Skills for testers today center on communication and foundational testing knowledge, not just technical ability. A tester who finds every bug but cannot explain the issue or argue its priority is blocked. Critical thinking about AI output matters because AI is toxically positive, confirming even wrong assumptions, which means testers must know their product well enough to catch what AI invents or misses.
Key Takeaways
- When QA teams are cut, users become the first line of defense and will find the bugs, making broken trust with a user base the real business cost.
- AI is toxically positive and will validate wrong inputs and incorrect assumptions, so testers must independently verify AI-generated test cases against real product knowledge.
- Communication skill is the hard limit on a tester’s effectiveness: finding bugs no one understands or acts on is a dead end, regardless of technical depth.
- Adapting communication to the audience, whether a longtime dev or a new C-suite member, determines whether a quality concern gets prioritized or ignored.
- AI will shift testing the way automation did: speeding up certain work and freeing time for exploratory and manual testing, not replacing the human tester.
Communication is the skill that decides whether your bugs matter
A tester who cannot explain what went wrong is stuck, no matter how good they are at finding issues. Tara Walton puts the basic human skills above everything else, and communication leads the list. You can be the best person in the world at spotting defects, but if you can’t make the case for why one matters, that is your ceiling.
The skill splits into two parts. Finding the problem is one. Naming a priority, framing it so the right person acts, is the other. The second part is where most of the value gets lost.
This becomes sharper with experience, not easier. After ten, fifteen, twenty years, the basics turn into second nature. You forget you ever learned them, and you start assuming everyone else has them too.
Why the testing fundamentals still carry the work
The core of testing is checking that things function and that users get the result they expect. Tara keeps returning to this baseline because it is easy to drift above it into technical detail or business politics and forget the calculator still has to reject alpha characters.
Edge cases are the clearest example. An experienced tester knows that a frustrated user will hit the enter key five hundred times because their internet is slow. They know to paste War and Peace into a text field and see what breaks. To a newcomer, the obvious question is “why would you even test that?”
The answer is the whole point of the job. You can ship the best software on the planet, but if it does not do what users thought it would do, you still have a problem. Functional correctness and matching user expectation are two different bars, and both have to clear.
When you train someone new, you cannot assume they know how to find an edge case. The instinct that feels automatic to you is a teachable method to them. Spelling it out is part of the work.
How AI handles test cases, and where it stops
AI is good at generating use cases from requirements, and that is genuine help. Feed it requirements and it produces coverage quickly. The gap shows up at the obvious human moves.
The tools do not think creatively. They miss the “what happens if I copy and paste War and Peace into this field” move that an experienced tester makes without thinking. That creative questioning, the steady stream of “what if” and “did you think about”, stays a human contribution.
So the questioning, the asking what if, and the conversations around them remain your job. AI extends your reach into routine coverage. It does not replace the instinct for the strange case.
Trust is the foundation, and it breaks faster than it builds
For Tara, trust has always been the base of quality work, with the user as priority one and stakeholders as priority two. The order matters: software that does not do what users need will not get used, regardless of how good it is otherwise.
Trust collapses fast and rebuilds slowly. Once it is broken, the climb back is long, and the memory of being burned does not fade. This holds for users facing a product and for teams facing AI-generated code.
The current wave of AI-assisted code arrives with unknown quality. Quality people are the ones who put trust back into that code by testing it. That makes the tester’s role a trust function, not just a defect-finding function.
Tara was slow to adopt AI herself because she saw the flaws first. The way through is to spend time with the tool and map its weaknesses. Knowing where the potholes are lets you route around them.
“I know where the potholes are. Let me show them to you so we can avoid them. Let’s navigate around them and then we can fix them.” — Tara Walton
AI is toxically positive, so critical thinking is the counterweight
AI agrees with you even when you are wrong. Tell it almost anything and it responds with “you’re right, that’s amazing, thank you for pointing that out.” Tara calls this toxic positivity, and it is a real risk for anyone leaning on the tool for analysis.
The defense is knowing your own product. If you do not know your business and your domain, you cannot tell when the model is making things up. The output sounds smooth and confident in both cases, correct and invented.
This shifts the work toward root cause analysis. Tara now spends more time taking what AI produced and asking “this doesn’t sound quite right, but I don’t know enough, tell me more.” The testing happens, but the center of gravity moves to verifying and digging.
That shift demands more knowledge, not less. You need the answers up front now instead of assuming them, because the model will not flag its own gaps.
Match your message to the person in front of you
How you report a finding depends entirely on who receives it. A developer you have worked with for ten years and a C-suite member who just joined need different framings of the same bug.
Tara asks the question directly up front: what helps you understand what I’m doing, and what is your stake in this? If a stakeholder does not care how many bugs you caught before release, you do not lead with that number. You connect the finding to something they already worry about.
