Blog | 13-Jan-2026
From Testers to Strategists:
The Evolving Role of the QA Professional
AUTHOR
Anesh Korla
Chief Technology Delivery Officer
Over the last year, almost every conversation I have had with technical leaders has started the same way. A quiet concern that everyone else seems to be moving faster with AI. That maybe they are missing something. That perhaps they are already behind.
If you are a technical professional today, you have probably felt this too. You wonder where all of this is headed, how fast it will change your role, and whether you should be worried at all.
The truth is far simpler and far more practical.
No matter where you are in your career, AI is not a foe. It is a friend. The real question is not whether AI will impact your role, but how you make it work for you so you can do your job better, faster, and with greater accuracy.
This is especially true in Quality Assurance.
QA has always been the final gatekeeper of technology delivery. When QA fails, everything else fails. It is a role built on accountability and precision. At the same time, it has historically been highly execution driven. Test case creation, execution, maintenance, regression cycles. Necessary work, but often repetitive and exhausting.
Based on my recent engagements, a clear shift is underway as we head into 2026. Many of these repetitive aspects of QA are increasingly being automated by AI. Rather than diminishing the role of QA professionals, this shift is elevating it.
QA engineers are moving away from manual execution and stepping into the role of quality strategists. The focus is shifting toward risk analysis, user impact, intelligent test design, and system resilience across complex environments.
The idea of zero touch QA or AI fully replacing human testers is already losing ground. What is actually emerging is a human in the loop model where AI amplifies human expertise instead of replacing it. The result is better customer experience, faster delivery, and more reliable systems.
Like it or not, the future is hybrid.
AI brings speed, scale, and consistency. Humans bring judgment, context, intuition, and accountability. Together, they create systems that can scale without compromising quality.
For QA professionals, staying relevant does not mean becoming data scientists overnight. It means building practical skills such as AI literacy, understanding and interpreting outputs, prompt design, and most importantly, validating and questioning what AI produces.
What has also changed is client expectations.
Clients are no longer asking whether AI can be applied to QA. They assume it will be.
That said, AI led QA is not without challenges.
Clients are no longer asking whether AI can be applied to QA. They assume it will be.
Data dependency.
AI is only as effective as the data it is trained on. Poor or biased data leads to unreliable results.
Trust and oversight.
Zero touch is not realistic. Over reliance on AI without human judgment can miss edge cases or create false confidence.
Organizational change.
Adoption requires cultural shift, training, and rethinking workflows and success metrics for an AI enabled world.
Automation of repetitive work, freeing up QA teams
Better test coverage and accuracy
Faster release cycles and reduced time to market
Lower costs through reduced manual effort
More stable and reliable systems in production
When implemented thoughtfully, the benefits are clear.
This perspective is not theoretical.
It is grounded in a recent enterprise engagement where we implemented an end to end AI powered test automation program across a complex technology landscape.
The focus areas included:
• Intelligent document validation, moving from manual checks to AI enabled verification at scale
• AI augmented test automation, accelerating test creation while improving resilience and embedding quality earlier
• Enterprise wide QA coverage, ensuring consistency, reliability, and risk management across interconnected systems
The impact was clear. Over 40 percent cost savings, faster release cycles, and more dependable QA outcomes at enterprise scale.
More importantly, it reinforced something fundamental. As we move into 2026, AI led testing is no longer an innovation story. It is becoming the baseline expectation for modern software delivery.
The organizations that will succeed are not the ones experimenting with AI in isolation. They are the ones deliberately combining AI speed and scale with human judgment, ownership, and context. That hybrid approach is quickly becoming the standard for building resilient systems and delivering consistent customer experience.
The Role of Humans in a More Automated Mortgage World
One of the biggest misconceptions we continue to see is the idea that mortgage technology success is about removing humans from the process. In reality, the opposite is true.
Mortgage operations rely on judgment, empathy, and accountability, especially in moments that matter most to borrowers. The right role for technology is to give people leverage better information, fewer distractions, and more time to focus on outcomes that require experience and care.
When technology and humans are designed to work together, organizations don’t just move faster—they make better decisions.
What This Means for the Industry
As we enter 2026, the mortgage organizations that will lead are those that:
Invest in data discipline before AI ambition
Design technology around real operational flow
Use automation to stabilize, not stress, delivery
Keep humans accountable at critical decision points
Focus relentlessly on customer and investor outcomes
This isn’t about chasing trends. It’s about building technology foundations that can absorb change—whether that change comes from market cycles, regulation, or customer expectations.