Slotegrator COO Olga Ivanchik outlines what most brands are getting wrong about AI. Instead of an optimisation tool, she argues, AI should be made a core part of business architecture, and the companies willing to make that leap will be the ones who perform best in the long run.
Even as so many companies are rushing to embrace AI, most still underestimate what it’s capable of.
At the moment, for example, B2C companies mostly use automation and machine learning as a means to accelerate the customer journey towards a decision point or reduce friction. Clothing retailers offer digital personal shoppers, health apps customise meal plans and exercise routines, and streaming services curate media according to the user’s tastes.
These use cases certainly boost sales and increase retention. There’s no doubt that soon they’ll reach a point where they smooth the customer journey so much the user isn’t even aware of them.
But too many companies are satisfied with leaving AI tools as an optional add-on. They view AI simply as a means to increase efficiency and improve UX. At a surface level, that makes perfect sense: if you’re using a new technology to optimise processes and reduce budgets, all while providing the same or better value to your customers, you must be doing something right.
But you could still be doing something more. AI isn’t just a way to keep up with your competitors; it can be the foundation of more imaginative, more effective and more strategic businesses.
Let’s look at iGaming. A vast majority of brands use AI chatbots for customer support. They also use automation for AML and compliance monitoring, as well as marketing content creation. It’s also common to use an algorithm to recommend new games a player might enjoy.
But some brands are moving further, integrating AI into core processes and releasing AI-first products. Those are the brands that will succeed in the long run are those that incorporate AI at a strategic level, not just as an optimisation tool.
The most obvious example is real-time personalisation. This goes beyond just making suggestions based on what players have enjoyed in the past; this is making sure players see exactly the right tailored offer at exactly the right time. For sportsbooks, these can be live, in-play bet recommendations. For casinos, a player might get a bonus right after they hit a run of bad luck. And all of it is carried out by AI agents that can learn and grow more effective over time.
AI is also highly effective at creating adaptive UX, conducting predictive LTV modelling, delivering well-tailored localisation and providing ongoing risk assessment and accurate fraud detection. In all these cases, scaling requires a level of processing power that human teams aren’t capable of.
When you shift from using AI to optimise established processes to building strategies based on AI’s capabilities, you can create features like table games with AI dealers, real-time odds and pricing models, and hyper-personalised game lobbies. This isn’t just elevating an experience; it’s creating a new experience altogether.
Let’s look at the two capabilities — optimisation vs. strategy — through the lens of fraud detection. On the one hand, you have the most obvious application: even before the AI era, human teams would struggle to process and verify ID documents, carry out ongoing threat analysis and identify and act on potential threats fast enough, making automation the only real option. It’s nothing if not effective; using AI to automate onboarding speeds up the signup process and reduces friction.
But now that cybercriminals have techniques such as deepfakes and synthetic IDs (fake identities composed of real, stolen personal data), mere automation isn’t enough. It’s easier than ever for fraudsters to slip through your defenses. Sometimes, a good enough deepfake can even help them pass a liveness check. And once they’re through your defenses, their patterns of behaviour are barely different from those of a real player, and even a trained security professional might struggle to detect them. That is until, of course, the damage has already been done.
This is where another, strategic layer of analysis is required. For example, an AI model integrated into your back office can provide ongoing behavioral monitoring and response according to presets. By going a step further, as we have in our platform, an AI assistant can quickly analyse the available data, and not just provide an overview, but make strategic recommendations on next steps.
Optimising processes and increasing efficiency is only the beginning of AI’s potential. In the near future, successful businesses will be the ones that use AI not just for optimising, but for strategising; not just to carry out automated actions, but to learn and act on its own. Companies that integrate AI into their operational core will be the ones elevating their industries to the next level, and those that leave it on the fringes will simply be left behind.
