The Intelligent Platform From Integration Layer to Decision Engine
Te AI opportunities in iGaming are huge. Platforms can already
recommend which promotion to run, flag a risky session before it becomes a problem, shape a customer journey in real time, and run a dozen third-party systems under one roof. Adding a smart assistant on top of that seems like a logical step in the AI transformation process, one that should help with day-to-day work immensely. Right? I want to back away from the hype for a moment and ask a bigger-
KATERYNA POZDNYSHEVA Chief Client Officer GR8_TECH
Waiting for a Miracle?
Ready, or
Kateryna Pozdnysheva, Chief Client Officer at GR8_TECH, argues that AI's potential to
transform iGaming operations depends less on the sophistication of the technology than on how well operators understand the platforms they already use. She explains why platform knowledge, better questions and shared responsibility between providers and operators
are essential to turning artificial intelligence into meaningful business results.
134 Are You
picture question that tends to get skipped when the conversation turns to features: are you actually ready to take advantage of any of this, or are you waiting for a miracle? What I mean is simple. AI is only as smart as the people working with
it, and it can’t know the platform you operate on better than you do. It works with what you give it and answers what you ask it. If your team doesn’t know the platform, no model on top will make up for that. As Chief Client Officer, I sit between our teams and our partners’
teams, and I watch how decisions actually get made on both sides. Te pattern I keep running into has very little to do with how smart the technology is and everything to do with how well the people using it understand what they’re working with.
AI ONLY ANSWERS THE QUESTION YOU ASK Here’s the part that gets lost in the conversation: AI doesn’t decide anything on its own. Someone has to ask it something first. Whether that’s a prompt typed into an assistant, a rule configured in a risk engine, or a goal set for a recommendation model, there is always a human request at the start of the chain. And the quality of the output is capped by the quality of that request. Before AI enters the picture, are you comfortable with the dashboards
and reports the platform already gives you? Do you know where to look when a cohort drops off, or when you need to see which promotion performed best last month? AI’s biggest value is in how much faster and more conveniently it gets you to an answer. It doesn’t pull answers out of thin air. It works on the data you point it at, and if you don’t know where that data lives, what it measures, and how deep it goes, you can’t feed it the right inputs, and you can’t tell whether what comes back is right.
Tis is the gap I see most often. Operators get excited about what AI
could do, add it to a platform they’ve only half explored, and then get frustrated when the results are underwhelming. AI doesn’t have a really big impact on any numbers; they conclude it isn’t that useful, and the tool gets blamed for a problem that started with the question. Any AI opportunity gets you exactly as far as you understand what to request of it. And you cannot request what you’ve never dug into.
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