Framing turns a complaint into a contribution. “You’re not doing a good job” is a horrible thing to say and a useless thing to say. “I found something I think we can improve” lands. Reframe the finding as help, point at the concern the person already named, and the conversation opens instead of closing.
Communication style is not the same as communication skill. Most people are not bad communicators, they have a preferred channel. Tara is visual: show her the screenshot or the video over a perfect step-by-step she cannot follow. Staying open to correction, asking for the format that works for the other side, is part of being good at this.
Get to the table, and ask if you have to
The way to survive the QA budget cycle is to stay in the developers’ minds, not the managers’. Tara makes herself a resource the devs come to: bring me your use cases and I’ll tell you what you’re missing. That is the expertise that earns a seat.
The cycle is familiar. When everything runs smoothly, leadership decides QA is unnecessary and cuts it to save budget. Then things go badly. Staying visible to the people who build the product is more durable than trying to convince the people who hold the budget.
If you are not in the planning conversation, ask to join it. “I’d love to know more about the upcoming features, can I sit in?” is a request that builds a relationship. You become part of the conversation rather than the person waiting at the end to deliver bad news.
These are relationships, and they rest on trust. You are not there to take up a seat or play the bad guy. You are there because you care about the product and want users to have the same faith in it that you do.
When you cut QA, users become your first line of defense
The argument against gutting a QA team is simple: the users will find the bugs instead. They find the ones testers miss already. Hand them all the defects and they will not be happy, and once their trust is gone, recovery is hard.
The bank login example makes it concrete. A small bank with a beautiful website and a login process that questions whether you are really you, suggests resetting a saved password every time, and turns a daily task into a fight. One person willing to act as a user for a minute would have caught it.
Richard left a bank for the same reason. The e-banking site looked like the early 2000s even after a refresh, and the usability was punishing for something he had to use every two days. Bad enough day-to-day experience moves customers to a competitor with a better app.
The fix costs almost nothing. One person pretending to be a user, walking the real flow, would have asked “what is happening here?” That single act of advocacy is what a QA function provides.
Standing up takes courage against a culture of forced positivity
Being the user’s advocate means speaking up in a meeting and saying the work is not headed the right way. That is not the mainstream move in many companies, and it takes nerve.
Toxic positivity shows up in teams too. Every release gets celebrated as fast and smooth while one tester sits in the corner pointing at hundreds of bugs parked on the backlog. The applause papers over the open defects.
Courage here is built on communication skill, not volume. You do not announce that someone failed. You reframe: here is something we can improve, did you notice this, what can I help with next time. Quality becomes an iterative process rather than a verdict.
The improvement that matters is often not a new feature. Sometimes it is fixing the typo. Version one shipped, that is good, and the question is what makes it better, which is rarely more features crammed in.
Use AI to check your tone, not to replace your voice
Text strips out facial expression and body language, so a quick Slack message can read as terse or mean when you meant nothing of the kind. Tara knows people who run a message through ChatGPT before sending it to a boss, asking what the tone sounds like.
This is a fair use of the tool. Ask it whether a draft reads as confrontational, and if it does, rewrite. A trusted friend who is good with words works the same way: “does this sound mean, or do I just need more coffee?”
Filtering tone is a learned skill. Very few people have it naturally, and getting words right in plain text takes practice. The tone check is a gut check, not a ghostwriter.
Keep your voice while you do it. Tara has phrases she always uses and pauses she always puts in, and she does not want to lose them to a model. Use AI for the gut check, stay authentic in the detail.
How testing changes over the next five years
The likely shift mirrors what happened with test automation. Automation was supposed to end manual testing and the slog that came with it. Instead it freed up time for hands-on, exploratory work that turned out to be necessary.
AI fits the same pattern. It will speed up automation and flag what needs a manual run-through. The manual skill of actually using the product, and exploratory testing, stay valuable. As long as humans use your software rather than only other software, there is human error in it, and human hands have to touch it.
A new specialty is forming around testing agents and systems that have to be judged on many dimensions instead of a yes-or-no result. That is a real skill, and it sits alongside the existing roles rather than erasing them.
Find the work that hooks you, by trying it
Tara wanted to be a developer and became a tester by accident, discovering she was good at the weird cases. Her happy place turned out to be back-end API test automation, strictly code. Exploring further, she found a strong pull toward UX and accessibility and went to learn it.
The field is wide enough to hold very different passions. Someone who wants nothing but exploratory testing has a place. Someone who writes AI prompts all day and loves it has a place. Quality is everyone’s responsibility, down to the salesperson who gets a thrill from finding a bug during a client demo.
You will not find your passion by thinking about it. You find it in the moment the work clicks, the light-bulb moment Tara watches her students hit when they get excited and hooked. Try the new tool a community member is playing with. It might be the thing.
If you dread one more manual test, that is a signal. Ask whether you can automate the thing instead. The range of work in QA, from automation to test data generation to non-functional testing across levels and types, is wide enough that there is somewhere to move.
