AI has become a powerful tool for QA – streamlining workflows, automating bug detection, and accelerating delivery.
While these efficiencies are helping us work faster, great QA still demands qualities that are distinctly human: the ability to think like a player, read between the lines, and apply the game sense needed to ensure the experience feels polished, intuitive, and fun to play.
In a talk given at the 2025 editions of Game Quality Forum and XDS, Kaley Hurst, our Chief Revenue Officer, and Kate Mitchell, our QA Director, explored how the key to great player experiences isn't just technically perfect games; rather, it's the strategic combination of AI efficiency and emotionally intelligent QA professionals.
This transformation is already reshaping how QA teams work. Below, we explore the importance of emotional intelligence and collaboration for the future of quality assurance in the age of AI.
What Is Emotional Intelligence & Emotional Quotient?
At the heart of what a QA tester does is to give feedback. Today, quality assurance is about building trust, influencing direction, and guiding development teams in making their game the best it can be. Communication isn't just part of our job; it is the job. You can find the most important bug in a game build, but if no one understands your report, it doesn't matter.
As tools help us generate data faster, it's the human layer – interpretation, empathy, and tone – that makes the difference. In other words, the real value of a tester isn't just what they say, it's how they say it.
This requires emotional intelligence – a set of skills that has become essential for modern QA professionals, defined as follows:
Emotional intelligence (EI) is the ability to understand and manage your own emotions while effectively reading and responding to others' emotions.
Emotional quotient (EQ) measures how well someone applies these skills in real-world situations.
Although AI handles many technical tasks well, it has significant gaps when it comes to these human emotional skills.
Collaboration: The Foundation of Quality Assurance
Collaboration means working as part of a collective (planning, coordinating, and sharing ideas) toward a common goal.
While agentic AI can be assigned goals and can complete tasks, it’s still limited by the data it was trained on, which limits its capacity for collaboration. AI can’t build or participate in community relationships in the human sense; it doesn't experience mutual care, reciprocity, or shared meaning, which are central to collective human work. AI can mimic empathy linguistically, but cannot truly feel or relate.
True collaboration requires reading between the lines, understanding unspoken concerns, and building trust over time. However, even for people, skills like emotional intelligence and the ability to collaborate well take work to develop.
Quotes
"The real value of a QA tester isn't just what they say, it's how they say it."
Building Human Collaboration Skills
At Side, we treat emotional intelligence as a core capability for QA. But self-reflection, empathy, and clear communication can’t be taught in a single training – they’re cultivated through ongoing practice and facing real-world challenges.
Here's how we approach cultivating EI across our teams:
Global QA Workshops to Build a Culture of Open Dialogue
Every month we host issue workshops with our QA managers across different regions. These are open sessions where they bring their blockers, challenges, and obstacles for collaborative problem-solving and peer coaching.
Having gathered feedback from our teams, the result is that our managers feel supported in their leadership challenges. From these sessions, we've seen that severe technical problems are rare. Rather, it's human challenges like communication breakdowns, misaligned expectations, and interpersonal conflicts that dominate the conversation.
This insight shaped our approach to upskilling. Rather than waiting for formal training, we actively develop each other. Testers shadow more experienced colleagues to learn different communication techniques and feedback approaches. Team members share how they've navigated difficult conversations or delivered challenging feedback.
This peer-to-peer approach develops exactly the kind of human insight that AI has difficulty replicating: the ability to read situations, adapt to different communication styles, and build genuine relationships. The only cost is time, but it's more effective than individual workshops because you're practicing with the actual people you work with every day.
The Fun Factor: Why Games Need Human Messiness
At a recent game conference we attended, attendees watched a playback of two AI bots playing against each other. The demonstration was meant to showcase AI's gaming capabilities, but the result was surprisingly lackluster.
Both AI systems played with technical precision. They moved to strategically optimal locations, calculated the most efficient paths, and executed moves that were mathematically sound. When one bot approached, the other would immediately retreat to the opposite side of the map, creating a perfectly logical but utterly predictable standoff. Neither bot was capable of winning because both were optimizing for the same metrics. They turned what should have been an engaging game into a tedious exercise in mechanical efficiency.
This demonstration made one thing clear: while AI can master the technical aspects of gameplay, it often misses what makes games actually enjoyable.
QA Testers Think Like Players
A game can be technically flawless and still fail to engage players. Understanding a game from the player's perspective requires insights that go beyond code functionality:
- Design and playability: Human testers recognize when a feature works as intended but feels wrong. They can identify when a difficulty curve is too steep, when controls feel unresponsive despite meeting technical specifications, or when a game becomes tedious despite being technically sound.
- Player psychology: QA professionals think like players, not like systems. They understand that players will try unexpected approaches, ignore tutorials, and create their own goals. Beyond technical bugs, they can identify when dialogue doesn't land, when a game mechanic breaks immersion, or when the overall experience doesn't align with the intended player experience.
AI can ensure bugs are documented properly and assist with workflow efficiency, but it cannot evaluate whether a game is fun to play. We need human messiness, chaos, creativity, and unpredictability to create it, because that's exactly what players bring to games.
Why the Future Belongs to Emotionally Intelligent Game Teams
AI is an incredible tool, and we're fortunate to live in an age where we get to partner with it. At Side, we’re actively exploring how to integrate and partner with both agentic AI and LLM-based AI in our QA processes. But the key word is partnership.
With the ability to parse through mountains of data in a fraction of the time humans can, LLM-based AI systems are seriously enhancing our ability to create technically perfect games and products. However, as we know, technically perfect isn't enough – QA's ultimate goal is ensuring amazing player experiences.
This is where cultivating EI and measuring EQ in quality assurance teams today becomes essential. The ability to understand players, communicate feedback constructively, and collaborate effectively across teams determines whether a game feels engaging or falls flat.
The future belongs to QA professionals who master this combination: those who can leverage AI's technical power while bringing the emotional intelligence and judgment required to shape truly unforgettable games.