Wojtek Sznapka, Co-founder & CEO of Gamblitude, has spent more than a decade working at the intersection of iGaming, data infrastructure and AI. Through Gamblitude, he is focused on a problem many operators still struggle to solve: turning fragmented operational data into a unified decision-making layer that can support real-time action, predictive modelling and AI-driven workflows across casino and sportsbook environments.
In this CasinoRank Interview, Sznapka discusses why many operators remain stuck in reactive analytics despite growing investments in AI and data tools. The conversation explores the structural challenges behind real-time decision-making, the increasing role of predictive models in retention and compliance, and why the next competitive divide in iGaming may come down to how effectively operators combine human expertise with autonomous systems.
Gamblitude is positioned as an AI data platform for iGaming. What gap in how operators were using data did you identify that led you to build the platform?
Wojtek Sznapka: We founded Gamblitude based on our firsthand experience, but the challenges we faced are ones we see all over the industry. My co-founder and I spent years on the operator side battling the exact problems we are solving today and we’re observing all over the industry.
Data is heavily fragmented and lacks consistent governance, leaving teams entirely dependent on analysts to answer even basic questions. Meanwhile, decision-making relies on delayed, retrospective reporting rather than real-time visibility.
This completely slows organizations down and leaves significant upside on the table, particularly when it comes to early player value detection, churn prevention, and bonus optimization.
We also see a massive push toward AI, yet most operators struggle to move beyond basic experimentation. The reason is simple: AI is only as good as the data foundation beneath it.
The industry is currently stuck on a legacy analytics model. To actually move forward, operators require a unified, well-governed, and real-time data layer. Only then can AI become a practical, everyday driver of decision-making. That is precisely the shift Gamblitude was built to deliver.
iGaming companies generate vast amounts of data across player activity, payments, and gameplay. Where do you see most operators underutilising this data today?
Wojtek Sznapka: Most operators are not underutilising data in terms of volume, but in terms of how effectively it is turned into decisions.
A large part of the data they already collect could be used much more proactively across several areas. For example, understanding player value much earlier in the lifecycle, identifying churn signals before they are visible in topline KPIs, optimising bonus spend based on actual incrementality or detecting operational and risk anomalies as they emerge, not after the fact.
The issue is that this data rarely exists in a form that is easy to operationalise. It is often spread across multiple systems, inconsistently defined and accessed through static reports or ad hoc analysis. As a result, teams tend to rely on simplified views and delayed insights, even though the underlying data is rich enough to support much more precise and timely decision-making.
This is where the gap becomes visible. Operators are sitting on data that could directly improve revenue, efficiency and risk management, but without a consistent, real-time and well-governed data layer, most of that potential remains unused.
Many operators have access to dashboards and analytics, yet decision-making often remains reactive. What prevents companies from becoming truly data-driven in practice?
Wojtek Sznapka: The main issue is that most organisations have analytics, but they do not have a data-driven operating model.
Dashboards alone do not change how decisions are made. In many cases, teams are still working with inconsistent KPI definitions, limited trust in the data, and a heavy reliance on analysts to answer even simple questions. This creates bottlenecks, slows everything down, and makes it difficult to act at the right moment.
Another factor is that insights are often disconnected from execution. Even if a team identifies a trend or an issue, there is no clear workflow that translates that insight into a concrete action. Without continuous monitoring, clear ownership and the ability to move quickly, decision-making naturally becomes reactive.
There is also a structural gap when it comes to AI. Many operators experiment with models or advanced analytics, but without a solid data foundation and consistent business logic, these initiatives remain isolated and do not translate into everyday decision-making.
Becoming truly data-driven requires more than access to data. It requires consistent definitions, self-service access across teams, real-time visibility, and systems that connect insight directly with action. Without that, dashboards remain informative, but not transformative.
From your perspective, how is AI changing the way operators approach player segmentation and personalization?
Wojtek Sznapka: AI definitely supports CRM specialists in the segmentation process, making it faster, more precise and less dependent on rigid rules. It helps identify patterns that would be difficult to capture manually and allows teams to work with more granular, behaviour-based segments.
Where it becomes a real game changer, though, is personalisation.
With AI, operators can move beyond simply adjusting what the player sees, like offers, bonuses or content. They can start interacting with players in a much more dynamic and proactive way. This includes anticipating needs, reacting to behaviour in real time, offering more relevant support and shaping the overall experience as it unfolds.
