By Christian Fiore — QA at Paisanos
I’ve been working for over a decade on highly complex digital projects, supporting product, technology, and business teams in fast-growth contexts where quality is non-negotiable and time is always running out.
It all started with a Notion doc full of bullet points, more than ten people connected on a call, and a question that echoes through every office when a product grows faster than its processes:
“Are we forgetting something?”
That’s how our regression sessions used to begin on complex projects. We wanted to cover everything, anticipate the unthinkable, leave no blind spots. Every feature had a direct impact on thousands (sometimes millions) of users. But over time, coordination became unsustainable. Meetings dragged on, documentation grew, and the feeling of fragility never really went away. We knew there had to be another way.
In this article, I want to share how, at Paisanos, we transformed those marathons into a more agile and intelligent process, and how our experience in demanding projects led us to actively collaborate in the evolution of a QA tool that now amplifies our work. A change that eased the team’s load and raised quality without slowing delivery. Spoiler: it’s not just about automating more, but about automating with judgment.
QA in high-complexity projects: the starting point
At Paisanos, we work with products that operate under constant pressure. Web and mobile applications that must perform flawlessly in real-world scenarios: traffic spikes, critical payment flows, multiple user profiles, and frequent releases. Each project brings its own challenges, from e-commerce platforms with intricate logic to entertainment systems with thousands of concurrent users.
For a long time, our quality approach relied heavily on manual practices. Extensive documentation, detailed test case design, manual regressions on every release, field-by-field validations. These practices were necessary, but hard to sustain at the pace the business demanded.
Automation arrived as partial relief. It brought consistency and speed, but also introduced new tensions: technical investment, constant maintenance, and dependence on specialized profiles. Not every project (and not every team) could absorb that cost without sacrificing agility.
Over time, we understood something essential: there is no single QA model that works for every scenario. On mobile, device and operating system fragmentation. On web, the diversity of browsers, resolutions, and session states. And when both worlds coexist, complexity multiplies.
Agility, continuous delivery, and an inevitable question
The adoption of agile methodologies and continuous delivery practices changed the game for good. More frequent releases, constant iterations, and a clear expectation from clients: move fast, but don’t break anything.
In that context, the question stopped being occasional and became permanent:
How do we ensure quality without slowing velocity?
The answer could no longer be limited to “automate more.” It required rethinking the role of QA within the team, bringing it into discovery, fostering a shared critical and analytical mindset, actively participating in ceremonies, and assuming that quality isn’t a final stage but a distributed responsibility.
Automation became a strategic decision. Combining exploratory testing, automated regressions, and deep business understanding became just as important as choosing the right tool.
When AI starts opening new possibilities
In that evolution process, artificial intelligence stopped being a distant promise and became a tangible enabler. It didn’t come to replace human judgment, but to amplify it.
AI can suggest test scenarios while people provide context, generate accessible scripts for teams without a technical background, or accelerate learning curves for new frameworks. In practice, this translated into greater inclusivity, shorter cycles, and more autonomous teams.
In an environment where the pressure for quality grows at the same pace as business speed, AI began to act as a guiding light. It doesn’t solve every dilemma, but it points toward a path where QA is no longer seen as a final checkpoint and instead becomes a strategic value engine.
A project where complexity became tangible
All of this evolution was put to the test in a specific project where challenges converged simultaneously.
The scene is probably familiar: when someone asks about the most critical edge cases, the mind starts listing scenarios one after another, an endless chain. In this project, regression sessions had turned into true corporate marathons.
More than ten people in every meeting (developers, QAs, product owners, analysts) each contributing valuable perspectives, institutional knowledge, and critical alerts. The issue wasn’t a lack of information, but the impossibility of systematizing it without relying on everyone being present at the same time.
With millions of users, multiple complex flows, and overlapping releases, every deployment could impact already-live functionality. The process was necessary, but clearly unsustainable.
The technical dilemma from our experience
Automation wasn’t an option, it was a necessity. But the traditional path came with obstacles we already knew well:
- Steep learning curves, hard to absorb in short- or mid-term projects.
- Constant maintenance, where every visual change meant fixing scripts.
- Complex test data management, with manual dependencies that were difficult to coordinate.
- Costly scalability, both in infrastructure and configuration time.
Given that landscape, the conclusion was clear: we needed to automate, without sacrificing agility or adding more friction to the process.
Collaborating to build the right tool: working with Autonoma
The question shifted:
How do we capture all that collective wisdom without relying on endless meetings? How do we democratize automation without turning the team into developers?
Exploring alternatives aligned with our reality, we began collaborating with Autonoma, not just as users, but as active contributors to the product’s evolution.
We shared real pains, concrete cases, complex flows, and edge scenarios. We showed how we worked, where processes broke down, and what we needed to move forward. That two-way collaboration made it possible to iterate the product on real contexts, not assumptions.
The no-code approach, intelligent test variable management, simple environment configuration, and parallel execution without additional infrastructure directly addressed problems we faced every day. In the process, we helped build a tool that now serves not only us, but other teams facing similar challenges.
Beyond efficiency: cultural and strategic impact
The biggest shift wasn’t technical, it was cultural. Freed-up time allowed us to expand scenario coverage and deepen analysis. Automation stopped being synonymous with reducing effort and became a way to redirect it toward where it adds the most value.
AI accelerated adoption, improved team adaptability, and reinforced a central idea: quality isn’t negotiated, it’s designed. And when technology empowers the team instead of constraining it, what once seemed impossible becomes part of everyday work.
At Paisanos, we believe artificial intelligence doesn’t replace human judgment, it amplifies it. We work with organizations that want to transform their processes without giving up quality, context, or speed. Because when technology adapts to people (and not the other way around), quality stops being a bottleneck and becomes a competitive advantage.
Questions and answers about QA and AI
How does artificial intelligence help improve QA processes in complex projects?
AI accelerates test scenario generation, lowers technical barriers, and expands coverage without losing human judgment. It acts as a facilitator that optimizes time and improves decision-making in high-complexity contexts.
Is no-code automation viable in enterprise projects?
Yes, when supported by a clear strategy. Well-designed no-code solutions make it possible to scale quality, reduce technical dependency, and maintain agility, even in products with millions of users.
Does AI replace the traditional QA role?
No. AI strengthens the QA role by freeing up operational time and reinforcing strategic analysis. Human judgment remains central for interpreting context, business needs, and user experience.
When is it time to rethink the QA approach in a digital product?
When regressions become unmanageable, delivery slows down, or quality depends on lengthy meetings. These are clear signs that the process needs to evolve.