Beyond the AI hype: where artificial intelligence is creating real value

Artificial intelligence is everywhere in the conversation around technology, but for EveryMatrix, the focus is increasingly on a simpler question: where does it actually create value?

Speaking to HIPTHER during the EveryMatrix Media Event in Copenhagen, Pablo Jensen, our Chief Technology Officer, discussed how the company is approaching AI across products, operations and regulated markets, while keeping human judgement firmly in the loop.

Pablo explored everything from practical uses cases and fraud prevention to data boundaries, scalability, and what an AI-enabled technology stack could look like by 2030.

Watch the full interview with Pablo Jensen below.

Two tracks for AI at EveryMatrix

EveryMatrix is approaching AI in two distinct ways: improving the products it delivers to operators, and making internal workflows more efficient.

“We are running two tracks of how we use AI. One track around using AI within our products in order to improve the product line that we serve for our clients, the operators. And then we have another track where we are focusing on how can we use AI in order to automate our processes, how we can work in better ways, how we can have better workflows in our different teams.”

Internally, that includes generative AI tools across engineering as well as teams such as legal, commercial and finance.

On the product side, EveryMatrix is using machine learning across its broader data platform, including use cases around bonus abuse, game recommendations, personalisation and player protection.

Pablo also highlighted the importance of data sovereignty, with client data remaining within dedicated environments, alongside ongoing work around local language models and controlled data usage.

Where AI is already delivering measurable value

The value of AI comes down to results. EveryMatrix is already tracking the performance of AI-driven functionality across areas such as Bonus Guardian and game recommendations.

“We can see it in our Bonus Guardian. We can see it in our game recommendation area. We have metrics around stay on page, game rounds and all that, so we are following these metrics carefully. We see good results in that.”

That focus on measurable impact helps distinguish practical AI applications from experimentation for its own sake.

The same principle applies to fraud prevention. By analysing large volumes of behavioural data, AI can help identify patterns that may indicate bonus abuse or other suspicious activity.

Pablo also pointed to similar techniques being used in system monitoring, where AI can help identify potential technical incidents before they develop into larger problems.

Keeping humans in the loop

Even as AI takes on a greater role in analysis and decision support, that human judgement remains essential.

In Bonus Guardian, for example, AI surfaces potential issues through a dashboard, but the final decision on what action to take remains with a person. “We always have this human in the loop.”

That principle also applies internally. Pablo explained that code is not moved into production without human oversight, while AI-driven functionality is designed so that important actions still involve human decision.

The same thinking can be seen in products such as Bet Builder, where AI can help generate or recommend combinations, but the player ultimately decides what to add to their bet.

Deploying AI across dozens of regulated markets and hundreds of clients brings another challenge: scale.

EveryMatrix relies on a significant hosting and DevOps infrastructure to manage updates across large numbers of systems while maintaining clear boundaries between individual client environments.

What could change by 2030?

Looking ahead, Pablo expects AI to influence much more than individual features.

One of the biggest opportunities is around how platforms are configured for operators.

Today, that process involves a significant amount of manual work, combining an understanding of regulation with each operator’s commercial objectives.

There is strong potential for AI to automate more of that process, helping configure technology in a way that is better aligned with what each operator needs.

Data security will become even more important.

“You will see many more closed environments client to client, where there is AI working on the data for that specific client.”

For EveryMatrix, the future of AI is therefore not simply about introducing more automation. It is about combining smarter technology with stronger data controls, measurable outcomes and human oversight.

That balance is likely to define how AI becomes embedded across both the EveryMatrix platform and the wider operator ecosystem over the coming years.