Creatit presents how AI is redefining critical iGaming processes through measurable, industry-ready applications

Artificial intelligence adoption in the iGaming sector has reached a pivotal moment. The conversation is no longer about future possibilities but about identifying concrete, high-impact applications that improve efficiency, reduce operational risks, and strengthen competitive performance. Against this backdrop, Creatit introduces an in-depth analysis of how AI can be deployed strategically across iGaming organisations, grounded in readiness, data clarity, and real use cases already reshaping the industry.

AI in your company – iGaming case study

AI is no longer a novelty in the iGaming sector. Across operators, studios, and affiliates, leaders increasingly agree that AI will reshape the industry. Yet the key question remains unchanged: How can AI be applied in a measurable, safe, ROI-positive way – without disrupting day-to-day operations?

Insights shared during the recent “AI in Your Company” workshop led for the iGaming sector by Bartek Borkowski, Managing Partner at createIT, point to one essential truth: effective AI adoption isn’t about tools. It’s about readiness, clarity of data, and operational discipline.

This article builds those principles and ties them to concrete use cases emerging across the industry.

The foundations – AI in your company

The first step in adopting AI is not selecting a model or planning the implementation. It is assessing whether the organisation is ready to use AI at all. iGaming runs on sensitive data, interconnected systems, and complex interdependencies across multiple departments. This is why questions around the legal compliance of public models, data quality, and organisational readiness are far more important than choosing a specific model.

AI becomes a meaningful bridge between departments only when employees understand how to use it and when processes are stable enough to be automated or augmented with models.

Contrary to popular belief, the most successful AI implementations are not the flashiest – they are the most rational. Not every workflow requires an intelligent model. Many day-to-day iGaming processes, from ticket routing to data validation, can run faster and more safely with simple automation. LLMs should be reserved for tasks that require interpretation, understanding unstructured data, or detecting patterns that humans cannot easily recognise.

Paradoxically, knowing when not to use AI is often the key to achieving the highest return on investment.

The ROI reality – Automation that pays for itself

And that return is tangible. In a sector with extremely low tolerance for errors and high time-related costs, AI-driven ROI typically comes from three areas: shortening processes, reducing mistakes, and accelerating delivery. Automating customer support generates immediate time savings. AI in game testing identifies issues before they reach production, preventing financial losses. Accelerating slot development cycles means more games reaching new markets sooner. This is not theoretical – such efficiencies can shift an operator’s or studio’s market position within a single year.

“When we run workshops for iGaming managers, we focus specifically on how to choose the right process, where to start, and whether teams are ready for it at all,” says Bartek Borkowski, who leads the AI in Your Organisation workshops tailored to the sector. “What I always emphasize is that the method of small steps works best. Our key recommendation is ask your teams which processes they hate the most – those are the ones worth starting with (if the ROI checks out).”

Selecting the right processes

The hardest part is choosing the right workflows. Successful companies start with the processes that generate the most frustration, where manual work truly hurts. They then evaluate their value: how much time the process consumes, what kinds of errors it creates, how frequently it repeats, what risks it carries, and whether tools already exist that can perform 80% of the work.

This structured approach eliminates costly experiments and helps build a project portfolio that genuinely improves business performance.

For more mature organisations already fluent in automation, the bottleneck is often not knowledge but capacity: workflow architects, AI engineers, QA automation specialists, and content operations experts. Partnerships with external technology teams create the most value here, enabling organisations to scale quickly without building large internal departments from the ground up.

Where AI already delivers value

When organisations are ready, AI opens entirely new avenues. In game production, models support prototyping, logic validation, documentation, and regression testing.
In player operations, AI validates bonuses, aggregates fraud signals, personalises CRM processes, and monitors game health around the clock.

In content operations, it enables multilingual scaling, improves SEO, and automates affiliate verification.

Increasingly, this progress depends on local model deployment and a persistent operational context in which AI works with organisational rules, data, and files. Local LLMs give operators and studios control over privacy while reducing dependence on cloud-based token costs. Persistent environments – where models retain project context – allow for automations that no longer require being “retrained” from scratch.

What comes next – 2025–2027

The trajectory is unmistakable. Game production pipelines will feature AI at every stage, from concept through certification. Player operations will move toward continuous intelligence, where RTP anomalies, fraud signals, UX issues, and behavioural patterns are analysed in real time. Personalisation will shift from luxury to baseline expectation.

Regression testing will become autonomous. Compliance teams will gain truly digital co-workers. AI will not replace people in iGaming. Instead, it will enable teams to work faster, more intelligently, and with greater focus on growth rather than hundreds of repetitive micro-tasks.

We do not need to wait until 2027 to see everyday operational problems solved quickly and safely by combining industry knowledge with technical expertise. A real example is game testing, specifically the solution developed by createIT.

How iGaming looked before PlayPatrol

The Problem: Casinos have thousands of games but cannot keep up with testing. They cannot test every game even once per day, nor verify whether games from certain providers load properly, or whether games are available across different geolocations.

Why it’s difficult: Limited time and a massive number of games. Testing requirements multiply with every new location.

Why existing approaches were not optimal: All solutions on the market required manually creating test scripts for every game, an extremely time-consuming process requiring a large QA team.

How the industry looks after PlayPatrol entered the market

The solution:

An AI model that recognises video elements, allowing it to autonomously navigate through a game to execute a basic scenario. The model learns games per provider, understanding recurring elements and mechanics typical for that provider. It also recognises whether a test was executed successfully and automatically classifies the results — crucial when thousands of tests are run daily.

What is required:

A test account with funds for gameplay. For heavy workloads (over 2,000 tests per day), 2–3 accounts are recommended to run tests in parallel. Each test normally takes a few to several dozen seconds, and one account can run only one test at a time.

Expected results:

Automated detection of issues related to game availability, loading failures, and gameplay-blocking errors across thousands of titles in a short time. Clear error reporting, replayable test videos for verification, and reporting game availability by provider across geolocations.

A reality check from an actual project:

• setup completed in under 7 days
• verification of 5,000 games across 50 providers in 7 days
• detection of over 900 issues before launching a new casino brand

The competitive edge – Treating AI as evolution, not a project

The companies that succeed will be those that treat AI as an operational evolution, not a one-off initiative. Those that assess readiness start with the highest-value processes, and implement technology with discipline rather than hype. The winners will be organisations that combine robust engineering expertise with a pragmatic approach to automation.

Now is the time to educate teams and adopt structured frameworks that turn strategy into action. One example is the material provided by createIT through their workshops and newsletter content.