So the future would be me QE and QA would be faded out. What are the key responsibliities of a QE in the AI era?
How do we make sure that accelerating testing doesn’t just mean testing the wrong things faster?
How did you implement your risk-based testing, what criteria did you use?
When developers take on more testing ownership, what does the new, evolved role of the dedicated QE professional look like? Do they become coaches, tool-builders, or something else?
With the revolution through AI, what will be the evolution of QA/QE in your opinion? Some jobs will no longer be needed, new jobs will take their place. What do you think the landscape will look like in a few years?
I’d say the biggest challenge when moving from QA to QE is the mindset shift. In QA, you’re mainly focused on verifying if things work, but in QE, it’s all about taking ownership of quality throughout the entire process. It’s not just about finding bugs at the end of the cycle – it’s about making quality a responsibility for everyone, from development to deployment. Sure, tech skills and culture are important, but the real struggle is getting the team to adopt this mindset that quality is everyone’s job, not just the QA team’s. Once the team gets on board with that, everything else starts to click.
The key difference between traditional QA and modern QE comes down to how success is measured. In traditional QA, the focus is on metrics like the number of test cases executed or defects found. However, in QE, it’s all about the impact.
You’re looking at things like coverage quality, test reliability, reduced production defects, and faster feedback cycles. The shift is from focusing on volume to focusing on value, making sure the tests that are run actually drive meaningful improvements in quality and speed.
To foster a mindset of “quality is everyone’s job,” leaders can start by embedding quality into the definition of done. This ensures that quality isn’t an afterthought but an integral part of the development process. Tying metrics to team outcomes helps align everyone with the bigger picture and encourages accountability.
Celebrating ownership moments, where developers take responsibility for their code quality, reinforces this mindset. Practices like pair programming, code reviews, and cross-functional demos are also great ways to show that QA isn’t the gatekeeper, quality is a shared responsibility across the entire team.
To help development teams internalize ownership of quality, it’s key to shift left in the testing process. This means involving quality checks earlier, like during development, rather than just at the end. Pair testing is another strategy, having devs and testers work together fosters collaboration and shared responsibility for quality.
Also, embedding QA champions in dev squads ensures there’s always someone advocating for quality from within. Finally, making testing tools and dashboards visible to everyone helps keep quality top of mind and encourages accountability across the whole team, without relying solely on QA as the bottleneck.
To transition from QA to QE, there are a few key skills you’ll want to pick up. First, automation is a big one – learning how to design frameworks, write scripts, and get comfortable with API testing is crucial. Understanding how to integrate CI/CD and using data analysis to improve testing processes will also set you apart.
On top of the technical side, soft skills matter too. Being able to coach your team and influence cross-functional teams to prioritize quality is just as important. Lastly, with AI becoming a bigger part of the testing landscape, getting familiar with AI-assisted test generation will definitely be an asset.
To effectively build a sense of quality ownership within dev teams, organizations need to shift from the traditional “QA as a gatekeeper” mentality. The key is to create an environment where everyone is responsible for quality, not just the QA team. Start by introducing transparent metrics that help developers understand the impact of their work on quality.
Automated feedback loops can make it easier for them to catch issues early. Integrating QA tools into their workflows ensures that testing is part of the development process, not an afterthought. Recognizing and rewarding proactive defect detection also motivates the team to take ownership. Lastly, providing accessible dashboards that visualize quality makes it more tangible and actionable for developers, fostering a culture where quality is everyone’s responsibility.
As a QE, advocating for a “quality is everyone’s responsibility” culture, especially under tight deadlines, starts with making quality visible and data-driven. You can leverage defect trends, coverage analytics, and execution insights to identify areas of high risk and focus efforts where they matter most.
Dashboards and AI analytics are great tools for this, as they help highlight which areas need attention and where testing will have the greatest impact. By presenting this data to the team, you can shift the focus from just meeting deadlines to ensuring the right quality is built in, enabling everyone to understand their role in delivering quality without sacrificing speed.
Definitely! As QE evolves, the focus should definitely shift towards the impact of our testing rather than just the number of test cases.
A single, well, executed test that prevents a major production issue is far more valuable than hundreds of tests that don’t add much value. At the end of the day, it’s all about effectiveness, testing smarter, not harder.
When tests can heal or adapt themselves in real time, the definition of “done” becomes dynamic. Instead of relying on rigid success criteria based on specific implementations, the focus shifts to ensuring the functionality works as expected.
The team needs to trust that these adaptive tests will continue to maintain proper coverage and reliability, even as UI or code changes occur. It’s about flexibility and maintaining confidence in test coverage, rather than sticking to a fixed definition of completion.
he impact of AI and agents on SRE and DevOps is significant. SREs will shift more towards policy enforcement, reviewing anomalies, and handling orchestration, while AI-driven tools can take over repetitive tasks. This will lead to the emergence of new roles focused on managing agents, autonomous monitoring, and enhancing cloud resilience.
These roles will complement traditional DevOps functions, allowing teams to be more proactive and efficient in their operations. As automation increases, DevOps teams will evolve, with a stronger emphasis on oversight and strategy rather than manual tasks.
When organizations rush to adopt QE practices, especially with the fast-paced implementation of AI and automation, they often face some common challenges. These include tool fatigue, resistance to change, and skill gaps. A good way to tackle these issues is to start small, launch pilot pipelines to test the waters before scaling up.
It’s also crucial to combine AI with human insight, as this helps avoid over-reliance on automation. Lastly, offering training and continuous learning opportunities will help build confidence in the team and prevent them from feeling overwhelmed by the new tools and processes.
A practical approach to scaling quality engineering (QE) in a growing organization involves a few key strategies. First, implement modular frameworks that allow teams to reuse and scale test cases easily. Centralized dashboards help monitor and manage the quality status across all projects in one place.
Consistent coding standards are crucial for maintaining uniformity across teams. It’s also important to allocate dedicated QE architects who can ensure test hygiene across projects and provide guidance. At the same time, empower individual teams to take ownership of testing responsibilities within their own projects, giving them the flexibility to adapt to specific needs while maintaining overall quality standards.
This balance of centralized oversight and decentralized ownership can scale efficiently as your organization grows.
To make sure your test automation suite is more than just a collection of scripts, it’s important to embed feedback loops that keep the process dynamic. When a test fails, it should provide actionable insights so developers can address issues quickly.
Additionally, maintaining clear and up-to-date documentation ensures that everyone knows what the tests are doing and why they’re important. Your tests should also integrate seamlessly into your CI/CD pipelines, offering immediate feedback during the development process, rather than just running without providing real-time value.
One practice that really helped break down the silos between Dev and QA was implementing weekly collaborative bug triage and paired exploratory testing sessions. By sharing the responsibility for defect analysis, both teams were able to build trust and reduce any friction that existed.
This not only helped with better communication but also fostered a true, shared culture of quality, where everyone felt accountable for the product’s success. It was a game-changer in getting both sides to work together seamlessly.
To successfully transition into a Quality Engineer role in the next 2-3 years, a QA professional should prioritize acquiring skills in automation architecture, AI-assisted test generation, and cloud-native testing. Familiarity with CI/CD processes will be key, along with performance monitoring and test data management.
Additionally, becoming proficient in observability and analytics tools will be increasingly important to ensure continuous improvement and quality at every stage of development. These skills will help align with the growing need for smarter, faster, and more scalable testing strategies.