In that sense, personalisation is no longer just about targeting, but about building a more responsive and adaptive relationship with players. That is where AI has the potential to significantly change how operators engage with customers.
Retention has become a key focus across the industry. How can data platforms help operators move beyond short-term engagement metrics toward more sustainable player relationships?
Wojtek Sznapka: That’s a great question, because retention is one of the areas where everything really comes together when you have the right data platform in place.
Retention is inherently a cross-functional problem. It depends on player behaviour, payments, gameplay, CRM activity, bonuses and often external signals as well. When all of that data is brought together and structured properly, and when you already have a good understanding of a given player, you can make a very significant impact.
This is where predictive models work particularly well. Churn models can identify early signals of disengagement before they become visible in standard KPIs, allowing teams to act sooner and more selectively. Value and LTV models help prioritise which players are actually worth investing in. Bonus optimization models can guide how much to spend and when, based on expected incrementality rather than broad assumptions.
The key difference is that retention stops being reactive and campaign-driven, and becomes much more targeted and economically rational. Instead of treating all players similarly, operators can focus on the right players, at the right moment, with the right level of intervention.
There is a very substantial upside here. In many cases, operators already have the data needed to improve retention, but without a unified, well-governed and predictive layer, they are not able to fully translate that into action.
Real-time data is often highlighted as a major advantage in iGaming. In practical terms, how does real-time insight change how operators respond to player behavior?
Wojtek Sznapka: Real-time data changes how teams can respond to player behaviour by giving them the ability to act while something is still happening, not after it has already played out.
In practice, it means teams can move from analysing past behaviour to actively managing ongoing situations. A CRM team can react to early signs of disengagement within hours (or minutes!) instead of days. A VIP team can identify high-value players as they emerge and engage them at the right moment – our predictive models give you the probability of a client becoming VIP in 24 hours from his first deposit. Risk and fraud teams can investigate unusual patterns as they develop, rather than after loss has already occurred.
What really matters is timing and context. Player behaviour in iGaming is often very dynamic, and many decisions lose value if they are delayed. Real-time insight allows teams to intervene when it can still influence the outcome, whether that is retaining a player, improving their experience, or managing risk.
But again, the key is not just speed. Real-time data needs to be structured, monitored and tied to clear actions. When that is in place, teams can prioritise effectively and respond in a much more precise and controlled way.
As regulatory requirements increase, data is also becoming central to compliance and risk monitoring. How do you see data platforms supporting operators in balancing performance with regulatory obligations?
Wojtek Sznapka: As regulatory requirements increase, data and AI become essential in making compliance both effective and operationally manageable.
AI that has access to reliable, well-governed data from multiple sources can significantly improve areas like AML and responsible gambling. Instead of relying only on static rules or retrospective checks, operators can monitor behaviour more holistically and identify patterns that may indicate risk much earlier.
This is particularly powerful in the context of predictive models. With the right data foundation, it is possible to detect early signals that a player may develop problematic behaviour from a responsible gambling perspective, often before it becomes visible through standard thresholds. This allows operators to intervene sooner and in a more measured way.
At the same time, a strong data platform ensures consistency, traceability and control. Centralised definitions, clear permissions and full visibility into how metrics are calculated make it easier to meet regulatory expectations while still enabling teams to act quickly.
In practice, this helps operators balance performance and compliance more effectively. Instead of treating them as competing priorities, they can be managed within the same data and decision framework.
Looking ahead, how do you see AI and data platforms evolving within iGaming, and what capabilities will define the next generation of operators?
Wojtek Sznapka: The clear direction is autonomy and efficiency, across cost, speed and quality of decision-making.
We are seeing a major shift across the market. Of course, iGaming is a highly regulated and trust-based industry, so this transition is happening more gradually than in some other sectors. But the trajectory is very clear. Operators that do not adopt AI and improve their efficiency in this way will simply fall behind and struggle to remain competitive.
One of the most compelling areas is the rise of autonomous agents. Even today, there are specific use cases where a certain level of autonomy can already be applied, for example in monitoring, anomaly detection or supporting operational decisions. Over time, this will expand into more complex workflows as trust in the systems and underlying data continues to grow.
Ultimately, the next generation of operators will be defined by how effectively they combine human expertise with AI-driven systems that can act faster, scale better and operate with a higher level of consistency.
With a background in digital media and a keen eye for emerging technologies, Ronaldo bridges the gap between players and platforms through clear, insightful reporting to the iGaming industry.
