The rise of the hybrid tester

I'm pretty fast on my laptop keyboard, usually typing around 60 words per minute, sometimes even more, thanks to my mom. She signed me up for a typing course when I was 12 because she thought it would be useful for my career someday, years before I could get my hands on a real computer. 

It turns out she was right, and it's still a handy skill today.  

When I graduated from the typing course, my mom gifted me an Olivetti typewriter. It was amazing at the time! I ended up typing all my school assignments instead of handwriting them, which was probably for the best since my handwriting was terrible back then (and I guess it never really improved because of that).

This is the model of the typewriter that my mother gave me as a gift.  

Years later, even though I had been using DOS and Unix computers for a while, getting to use a mouse for the first time on Windows 3.11 and Word 6.0 was a magical experience.  

Being able to print exactly what I saw on the screen, along with the ability to edit text instantly, was a game-changer, and don’t get me started on the grammar and spell-check tools.

Word 6.0 brings back so many great memories.  

Word 6.0 and I became one, helping me work faster and better. Today, it's hard to feel those kinds of breakthroughs.  

We live in a time where computers and mobile devices are incredibly powerful, GUIs are standard, and the internet has become essential like oxygen.  

At that time, office productivity tools were just starting to become mainstream: word processors, spreadsheets, Xerox machines, fax machines, and more. It was a big leap forward in terms of productivity and efficiency.  

With the rapid growth of AI today, it feels like we’re experiencing another major breakthrough all over again.  

Let’s be clear: in the software development field, we are all knowledge workers. We don’t break a sweat physically to get our work done, we’re using our brains.  

As outlined in this IBM article, a knowledge worker is a professional who generates value for the organization with their expertise, critical thinking and interpersonal skills. They’re often tasked with developing new products or services, problem-solving, or creating strategies and action plans that will drive better business outcomes. Knowledge workers have formal training or significant experience, are skilled communicators and can learn and adapt to a shifting work environment.  

As we frequently hear in the news, the supply of developer talent isn’t keeping up with the growing demand for new digital services. In response, developers are becoming early adopters of AI agents and copilots to accelerate and enhance their work.  

There's no doubt that this technology is still in its early stages. It's not perfect, and the output from AI can sometimes be completely inaccurate. In addition to that, we still have significant challenges to tackle regarding ethics, bias, privacy, and security. Getting AI right is a journey that still lies ahead of us.  

These days, AI is, in many ways, brilliantly stupid, and that's fine. No early technology is perfect. 

But, despite the mix of fear and skepticism we hear from many companies and some individuals in LinkedIn posts, AI has been rapidly infiltrating the software development industry, whether we like it or not.  

As developers accelerate their work through agile practices, DevOps, and widespread adoption of AI, testing becomes the bottleneck.  

In this survey from GitHub on AI’s impact on the developer experience, it highlights that “waiting on builds and tests is still a problem. Despite industry-wide investments in DevOps, developers still say the most time-consuming thing they’re doing at work besides writing code is waiting on builds and tests. Notably, developers say they spend the same amount of time waiting for builds and tests as they do writing new code”.  

This is a call to action for those of us in the software testing and quality engineering space. As the ultimate knowledge workers, we should remain open-minded about experimenting with the latest wave of AI tools.  

The more we experiment with these new AI tools, the better we can understand their value and limitations, as well as how they can be integrated into our workflow.  

To be real, I love getting my work done quicker and better. Like I mentioned before, I switched from handwriting to typewriters and then to word processors. I really appreciate how these changes have helped me out.  

In the testing field, much of our work involves routine, procedural, or algorithmic tasks. Trust me, these are the perfect candidates to take advantage of AI tools.  

Can a computer do what I do faster and more cheaply? Count me in, so I have more time to focus on creative or strategic tasks.  

Or I could use AI to generate a variety of possible testing ideas, so I can refine them with my experience and creative vision. Absolutely, I'm on board!  

I don’t expect AI to mimic creative human thought processes anytime soon, probably not in my lifetime, but I’m more than happy to take advantage of any leverage it can provide at its current stage.  

If it can take care of the happy path and greenfield cases with little input from me (or light review), that would free me up to dive into the tricky corner cases or the ones that really need my experience, creativity, or a collaboration with a subject matter expert (SME). That would definitely save me some time!  

At the end of the day, AI should be seen as a tool to augment, not replace, human testers. By automating menial tasks, AI frees us up for meaningful and innovative work.  

We don’t need to fear AI taking our jobs; we need to be concerned about the people using AI taking our jobs (yes, I know! I've seen plenty of LinkedIn posts where people call this a silly statement, but I still believe it's true).

I came across a report from Capgemini on how companies are harnessing the value of generative AI. It highlights the benefits realized from GenAI over the past year, and while the results aren't extraordinary, they are definitely positive. Keep in mind that GenAI has only been mainstream for about 18 months. While we haven't seen anything groundbreaking yet, the future looks promising with ongoing advancements in GenAI and improvements in other AI methods. 

I believe the key to success lies in mastering AI tools to gain a significant competitive advantage.  

To stay relevant, it's essential to embrace these tools and learn what and how to delegate effectively to AI. Sometimes, certain tasks need to be carried out manually by humans. That’s fine, as the goal is not replacement but augmentation.  

Before we know it, we’ll see the rise of the hybrid tester, QA/QE professionals working in tandem with AI.  

The future of work isn’t likely to be about humans versus machines, but rather about working together with AI.  

I'm glad you made it to the end of the article! I’d like to ask a favor: please share in the comments which AI tools and methods you're using. Feel free to share your success stories as well as any failures you've faced, so we can all learn from our collective knowledge.

About the author

Cristiano Caetano is an entrepreneur and product expert with extensive experience in software testing, B2B SaaS, and marketplaces. Founder of Zephyr Scale, the top-selling app in the Atlassian ecosystem, he is now the VP of Product Marketing at Katalon, where he continues to drive innovation in the tech space.

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