As operators grapple with increasingly complex datasets, turning information into actionable insight remains an industry challenge. Gabriel Kolawale, Director of Product at Hub88, shares how AI-powered tools such as Page Insights are reshaping data analysis as decision-making ‘co-pilots’.
We hear a lot about ‘data-driven decision-making’ across the industry, but many operators still struggle to process player data efficiently. What is holding them back?
The industry has no shortage of data with operators processing millions of bets per year, but the real challenge for most is turning it into strategy and action. According to analysis by Gartner, by 2027 50% of business decisions are expected to be ‘augmented or automated by AI agents’, yet many operators are still working with static and fragmented data.
While more data than ever is being collected, a significant portion of it goes underutilised because it is too time-consuming to interpret. In online casino, where margins and player behaviour can shift quickly, that delay can create a real competitive disadvantage.
Contextual intelligence is the missing piece. Technology that not only aggregates data but understands it in real-time. Operators need systems that surface what matters to them, when it matters and enable immediate action.
You’ve introduced Page Insights within Hub AI. What problem does this solve?
Page Insights is designed to remove the friction operators experience between data and decision-making. Traditionally, analysing performance, whether by country, game or individual player, required manual filtering and cross-referencing.
With Page Insights that process is now instantaneous. As soon as a user opens a data-heavy page in the Operator Backoffice or Supplier Zone, the system generates easy to digest visual dashboards alongside AI-generated summaries that highlight key patterns or movements.
The AI actively draws attention to anomalies, growth spikes and underperformance, which are often the most commercially relevant signals. Small percentage shifts can translate into significant revenue impact, so the immediacy this offers is invaluable.
How is this different from traditional BI or analytics tools?
Traditional BI tools are powerful, but they were designed for a different pace of decision making. Many sit outside the core platform, require manual configuration and depend on specialist users to extract value.
Across many adjacent industries, such as FinTech, e-commerce and SaaS, there is a shift towards embedded analytics. The idea is that insights should exist where decisions are made, not in a separate environment that adds friction.
Page Insights reflects this. It is fully integrated into the workflow and automatically adapts to the context of the page that users are on. It does not require setup, report building or any delay. Operational teams can respond quicker to performance changes across multiple markets and partners.
Can you share a practical example of how this works in action?
One use case for Page Insights is our country-level performance analysis in the Supplier Zone. Traditionally, suppliers might export data into spreadsheets or custom build reports to understand which regions are driving revenue.
With Page Insights, that entire process is condensed into a single view. Users can immediately see top-performing countries, identify which markets are gaining momentum and switch between metrics such as GGR, turnover, actives and average bet based on their business needs.
What adds real value is the AI-generated HubAI Insights Sidebar. For instance, it might highlight a 15% to 20% growth spike in a specific region or flag a decline that warrants investigation. These are the kinds of insights that drive commercial decisions.
What role does Context Mode play in making data more actionable?
Context Mode is where the tool moves from visual analytics to interaction. Instead of navigating dashboards or building queries, users can simply ask questions in natural language and the AI responses based on the data currently in view.
This reflects a broader trend we have observed in AI adoption. We have seen across multiple sectors that natural language querying is ‘democratising’ data analysis.
In practical terms, this means an account manager or commercial team member can ask questions such as ‘who is the top performer’ or ‘what is the total GGR for the top five partners’ and get an immediate, contextual answer they can trust. It removes reliance on technical teams for everyday analysis, freeing up their resources for more complex work.
What does this mean for the future of decision-making in iGaming?
In this context, AI is evolving from a reporting tool to a co-pilot that surfaces insights, anticipates trends and increasingly recommends actions.
In a competitive landscape where operators are expanding into new markets and managing more complex ecosystems at pace, speed of insight will be a key differentiator. Those who can identify and act on trends faster, whether that’s a high performing market or a declining segment, will have the advantage.
We have invested significantly in our product offering over the past 12 months and continually seek to introduce innovative tools that simplify complexity for our partners. The goal is to reduce the distance between data and action as much as we can. Page Insights is an important step in that direction.
