In conversatiom with iGamingExpert, Slotegrator Product Owner, Maksym Shtun, analyses the current state of AI adoption in the iGaming industry, highlighting the dangers of misuse and outlining the company’s plans for incorporating AI into its processes.
How would you describe the overall usage of AI within the industry at the moment? Is its full potential being realised?
The industry’s approach to AI is currently shifting from observation to active integration. The sector has definitely become bolder and more proactive in experimenting with these technologies compared to previous years.
However, we are still far from reaching its full potential. Currently, there is a significant gap between technical capability and practical application.
This is primarily due to ongoing concerns regarding data security, the need for robust human oversight, and a deeper understanding of the technology’s long-term impact. The industry is moving fast, but it is doing so with a necessary degree of caution to ensure stability and trust.
Is there a danger of leaning into AI too much in a way that could potentially be detrimental to the overall product?
The danger isn’t in using AI — it’s in misunderstanding what AI actually is. People sometimes treat it as a deterministic system, like a calculator that always returns the same output for the same input. But AI doesn’t work that way. There is inherent variability in how it responds, and that deviation compounds when you stack decisions on top of each other.
The moment a team starts offloading 100% of critical decisions to AI — whether that’s content generation, logic handling, or user-facing outputs — they lose the ability to guarantee a consistent experience. And consistency is what users trust.
The correct approach is to treat AI as a capable collaborator, not an autonomous operator. Humans still need to define the boundaries, validate the outputs, and own the outcomes. If something goes wrong and you can’t explain why the AI made that call, you’ve already lost control of your product. That’s not an AI problem — that’s a process problem.
How does Slotegrator currently utilise this technology, and do you plan to step this up? If so, in what way?
Our current approach is highly pragmatic. We see great value in utilising LLMs combined with RAG (Retrieval-Augmented Generation) architectures. This allows us to implement effective, streamlined solutions for processing complex documentation and turning static knowledge bases into interactive, high-precision resources.
In addition to data processing, we are integrating AI into our creative workflows. It has become an essential tool for rapid prototyping, generating mockups, and exploring visual concepts, which significantly accelerates the ‘ideation-to-execution’ cycle without replacing human oversight.
Looking ahead, we recognise the potential in MCP (Model Context Protocol) solutions for creating more connected environments. However, our interest here is very specific: we see it as a way to enable tightly scoped, isolated functionality for particular tasks.
For us, it’s about maintaining full transparency and ensuring that any AI integration operates within a strictly defined perimeter. It’s not about broad access, but about precision and control over how the technology interacts with our internal tools.
What role does client feedback play in shaping how you utilise not just new tech like AI, but your overall roadmap?
Client feedback acts as an important signal for potential optimisation. When we see recurring inquiries or similar requests for specific functionalities, it suggests that there might be a broader opportunity to enhance the user experience.
However, we approach this with a focus on suitability. First, we evaluate whether a process actually benefits from automation and if it aligns with how our partners interact with the system.
If automation is the right path, we then determine the most effective tool for the task. AI is a powerful option for handling complex or unstructured data, but we only apply it where it offers a clear advantage over traditional methods.
Our roadmap is shaped by this pragmatic approach: identifying the right solution for each specific case, whether that involves AI or a more conventional technical improvement.
Following on from the previous question, what else can we expect from Slotegrator as the company keeps driving forward?
What you can expect from us is a consistent push toward giving operators better tools to make smarter, faster decisions. That’s the thread running through everything we’re building right now.
With Casino Builder, the focus is on reducing the time between an idea and its execution. We’re integrating AI-driven features that help operators configure, adjust, and fine-tune their platforms with much greater precision — so decisions that used to take days of back-and-forth can be made confidently in a fraction of the time.
Beyond the builder itself, we’re investing heavily in the way operators access information and guidance. Static documentation only gets you so far. We want to replace that with intelligent, context-aware support that surfaces the right answer at the right moment — so operators aren’t guessing, they’re acting on accurate, timely insight.
The broader direction is this: every feature we ship should either remove friction from an operator’s workflow or sharpen the quality of the decisions they’re making. Speed matters, but speed without accuracy creates problems downstream.
Our goal is both — give operators the confidence to move quickly because the tools they’re working with are reliable and built around how they actually operate.
