Your feed, your group chat and everything you read is stuffed to the brim with AI opinions. Some of us have gone all in with elaborate workflows and can’t make a decision without running it by ChatGPT. Others are dabbling in meal planning, email edits and light consultations with the agent. And some of us remain cautious and even a little intimidated by what AI will mean for our lives, our jobs and the world at large.
So as professionals in games research, let’s approach AI the same way we approach everything else in this field: carefully, pragmatically, and with a healthy mix of skepticism. The goal isn’t to worship the tech, or write it off entirely. The goal is to understand what it can (and can’t) do, and where it can actually help us do our jobs better.
Let’s break it down.
Research is great. But research is also… a lot.
You know the drill: before a single player hits “Start,” there’s already a mountain of prep behind the scenes. Aligning on goals. Reviewing the build. Defining the player profile. Writing screeners, crafting surveys, building interview guides. Then comes the fieldwork: running sessions, reacting in real-time, juggling schedules and last-minute tweaks.
And then there’s the analysis—sorting through hours of gameplay footage, identifying moments of insight, coding open-ended responses, building summaries, presentations, reports.
Great research takes time. And energy. And brainpower. But what if AI could give us some of that time back?
Where AI really shines
AI isn’t here to replace researchers (or players!). It’s here to assist us, especially with the more tedious or time-consuming parts of the process. Here’s where it can help today:
Prep Work
- Generate starter templates for surveys, interview guides, or screeners based on past projects
- Repurpose frameworks for new studies with a couple of smart prompts and a human sanity check
Analysis
- Auto-annotate gameplay videos to surface moments that matter—no need to watch every minute manually
- Quickly theme and code open-ended feedback or transcripts. What used to take days, now takes minutes
- Visualize responses with AI-generated charts and summaries (actually helpful, not just pretty)
Reporting
- Draft an executive summary to get the team aligned faster
- Create a “first pass” report that you can refine and enrich with your own insights
At PlaytestCloud, our First Findings feature does exactly that: giving you AI-assisted insights as soon as your playtest ends. You still get the raw footage and full transcripts, but now there’s an instant layer of support to help you hit the ground running. And let’s face it: who has time to watch all the videos?!
First Findings is a launchpad for deeper research. It helps pre-categorize trends and identify themes, but each AI insight links directly to clips of gameplay recordings that justify the point. Researchers can (and should!) always double-check these clips to make sure they’d make the same assessment. If the robots get it wrong, it’s quick work to edit the annotation yourself. Which leads me to my next point…
What AI can’t (and shouldn’t) do
AI is powerful—but it’s not perfect. And more importantly, it’s not human. That matters in games research, because we’re not just counting clicks or compiling stats. We’re watching players, listening to them, interpreting what they say and what they mean—even when those things don’t align. Sure, even researchers bring our own bias into things, and sometimes we misinterpret findings or adjust them to align with our existing hypotheses (it happens). But relying 100% on AI to draw conclusions and identify challenges would be risky territory, especially with such crucial development decisions at stake.
Here’s where AI struggles:
Context & Nuance
- Players mislabel things all the time. They call a “crafting menu” a “shop,” or mix up character names
- Humans can spot these mixups using context, facial expressions, or memory of earlier sessions. AI? Not so much.
- Sarcasm can be hard to pick up on, and a wry comment delivered as fact could throw off the AI interpretation.
Domain Knowledge
- AI won’t automatically know that your game’s tutorial issue is common in roguelikes, or that most farming sims solve similar UX pain points with a toggle menu (this is where your researchers come in!)
- That kind of insight comes from experience, pattern recognition, and knowing the genre inside and out.
Junior Development
There’s also a long-term risk: many researchers cut their teeth on “boring” tasks like reviewing transcripts or formatting slides. These aren’t just chores—they’re learning opportunities. Automating everything could mean fewer growth paths for juniors, unless we actively make space for mentorship and skill development alongside the tools. Sometimes doing things the “hard” way builds character (I sound like my grandpa here don’t I?).
So where do we go from here?
AI is not inherently good or bad. It’s a tool. Like any tool, the outcome depends on who uses it, how, and what for.
Use it to:
- Get a head start on data synthesis
- Surface themes quickly so you can dig deeper
- Clear your plate for more strategic work—triangulating data sources, collaborating with your team, or planning the next study
Just don’t use it as a substitute for critical thinking or human insight. That’s not what it’s good at.
If you're generating solutions or creative fixes? Still trust your brain first. If you're writing a report for stakeholders who'll act on it, double check everything. But if you're staring down hours of footage or pages of free-text responses, AI might just be the best intern you've ever had.
And who knows—maybe that junior researcher on your team will be the one who figures out how to use it best.
TL;DR:
AI won’t do your job for you. But it can help you do your job faster—and free up time for the parts that really matter. Use it wisely, check it carefully, and make sure it works for you.
