Hi everyone,
I’m trying to understand how Software Quality Assurance / Testing will evolve in 2026 as AI copilots and agent-style tools become common in development and delivery.
From what I’m seeing, AI can already help with test generation, test data, faster regression, and summarizing failures. At the same time, teams are expecting QA to focus more on risk-based strategy, continuous quality (shift-left + shift-right), and governance/security—especially when the product includes AI features.
I’d love to get the community’s thoughts on:
What will QA engineers spend less time on in 2026 (because AI will automate it)?
What will become the core “must-have” QA skills (strategy, automation, observability, security, performance, AI testing, etc.)?
For someone planning a career path: which QA roles will be more “high value / higher pay” in 2026?
If you’re leading QA today: what are you changing in your team/process right now to prepare for 2026?
If you have real examples (tools, team structure, metrics, success/fail stories), please share. Thanks!
I’d like to think my time was fairly optimised before, 60 percent of my time on actual discovery and learning focused testing. I’d rather that was 80.
Here are a few things I feel I going to continue to do more of.
Automation - the ROI in many cases was not there before but now in a few hours I’m getting some basic health check level coverage automated.
Developer tools IDE usage. I can interrogate the code, ask a about root cause a risk I have found with hands on testing for more insight at code level, even its it to assess a feature for risks directly.
Vibe coding tools to run testing experiments. Clone the source code and add mutants so I can test how good my automated health check actually is. Often as a tester I think oh I could do with a simple tool to help me test something better, I suspect I’ll explore this more.
Information repository agents, yes I could sit with the designer or the developer with my bucket load of questions but this provides a potential other option to gather the information i need.
Research, questioning and generating risk hypothesis. Empowering more and deeper testing.
So in the above I have focused on doing more and deeper testing which I feel will also in time come with its own natural efficiency boost.
I am wary of faster and cheaper goals, they seem currently a bit more suited if my activities had leaned more to mechanical strength ones but that was rarely the case so my focus has been on more and not less.
I caveat this, my role and contribution is often way off mainstream and faster and cheaper may end up losing sight of the value of the model I work in, that risk has always been there but there will definitely a few more angles to this on the horizon.
I think a lot of what QA does today that feels exhausting is going to fade into the background by 2026. Writing and maintaining repetitive test cases, chasing requirement changes, manually triaging failures, and keeping test artifacts in sync are all areas where AI already helps and will probably become table stakes. That work does not disappear, but it becomes less manual and less time consuming.
What seems to increase in value is judgment. Understanding risk, deciding what is worth testing deeply versus lightly, knowing when automation is lying to you, and connecting test results back to real user impact. On teams I have worked with, tools like Tuskr, Qase, and even TestRail start to matter less for their feature lists and more for how much friction they remove from day to day QA work. When the tool stays out of the way, QA can spend time thinking instead of maintaining.
Career wise, I would bet on roles that sit close to product and architecture rather than pure execution. People who can design quality strategies, reason about complex systems, validate AI behavior, and communicate risk clearly to stakeholders are already harder to replace. The testers who only execute scripts will struggle, but the ones who understand why a system fails will probably be more valuable than ever.
I think this highlights an interesting starting point “Writing and maintaining repetitive test cases,” I do not do that, have not done it in multiple decades and I no longer directly know testers who do this.
It did disappear for a lot of teams and those other things you mention like judgement and understanding risk replaced it. What may have been key to getting those advantages and removing waste was when the model evolved it was driven by human testers and it brought those very human strengths with it.
AI has on the face of it may have brought it back to the forefront as a good idea. This time for those that missed on the human evolution of that model and will only experience an AI driven evolution now, it may miss out completely on those values, it may bring other ones.