Artificial intelligence in online gambling tends to raise an uncomfortable question.
Is the technology being used to understand players better so they gamble more—or to recognise when they should gamble less?
In 2026, Anthony Rus, CEO of Belgium’s PepperMill Casino, has offered an interesting answer.
Rather than announcing an AI system for predicting game outcomes, manipulating odds or squeezing additional engagement from individual players, PepperMill has deployed artificial intelligence in an area where the technology may have far greater long-term value: identifying potentially harmful gambling behaviour earlier.
That makes PepperMill’s approach worth watching wéll beyond Belgium.
From Reactive Protection to Earlier Detection
In January 2026, PepperMill Casino announced a strategic partnership with Danish safer-gambling technology specialist Mindway AI.
At the centre of the agreement is GameScanner, Mindway’s AI-based player-monitoring technology. It is being integrated into PepperMill’s Belgian operations to help detect patterns associated with at-risk and problem gambling.
The distinction is important.
Traditional responsible-gambling controls often depend on fairly obvious events: a player reaches a financial limit, requests exclusion, contacts support, or exhibits behaviour severe enough for a human employee to notice.
AI can potentially move the intervention point earlier.
Instead of waiting for one dramatic red flag, GameScanner examines patterns across gambling behaviour. Mindway describes the system as combining artificial intelligence, neuroscience and assessments made by gambling experts. Its algorithms learn from expert-reviewed player patterns and produce explanations for why a player has been assigned a particular risk profile.
That last point matters almost as much as the detection itself.
In a sensitive field such as gambling, simply telling an employee that “the algorithm thinks this customer is risky” is not enough. Staff need to understand what triggered the alert and decide what response is appropriate.
Mindway therefore describes GameScanner as explainable AI, rather than an entirely opaque black box. Human expertise remains part of the model and the subsequent intervention process.
The Clever Part: The AI Isn’t Playing the Game
There is another reason PepperMill’s implementation deserves attention.
Consumers increasingly associate AI with algorithms designed to influence almost everything they see online. In gambling, that can create understandable suspicion about whether algorithms might somehow affect the games themselves.
PepperMill draws a clear line between the two.
Its own responsible-gaming information states that game outcomes are determined randomly using tested Random Number Generators. Its FAQ likewise says approved RNG technology is required from its game providers.
So the interesting AI application is happening around player behaviour, not inside the roulette wheel or slot result.
That is an important model for responsible adoption of AI.
Use algorithms where pattern recognition can add something useful. Keep them away from areas where they could undermine trust in game fairness. And retain humans where judgment, context and intervention matter.
AI as a “Virtual Psychologist”
Mindway uses the phrase “virtual psychologist” to describe GameScanner.
It is provocative marketing language, but the underlying idea is straightforward.
Problem gambling rarely begins with a single easily identifiable event. Warning signs can emerge as combinations of behavioural changes: playing more frequently, increasing intensity, chasing losses, changing normal gambling routines or displaying other deviations from previous behaviour.
A computer can continuously evaluate large numbers of accounts in a way that a responsible-gambling team cannot manually replicate.
Mindway says GameScanner detects at least 87% of the at-risk and problem-gambling cases identified by human experts, a performance figure it says has been validated by Gaming Laboratories International.
The practical value, however, is not replacing those experts.
It is helping them decide where to look first.
That makes the technology particularly interesting as an example of human-AI collaboration. The machine does what machines are good at—continuous monitoring and pattern detection—while responsible-gambling professionals retain a role in interpreting cases and deciding how to respond.
PepperMill CEO Anthony Rus described the adoption as a combination of “data-driven insights and behavioural science,” positioning the technology as a way to raise the company’s standard of player protection.
Why the Timing Matters for PepperMill
PepperMill’s AI adoption becomes more significant when viewed alongside the company’s other moves in 2026.
The operator has continued widening its content offering. In August, for example, Amigo Gaming announced a strategic agreement that brought its slot portfolio to PepperMill as part of the supplier’s European expansion.
PepperMill has also been building visibility outside the casino lobby.
In July, SK Beveren announced PepperMill & Friends as its new main sponsor for the following two seasons after an existing relationship dating back to 2024. The agreement followed Beveren’s promotion to Belgium’s top football division.
These may seem like separate stories—AI, new games and football sponsorship—but strategically they connect.
Greater content variety can attract more activity. Bigger sponsorships create greater brand reach. Greater reach creates greater responsibility.
If an operator wants to expand its audience while arguing that player protection remains central to its business, responsible-gambling technology has to scale alongside the marketing.
That is where PepperMill’s 2026 AI investment starts to look less like a standalone compliance tool and more like infrastructure.
AI Could Become a Competitive Advantage—But Only If It Is Governed Well
There is a temptation to describe any AI deployment as innovation.
That would miss the real test.
A system monitoring gambling behaviour handles highly sensitive signals about individuals. Good implementation therefore requires more than an impressive algorithm. It requires clear risk criteria, appropriate human oversight, secure handling of player information, sensible interventions and mechanisms for reviewing questionable classifications.
PepperMill already operates several conventional responsible-gambling controls alongside the new technology. Its current player-protection information includes deposit limits, cooling-off options, self-exclusion guidance and access to support resources.
AI is therefore most useful when it adds an earlier warning layer to those existing safeguards—not when it becomes a substitute for them.
And that may be the broader lesson for the gambling industry.
The most convincing use of artificial intelligence is not necessarily the one with the most automation. It is the one where automation improves a decision humans already have a responsibility to make.
The Bigger 2026 Story
AI is becoming embedded across digital businesses, and gambling will be no exception.
Operators can use sophisticated analytics for personalisation, marketing, fraud prevention, customer service and countless operational tasks. But the applications that ultimately build the most trust may be those that deliberately work against short-term commercial incentives.
Detecting a customer who is becoming vulnerable can lead to an intervention that reduces their gambling activity. In purely transactional terms, that might look counterintuitive.
In a regulated entertainment business seeking sustainable relationships with customers, it makes considerably more sense, for all regulated and listed casino companies.
PepperMill Casino’s 2026 experiment therefore raises a larger question for the industry:
What if the most intelligent gambling algorithm isn’t the one that knows what a player wants to play next; but the one that recognises when continuing to play may no longer be a good idea?
That is a version of AI innovation worth paying attention to.