AI and Testers and Jobs

I was recently asked a simple question by an experienced software professional:

Are testers going to be out of work because of AI?

That is a question I've heard repeatedly over the past 12–18 months. What's interesting is that I've heard some version of this question throughout my career. Every major technology shift—from fourth-generation languages to test automation to Agile to AI—has prompted concerns that testers will become obsolete.

Those conversations have taught me a few lessons.

Learn Continuously

Every significant technology shift creates uncertainty. Today it's AI. Before that it was Agile, automation, cloud computing, and countless other innovations. The professionals who thrive through these changes are rarely the ones who resist them. They're the ones who stay curious.

Learn the new techniques. Explore the new approaches. Most importantly, examine how you solve problems. If your approach to software development or testing is largely unchanged from five or ten years ago, it may be time to reassess. The challenge isn't simply learning new tools - it's learning new ways of thinking.

For years I've argued that the most important challenge in software development and testing is understanding why software is being built. What problem is it solving? Why does it matter? How is it different from existing solutions? These questions are more valuable than any specific tool knowledge.

Of course, tools matter too. As new technologies emerge, take the time to explore them. Read about them, evaluate them critically, and whenever possible, try them yourself. Marketing materials rarely tell the whole story. Hands-on experience will quickly reveal whether a tool genuinely improves your work or simply repackages existing capabilities.

The goal isn't to chase every trend. The goal is to understand what is changing, determine what is useful, and continuously improve how you work.

Applying What You Learn

Learning about AI, new testing techniques, or emerging tools is interesting. Applying them is what creates value.

The question isn't whether a new approach or tool is perfect. The question is whether it helps you work more effectively. The only way to answer that is to try it.
Experiment. Use new techniques on real work. Evaluate the results. Keep what works and discard what doesn't.

You won't get everything right the first time. No one does. The professionals who adapt successfully aren't the ones who always pick the right tool or follow the perfect process. They're the ones who continuously learn, experiment, and improve.

In a rapidly changing industry, that ability to adapt may be more important than any specific tool or technology.

Changes in Thinking and Working

Technology has always changed how work gets done. For decades, tools have been developed to automate established practices and perform routine tasks faster than people can. AI is simply the latest example.
If your contribution is limited to taking requirements at face value and creating test cases from them, you should ask a difficult question: how is that fundamentally different from what modern AI tools can already do?

The real value of testing has never been documenting expected behavior. It has been challenging assumptions.

Do the requirements make sense? Are they consistent? Are there gaps? What happens when users do something unexpected? How can the solution fail?

Those questions require critical thinking and multiple perspectives.

In the ideal Agile team, developers, testers, analysts, and business stakeholders collaborate to explore requirements before development begins. Everyone contributes to refining the work and identifying risks. In reality, many teams fall victim to confirmation bias, assuming everyone has the same understanding of the problem and the same interpretation of the requirements.

When that happens, important questions go unasked.

Good testers bring a different perspective. They look beyond what is written and explore what might be missing. They challenge assumptions, identify risks, and ask the questions others may not think to ask.

That's the work that is difficult to automate. And it is the work that becomes increasingly valuable as tools become more capable.

AI can:

• Generate test cases
  
• Analyze requirements (based on some model)
  
• Identify patterns in large amounts of data.

But it still depends on people to determine whether the right problem is being solved in the first place.

"How can this fail?"

As tools become better at generating code, tests, and documentation, the ability to uncover hidden assumptions and alternative perspectives becomes even more important.

So What Can We Do?

Will AI replace testers? Some testing activities certainly will be automated by AI agents and other tools. Some already are. The more important question is whether we are doing work that can be easily automated in the first place.

If our value comes from following instructions, generating test cases, and executing repeatable processes, we should expect increasingly capable tools to compete for that work. If our value comes from understanding problems, uncovering risks, challenging assumptions, and helping teams make better decisions, our contribution becomes much harder to replace.

Learn continuously. Adapt continuously. Think critically.

Those skills have survived every major technology shift I've seen, and I suspect they'll remain valuable through this one as well.