Games rarely fall over on ideas. They fall over on repetitive work that scales with the project: thousands of test cases after every build, another language to wrangle before launch, reports someone assembles by hand instead of making the game. The bigger the title and the more frequent the updates, the clearer it becomes that gaming automation has stopped being a curiosity and turned into a condition for hitting the date.
In our experience, the hard part is not “adding AI” but wiring automation into an existing pipeline so the team trusts it. We have no case study from a game studio, and we say so plainly. What we do have is real AI/Data competence inside the RITS Group (our AI/LLM/data teams build LLM, AI and data solutions; we have delivered GenAI, RAG and LLM projects, including an AI knowledge assistant for an insurance client) plus engineers who automate processes. Those skills carry over to game production, because the problems rhyme: lots of repetitive tasks, lots of data and a high bar for quality.
Where does game production waste the most time?
Before we automate anything, we hunt for the places where time simply leaks. It is usually the same ground: clicking through builds by hand looking for regressions, moving text back and forth between spreadsheets and the engine, assembling patch notes and reports from several tools, handling player tickets that repeat by the hundred.
Each of these shares one thing: a person is doing work a machine would do faster and without fatigue. So our first move is not to deploy a tool but to measure where the biggest hidden cost sits, because that is what sets the order of automation.
How do automated QA and regression tests take load off the team?
Testing is where automated QA pays back the fastest. Checking the whole game by hand after every change is unsustainable with frequent builds, so we build a layer of tests that runs without a human wherever that makes sense:
- regression tests triggered automatically after each build, making sure a fixed bug does not return,
- smoke and boot tests confirming the game launches at all on the target platforms,
- performance tests measuring FPS, memory usage and load times across versions,
- level-walking bots that surface blocked paths, geometry traps and soft-locks.
The AI layer adds anomaly detection on top: a model learns what a “normal” session looks like and flags deviations a rigid script would miss. Testers do not disappear; they shift to where human judgement counts - control feel, balance, the moments where the game simply is not fun.
How do you build a game localisation pipeline on automation and AI?
Localisation is a classic pre-launch flashpoint, because it grows linearly with the number of languages. We turn it into a repeatable pipeline instead of rescuing it by hand in the final week. Strings leave the engine automatically, a translation model drafts the first version while preserving variables and tags, and the system guards terminology consistency and string length so the UI does not break.
The point is that AI prepares the material, it does not close the process. A translator and a localisation specialist verify tone, cultural context and the jokes a model will not feel. That setup shortens the time from “EN text ready” to “10 languages ready” and removes the manual copying that is practically begging for a typo in the menu.
How does AI support content generation under human supervision?
Here we tread carefully, because content generation is where quality and rights are easiest to lose. We treat models as an accelerator for creators, not a replacement. In practice AI works best in early phases and in supporting work:
- concept variants and sketches an artist picks a direction from,
- placeholders and greyboxes for testing levels before final assets exist,
- NPC dialogue and helper text as a first draft for a writer to edit,
- supporting tools, such as tidying design notes or suggesting names.
The final shape always passes through a human. That is not courtesy to the creative team; it is the condition for repeatable quality and legal safety, which we cover below.
How do you automate back-office processes in a game studio?
Production is more than the engine and the assets. A lot of time goes to the back-office: reporting, ticket flow, syncing data between tools, handling community. That is natural ground for process automation and RPA, because the tasks are structured and repetitive.
- build and release workflows chaining compilation, testing and publishing into one line,
- automated reports assembled from analytics, the bug tracker and the stores, with no manual gluing,
- ticket triage where a model pre-classifies and groups recurring player problems,
- data synchronisation between production, publisher and platform systems.
An AI agent can act as the connective layer here: it reads a ticket, assigns a category, suggests a fix and leaves the decision to a human. The reclaimed time goes back to the team, which makes the game instead of clicking through reports.
How do you reconcile a human in the loop with quality and content rights?
Automation without control is a risk, not a saving. So we design every process with a human in the loop at the point where a quality or publishing decision is made. The model proposes, a person approves, and the system records who accepted what.
We watch content rights separately. We establish what data models are trained and queried on, we label AI-generated material and keep it away from areas where a licence or copyright is uncertain. A clean content provenance chain is now part of due diligence with a publisher and an investor, not a lawyer’s whim. We automate to speed production up, not to drop the studio into a rights dispute.
Which AI and automation skills do we provide?
We do not sell a boxed product; we match people to a specific bottleneck in your production. To AI and automation projects we bring:
- AI/ML and LLM engineers (prompt engineering, RAG, model integration) from the RITS Group,
- automation and QA engineers building test pipelines and CI/CD,
- data specialists who tidy telemetry and analytics data,
- architects who arrange the whole thing so it stays maintainable after we leave.
We fill single roles in a team augmentation model or provide a dedicated team, depending on how deep the skills gap runs.
How do we do it at RITS?
We join as a nearshore partner from Poland, with genuine AI/Data competence but without promising miracles we cannot deliver:
- we start with an audit of where automation pays back the fastest, not with forcing a model in,
- we build GenAI, RAG and LLM solutions on the RITS Group’s experience, including an AI knowledge assistant for an insurance client,
- we design processes with a human in the loop and a well-kept content rights chain,
- we verify skills before the start and work in a close time zone, so the team joins your reviews and meetings without a long ramp-up.
Most often we do this as an AI adoption engagement, backed by team augmentation where specific hands are missing.
Where do you start with automation in game production?
Start from the single most painful area - usually QA or localisation - and measure how much time it eats by hand today. That defines the first project, measurable and easy to defend to the board. Only then do we extend automation to further processes, because that is how a team builds trust in the tools. We covered the differences between engagement models in our article on IT engagement models.
Want to wire automation and AI into your game production? Let’s talk - we’ll point to where it pays back fastest and match a team.