Pradeep Govindasamy is the Co-Founder, President and CEO of QualiZeal.
We’re at the beginning of a new era in quality engineering, one shaped by agentic AI. While generative AI has captured global attention, the real transformation in software testing is only just beginning. I believe we’re now entering a phase where AI isn’t just assisting people in testing tasks. It’s becoming autonomous, goal-driven and capable of acting with intelligence across the lifecycle.
At QualiZeal, we’re witnessing this shift firsthand. As someone who has spent years in the testing space, I can confidently say that AI is not a far-off future. It’s here, being built into our processes today, and it’s already beginning to disrupt how we think about quality at scale.
The Disruption Is Already Underway
Software development and testing are the two most critical pillars in any IT application lifecycle. To get a product into the hands of customers, you first build it, then test it and only then can you ship it. We’ve seen how tools like GitHub Copilot have revolutionized development. Now, that same level of AI adoption is happening in software testing.
This is no small market—it’s a $100 billion global industry. And just as smartphones once disrupted legacy devices like BlackBerry, AI is poised to transform testing in a similar way. Every phase of the software testing lifecycle—test case preparation, test design, test data management, performance testing, site reliability engineering—is now being infused with AI to increase efficiency, productivity, and ultimately software quality.
From Automation To Intelligence
Before we talk about agentic AI, we need to understand the evolution. The first step in embracing AI is automating repetitive, rule-based tasks. Once you have robust automation in place, AI capabilities can be layered on top to improve every phase of testing.
But agentic AI goes one step further.
With standard AI, we build prompts, define logic and teach the models how to behave. With agentic AI, we create systems that learn, adapt and act autonomously. These agents follow instructions and understand intent. They can analyze changes in the system, adjust automation scripts accordingly and execute tests without human intervention.
For example, imagine a scenario where a company updates its checkout process, maybe tariffs or payment options change. In the past, a QA team would have to manually identify changes, rewrite test scripts and rerun tests. With agentic AI, the system learns what’s changed, modifies the scripts, self-heals when errors occur and continues testing. It even generates a report outlining what it changed and why.
This self-healing, self-optimizing capability sets agentic AI apart from traditional automation. And it’s a game-changer.
Real Business Impact
We’re seeing both technical benefits and measurable business outcomes. With agentic AI, the cost of quality is decreasing. From my observation, the industry average today is about 18%, but with AI-infused testing, we anticipate a 5% drop, driven by reduced manual effort and increased efficiency. In maintenance alone, we’ve seen a reduction from 20% of team capacity to less than 5%.
Even more importantly, release cycles are accelerating. Time to market (TTM) has gone from quarterly to weekly, and now, with agentic AI and DevOps practices, to daily releases. The entire production throughput is becoming faster and more reliable. And decision-making is more seamless because agentic systems provide full transparency through real-time reporting, eliminating the need to compile data across disparate systems.
Preparing For What’s Next
Organizations looking to lead in this space must prepare now. I always say this moment is not just about catching up—it’s about disrupting yourself before you get disrupted. Companies that wait too long will miss the opportunity to lead. Those who invest now will be in a position to capture market share and build the next generation of testing capabilities.
This preparation requires both a top-down and bottom-up approach. Leadership must allocate budgets, not just wait for client-driven funding, and teams must be empowered to get trained, certified, and exposed to different AI models. AI isn’t just a CIO or CTO conversation anymore. It’s happening at the board level, and for good reason: this is the foundation for long-term competitiveness.
I recommend organizations push their teams to reach at least level three in AI readiness: basic execution. Core functions like engineering and QA need to go further, while ancillary teams like finance and marketing should also gain exposure.
Trust And Responsibility
Of course, with great power comes responsibility. We need to ensure agentic systems operate ethically, transparently and securely. Especially in regulated industries like healthcare, insurance or banking, any AI-driven decision, no matter how small, can have massive consequences.
That’s why testing the AI itself is just as important as using AI for testing. There’s a growing demand for AI-specific test engineers who can validate agentic systems through high-end exploratory techniques. Traditional testing models like equivalence partitioning or boundary analysis must now be complemented with new approaches tailored to AI behavior.
In the near future, eight to 10 new job roles will emerge specifically to test and validate agentic AI systems. These won’t be optional. They’ll be mission-critical.
Looking Ahead
We estimate that full-scale AI maturity across the testing lifecycle will arrive around 2027. Between now and then, we’re in the planning and education phase, training models, customizing LLMs and building the necessary infrastructure. Implementation will accelerate in 2026, and by mid-2027, I expect the majority of enterprise QA environments to be agentic by design.
This is a once-in-a-generation opportunity for testers, developers and technology leaders. Gen Z professionals, especially those raised in a digital-native world, will have an edge. They can adopt these tools faster, and many will find themselves building careers in entirely new domains.
We’re not just building testing systems anymore. We’re building trusting systems. Platforms that learn, adapt and support business continuity without human babysitting. That’s the future of QA. That’s where agentic AI takes us. And the companies that embrace it today? They’ll be the ones defining quality tomorrow.
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