is reshaping software quality and the people responsible for it.
Voices in the Conversation
The series brings together perspectives from across software testing, product, AI, and quality engineering. These voices have watched AI change how software gets built, and they are examining what that means for how software earns trust.
- Vu Lam brings the founder's perspective: a long view of how testing has evolved across agile, automation, and now AI-assisted delivery.
- Cristiano Caetano brings the product architect's perspective: how testing platforms should support real engineering workflows, not just isolated features.
- Alex Martins brings the perspective of the Quality Engineering leader trying to use AI to accelerate testing: what it actually means to build AI into testing workflows and separate practical value from hype. Read his take on how AI is creating more capacity for testers.
- Mush Honda brings the quality engineering perspective: how teams turn tools into process, evidence, accountability, and better release decisions.
Together, these perspectives examine a fundamental question: what does quality require when AI can do more of the work?
A Point of View on AI and Testing
AI accelerates testing. People lead quality.
Code can now be generated in moments. Tests can be created, executed, analyzed, and maintained with increasing autonomy. But more code, more tests, and faster delivery do not automatically produce better software. Teams still need to review AI-generated test cases before they trust them.
The future tester will not compete with AI on speed. Their value will increasingly come from what AI cannot provide on its own: judgment, context, accountability, and confidence.
These ideas are spelled out in the series' Manifesto for Testing in the Age of AI, which sets out five commitments for how testing should evolve.
Questions Shaping the Conversation
Who Owns Quality in the Age of AI?
As AI contributes more code and testing work, accountability becomes more complex. Who validates AI-generated output? Who accepts the risk? Who decides whether the evidence is sufficient? And who is ultimately accountable for what reaches customers?
Quality may increasingly become everyone's responsibility. But shared responsibility can easily become unclear responsibility. For a closer look at this problem, see why independent validation comes first in AI agent governance.
What Happens to the Human Tester?
AI will automate more testing activities, but testing has never been only about executing test cases. Strong testers understand systems, challenge assumptions, uncover unexpected risks, and interpret whether the available evidence actually supports a release decision.
As AI takes on more execution, the better question is: where does human judgment create the greatest value? Our data on whether AI will replace manual testers offers one starting point, and the second discussion in the series, Manual Testing Is Dead. Automation Is Dead. What's Really Changing?, takes it further.
How Should Testers Evolve?
The strongest signal from the market right now is not anxiety. It is ambition. A growing number of professionals are reframing their roles around AI-augmented testing. They want to evolve, and they want to know how. We will give them a practical path.
What Comes Next
This month, The Future of Software Testing will begin exploring the questions shaping the next era of software quality. The goal is not to arrive at one definitive answer, but to bring sharper perspectives into the conversation and invite the testing community to challenge them.
What feels incomplete? Where do you disagree? What is the industry still avoiding?
The value of this series will not only come from what we publish. It will come from the conversation it opens. This is our invitation to examine that future together. Subscribe to the series to follow new discussions as they publish.
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